Operations Research Proceedings 2012

In the context of growing demand for individualization and increasing propagation of modularization and mass customization, it is necessary to combine the opportunities of product individualization with an appropriate pricing system. In contrast to patterns of pure product line design and pricing, the new concept of pricing kits provides a strategy which transfers the flexibility of product configurations to price setting. A mixed-integer programming model in order to determine an optimal pricing system for all product components as well as promising extensions for the combined use with complete product strategies are proposed. It is shown that the new approaches are superior to established methods of pure product line design, especially in cases of uncertain parameters.

It is well-known that sparsity (i.e. having only a few nonzero coefficients) is a desirable property for cutting planes in mixed-integer programming. We show that on the MIPLIB 2003 problem instance set, using only 10 very dense cutting planes (compared to thousands of constraints in a model), leads to a run time increase of 25 % on average for the LP-solver. We introduce the concept of dual sparsity (a property of the row-multipliers of the cut) and show a strong correlation between dual and primal (the usual) sparsity. Lift-and-project cuts crucially depend on the choice of a so-called normalization, of which we compared several known ones with respect to their actual and possible sparsity. Then a new normalization is tested that improves the dual (and hence the primal) sparsity of the generated cuts.

This article gives an overview of the results of the author’s PhD thesis [

8

]. The thesis deals with the mathematical optimization for the efficient use of railway infrastructure. We address the optimal allocation of the available railway track capacity—the track allocation problem. This track allocation problem is a major challenge for a railway company, independent of whether a free market, a private monopoly, or a public monopoly is given. Planning and operating railway transportation systems is extremely hard due to the combinatorial complexity of the underlying discrete optimization problems, the technical intricacies, and the immense sizes of the problem instances. Mathematical models and optimization techniques can result in huge gains for both railway customers and operators, e.g., in terms of cost reductions or service quality improvements. We tackle this challenge by developing novel mathematical models and associated innovative algorithmic solution methods for large scale instances. We made considerable progress on solving track allocation problems by two main features—a novel modeling approach for the macroscopic track allocation problem and algorithmic improvements based on the utilization of the bundle method. This allows us to produce for the first time reliable solutions for a real world instance, i.e., the Simplon corridor in Switzerland.

This article gives an overview on some of the results of the author’s PhD thesis [

11

]. The thesis deals with the problem of integrating passengers’ routing decisions in the optimization process in public transportation problems, in particular the problems of line planning, timetabling and timetabling, where the objective is the minimization of the passengers’ overall travel time. In this article we concentrate on some aspects of integrating routing decisions in the delay management problem.

Information sharing is frequently discussed as a main enhancer for supply chain cooperation. This holds particularly true for environments that are characterized by asymmetrically distributed information concerning, e.g., cost parameters or end-customer demand. Yet, if the supply chain parties are profit-maximizing and fully rational, credible information sharing might not be established due to misaligned incentives. In this context, non-linear contract schemes are intensively discussed in the supply chain coordination literature, since they coordinate the supply chain to a second-best outcome. The present work applies methods of non-linear optimization in order to obtain optimal contracting schemes (so-called screening contracts) in a strategic lot sizing framework. The validity of the applied theory is tested via laboratory experiments. This approach allows for identifying the main critical assumptions within the theory while showing that non-predicted behavior leads to a deterioration of supply chain performance. Interestingly, the experiments reveal that—in contrast to standard assumptions—the impact of information sharing is ambiguous and dependent on several factors, such as contract flexibility and complexity. The experimental results form the basis for a behavioral principal-agent model. The model gives valuable insights on how the interaction of information sharing and information processing impacts the supply chain performance.

We investigate Value-at-Risk (VaR) estimates based on extreme value theory (EVT) models combined with time varying parametric copulas against competing parametric approaches accounting for dynamic conditional correlations feasible to higher order portfolios. Tails of the return distributions are modeled via Generalized Pareto Distribution (GPD) applied to GARCH filtered residuals to capture excess returns, linked via constant and time varying copulas. Drawing on this EVT-GARCH-Copula, we evaluate portfolios consisting of German Stocks, market indices and FX-rates. However, the empirical results support the dynamic EVT-GARCH-Copula approach, as 99 % VaR forecasts clearly outperform parametric estimates stemming from competing dependency approaches.

Problems modeled as a sum-of-ratios arise naturally when several rates (objectives) have to be optimized simultaneously. The linear sum-of-ratios problem is also used for computing nondominated solutions in multiobjective linear fractional programming problems when the weighted-sum is applied to the objective functions. We previously developed a Branch & Cut algorithm for computing solutions, considering a pre-defined error, for this kind of problems. The algorithm has a good performance for problems of medium dimensions (less than roughly ten ratios), even considering a very small pre-defined error. In this text we propose a combination of particle swarm optimization (PSO) techniques with the Branch & Cut algorithm in order to improve the performance of the computations for problems of higher dimensions. We present computational results for problems with up to twenty five ratios.

This research presents a novel framework for selecting Socially Responsible Investment (SRI) portfolios. The Hedonic Price Method (HPM) is applied to obtain an evaluation of SRI criteria that is integrated into a multi–objective mathematical programming model. This approach allows us to obtain a portfolio whose financial performance is similar to which the investor would have reached if he or she had not taken into account Social, Ethical and Environmental (SEE) characteristics when making his or her investment decisions. This methodology is applied to portfolios composed of socially responsible and conventional mutual funds domiciled in Spain.

Industrial compressed air systems are important contributors to energy demand and energy costs. Thus, numerous measures to increase their energy efficiency have been proposed. Yet it often remains difficult for decision-makers to evaluate the different conceivable measures. Difficulties arise among others from uncertainties related to the evaluation of relevant criteria and from diverging interests within a company. As a mean to facilitate the decision-making processes for evaluating energy-efficiency measures, this paper suggests a fuzzy version of the PROMETHEE method which allows explicitly dealing both with multiple decision-makers and evaluations under uncertainty.

A risk measure determines the quantity of an asset that needs to be kept in reserve in order to make the risk taken by an investor acceptable. In the last decade coherent measures of risk meeting a set of four desirable properties gain in importance. We prove the

Competitive Ratio

to be coherent since it satisfies the four required axioms. We explain risk management in online conversion problems, and show how the

Competitive Ratio

can be used to manage the risk.

It is known from literature that many models of financial mathematics are based on the assumption of normality returns. Thus, the normal distribution is not a single model to fit the log-return distributions. It is very important to consider an alternative class of probability distributions which is able to model the effects caused by asymmetric data. This paper is a survey about the log-returns of Certified Emission Reductions (CERs), carbon credits generated by projects of the Clean Development Mechanism. The contracts are priced through the binomial lattice model proposed by Cox. Therefore, the model is discussed in order to represent the random parameter behaviors of CERs contracts and evaluate the benefits and exposure to them.

