Computational Logistics

At each generation of an ant algorithm, each ant builds a solution step by step by adding an element to it. Each choice is based on the

greedy force

(short term profit or heuristic information) and the

trail

system (central memory which collects information during the search process). Usually, all the ants of the population have the same characteristics and behaviors. In contrast in this paper, a new type of ant metaheuristic is proposed. It relies on the use of ants with different personalities. Such a method has been adapted to the well-known vehicle routing problem, and even if it does not match the best known results, its performance is encouraging (on one benchmark instance, new best results have however been found), which opens the door to a new ant algorithm paradigm.

In this work, we investigate the potential benefits of introducing relay stations in the round-trip ridesharing problem. In the classical round-trip ridesharing system, the pick-up and drop-off locations for the rider don’t differ from his origin and destination, respectively, for both outgoing and return trips. This system is straightforward but inflexible and unbalanced as it puts the whole detour effort on the driver’s shoulders. In this paper, we propose to consider a meeting location as flexible and to determine its optimal position minimizing total travel cost for the round-trip. The meeting locations correspond to relay stations in which riders find ridesharing vehicles. The introduction of relay stations in the round-trip ridesharing problem creates dependency between the outgoing and return trips, in the sense that the relay stations must be chosen in such a way as to minimize the combined travel cost of the outgoing and return trips. In this setting, the rider is supposed to drive to the relay station with his private car and to park it there, so the return trip has to drop him there to get his car back. We present efficient algorithms to solve this problem and, finally, we perform a comparative evaluation using a real road network and real dataset provided by a local company. Our numerical results show the effectiveness of our system, which improves participants’ cost-savings and matching rate compared to the classical round-trip ridesharing system.

This paper presents a hierarchical model that incorporates strategic, tactical, and operational decisions of cash transfer management system of a bank. The aim of the model is to decide on the location of cash management centers, the number and routes of vehicles, and the cash inventory management policies to minimize the cost of owning and operating a cash transfer system while maintaining a pre-defined service level. Owing to the difficulty of finding optimal decisions in such integrated models, an iterative solution approach is proposed in which strategic, tactical, and operational problems are solved separately via a feedback mechanism. Numerical results show that such an approach is quite effective in reaching at greatly improved solutions with just a few iterations, making it a very promising approach for similar models.

We consider a real fuel supply vessel routing and scheduling problem faced by a Hellenic oil company with a given fleet of fuel supply vessels used to supply customer ships outside Piraeus Port. The supply vessels are loading fuel at refineries in the port area before delivering it to a given set of customer ships within specified time windows. A customer ship may place orders of more than one fuel type, and all orders placed by a customer ship do not have to be serviced by the same vessel, meaning customer splitting is possible. Fuel transported to the customer ships is allocated to compartments on board the supply vessels, and fuels of different types cannot be mixed in the same compartment. The objective is to design routes and schedules for the supply vessels while maximizing the company’s profit. We propose a mixed-integer programming (MIP) model for the problem and provide a computational study based on real instances.

We study an extension of the delivery dispatching problem (DDP) with time windows, applied on LTL orders arriving at an urban consolidation center. Order properties (e.g., destination, size, dispatch window) may be highly varying, and directly distributing an incoming order batch may yield high costs. Instead, the hub operator may wait to consolidate with future arrivals. A consolidation policy is required to decide which orders to ship and which orders to hold. We model the dispatching problem as a Markov decision problem. Dynamic Programming (DP) is applied to solve toy-sized instances to optimality. For larger instances, we propose an Approximate Dynamic Programming (ADP) approach. Through numerical experiments, we show that ADP closely approximates the optimal values for small instances, and outperforms two myopic benchmark policies for larger instances. We contribute to literature by (i) formulating a DDP with dispatch windows and (ii) proposing an approach to solve this DDP.

In order to reduce the greenhouse effect caused by road haulage, new methods for transportation planning need to be developed. The amount of combusted diesel on a route segment of a tour depends to a large extend on the travel distance, the curb weight and the actual payload of the vehicle traversing that segment. Both, using an adequate mixed fleet and allowing split deliveries, open up options for reducing greenhouse gas (GHG) emissions caused by transportation. A MIP-model for a GHG based vehicle routing problem with a mixed fleet and split deliveries is presented. To demonstrate the achieved potential for emission reduction, we analyze results of applying our model to instances generated for homogeneous and mixed fleets.

