Skip to main content

Über dieses Buch

This book constitutes revised selected papers from the 5th International Conference on Operations Research and Enterprise Systems, ICORES 2016, held in Rome, Italy, in February 2016.

The 14 papers presented in this volume were carefully reviewed and selection from a total of 75 submissions. They are organized in topical sections named: methodologies and technologies; and applications.



Methodologies and Technologies


An Investigation on Compound Neighborhoods for VRPTW

The Vehicle Routing Problem with Time Windows (VRPTW) consists of constructing least cost routes from a depot to a set of geographically scattered service points and back to the depot, satisfying service time intervals and capacity constraints. A Variable Neighbourhood Search algorithm which can simultaneously optimize both objectives of VRPTW (to minimize the number of vehicles and the total travel distance) is proposed in this paper. The three compound neighbourhood operators are developed with regards to problem characteristics of VRPTW. Compound neighbourhoods combine a number of independent neighbourhood operators to explore a larger scale of neighbourhood search space. Performance of these operators has been investigated and is evaluated on benchmark problems.
Binhui Chen, Rong Qu, Ruibin Bai, Hisao Ishibuchi

Product Modularization Using Cuckoo Search Algorithm

Modularity plays an important role in managing complex systems by dividing them into a set of modules that are interdependent within and independent across the modules. In response to the changing market trend of having large varieties within small production processes, modular design has assumed significant roles in the product development process. The product to be designed is represented in the form of a Design Structure Matrix (DSM). The DSM contains a list of all product components and the corresponding dependency patterns among these components. The main objective of this paper is to support design of products under modularity through clustering products into a set of modules or clusters. In this research, Cuckoo Search optimization algorithm is used to find the optimal number of clusters and the optimal assignment of components to clusters. The objective is minimizing the total coordination cost. Results obtained show an improved performance compared to published studies.
Hayam G. Wahdan, Sally S. Kassem, Hisham M. E. Abdelsalam

A Family of Models for Finite Sequential Games Without a Predetermined Order of Turns

Situations where individuals interact for a long time in different periods of time are usually modelled with simultaneous games repeated for each period of interaction. Therefore, decisions are made at the same time. Even models that are sequential assume the knowledge of the order in which players make their decisions. In this work a basic model for games with finite strategies sets is presented where the order of turns is not known beforehand, but is represented by a random variable. Other models are derived from the first one, allowing the study of more general cases, by changing the assumptions on different components of the game. A variation for bayesian games is presented as well. For all these models a series of results is obtained which guarantees the existence of Nash equilibria. A theoretical example and a possible application for drafting athletes are shown as well.
Rubén Becerril-Borja, Raúl Montes-de-Oca

Solving Chance-Constrained Games Using Complementarity Problems

In this paper, we formulate the random bimatrix game as a chance-constrained game using chance constraint. We show that a Nash equilibrium problem, corresponding to independent normally distributed payoffs, is equivalent to a nonlinear complementarity problem. Further if the payoffs are also identically distributed, a strategy pair where each player’s strategy is the uniform distribution over his action set, is a Nash equilibrium. We show that a Nash equilibrium problem corresponding to independent Cauchy distributed payoffs, is equivalent to a linear complementarity problem.
Vikas Vikram Singh, Oualid Jouini, Abdel Lisser



Distributed Patrolling with Two-Speed Robots (and an Application to Transportation)

We initiate the study of patrolling a unit interval with primitive two-speed, autonomous robots, i.e. robots without memory, no communication capabilities and no computation power. Robots have only two moving-states, one for patrolling and one for walking, each associated with a direction and speed. The robots are moving perpetually, and their moving-states and moving directions change only when they collide. Such a dynamic system induces the so-called idleness for patrolling a unit interval, i.e. the smallest time interval within which every point of the domain is patrolled by some robot. Our main technical contribution is an analytic study of the induced dynamic system of robots, which allows us to decide efficiently whether or not the system converges to a stable configuration that is also shown to be optimal.
As a warm-up for our main result, we show how robots can be centrally coordinated, carefully choosing initial locations, so that the induced idleness is optimal. Our main result pertaining to the idleness of primitive robots follows by a technical analysis of their collision locations, which we show, under some conditions, converge to the optimal initial locations for non-distributed robots.
Our result finds an application to a transportation problem concerning Scheduling with Regular Delivery. In this optimization problem, an infinite quantity of a commodity, residing at an endpoint of an interval, needs to be transported to the other endpoint. To that end, we show that the already established patrolling schedules of an interval correspond to optimal strategies that guarantee that the flow of the commodity is the largest possible.
Jurek Czyzowicz, Konstantinos Georgiou, Evangelos Kranakis, Fraser MacQuarrie, Dominik Pajak