The energetic use of biomass offers considerable advantages with respect to sustainable development. Nevertheless, concerns exist about ecological aspects, such as adverse effects on biodiversity by promoting monocultures, as well as about social and ethical ones. The latter include for example the competition with food crops. Therefore, it becomes obvious that the assessment of concepts for biomass usage should not only be based on economic factors, but on a multi-criteria deliberation. Hence, methods of Multiple-Criteria Decision Analysis seem to be suitable in this case. At first, an approach based on PROMETHEE for the assessment of different concepts of biomass usage, which has already been applied exemplarily for a rural area in southern Lower Saxony, is presented. Thereafter, the focus of this paper is to show how this approach could be enhanced to make it more suitable for the sustainability assessment of biomass potentials on a broader, regional scale.

The Outranking method PROMETHEE comprises as a visualization method Geometrical Analysis for Interactive Aid (GAIA). Another visualisation method is Multidimensional Scaling (MDS) linked with Property Fitting. This paper compares the two different approaches.

Energy-efficient operation of large telecommunication networks is an important issue in the near future, especially in the light of climate change. We study the question how much power is necessary to operate a network with state-of-the-art hardware during peak and low-traffic times. The study respects realistic side constraints, such as protection requirements and routing schemes, and takes the special structure of an extensive nation-wide optical network, including backbone and regional sections, into account.

In this work, we focus on the integrated planning of the following problems faced within the context of seaside operations at container terminals: berth allocation, quay crane assignment, and quay crane scheduling. First, we formulate a new binary integer linear program for the integrated solution of the berth allocation and quay crane assignment problems called BACAP. Then we extend it by incorporating the crane scheduling problem as well, which is named BACASP. Although the model for BACAP is very efficient and even large instances up to 60 vessels can be solved to optimality, only small instances for BACASP can be solved optimally. To be able to solve large instances, we present a necessary and sufficient condition for generating an optimal solution of BACASP from an optimal solution of BACAP using a postprocessing algorithm. We also develop a cutting plane algorithm for the case where this condition is not satisfied. This algorithm solves BACAP repeatedly by adding cuts generated from the optimal solutions at each trial until the aforementioned condition holds.

In this paper we apply a genetic algorithm for solving the facility layout problem. The focus is on departments of unequal sizes. The objective is primarily to minimize material flow factor cost. The chosen genetic method bases on an implemented space-filling curve which continuously connects each unequal area department in order to avoid disjoint areas. In contrast to the existing literature, which applies sweeping, spiral or Hilbert-type patterns, we propose Peano-type patterns. The original Peano curve has to be adjusted in order to be applicable to the considered facility layout problem. We describe the generation of the Peano curve and the applied genetic search. Further, we provide results from several test problems that demonstrate the very good suitability of modified Peano curves for the unequal area facility layout problem.

We provide an overview of our main results about studying Linear Programming Problems whose coefficient matrix is subject to uncertainty and the uncertainty is modeled through a multi-band set. Such an uncertainty set generalizes the classical one proposed by Bertsimas and Sim [

3

] and is particularly suitable in the common case of arbitrary non-symmetric distributions of the parameters. Our investigations were inspired by practical needs of our industrial partner in ongoing projects with focus on the design of robust telecommunications networks.

This paper deals with the approximate approach to the large p-median problems based on the set-covering formulation. This approach pays for its shorter computational time or smaller demanded memory by losing its accuracy. The accuracy can be improved by more suitable determination of the dividing points which are used for the distance approximation. This contribution suggests that some network distances from the customers to the possible service center locations can be considered relevant, and are expected to belong to the optimal solution. We proposed to formalize the outlined notion of relevance, and based the dividing point selection on it. Hereby, we are studying the impact of the dividing point determination on the effectiveness of the presented approximate approach.

Traditional approaches to the public service system design can be seen as a resource allocation problem with a central planner, where social costs are minimized. Even though all tax payers share the costs of the system construction and its operation, not all citizens have the same access to the service. There are two different ways of evaluating the resulting quality of the general system. The utilitarian approach prefers solutions which maximize the system utility. The fair approach, on the other hand, takes into account the preferences of individual users and their rights to have an equal access to the provided services. In this paper we focus on reformulation of the public service system design problem to the utility maximization problem, where both, the system and fair approaches will be studied. We evaluate the impact of the used utility functions on the location of service centres. We compare the distribution of located centres in comparison with other approaches, and we explore its impact on the price of fairness.

The reliable functioning of an electrical power grid is dependent on the proper interaction between many of its elements. What is critically important is its ability to keep the frequency across the entire system stable. Considering a simple mathematical model, representing the network of coupled oscillators, we study the stability of frequency synchronization. This model can be interpreted as the dynamical representation of frequency synchronization between the power producing and power consuming units. Assuming a uniform network, we analytically derive the formula estimating the relation between the minimum coupling strength required to ensure the frequency synchronization and the network parameters. This minimum value can be efficiently found by solving a binary optimization problem, using universal solver XPRESS, even for large networks,. We validate the accuracy of the analytical estimation by comparing it with numerical simulations on the realistic network describing the European interconnected high-voltage electricity system, finding good agreement. Moreover, by repeatedly solving the binary optimization problem, we can test the stability of the frequency synchronization with respect to link removals. As the threshold value changes only in few cases, we conclude that the network is resilient in this regard. Since the synchronization threshold depends on the network partition representing the synchronization bottleneck, we also evaluate which network areas become critical for the synchronization when removing single links.

In this paper a class combinatorial optimization problems with uncertain costs is discussed. This uncertainty is modeled by specifying a scenario set containing

K

distinct cost vectors. In order to choose a solution the Ordered Weighted Averaging aggregation operator (OWA) is used. For most classical problems, for example network problems, minimizing OWA is NP-hard even for two scenarios. In this paper some positive and negative approximation results for the problem are shown.

This paper deals with two Recoverable Robust (RR) models for combinatorial optimization problems with uncertain costs. These models were originally proposed by Büsing (2012) for the shortest path problem with uncertain costs. In this paper, we generalize the RR models to a class of combinatorial optimization problems with uncertain costs and provide new positive and negative complexity results in this area.

This paper presents new solution approaches capable of finding optimal solutions for the Hydrothermal Scheduling Problem (HSP) in power generation planning. The problem has been proven to be NP-hard and no exact methods have been able to tackle it, for problem sizes of practical relevance. We explore three approaches. The first method is an iterative algorithm that has been successfully used previously to solve the thermal commitment problem. The two other methods are “Local Branching” and a hybridization of “Particle Swarm Optimization” with a general purpose solver. Computational experiments show that the iterative piecewise linear approximation method outperforms more elaborated approaches, indicating that recourse to matheuristics for solving this problem is not necessary.