Liquefied Natural Gas (LNG) is becoming a more crucial source of energy due to its increased price competitiveness and environmental friendliness. We consider an inventory routing problem for inland distribution of LNG from storage facilities to filling stations. Here, an actor is responsible for the inventory management at the storage facilities and filling stations, as well as the routing and scheduling of a heterogeneous fleet of vehicles. A characteristic of the problem is that a constant rate of LNG evaporates each day at the storage facilities and filling stations. This is in contrast to maritime LNG inventory routing problems where the evaporation is considered at the ships only. The combined LNG routing and deteriorating inventory management problem is modelled with both an arc-flow and a path-flow formulation. Both models are tested and compared on instances motivated from a real-world problem.

We propose a matheuristic approach to solve several types of vehicle routing problems (VRP). In the VRP, a fleet of capacitated vehicles visits a set of customers exactly once to satisfy their demands while obeying problem specific characteristics and constraints such as homogeneous or heterogeneous fleet, customer service time windows, single or multiple depots. The proposed matheuristic is based on an ant colony optimization (ACO) algorithm which constructs good feasible solutions. The routes obtained in the ACO procedure are accumulated in a pool as columns which are then fed to an integer programming (IP) optimizer that solves the set-partitioning (-covering) formulation of the particular VRP. The (near-)optimal solution found by the solver is used to reinforce the pheromone trails in ACO. This feedback mechanism between the ACO and IP procedures helps the matheuristic better converge to high quality solutions. We test the performance of the proposed matheuristic on different VRP variants using well-known benchmark instances from the literature. Our computational experiments reveal competitive results: we report six new best solutions and meet the best-known solution in 120 instances out of 193.

We solve the liner shipping fleet repositioning problem (LSFRP), a key problem in the liner shipping industry, using a hybrid reactive tabu search and simulated annealing algorithm in combination with novel local search neighborhoods. Liner carriers reposition vessels between services in order to add, remove or modify services in their network. Repositioning vessels costs between hundreds of thousands and millions of dollars, meaning finding cost efficient repositioning plans is an important goal for liner carriers. We introduce a reactive tabu search approach and hybridize it with simulated annealing, allowing us to find a combined $147,000 in additional profit over the state-of-the-art approach across 44 public LSFRP instances.

The risk analysis and vulnerability quantification in the global maritime transportation networks are important to maintain the healthy economy in today’s world. In this paper, we analyze the auto identification system (AIS) data that provides us with the real-time location of vessels. The AIS data of a Japanese company was used to compute the throughputs of the ports for the vessel it operates and the topology of the global maritime transportation network during a certain time period. Firstly, we computed the conventional un-weighted node-level characteristics and compared it with the port throughput. This comparison shows the statistically significant correlations, especially, with the in-degree and the Page-Rank. Secondly, we modeled and simulate to quantify the vulnerability and importance of each port identified from the AIS data. The simulation results indicate that Singapore is the most robust and influential port when disrupted. In addition, we introduce a method to compute the vulnerability and importance analytically. Subsequent research will be required to extend the proposed analysis to the complete data sets for all cargo-ships and utilize the high performance computing technologies to accelerate the computation.

In this paper we study a maritime pickup and delivery problem with time windows and stowage constraints. The problem is inspired by a real life ship routing and scheduling problem from a sub-segment of shipping known as

project shipping

. What is unique about this sub-segment is that they transport unique and specialized cargoes, that make stowage onboard the ships complex, and thus must be considered when creating routes and schedules for the ships. To solve this problem we propose a branch-and-price method, including a novel way of solving the subproblems using a labeling algorithm. To speed up the solution process we relax some of the restrictions in the subproblem, giving us a simpler subproblem that may produce infeasible paths. The computational results show the branch-and-price method provides optimal solutions to many instances previously unsolved by the literature, and that it is competitive with the tabu search heuristic for many instances.

An autonomous vessel can improve the intelligence, efficiency and economy of shipping logistics. To realize autonomous navigation control of underactuated vessels (USVs), this paper presents a controller that can make USV track a reference trajectory with only 2 inputs, i.e., surge and yaw. A nonlinear state-space model with 2 inputs considering environmental disturbances induced by wind, current and waves for a 3 degree of freedom (DOF) surface vessel is considered. Based on this model, a trajectory tracking controller using Nonlinear Model Predictive Control (NMPC) is designed. System constraints on inputs, input increment and output are incorporated in the NMPC framework. Simulation results show that the controller can track an ellipse trajectory well and that the tracking errors are within acceptable ranges, while system constraints are satisfied.