Inventory Routing with Explicit Energy Consumption: A Mass-Flow Formulation and First Experimentation

Energy efficiency is becoming an important criteria for the inventory systems. Our aim is to explicitly integrate the energy into the existing Inventory Routing Problem (IRP). The problem is based on a multi-period single-vehicle IRP with one depot and several customers. An energy estimation model is proposed based on vehicle dynamics. A mass-flow based Mixed Integer Linear Programming (MILP) formulation is presented. Instead of minimizing the distance or inventory cost, energy minimization is taken as an objective. Benchmark instances for inventory routing are adapted for energy estimation and experiments are conducted. The results are compared with those of the distance/inventory cost minimization.
Yun He, Cyril Briand, Nicolas Jozefowiez

Delineation of Rectangular Management Zones and Crop Planning Under Uncertainty in the Soil Properties

In this article we cover two problems that often farmers have to face. The first one is to generate a partition of an agricultural field into rectangular and homogeneous management zones according to a given soil property, which has variability in time that is presented by a set of possible scenarios. The second problem assigns the correct crop rotation for those management zones defined before. These problems combine aspects of precision agriculture and optimization with the purpose of achieving a site and time specific management of the field that is consistent and effective in time for a medium term horizon. Thus, we propose a two-stage stochastic integer programming model with recourse that solves the delineation problem facing a finite number of possible scenarios, after this we propose a deterministic crop planning model, and then we combine them into a new two-stage stochastic program that can solve both problems under ucertainty conditions simultaneously. We describe the proposed methodology and the results achieved in this research.
Víctor M. Albornoz, José L. Sáez, Marcelo I. Véliz

Towards the PhD Degree: A Reflection and Discussion of the PhD Process

In recent decades the literature aimed at supporting postgraduate students in their PhD studies developed notoriously. This paper presents a reflection on managing the PhD process and accomplishing the doctoral thesis, based on this literature and the author’s PhD experience.
The contents of presentations on this topic delivered to science and engineering audiences are first summarized (the latest having taken place at ICORES 2015 and 2016). Feedback from audiences is then described and the most significant issues raised in discussions listed. The concluding remarks reflect on the importance of disseminating this subject among the science and engineering academic community.
Marta Castilho Gomes

Competition and Cooperation in Pickup and Multiple Delivery Problems

Logistics is a highly competitive industry; large hauliers use their size to benefit from economies of scale while small logistics companies are often well placed to service local clients. To obtain economies of scale, small hauliers may seek to cooperate by sharing loads. This paper investigates the potential for cost savings and problems associated with this idea. We study dynamic scheduling of shared loads for real-world truck haulage in the UK and model it as a dynamic pickup and multiple delivery problem (PMDP). In partnership with Transfaction Ltd., we propose realistic cost and revenue functions to investigate how companies of different sizes could cooperate to both reduce their operational costs and to increase profitability in a number of different scenarios.
Philip Mourdjis, Fiona Polack, Peter Cowling, Yujie Chen, Martin Robinson

Exploring Techniques to Improve Large-Scale Drainage System Maintenance Scheduling Using a Risk Driven Model

Gully pots are part of the infrastructure of a storm drain system, designed to drain surface water from streets. Any broken or blocked gully pot represents a potential cause of flooding, for instance during periods of intense or prolonged rainfall. Regular cleaning is necessary for gully pots to function effectively. We model gully pot maintenance as a risk-driven problem, and evaluate the maintenance quality of maintenance strategies by considering the risk impact of gully pot failure and failure behaviour. The results suggest investment directions and management policies that potentially improve the efficiency of maintenance. We find that the current maintenance quality is significantly affected by untimely system status information. We propose that low-cost sensor techniques might be able to improve timeliness of status information, and use simulation results to show the behaviour and advantages that might arise in a range of real-world scenarios.
Yujie Chen, Fiona Polack, Peter Cowling, Philip Mourdjis, Stephen Remde