Optimisation models for energy environmental planning based on the concept of economic equilibria share a common flaw that stems from their neoclassic roots: hypothesis of a perfect information and hypothesis of perfect economic rationality. A way to circumvent this issue consists in soft-linking data from sociological surveys that determine technical coefficients for MARKAL model, creating a hybrid approach of coupling a deductive engineering model with typical inductive methods of social sciences. Behavioural changes are described as virtual technologies with usual technology attributes. The method is illustrated on a case of lighting bulbs. The goal of the approach is to build long term policies that are not solely based on technology progress but also taking into account social change.

Europe aims for internal market in electricity. The target is to ensure the efficient use of interconnections, increase in social welfare and electricity price convergence. However, different market designs across Europe may create a threat to the efficiency of market integration. This paper presents results of a case study of the current operational principles of cross-border trade between Russian energy+capacity market and Nordic energy-only market. The results suggest that interraction of markets with different designs may lead to the non-optimal use of the interconnection.

Markowitz’s classical mean-variance approach for portfolio selection considers only single-period investments. It has, therefore, received very little attention in the context of long-term investment planning. Nevertheless, considering dynamic aspects, already Markowitz [

12

] mentioned the attractiveness of multi-period portfolio selection problems for portfolio readjustments during the planning horizon. The direct application of the mean-variance model to multi-stage portfolio problems, however, causes many difficulties. A number of studies have tackled such difficulties, providing suggestions on how the dynamic aspects of portfolio optimization should be considered. One of these suggestions is a reallocation methodology that is based on scenario analysis and a tree approach [

14

]. In this paper, we apply this methodology to power generation assets, in order to capture the continuously changing values of the economic as well as technical parameters considered when evaluating investments in power plants.

Decentralized power generation may lead to operational problems in electricity distribution networks, such as current overloads and too large voltages deviations. Storage systems can have a beneficial effect to alleviate these problems. However, these systems are relatively very expensive and have not been applied much in electricity grids in Europe. We present an optimization model which can be used to support the analysis of the operational benefits and investment cost of storage systems. It makes use of a simulated annealing approach to find good storage configurations, with a linear programming model to determine the load and optimal storage control, maintaining all the loadflow constraints. Our model seems to be an interesting approach to solving the storage location problems, and a promising approach to solve other investment problems.

Most European electricity markets know the principle of Balanced Responsible Parties (BRP) which are entities in charge of ensuring the energy balance over each settlement period on their balance area. We present one of the many challenging problems that must be solved to increase penetration of Electrical Vehicle (EV) in Smart Grids: the valorization of the storage capacity owned by an Electrical Vehicle Rental Service (EVRS). Our purpose is to present a workable business model in the context of European BRPs, and to describe an industrial optimization tool which conjointly minimizes EV charging cost and increases the revenue of a BRP. Electrical Vehicles are consuming power when they are used to transport people. During transport, the flexibility of the battery is not available for grid services. But when idle, the optimizer can define if the vehicle battery has to be charged for future transport or if it can be used to store energy for different potential future usages (either injection in the grid or transport). The decisions are based on the forecast of vehicles reservation (speculation about the transport service usage) and on the price the BRP is exchanging electricity.

First, we describe an Electric Vehicle Fleet Optimizer (EVFO) used on the EVRS side to optimally schedule EV charging cycles by provinding a mathematical program. Then we present how to make it interact with the BRP side tool which usually consists in an optimization tool scheduling and dispatching their generation portfolio. We choose to use a demande-response scheme to achieve this since this is an interesting way to contribute relieving electricity industrial problems [4] with a real potential [5]. We finally conclude on providing some hypothesis under which we can ensure our demand-response scheme to converge.

Currency options on a future confer the right but not the obligation to trade the underlying currency future at a pre-agreed price (the strike). Most currency options trade over-the-counter (OTC). That means that they do not follow standard rules for option contracts. They can, in principle, have arbitrary maturities, strike prices, and volume. Valuing OTC options is complex and various models have been developed for that task. Here, we present a Financial Decision Support System that helps in gauging the probable price of a curreny option within the next thirty minutes. The forecast horizon is continuous and also an input to the model. We train an artificial neural network on past price data, using tick prices of option and underlying future of the past two hours. Fuzzy coding of time-points increases the robustness of our model. Error on the out-of-sample data set is small. Interestingly, we do not need additional data to obtain satisfactory results. The tick data time series of option and future prices contains enough information to lead to a good forecast. Especially, we do not use volatility or interest rate data. The neural network is, in most cases, able to correctly forecast the main troughs and peaks of the following thirty minutes. An ensemble of neural networks further smoothes the result. Our Financial Decision Support System is useful for both sellers and buyers of options. Sellers get a tool that is calibrated on the latest market data. Buyers can judge, whether they should hedge their risks now or rather wait a short amount of time.

This paper proposes an efficient version of the finite element method (FEM) in option pricing. In this study, we determine element lengths from the curvature of the PDE endogenously. Our method consists of two algorithms, the coarsening and the refinement of the elements. The model makes the element larger if the curvature of the local domain is low, and smaller if it is high at each time step. We apply this approach to one-dimensional options, a European up-and-out call option, and an American put option. As a result, we find that this method is able to reduce the experiment time while the accuracy remains at a comparable level.

Within the context of the present work an experimental study is conducted which executes negotiations between the agent and bidders. In these contract awards the opportunity for bribes is simulated. The following issues are analyzed: on the one hand the willingness to be dishonest respectively to accept bribes and on the other hand the effect of different detection probabilities and the possibility of leniency policy. The new idea is the simulation of bribes and as a further step the consequences of leniency policy. Our motivation was created by recent developments of increasing cases of corruption worldwide. Quite a number of corruption cases are in the field of huge projects mainly in construction industry, building sector in general and energy sector and of course in all sorts of supplies of services. We ran an experiment and report some interesting results.

We construct and investigate a mathematical model of a two-stage market. Strategies that correspond to the subgame perfect equilibrium are determined depending on the parameters of the model. We compare the subgame perfect equilibrium with the Nash equilibrium of a one-stage model. The main contribution is a consideration of a random factor that affects the outcome in the spot market.

Simple bargaining problems with transferable utility are considered. By associating a quasi-additive cooperative game with each one of them, a

Shapley rule

for this class of problems is derived from the Shapley value for games. The analysis of this new rule includes axiomatic characterizations and a comparison with the proportional rule.

We consider a game corresponding to the uniform price supply function auction for a homogenous good. We present best response dynamics analysis for a repeated auction game and show that under a linear marginal cost function, it converges to the SFE with a geometric rate. However, for a fixed marginal cost and limited production capacity, the best reply may not exist at some step so the dynamics does not converge to SFE in general.

The paper presents an approach to the selection of healthcare programmes based on cost and effect information estimates, both being subject to uncertainty. The decision maker is assumed as risk-averse, but it is argued why risk aversion in public health will often only refer to total cost but not to total effect. A recent theoretical result is used to show that in the obtained multi-objective stochastic program, the random variables for cost and for effect can be decoupled. This produces a mean-mean-risk model for which the Pareto frontier can be determined. The approach is applied to a class of health programme portfolio problems studied before by other authors, and illustrated by an example. For risk, a measure derived from absolute semideviation as well as the budget overrun probability are used alternatively. The semideviation-based choice usually leads to a degeneration of the Pareto frontier to a curve for which only expected cost and expected effect count. This does typically not happen for the budget-related measure.