This paper investigates cooperative waterborne AGVs which are appealing for intelligent transport in port areas. The systems are with independent dynamics and objectives but coupling constraints due to collision avoidance requirements. The goal is to develop an algorithm that locally solves subproblems while minimizing an overall objective in a cooperative distributed way. The proposed approach is based on the decomposition-coordination iterations of the alternating direction method of multipliers (ADMM). In particular, we separate the variables into two sets: one concerned with collision avoidance constraints, and the other concerned with local problems; and add consensus constraints for these two. Local subproblems can be solved fully in parallel. Furthermore, optimization problems are formulated and solved in a receding horizon way. Successive linearizations of nonlinear vessel dynamics as well as the non-convex collision avoidance constraints about a shifted optimal trajectory from a previous step are implemented to maintain a trade-off between computational complexity and optimality. Simulation results are presented to illustrate the effectiveness of the proposed algorithm for cooperative distributed collision avoidance.

We present a mathematical model for the liner shipping network design problem with transit time restrictions on the cargo flow. We extend an existing matheuristic for the liner shipping network design problem to consider transit time restrictions. The matheuristic is an improvement heuristic, where an integer program is solved iteratively as a move operator in a large-scale neighborhood search. To assess the effects of insertions/removals of port calls, flow and revenue changes are estimated for relevant commodities along with an estimation of the change in the vessel cost. Computational results on the benchmark suite

LINER-LIB

are reported, showing profitable networks for most instances. We provide insights on causes for rejecting demand and the average speed per vessel class in the solutions obtained.

High precision positioning for a ship is important during logistics automation on water. This paper has proposed a positioning system of model ship based on monocular vision. Firstly an image of the experiment pool can be obtained by the camera on shore before the image is filtered and corrected in the preprocessing. Then the system uses binarization area and color threshold to identify a ship target. Finally ship real-time information which includes position, navigation speed and course can be obtained by transforming the image coordinate into the inertial coordinate, and the ship track will be displayed on the positioning software at the same time. Experimental results show that real-time and positioning precision of this system can meet automatic navigation demand for model ship.

A considerable shift from road transport to inland navigation and railway is expected in the following years in France. By focusing on inland navigation, this expected increase of transport will require an efficient management of the infrastructure and water. Inland navigation requires water levels kept within the navigation rectangle. Hence it is necessary to design efficient control algorithms for water levels. Model Predictive Control (MPC) is proposed to regulate the water level of canals with locks. This controller maintains the level close to the navigation objective by rejecting disturbances mainly caused by lock operations. In this paper, MPC is designed by considering realistic constraints on the dynamics of the gates and the available supplied discharges. It allows taking into account several operating conditions that correspond to normal, drought and flood situations. MPC strategy is tested on a numerical simulation of the Cuinchy-Fontinettes reach that is located in the north of France.

Recent statistics show that large container terminals can process more than 30 million containers a year, and are constantly in search for the better ways to optimize processing time, deliver high quality and profitable services. Some of the terminal decisions are, however, dependent on externalities. One of those is the ship loading process. Based on the stowage plan received by liner shippers, terminal operators plan the execution of load and discharge operations. In this paper we present a literature review for the Ship Loading Problem, where stowage and loading sequencing and scheduling are integrated to improve the efficiency of the ship handling operations. We present a survey of the state-of-the-art methods and of the available benchmarking data.

Nowadays, Short Sea Shipping (SSS) is an essential part in European multi-modal transport system, representing approximately thirty-seven per cent of intra-Community transactions in tonnes per kilometre (tkm). Since 2001, the European Shortsea Network (ESN) in partnership with the short-sea Promotion Centres (SPC) of each Member State of the European Union (EU) have managed to make significant progress in the promotion and development of this mode of transport.

This paper aims to assess and analyse the SSS of containerised goods in Portugal and its articulation with other EU routes and also other transport modes. The current SSS infrastructure, how the sector is organized, as well as the future perspectives for the sector are also analysed for the case of Portugal.

The analyses are based on a survey that was carried out on the logistics operators, navigation agents, freight forwarders, and the leading imports and exports manufacturers in Portugal.