This paper tests the forecasting and trading performance of popular linear and non-linear forecasting models in predicting spot and forward freight rates in the dirty tanker shipping market. Maritime forecasting studies using neural networks are rare and only focus on spot rates. We build on former investigations, but we extend our study on freight rates derivatives. Our conclusion is, that non-linear methods like neural networks are suitable for short-term forecasting and trading freight rates, as their results match or improve on those of other models.

We use Historically Consistent Neural Networks (HCNN) to forecast intraday highs and lows of liquid and volatile stocks. To build our forecast model we only use easily available open-high-low-close (OHLC) data. This is a novel application of HCNN to intraday data. It is important to note that model performance evaluation does not need tick data, which is more difficult to obtain and to handle. However, there is only few academic literature on forecasting intraday high-lows with neural networks. The present study aims at closing this gap. We measure the economic performance of a strategy using forecast high-low data. The strategy is intraday. It exits all positions at the close. This reduces the risk of being caught in abrupt price moves without the ability to exit the position. We test the strategy on a sample of S&P500 stocks. It turns out that profit and reward to risk ratios are attractive and confirm the good results of previous studies on an emerging market.

The model utilized in our paper studies the impact of interacting aggregation and measurement errors in two-stage Activity-Based Costing systems (ABC systems). Based on a simulation study we provide the following first results: (1) The sequence of occurrence of the considered errors is crucial for having either decreasing or partially increasing overall accuracy of reported product costs. Thus, negative interaction effects may overcompensate first-order effects. (2) The type of the aggregation error has a substantial impact on the interaction effects with a measurement error and (3) some discussed interaction effects in the literature can be explained by technical assumptions and restrictions. We propose a solution for further simulation studies of ABC systems.

Condition-based maintenance is analyzed for multi-stage production with a separate maintenance unit. Deterioration depends on multiple production para-meters, and maintenance has to keep up predefined operational availabilities per production stage. Two problems are of interest: Determining machine conditions that require a release of preventive maintenance jobs, and scheduling maintenance jobs of different production stages. Considering continuous condition monitoring, a specific priority rule for scheduling is developed, factors determining the release situation are operationalized and effects of choosing the triggering condition are identified. The performance of the priority rule and a stochastic approach to determine optimal triggering conditions are analyzed by simulations.

We propose an exact solution method based on semidefinite optimization for simultaneously optimizing the layout of two or more cells in a cellular manufacturing system in the presence of parts that require processing in more than one cell. To the best of our knowledge, this is the first exact method proposed for this problem. We consider single-row and directed circular (or cyclic) cell layouts but the method can in principle be extended to other layout types. Preliminary computational results suggest that optimal solutions can be obtained for instances with 2 cells and up to 60 machines.

Within the tubes production process steel bars are cut into pieces before they are heated and rolled. In order to avoid waste of input material the cuts of the input material have to be optimised which is a classical one-dimensional cutting problem. A special characteristic of the tubes production is that the same output can be reached with different cutting lengths. Thus, the minimisation of input material gets more difficult and the known algorithms have to be adapted.

Infrastructure is a success factor for e-mobility. It is needed to operate e-vehicles and to convince individuals and firms to switch; the most important component of it is the charging station system. Since not all e-vehicles will charge at home, a significant part will use public charging stations. Investors will only install and operate charging infrastructure if there are profitable prospects. Because lacking historical data for forecasting, we propose a system dynamics model to capture factors and policies facilitating or impeding the buildup of public charging infrastructure. Yet, there is a “chicken-egg” problem: without infrastructure diffusion and growth of the e-vehicle market will not start off. Without enough market potential, investors will be reluctant to invest in infrastructure. We concentrate on so called Alpha Cities like Singapore, because they will presumably lead the way for e-mobility. Our analysis allows to estimate potential market size of public charging infrastructure depending on the city and several endogeneous and exogeneous variables. An important finding is the recommendation to offer a combination of charging services.

We present a decentralized heuristic applicable to multi-agent systems (MAS), which is able to solve multiple-choice combinatorial optimization problems (MC-COP). First, the MC-COP problem class is introduced and subsequently a mapping to MAS is shown, in which each class of elements in MC-COP corresponds to a single agent in MAS. The proposed heuristic “COHDA” is described in detail, including evaluation results from the domain of decentralized energy management systems.

Sustainable mobility, based on battery-powered electrical propulsion concepts, has been rediscovered in 2008 through emerging technology developments and enviromental requirements. Since 2009 a charging infrastructure for transportation on the basis of electrified powertrains has been developed all over Europe. Access to the charging stations is monitored by intelligent Triple-A-Systems (AAA - Authentication, Authorisation and Accounting) for reasons of security, customer loyalty and accounting. Similar to roaming in mobile communications, it should be possible for pioneers of e-mobility to have access to public charging stations of different operators. This article describes an efficient and future-oriented architecture of Clearinghouse systems that will provide interoperability and information exchange for e-mobility applications.

Offshore wind energy has developed rapidly in the last 20 years. However, the development of the technical aspects has been in the foreground for most of the time. Due to high costs of more than billion the majority of the realized projects has been implemented within the framework of corporate finance. Further goals concerning the expansion of renewable energies have been set by governments in various countries. In this context the importance of the economic viability of alternative financing concepts like project financing is becoming increasingly evident. This paper provides a decision support tool (DST) for analyzing and evaluating the project value of offshore wind energy projects within the framework of project financing. The DST is based on a cash-flow model in which the project value is calculated by applying the discounted cash-flow (DCF) method. A Monte Carlo-simulation is used to take the project risks into consideration. In the course of that the cash-flow-at-risk (CFaR), an adjustment of the value-at-risk (VaR) approach, is used to quantify the influence of risk factors and the effects on the project value. In order to be able to consider the requirements of debt capital providers in the context of project financing key figures like the debt service cover ratio (DSCR) are calculated. Therefore, a data base that supports decisions of many project participants is provided. Finally, a case study for a fictitious offshore wind park in the German North Sea is conducted. It is shown that in the framework of the existing feed-in tariff in Germany an offshore wind park project generates adequate returns for investors and provides sufficient debt service coverage.

Range limitations of battery electric vehicles require new approaches to over-come usability restraints. To reduce size, cost and mass of battery-packs some manufacturers integrate small combustion engines into their concepts. These range-extended electrical vehicles (REEV) strongly depend on climate conditions and driving cycles. Significant amount of power is needed for heating and cooling of batteries and passenger compartment. Dynamic simulation of three alternative REEV-concepts has been conducted. Applying a combined-heat-and-power range-extender and optimizing its output power to climate profile and driving cycle a significant amount of could be saved in comparison to standard REEV.

The share of renewable energy in today’s power grids is continually increasing. However, it is notoriously difficult to accurately forecast renewable electricity sources like wind and solar production with the granularity that energy providers require. To compensate for the fluctuating production and forecast errors, energy providers have to use expensive control energy. This partly negates the positive effect of renewables. Various ideas for load smoothing on the production side have been suggested. Here, we focus on load shifting on the consumer side: electricity rates that may vary in hourly intervals can influence smart devices in private consumer households. With real-time pricing (RTP) the energy provider can send high prices when production is behind forecasts. On the other hand, prices should be cheap when the production exceeds the forecast. Cheap rates would incite electricity consumptions. The challenge is to identify the price signal that will result in the desired load shift at consumers. As the behavior of smart devices is still unknown today we use a simulation prototype and train an artificial neural network with simulation data. As it turns out the neural network leads to good results and achieves hit rates in the task of mapping the desired load shift to a price signal. This hit rate only slightly decreases when we submit the price model to some constraints that increase consumer-friendliness. The advantage of using a neural network is that it can adapt to a slowly changing mix of smart devices in households. By regularly retraining the network we are able to react to the future reality.

In a quantitative scenario analysis ways, in which the consumption of fossil fuel in the German transport sector (about 700 TWh p.a.) can be replaced by renewable fuel until 2050, are discussed. Especially the impact of policies on the replacement of fossil fuels with renewable fuels is investigated. A linear optimization model is developed with the objective to minimize yearly total cost of fuel, taking into account total demand and capacities of various fuels, e.g. production capacities. The implementation of the model is done in Excel and the optimization is automated using VBA. For the creation of scenarios various parameters are modified: energy taxes exemptions, penalties on the emission of carbon-dioxide-equivalents and major restrictions for renewable fuels. Parameter dependent results show optimized fuel combinations for every year until 2050. Results from scenario analyses show further that it is possible to increase the share of renewable fuel sources in total consumption to 100 % until 2050. This can be achieved by a declining population, by constant mobility preferences and by more efficient vehicles. Furthermore, it is necessary to support the substitution of fossil fuels with bio-fuels, hydrogen and renewable electricity by using policies for a sustainable change. Simulations show that long-term energy taxes exemptions with long-term tax on emissions of carbon-dioxide-equivalents lead to significantly better results.

The task of planning photovoltaic power plants is far from trivial. This complex optimization problem combines aspects of placement problems and constraint satisfaction problems, and several interdependent goals must be considered. This paper describes the main issues of the design task and it contains an algorithmic approach to deal with the multicriterial aspects. We decompose the planning process into several subproblems, which can be solved with specialized fast heuristics. In addition, we provide a decision support concept to allow the engineer to intuitively navigate on the multidimensional solution set.

Followed by the liberalization of the European market for electrical energy, the amount of transmitted power between the countries increases and thus affects electricity prices. Therefore, the loads of the weakly built interconnection lines between countries approach their physical limits. Furthermore, the construction of decentralized generation units, especially renewable power generation, changes the generation structure as a whole. In order to analyse the changes in the European power system, the Institute of Electric Power Systems in Hannover is developing an integrated grid and power market simulator to analyse possible future scenarios in electric power systems. This simulation tool calculates a power plant schedule in order to run power system simulations based on the loads given by the schedule.

This paper presents a simulation study on the perfomance of public transportation networks offering navigation services. Car-like navigation for passengers in public transport is still a vision, but several projects try to close the remaining gap. Customer-oriented delay management is a value adding service enabled by navigation. In our study we ask for the benefits generated by navigation and by delay management that takes the passenger routes into account. Particularly we distinguish two groups of passengers, the first one using navigation and the second one traveling in the traditional manner.

Environmental and operational loads are design drivers for steel support structures of Offshore Wind Turbines. Besides common design and installation factors a holistic design includes sustainability dimensions which will dominate decision processes and cost effectiveness of future renewable constructions. Within a national research project a life cycle sustainability assessment was developed for steel constructions of renewable focussing on Offshore Wind Turbines. To describe the sustainability, categories and criteria were defined and their applicability was analyzed for selected reference steel support structures of Offshore Wind Turbines.

Today’s discussion of new mobility concepts mirrors the changing public awareness regarding a rethinking of tomorrow’s mobility. Car sharing has become a very important topic in the last years. Market dynamics has grown significantly due to the car sharing market entry of renowned, global automobile manufacturers. These manufacturers recently have noticed the potential of car sharing to introduce new technologies like e-mobility or mobile systems’ car integration. While car sharing already has many and various customers, the Leibniz Universität Hannover now focuses on developing new ways to interest students in this form of individual and new mobility. The discussed quantitative car sharing model for student customers is developed from a general business model for car sharing in Hannover (Quicar by Volkswagen). Facing student customers the business model has to deal with two basic challenges. On the one hand, different students’ needs of mobility must be addressed and on the other hand students’ willingness to pay must be addressed. The development of a student specific price model includes car sharing rates for driving as well as parking on a minute pay per use basis. In the first step use cases are identified in order to illustrate typical students’ mobility needs. Then these cases provide the basis for a student online survey to analyze the students’ willingness to pay (500 responses). Finally the calculation of student car sharing rates maximizes the car sharing providers’ revenue. An optimization problem under the constraints (a) student car sharing rates must not exceed their willingness to pay and (b) students car sharing rates must not deviate by more than 25 % from the general car sharing rates is solved.

Recently biodiesel has become more prominent in countries of the European Union (EU). The rapidly increasing domestic production and consumption of biodiesel is accompanied by increasing trade flows. It is questionable if these trade flows are caused mainly by EU regulations concerning trade or concerning the bioenergy sector. A sector-specific analysis taking industry patterns into consideration is necessary to evaluate the impact of these two policy areas on trade flows. The obtained results suggest that while the mandatory biofuel blending quota has a positive impact, investment subsidies cannot be shown to have any effect and trade integration might even have a trade inhibiting effect among EU members.

Today only 13 % of Germany’s energy demand of 2,500 TWh (1 Terawatt for p.a. for living, traffic and electricity is met with renewable energy sources. Total energy costs reach 300 billion Euro per year and imported fossil fuels reach 100 billion Euro per year. Significant parts of renewable power plants, e.g. wind turbines and photovoltaic cells, are also mainly imported, today. For the next decades Germany needs a careful and energy/cost-efficient transformation to a reliable and affordable energy system with a rapidly increasing share of renewable energy sources. Trillion of Euro total investments are necessary to build up renewable energy generation, storage, transformation and transportation. Power plants, vehicles, heater/air conditioner and boiler etc. must be replaced step by step in the next decades. Finance and risk management alternatives are important. Value creation chains and networks and the effects on the German labor market, Germany’s state taxes, Germany’s gross national product, and Germany’s trade balance are interesting, too. For holistic, techno-economic scenario analyses and optimization a multi-criteria, long term simulation and optimization model with many constraints is discussed which forms the basis for an interdisciplinary research project that has already started

Current results of revenue management and pricing policy research enable organizations to identify and realize optimal sales strategies and decisions. In response, enterprises which have to deal with the results of pricing policies are compelled to optimize their purchase. New developments in the aviation sector and airline industry along with a highly competitive flight ticket market and the importance of strategic purchase reveal the necessity of creating new solutions based on quantitative models. A decision-making process is outlined in order to support travel contract negotiations, an optimal supplier selection and allocation of flight tickets considering traditional airlines trade off against low-cost carriers, which provide less routes but usually lower costs.