In traditional approaches, the objective of yard crane (YC) dispatching is usually to minimize makespan of YC operations or to minimize vehicle waiting time. However, one of the most important objectives of terminal operation management is to minimize total weighted vessel turnaround time. We prove that minimizing the maximum tardiness of vehicle jobs from one vessel will minimize the vessel’s turnaround time. Therefore minimizing the total weighted maximum tardiness of all YCs’ jobs will minimize the total weighted vessel turnaround time. We propose algorithm MTWMT to minimize total weighted maximum job tardiness. We compare the performance of our algorithm with the optimal algorithms RBA* and MMS-RBA* from earlier studies. RBA* minimizes total vehicle waiting time while MMS-RBA* minimizes makespan. Experimental results confirm that MTWMT is most effective in reducing total weighted vessel turnaround time.

While inland vessels are becoming more important in container transport between terminals in the port and the hinterland, in a large sea port like port of Rotterdam, only 62% of the inland vessels leave the port on time. Poor alignment of inland vessels leads to significant uncertainty in waiting times of vessels at terminals, low utilization of terminal quay resources, and long sojourn time of vessels in the port. To improve this, it is important to find the sequences in which vessels visit different terminals in an efficient way. This paper presents a novel approach to find the visiting sequence of groups of inland vessels in a large seaport with the objective of better alignment of activities. The approach consists of two phases, namely single vessel optimization and multiple vessel coordination. The single vessel optimization problem is first solved using mixed integer programming, preference-based and utility-based coordination rules are then used to solve the multiple vessel coordination problem. We evaluate the performance of the proposed approach with respect to departure time of the last vessel, total sojourn time and waiting time. Simulation results show improvements with earlier departure time, as well as much shorter sojourn time and waiting time.

The Automatic Identification System (AIS) is used to identify and locate active maritime vessels. Datasets of AIS messages recorded over time make it possible to model ship movements and analyze traffic events. Here, the maritime traffic is modeled using a potential fields method, enabling the extraction of traffic patterns and anomaly detection. A software tool named STRAND, implementing the modeling method, displays real-world ship behavior patterns, and is shown to generate traffic rules spontaneously. STRAND aids maritime situational awareness by displaying patterns of common behaviors and highlighting suspicious events, i.e., abstracting informative content from the raw AIS data and presenting it to the user. In this it can support decisions regarding, e.g., itinerary planning, routing, rescue operations, or even legislative traffic regulation. This study in particular focuses on identification and analysis of traffic rules discovered based on the computed traffic models. The case study demonstrates and compares results from three different areas, and corresponding traffic rules identified in course of the result analysis. The ability to capture distinctive, repetitive traffic behaviors in a quantitative, automatized manner may enhance detection and provide additional information about sailing practices.

The aim of this paper is to propose an approach for forecasting passenger (pax) demand between airports based on the median pax demand and distance. The approach is based on three phases. First, the implement of bootstrap procedures to estimate the distribution of the mean pax demand and the median pax demand for each block of routes distance; second, the estimate pax demand by calculating boostrap confidence intervals for the mean pax demand and the median pax demand using bias corrected accelerated method (BCa); and third, by carrying out Monte Carlo experiments to analyse the finite sample performance of the proposed bootstrap procedure. The results indicate that in the air transport industry it is important to estimate the median of the pax demand.

The computational challenge offered by most traditional network flow models is modest, and large scale instances can be solved fast. The challenge becomes more serious if the composition of the flow has to be taken into account. This is in particular true for the pooling problem, where the relative content of certain flow components is restricted. Flow entering the network at the source nodes has a given composition, whereas the composition in other nodes is determined by the composition of entering flows. At the network terminals, the flow composition is subject to restrictions. The pooling problem is strongly NP-hard. It was recently shown that at least weak NP-hardness persists in an instance class with only two sources and terminals, and one flow component subject to restrictions. Such instances were also shown to be solvable in pseudo-polynomial time if a particular fixed-size instance class, called atomic instances, can be solved in terms of a constant number of arithmetic operations. This work proves existence of such a closed-form solution to atomic instances.

Containers stowage optimization is a long-standing problem in the maritime industry. Since the problem was shown to be NP-hard, it is computationally challenging to obtain an optimal solution. We first review an efficient 2-phase block stowage scheme which can generate a feasible initial solution in just a few minutes. Since the algorithm relies heavily on checking whether any constraints will be violated by stowing a container at a specific location, we investigate the impact of changing the order in which the constraints are checked on the execution time of the algorithm. We evaluate seven different strategies for ordering the sequence in which the constraints are tested. Experiments based on real stowage instances show that, by strategically reordering the constraints test sequence, we can achieve 2 times speedup on the stowage planning algorithm on average, and up to 33 times speedup in certain instances.