In broadcasting advertisements, commercial breaks are offered to heterogeneous clients with uncertain demand. Interested in the efficient utilization of its limited airtime inventory, the TV station has to decide simultaneously which requests to accept or to deny and when ad spots should be scheduled. In addition, there may be clients that have preferences for specific ad slots with cancellation rates depending on provider’s slot assignments. Provoking the abortion of reservations or even relationships with the provider in traditional optimization, we provide a mathematical model considering both combinatorial aspects of the problem and said customer preferences. Furthermore, a simulation is performed in order to test the efficiency of the proposed approach.

Differentiated pricing is among the widely practised Revenue Management (RM) tactics in which a firm offers its products/services at differentiated prices to distinct markets. Earlier researches have shown that the benefits from differentiated pricing are evident when the market segmentation is assumed perfect which are regarded as distinct markets with deterministic demands. In perfect market segmentation customers associated with a market segment do not cannibalize (move) between market segments. However, it is not uncommon to notice that the market segmentation a firm exercises is seldom perfect, and due to imperfect segmentation customers cannibalize between market segments which is also referred as demand leakage. In addition to this, the demand is often uncertain, and thus a firm also experiences short sales and leftovers due to uncertain demand. This research addresses the issue of establishing an integrated framework to optimize price differentiation strategy, pricing, and order quantity for a firm that experiences demand leakage. The models to determine the optimal market segmentation strategy, pricing, and order quantities for a firm are developed facing price dependent deterministic demand, stochastic demand, and when the demand is stochastic, yet the distribution is unknown. The models are analyzed to identify the optimal pricing, order quantities, and price differentiation strategy. Numerical experimentation show that optimizing the price differentiation strategy (market segmentation) along with optimizing the joint pricing and order quantity decisions price significantly improve the revenue to a firm although it experiences customer cannibalization. This paper, however, only highlights the deterministic model and its analysis.

This paper uses a bi-level optimization model to formulate a specific type of capacity reservation contracts, namely take-or-pay contracts, where a buyer reserves a portion of a supplier’s capacity before demand is realized with discounted price. At first, we formulate the lower-level problem and solve a non-linear optimization model where a buyer decides on the amount of capacity to be reserved given the discounted and normal unit capacity price, demand probability distribution and maximum available capacity. Afterwards, we construct the upper-level model where there are a supplier and multiple buyers and the supplier must choose the discounted price and maximum available capacity for each of the buyers. Enforced by the behaviour of the model, we create a bi-level real-valued genetic algorithm to find good solutions for the model.

In this paper, we consider two dedicated parallel machines scheduling problem with precedence relations to minimize makespan. Complexity and approximation results are presented.

In this paper, we present MILP-formulations for project scheduling problems subject to general temporal constraints, where the fluctuation of resource utilizations within the project duration is to be minimized (resource leveling). Particularly, we consider a resource leveling objective function that coincides with the cumulative costs arising from increasing or decreasing the requirements of resources. The resulting “total adjustment cost problem” has achieved little attention in the literature, although it seems to be of considerable importance in real-life applications. A comprehensive performance analysis is provided showing that medium- as well as large-scale instances can be solved to optimality using CPLEX 12.4.

The weighted earliness tardiness parallel machine scheduling problem is NP hard. It is herein approximately solved using a decentralized multi-agent system (MAS). MAS acts as a moderator of two types of agents: free jobs, and groups of jobs assigned to machines. MAS yields good solutions’ quality in reduced run times as evidenced by the computational results. Most importantly, it can be easily adapted to similar problems.

When scheduling on parallel machines, these may exhibit periods of unavailability, due to maintenance or failures, or due to jobs that must be executed at certain predefined times. We consider the problem of non-preemptively scheduling a given set of tasks on identical processors with periods of unavailability to minimize the maximum completion time. This problem is strongly NP-hard, thus polynomial approximation algorithms are being studied for its solution. Often considered approximation algorithms for multiprocessor scheduling and generalizations thereof are LPT (largest processing time first) and Multifit with their variants. We give a simple polynomial Multifit-based algorithm, the schedules of which end within 1.5 the maximum between the end of the optimal schedule and the latest end of a downtime when there are at most two downtimes on each machine. Even when there is at most one downtime on each machine, no polynomial algorithm can insure a better worst-case bound for this problem unless P=NP. For the case when there is at most one downtime on each machine we also present a simple LPT-based algorithm which has the same property. We also give details of the upper bound proofs.

In projects with alternative modalities, the jobs that actually have to be implemented are not known beforehand, i.e. the project structure is unknown. However, established formulations of the resource-constrained project scheduling problem (RCPSP) assume the project structure to be given in advance. In this paper, the RCPSP is extended by a model-endogenous decision on the project structure in order to include those alternative modalities. The requirements on the project structure decision problem are explained and it is shown that this problem occurs, e.g., with scheduling the turnaround at airports. As a solution approach aspects of a genetic algorithm are presented.

In this paper, an integrated agent-based (AB) and system dynamics (SD) model is developed to study the market share evolution of light duty vehicles in Iceland. The model takes into account the conventional internal combustion engine vehicles that are currently dominant in the market and the alternative fuel vehicles including various types of electric vehicles, bio-fuels and fuel cell vehicles. SD approach is used to simulate the development of continuous, homogenous and aggregate variables of the energy supply system over time. The AB approach is employed to study the consumer behavior and market share evolution of passenger vehicles. Different vehicles compete for market penetration through a vehicle choice algorithm that accounts for social influences and consumers’ attractiveness for vehicle attributes. The linking variables between AB and SD components are identified and then these two bottom-up and top-down approaches are integrated to provide a comprehensive framework. The main results provided by the application of this modeling approach for the case study of Iceland are market share evolution of various vehicle types and consumers’ fuel demands during the period 2013–2050.

Power demand of private households shows daily fluctuations and is expected to rise with the introduction of power intense technologies like battery electric vehicles (BEV) and heat pumps. This additional demand, especially when it remains unmanaged, will lead to an increase in fluctuations. To balance demand, demand side management may be deployed by utilities. The aim of the paper is to develop a concept for modeling demand side management as interaction between utility and households. The model considers both, a structural and a behavioral level. On the structural level, energy usage and flows are modeled as a mathematical network flow problem. The behavior level represents the consumers’ behavior and the utility-consumer interaction as an agent-based model.