Consider the situation where some items arrive to a storage location where they are temporarily stored in bounded capacity LIFO stacks until their departure. We consider the problem of deciding where to put an arriving item with the objective of using as few stacks as possible. The decision has to be made as soon as an item arrives and we assume that we only have information on the departure times for the arriving item and the items currently at the storage area. We are only allowed to put an item on top of another item if the item below departs at a later time. We assume that the numbers defining the storage time intervals are picked independently and uniformly at random from the interval [0, 1]. We present a simple polynomial time online algorithm for the problem and prove the following: For any positive real numbers

$$\epsilon _1, \epsilon _2 > 0$$

there exists an

$$N > 0$$

such that the algorithm uses no more than

$$(1+\epsilon _1)OPT$$

stacks with probability at least

$$1-\epsilon _2$$

if the number of items is at least

N

where

OPT

denotes the optimal number of stacks. The result even holds if the stack capacity is

$$o(\sqrt{n})$$

where

n

is the number of items.

Logistic Service Providers (LSPs) offering hinterland transportation face the trade-off between efficiently using the capacity of long-haul vehicles and minimizing the first and last-mile costs. To achieve the optimal trade-off, freights have to be consolidated considering the variation in the arrival of freight and their characteristics, the applicable transportation restrictions, and the interdependence of decisions over time. We propose the use of a Markov model and an Approximate Dynamic Programming (ADP) algorithm to consolidate the right freights in such transportation settings. Our model incorporates probabilistic knowledge of the arrival of freights and their characteristics, as well as generic definitions of transportation restrictions and costs. Using small test instances, we show that our ADP solution provides accurate approximations to the optimal solution of the Markov model. Using larger problem instances, we show that our modeling approach has significant benefits when compared to common-practice heuristic approaches.

Renewed attention has emerged for the topic of intermodal transportation. Since a couple of years, research has focused on the concept of

synchromodality.

Synchromodality refers to creating the most efficient and sustainable transportation plan for all orders in an entire network of different modes and routes, by using the available flexibility. In this paper we provide an overview of relevant research around three topics related to the case of European Gateway Services, the network orchestrator of container transportation network in the Rotterdam hinterland. For each topic we describe studies with practical relevance and recent results. Finally, we conclude by describing topics for further research with relevance for future practical developments.

Roughly one third of the food is lost worldwide yearly. Much of the loss happens during transport. With the emergence of information and communication technology, new intelligent ways of arranging food transport can be developed. By making transport more intelligent and efficient, the loss of food can be reduced. This survey performs a review on three critical aspects of food transportation, for considerations of an intelligent food transport system. Firstly, the indicators that reflect and factors that affect food quality are discussed. Then, shelf life modeling approaches are analyzed. Thirdly the impacts of shelf life and information technology on the transport system are discussed. Although great achievements have been made, there is much room for further research, as this survey points out, in order to establish a quality-oriented transport system for less food loss.

A number of cities around the world have adopted urban consolidation centers (UCCs) to address challenges of last-mile deliveries. At the UCC, goods are consolidated based on their destinations prior to their deliveries into city centers. Typically, a UCC owns a fleet of eco-friendly vehicles to carry out such deliveries. Shippers/carriers that make use of the UCC’s service hence no longer need to be restricted by time-window and vehicle-type regulations. As a result, they retain the ability to deploy large trucks for the economies of scale from the source to the UCC which is located outside the city center. Furthermore, the resources which would otherwise be spent in the city center can then be utilized for other purposes. With possibly tighter regulation and thinning profit margin in near future, requests for UCC’s services will become more and more common, and there is a need for a market mechanism to allocate UCC’s resources to provide sustainable services for shippers/carriers in a win-win fashion. An early work of our research team (Handoko et al.,

2014

) proposed a profit-maximizing auction mechanism for the use of UCC’s last-mile delivery service. In this paper, we extend this work with an idea of rolling horizon to give bidders more flexibility in competing for the UCC’s resources in advance. In particular, it addresses the needs of many shippers/carriers to be able both plan deliveries weeks ahead and at the same time bid for the UCC’s service at the last minute. Under our rolling horizon framework, the capacity of the same truck is up for bid in several successive auctions. To allocate truck capacities among these auctions under future demand uncertainty, we propose a virtual pricing mechanism which makes use of Target-oriented Robust Optimization techniques.