This contribution focusses on an innovative IT-based decision support concept for turning on Germany’s energy transition. Considering the historical development of renewable energy in Germany over the last 20 years, the success story of green energy has to be interpreted to understand Germanys beginning energy transition. Being a core part of the actual electricity economy, renewable energy sources are still discussed and consequently bring new challenges for the interaction with the energy system itself. By modeling the latest project “Nord.Link” within the actual electricity system with the help of System Dynamics, the active public should be able to understand the complex interactions. System Dynamics modeling supports the comprehension about the main interdependencies related to the successful market integration of new sustainable technologies, which are essential for Germanys energy transition in the next 20 years. Therefore it can be mentioned that System Dynamics is trying turning on the energy transition process in Germany. This contribution focusses especially on the IT-based decision support component.

Digital medical images must be stored or transmitted via internet in a secure way to ensure authenticity and preserve image quality to avoid mistake in medical diagnosis. A multiple watermarking scheme can serve these purposes. The multiple watermark consists of a robust part and a fragile part. The robust part is embedded in Region of Non-Interest within a medical image, usually containing ownership and medical record of a patient. Meanwhile, a fragile part is embedded in Region of Interest within a medical image which it can detect a tampering or modification in the medical image. In this paper, we implement Reed Muller Code in multiple watermarking based on Wavelet and Hash Block Chaining to achieve the purposes mentioned above. We show that the proposed scheme can detect some tampering and increase robustness from various attacks.

Railway traffic now and in future faces ever-growing challenges. On the one hand, infrastructure measures must be planned and in the medium respectively long term corresponding operating programs have to be generated. On the other hand, in the short term economical and political reasons exert increasing influence on the requirements of timetabling as well. Consequently, it exists the need for highly optimized, automated algorithms and its corresponding intelligent conjunction. A state-of-the-art realization is reflected in the software system TAKT. The implementation offers a complete new approach to solve the problems like computing conflict-free, optimized time tables or searching, optimizing and maximizing rail freight transport train paths based on an existing operating program.

The augmented use of renewable energy sources will increase the volatility of the residual load in the next few years. Under these circumstances, the existing numerical methods for solving the unit commitment problem reach the performance limits in terms of their rate of convergence. Therefore new methods with a higher convergence are necessary in the unit commitment optimization. This paper intends to exemplify the use of Proximal Bundle Methods in large scale unit commitment.

In digital communication systems, blind equalizers are used to combat inter-symbol interference (ISI), by only exploiting some statistical and geometrical properties of the transmitted signals. One of the first schemes of blind equalization is the constant modulus algorithm (CMA) which is known to be robust and requires simple hardware implementation. However, for quadrature amplitude modulations (QAM), used in high data-rate communications, the CMA leads to not sufficiently low residual errors. Our contribution presents a hybrid scheme based on the combination of the extended constant modulus algorithm (ECMA) and a modified version of a Gaussian criterion matched to the alphabet of the QAM symbols. The derived algorithm is tested on 512-QAM signals, where significant improvements in terms of mean square error (MSE) are obtained.

In this paper, periodic event scheduling problems (PESP) are encoded as satisfiability problems (SAT) and solved by a state-of-the-art SAT solver. Both the encoding, based on order encoded domains, and the valid use in terms of a proof are presented. The experimental evaluations suggest that the SAT-based approach outperforms constraint-based PESP solvers, which were considered to be the best solvers for PESP. This opens the possibility to solve more complex and larger real-world instances, such as timetabling for public railway transport networks.

The DB Netz AG is the most important railway infrastructure company in Germany and operates a railway network with a total length of about 34,000 km. One of the main tasks is to generate timetables to sell train-paths or enhance the infrastructure by eliminating bottlenecks. The creation of a timetable for the complete network is complex and time-consuming so there are efforts to atomize this process in essential parts. The software-system TAKT of the professorship “Verkehrsstroemungslehre” at the TU Dresden automatically generates timetables for complex railway networks on the base of periodic event scheduling problems (PESP) by using specialized algorithms. It provides practically useful solutions in appropriate runtimes because of its up to date techniques like a SAT-solver and an innovative routing-algorithm, which finds a well-suited train-path in the network automatically. To calculate a timetable for the whole Eastern German long-distance, regional and local traffic the solver needs a computation time of only few hours. In summary with the use of TAKT at DB Netz timetables for passenger and freight traffic can be generated automatically with a great quality in a very short time.

By means of semi-Markov process, a problem of rational organisation of task processing scheduling is solved for a GRID-System, where algorithms are realized, in which a task package is accumulated prior to resource allocation or intermediate pools of tasks from global queues are formed in accordance with task priorities. Verified distribution of amounts of tasks waiting for resources to be allocated, is used to specify a goal function as a function of a package size limit value. An optimal package size value is specified as a point of minimum of an introduced function.

When baggage is disposed to baggage belts to be picked up by passengers, we often face situations of imbalanced load distribution throughout belts. Thus, passengers might be unnecessarily crowded tightly around some belts whereas other belts remain unoccupied at the same time, which is an unpleasant situation for passengers as well as for airport staff. Moreover, imbalanced load distribution over a longer period of time leads to unsynchronized maintenance intervals. To address this problem within an existing Constraint Programming approach, we present measures for both kinds of imbalance which are equivalent to minimizing the variance of load distribution but allow for monotonous lower and upper bounds as well as for effective (partial) computation. Results of our approach are illustrated with real planning data of a German airport.

Level of repair analysis (LORA) is often defined as the problem of determining whether a component should be repaired or discarded upon its failure, and the location in the repair network to do such work. A related problem is the determination of the optimal number of spares for a given piece of equipment. Although LORA and spare provisioning are interdependent, they are seldom solved simultaneously due to the complex nature of the relationships between spare levels and system availability. In this paper, we propose to apply a multi-objective genetic algorithm (specifically the Non-dominated Sorting Genetic Algorithm II) with optimization objectives of repair costs (e.g., spare parts, spares transportation, spares storage) and spare parts availability. The approach uses a Monte Carlo simulation to generate scenarios based on a dataset which includes the expected failures of the equipment and their associated probabilities. The objective functions are computed at each genetic algorithm generation based on the generated scenarios. An example that can be used for a trade-off analysis is provided.

Fashion discounters face the problem of ordering the right amount of pieces in each size of a product. The product is ordered in pre-packs containing a certain size-mix of a product. For this so-called lot-type design problem, a stochastic mixed integer linear programm was developed, in which price cuts serve as recourse action for oversupply. Our goal is to answer the question, whether the resulting supply strategy leads to a supply that is significantly more consistent with the demand for sizes compared to the original manual planning. Since the total profit is influenced by too many factors unrelated to sizes (like the popularity of the product, the weather or a changing economic situation), we suggest a comparison method which excludes many outer effects by construction. We apply the method to a real-world field study: The improvements in the size distributions of the supply are significant.

We consider a multi-allocation problem where the transport is handled by complete (integer-valued) trucks. It consists of two parts: A number of hubs are chosen out of a given set of depots; then the given transport relations are individually assigned to two hubs, one hub or direct transport. Having four-index variables for the routing and integer variables for the trucks, this MIP becomes difficult. Our heuristic approach gives much better results than a Cplex implementation and can be used to generate a restricted problem which can again be given to Cplex. The idea is as follows: Considering the whole network as collection of

n

trees sending goods to a chosen depot, we improve the total costs step by step: For that we always take the edge with most expensive transport and try to find a new route for it. In our paper we will explain the theoretic ideas, point out different possibilities and connect them to computational results.

Screening contracts (or non-linear "menu of contracts") are frequently used for aligning the incentives in supply chains with private information. In this context, it is assumed that all supply chain parties are strictly (expected) profit maximizing and, therefore, sensible to even arbitrarily small pay-off differences between contract alternatives. However, previous behavioral work on contracting under asymmetric information in supply chains shows that agents (buyers) are not always strictly profit maximizing. The contribution provides researchers and managers an approach on how to account for such an insensitivity to arbitrarily small pay-off differences. The results highlight that supply chain performance losses can be substantially reduced under the behavioral robust contract.

The integrated scheduling of production and transport systems is a NP-hard mixed-integer problem. This paper introduces a genetic algorithm (GA) that addresses this problem by decomposing it into combinatorial and continuous subproblems. The binary variables of the combinatorial subproblem form the chromosomes of each individual. Knowledge-based evolutionary operators are deployed for reducing the solution search space. Furthermore, dependent binary variables are identified which can be efficiently determined rather by a local search than by the evolutionary process. Then, in the continuous subproblem, for fixed binary variables, the optimization problem turns into a linear program that can be efficiently solved, so that the fitness value of an individual is determined.

This paper presents a new mixed-integer linear programming (MILP) model for facility location for the simultaneous design of forward and reverse supply chains. The main contribution of this study is the formulation of a dynamic (i.e., multi-period), multi-echelon, multi-commodity capacitated facility location problem in a strategic planning context. To assess the long-term impact of the problem, our model intends to maximize the net present value (NPV) of expected cash flows for the whole supply chain. We take into consideration various features of the problem: location, capacity allocation, processing-distribution system, supplier selection and supply chain subcontracting. We examine issues related to relocation and capacity expansion under increasing deterministic, dynamic product demands and returns. Numerical results for the issues presented are given through a case study from the generated test data.

We consider a supply chain with one supplier and one retailer in which the supplier produces a single product demanded by the retailer. The two parties in the supply chain negotiate on a contract for the delivery of the product, and the contract specifies the number of firm commitments and options. The retailer who aims to increase his own profit without any concern about the supplier’s profitability increases the uncertainty on the supplier’s side by changing the cost structure of the contract. In this research, we integrate "contracts as reference points" theory (Hart and Moore Ouarterly J Econ 123:1–48, 2008) into quantity flexible contracts to price the indirect effects of retailer’s behavior on his own payoff function. Under voluntary compliance, we show that there is a trade-off for the retailer between decreasing the option cost and increasing the indirect behavioral cost that is imposed by the supplier when the uncertainty on her payoff function is increased by the retailer. Apart, we also show that the indirect cost of the retailer increases as the supplier becomes more critical to the retailer.

In this study, we consider an inventory system for a single item, which is being used in the military operations. In the current system, there is a two-echelon inventory setting which consists of a single stock point, the central depot, in the upper echelon and several stock points, the bases, in the lower echelon. A continuous review base-stock policy is used by all facilities. The military headquarters responsible for inventory management identified improvement opportunities such as acquiring repair ability and changing the structure of the supply chain from a two-echelon model to a single echelon model. Considering these opportunities, we investigate four different alternative inventory systems, single-echelon and two echelon models with or without repair ability. Our objective is to determine if and under what conditions each alternative results in lower costs.

Multi-objective equilibrium models of traffic assignment state that users of road networks travel on routes that are efficient with respect to several objectives, such as travel time and toll. This concept provides a general framework for modelling traffic flow in tolled road networks. We present the concept of time surplus maximisation as a way of handling user preferences. Given a toll, users have a maximum time they are willing to spend for a trip. Time surplus is this maximum time minus actual travel time. A rational user can be assumed to maximise time surplus, leading to the definition of time surplus maximisation bi-objective user equilibrium. We propose to use such models on the lower level of bi-level models for pricing in road networks under multiple upper level objectives such as minimising total travel time and emissions. In such a model a multi-objective optimisation problem at the upper level is combined with a multi-objective equilibrium problem at the lower level.

For single-pair shortest path problems, Contraction Hierarchies (CH) provide very small query times together with a very low space overhead for the graph. During a preprocessing every node is assigned a distinct rank. Queries are performed using an alternating bidirectional Dijkstra search that only expands towards higher ranked nodes. In its original version CH do not take turn restrictions (TR) into account. This may lead to illegal routes whose length may considerably differ from legal ones. Incorporating TR widens the scope of application for CH, e.g., car navigation or precise traffic simulations. A way to consider TR is to switch from a node-based to an edge-based search, implicitly allowing nodes to be settled more than once. Compared to the original CH query this increases the size of the search trees substantially. A more space efficient adaptive algorithm combining node-based with on-demand edge-based search is presented, where nodes may only be settled multiply if the encountered path towards them traverses a (possible) TR. The number of iterations in this approach is at most as high as the edge-base search. Depending on the number of TR and the average node degree in the graph our approach outperforms the edge-based search by far.

In the field of robust flight optimization, disruption and delay propagation play a central role. Recently, huge amounts of data became available for analysis of these delays. For example, empirical distribution functions can be estimated and used as input for scheduling systems. But for a better understanding of the underlying mechanisms, distribution models with explicit dependencies and interpretable parameters are more desirable. In this paper we present new patterns in flight delay data and an initial model of the underlying physical processes.

Battery electric vehicles seem to offer great opportunities in context with ecological compatibility of urban transport systems. Along with the technical innovations appropriate operating models are required. In this contribution we propose an extension of the Vehicle Routing Problem, which adapts most of the needs for operating electric vehicles. This includes an objective function that considers energy consumption depending on driving resistances and loading weight, as well as additional restrictions to handle the maximum range depending on battery capacity and recharging options. To illustrate the problem a set of test instances is solved. For this purpose we present an adapted tabu search heuristics. The results are analyzed in respect of changes compared to distance-based optimization and the influence of battery capacity.

In transport networks consisting of nodes with surpluses or deficits of products, central transshipment facilities (hubs), and interconnecting links, strategic and tactical network design problems, as well as operational routing problems have to be solved. Strategic network design problems determine the number and location of hubs within the network.