We consider the direct long-haul transportation network of a parcel service provider where transports are carried out using swap bodies. Our focus is on residual volumes, which are not enough to fill a swap body, and investigate how consolidation using hubs can lead to cost reduction through better capacity utilization. We developed a corresponding model minimizing total costs consisting of transportation costs for the swap bodies and costs for the additional sorting required in the hubs.

Bundling freight flows are significant important in intermodal transport, as all stakeholders involved, e.g. infrastructure owners and transport providers have to find the best solution to meet the transport demand with reasonable cost. Therefore, consolidation and spilt of the freight are inevitable during the transport. As a result, extensive operations must be performed to ensure the exchange between freight flows. The aim of this paper is to give an integrated perspective of the railway intermodal bundling operations. Key operations in bundling rail freight are addressed and discussed, literature in relevant area are classified according to the characteristic of operation. Finally, current trends and limitation in these topics are discussed.

Recent and future technology development make intelligent transport systems a reality in contemporary societies leading to a higher quality, performance, and safety in transportation systems. In a big data era, however, efficient information technology infrastructures are necessary to support real-time applications efficiently. In this paper, we review different control structures based on model predictive control and embed them in cloud infrastructures. We especially focus on conceptual ideas for intelligent road transportation and explain how the proposed cloud-based system can be used for parallel and scalable computing supporting real-time decision making based on large volumes and a variety of data from different sources. As such, the paper provides a novel approach for applying data-driven intelligent transport systems that utilize scalable and cost-efficient cloud infrastructures based on model predictive control structures.

Freight networks are more exposed to unforeseen events leading to delays compromising the delivery of cargo on time. Cooperation among different parties present at freight networks are required to accommodate the occurrence of delays. Cargo assignment to the available transport capacity at the terminal is addressed using a Model Predictive Control approach in this paper, taking into consideration the final destination and the remaining time until due time of cargo. A cooperative framework for transport providers and intermodal hubs is proposed in this paper. The cooperation is based on information exchange regarding the amount of cargo at risk of not reaching the destination on time. The terminal searches for a faster connection at the terminal to allocate the cargo at risk such that the final destination is reached on time. The proposed heuristic is a step towards sustainable and synchromodal transportation networks. Simulation experiments illustrate the validity of these statements.

Port-related emissions are a growing problem for urban areas often located directly next to ports highly frequented by trucks and vessels. Empty truck trips are responsible for a significant share of these emissions. Truck appointment systems (TASs) allow scheduling of truck arrivals and enable collaboration among truckers. Though, TASs leveraging the potential to reduce avoidable emissions due to empty trips have hardly been studied. We aim to show how a TAS following this idea may be designed and evaluate the approach. We thus review requirements for a collaborative TAS and develop a discrete-event simulation model to assess coordinated truck appointments in a practical case of drayage. The results indicate that the approach effectively reduces port-related truck emissions, but might create congestion in the port. The considered case refers to drayage processes, but may also be transferred to the hinterland. The developed simulation model assumes a generic truck appointment process and may also serve to analyze diverse cases.

The substantial amounts of information that must be gathered, preserved, and used to analyse environmental and ecological impacts on seaports such as the international standards, deserve a direct way to manage and improve those impacts in a seaport through a systematic environmental management system (EMS). We present an artefact called the conceptual intelligent decision-making support module (

i

-DMSS) to enhance cooperative seaport decision-making (COSEADM) in environmental and ecological sustainability. Three interrelated activities of data collection, descriptive and normative modelling, incorporate processes of handling the decision-making side and processes integrating engineering requirements to produce the conceptual

i

-DMSS module. We include two data-driven models to handle the decision-making side of this module and automatically induce domain knowledge. Besides, we deploy and standardise the data-driven models and use the Predictive modelling markup language (PMML) to show advantages of data interoperability. Finally, we offer the rationale of the ontological process to anticipate and provide illustration of how to describe concepts in regard to COSEADM for environmental and ecological sustainability. This module demonstrates how the capture and interoperation of information and decisional structures can be managed.

This paper studies the computation of so-called order-up-to levels for a stochastic programming inventory problem of a perishable product. Finding a solution is a challenge as the problem enhances a perishable product, fixed ordering cost and non-stationary stochastic demand with a service level constraint. An earlier study [

7

] derived order-up-to values via an MILP approximation. We consider a computational method based on the so-called Smoothed Monte Carlo method using sampled demand to optimize values. The resulting MINLP approach uses enumeration, bounding and iterative nonlinear optimization.

This paper investigates transport services pricing problems faced by intermodal freight transport operators with fixed transport capacities in an intermodal freight transport network. We first present an optimal intermodal freight transport planning model to minimize the total transport cost. This model captures modality change phenomena, due time requirements, and the possibility to subcontract transport demands. A cost-plus-pricing strategy is proposed to determine the service price as the sum of the operational cost and the targeted profit margins of transport operators under different transport scenarios, i.e., self-transporting, subcontracting, and a combination of them. For the reference transport demand specified by a customer, a list of service packages with different due times, demand sizes, and the determined service prices will be offered to the customer. Based on the urgency of delivering containers and the prices of different service packages, the customer will make the final selection decisions. A case study is given to illustrate the planning model and our proposed pricing strategy.

Today’s company competiveness is favoured by product customisation and fast delivery. A strategy to meet this challenge is to manufacture standard items to stock for product customisation. This configures a hybrid environment of make-to-stock and make-to-order. To explore the advantages of this requires good understanding of production control. Thus, we study production under hybrid MTS-MTO, organising the system in two stages. The 1

st

manufactures items to inventory, which are then customised in the 2

nd

. We analyse how the percentage of tardy orders is affected by the inventory of items required to achieve a given fill rate. The impact of two mechanisms for releasing orders to both stages is also analysed. Results of a simulation study indicate that most of the reduction on the percentage of tardy orders is achieved by a moderate increase in the stock level of semi-finished products. Moreover the percentage of tardy orders decreases if suitable controlled release of orders is exerted.

Supply chains are becoming more complex and vulnerable due to globalization and interdependency between different risks. Existing studies have focused on identifying different preventive and reactive strategies for mitigating supply chain risks and advocating the need for adopting specific strategy under a particular situation. However, current research has not addressed the issue of evaluating an optimal mix of preventive and reactive strategies taking into account their relative costs and benefits within the supply network setting of interconnected firms and organizations. We propose a new modelling approach of evaluating different combinations of such strategies using Bayesian belief networks. This technique helps in determining an optimal solution on the basis of maximum improvement in the network expected loss. We have demonstrated our approach through a simulation study and discussed practical and managerial implications.

The Mexican government studies the possibility of building a new international airport (NAICM) in ZFLT or Tizayuca. The aim of this paper is to determine which the best location site of the NAICM is (ZFLT or Tizayuca) considering the maximization of the sum of expected air pax demand as main factor. To solve such problem, we propose: a mathematical formulation with the objective of maximizing the sum of expected air pax demand and a methodology to estimate air pax demand at each demand point based on an index to measure wealth. Results indicate that Tizayuca is the place where the NAICM should be located for a catchment area smaller than 500km or 4 hours travel time, and ZFLT is the place where the NAICM should be constructed for a catchment area longer than 500km or 4 hours travel time.

Optimization models for railway traffic rescheduling in the last decade tend to develop along two main streams. One the one hand, train scheduling models strives to incorporate any relevant detail of the railway infrastructure having an impact on the feasibility and quality of the solutions from the viewpoint of operations managers. On the other hand, delay management models focus on the impact of rescheduling decisions on the quality of service perceived by the passengers. Models in the first stream are mainly microscopic, while models in the second stream are mainly macroscopic. This paper aims at merging these two streams of research by developing microscopic passenger-centric models, solution algorithms and lower bounds. Fast iterative algorithms are proposed, based on a decomposition of the problem and on the exact resolution of the sub-problems. A new lower bound is proposed, consisting of the resolution of a set of min-cost flow problems with activation constraints. Computational experiments, based on a real-world Dutch railway network, show that good quality solutions and lower bounds can be found within a limited computation time.

This paper deals with the problem of locating the minimal number of parking areas being necessary for dangerous goods in the European Road Transport Network. To obtain a near-optimal solution for this problem, we introduce the design of a new graph-based algorithm to locate parking areas in such a way that drivers can obey the regulations related to driving and resting times. This restriction is imposed to the problem as follows: each point in the European Road Transport Network has to be at a distance lower than 200 km from a parking area in the Network.

A model for capacity analysis of freight transport with a time aspect is presented and applied. The model involves the analysis of the impacts of investments in possible new connections. In this capacity analysis we combine the knowledge of origin and destination of the demand with business knowledge of desired departure and arrival times. The model for the analysis is an integer multi-commodity flow model. From the output of this model congested track segments are found together with the time of week the congestion occurs. To illustrate the application of the model a capacity analysis of the railways in Denmark and Southern Sweden based on forecasts of the transportation flow in 2030 is presented.

The supply order management problem (SOMP) is an important planning problem in the offshore oil and gas industry. The SOMP consists of determining, for a given departure for an offshore supply vessel following a fixed route, which orders to and from offshore oil and gas installations to serve and which orders to postpone. We propose a mixed-integer programming (MIP) model for this purpose. By setting up the model in a number of ways and running simulations using the model, we compare the performance of various planning strategies. As a case study, we use data from Petrobras, which is the main state-owned oil company in Brazil. It is shown that reductions in the amount of critical order delays can be achieved with careful planning using the proposed MIP model.

This paper gives an over view of real time traffic management of the railway network in case of disturbances. After briefly introducing the problem of disturbance management and basic mathematical formulations, this paper overviews the existing literatures according to the typologies of traffic and levels of detail in the infrastructure models used for railway traffic network representation. A precise placement is made based on the effect of management decisions towards the various stakeholders. The application of these models in real life railway system is discussed based on the special constraints considered, the size of the railway network and the calculation time. Most railway disturbance management models are tested in an experiment setting at present, and if applied in practice they can be helpful to dispatchers to provide a higher quality service for all stakeholders involved.

This paper develops a new decision making method for optimal planning of railway maintenance operations using hybrid Model Predictive Control (MPC). A linear dynamic model is used to describe the evolution of the health condition of a segment of the railway track. The hybrid characteristics arise from the three possible control actions: performing no maintenance, performing corrective maintenance, or doing a replacement. A detailed procedure for transforming the linear system with switched input, and recasting the transformed problem into a standard mixed integer quadratic programming problem is presented. The merits of the proposed MPC approach for designing railway track maintenance plans are demonstrated using a case study with numerical simulations. The results highlight the potential of MPC to improve condition-based maintenance procedures for railway infrastructure.

Order picking is a labour-intensive operation and so it represents significant expenditures, with any underperformance leading to high operational costs for the entire supply chain. Hence, this manuscript reports a research aimed to enhance the order picking process at an automated warehouse. The research included an assessment and restructuring of the storage assignment in the picking area and the picking process, namely the routing method.

A simulation model was developed to assess the performance of the order picking, taking in account multiple scenarios, in a total of sixteen scenarios (including three storage assignment policy and five routing methods plus a base case), varying in storage assignment policy and routing method.

The results evidence that gains above 30% could be achieved in certain scenarios (e.g.: class-based storage assignment policy with s-shape routing).

We are concerned with the routine crowd management problem after a major event at a known venue. Without properly design complementary transport services, such sudden crowd build-ups will overwhelm the existing infrastructure. In this paper, we introduce a novel flow-rate based model to model the dynamic movement of passengers over the transportation flow network. Based on this basic model, an integer linear programming model is proposed to solve the bus transit problem permanently. We validate our model against a real scenario in Singapore, where a newly constructed mega-stadium hosts various large events regularly. The results show that the proposed approach effectively enables routine crowd, and achieves almost

$$24.1\%$$

travel time reduction with an addition of 40 buses serving

$$18.7\%$$

of the passengers.

We describe the construction, calibration and application of a discrete-event simulation model to estimate the potential effects of changes in airport infrastructure, operating procedures, and traffic intensity upon system performance. Logistic and regression models provide time-varying parameters for probability distributions used for physical processes. Detailed event logs of simulated aircraft activity and corresponding logs of actual aircraft operations allow us to validate the model and analyze the effects of normal disruptions and extraordinary events. With multivariate statistical analysis, we assess the influence of design capacity, airline scheduling practices and uncontrollable events on flight delays. We also estimate the effects of selectively removing airport assets from service for major maintenance.

Vertical flight planning concerns assigning optimal cruise altitude and speed to each trajectory-composing segment, such that the fuel consumption is minimized, and the arrival time constraints are satisfied. The previous work that assigns continuous speed to each segment leads to prohibitively long computation time. In this work, we propose a mixed integer linear programming model that assigns discrete speed. In particular, an all-but-one speed discretization scheme is found to scale well with problem size with only negligible objective deviation from using continuous speed. Extensive experiments with real-world instances have shown the practical effectiveness and feasibility of the proposed speed discretization approach.