A Comparison of One-Pass and Bi-directional Approaches Applied to Large-Scale Road Inspection

Gaist Solutions Ltd. carries out national-scale road inspection surveys in the UK. Visual inspection is used to identify the need for road maintenance. An inspection vehicle that monitors one side of the road needs two traversals to monitor a typical road, whereas a vehicle with cameras that record both sides of the road only requires a one-pass approach. To determine whether the one-pass approach affords any real cost advantage, we analyse road networks of six typical UK cities and the county of Norfolk using a range of exact and heuristic methods, and extrapolate from our results to estimate the cost-effectiveness of these two approaches for the road network of the UK. Our analysis approach is based on the Chinese Postman Problem (CPP), using graph reduction to allow effective computation over very large data sets.
Yujie Chen, Fiona Polack, Peter Cowling, Stephen Remde

Assessment of Risks in Manufacturing Using Discrete-Event Simulation

Due to globalisation, supply chains face an increasing number of risks that impact the procurement process. Even though there are tools that help companies address these risks, most companies, even larger ones, still have problems for adequately quantifying the risks on their current process as well as on alternative process. The aim of our work is to provide companies with a software supported method for quantifying procurement risks and establishing adequate strategies for risk mitigation at an optimal cost. Based on the results of a survey on risk management practices and industrial needs, we developed a tool that enables them quantifying these risks. The tool makes it easier to express key risks via a process model that offers an adequate granularity for expressing them. A simulator incorporated in our tool can efficiently evaluate these risks through Monte-Carlo simulation technique. Our main technical contribution lies in the development of an efficient Discrete Event Simulation (DES) engine, together with a Query Language that can be used to measure business risks from the simulation results. We show the expressiveness and performance of our approach by benchmarking it on a set of cases that are taken from industry and cover a large set of risk categories.
Renaud De Landtsheer, Gustavo Ospina, Philippe Massonet, Christophe Ponsard, Stephan Printz, Sabina Jeschke, Lasse Härtel, Johann Philipp von Cube, Robert Schmitt

Ramsey’s Discrete-Time Growth Model: A Markov Decision Approach with Stochastic Labor

In this paper, we study a Markov Decision Process version of the classical Ramsey’s Growth model where the evolution of the labor component is assumed to be stochastic. As this is a discrete-time model, it is much easier to study the corresponding long-run behavior of the optimal strategies, as it would be for a continuous version. A set of natural conditions in the Euler Equation context are presented that guarantee a stable long-term behavior of the optimal process.
Gabriel Zacarías-Espinoza, Hugo Cruz-Suárez, Enrique Lemus-Rodríguez

An Investigation of Heuristic Decomposition to Tackle Workforce Scheduling and Routing with Time-Dependent Activities Constraints

This paper presents an investigation into the application of heuristic decomposition and mixed-integer programming to tackle workforce scheduling and routing problems (WSRP) that involve time-dependent activities constraints. These constraints refer to time-wise dependencies between activities. The decomposition method investigated here is called repeated decomposition with conflict repair (RDCR) and it consists of repeatedly applying a phase of problem decomposition and sub-problem solving, followed by a phase dedicated to conflict repair. In order to deal with the time-dependent activities constraints, the problem decomposition puts all activities associated to the same location and their dependent activities in the same sub-problem. This is to guarantee the satisfaction of time-dependent activities constraints as each sub-problem is solved exactly with an exact solver. Once the assignments are made, the time windows of dependent activities are fixed even if those activities are subject to the repair phase. The paper presents an experimental study to assess the performance of the decomposition method when compared to a tailored greedy heuristic. Results show that the proposed RDCR is an effective approach to harness the power of mixed integer programming solvers to tackle the difficult and highly constrained WSRP in practical computational time. Also, an analysis is conducted in order to understand how the performance of the different solution methods (the decomposition, the tailored heuristic and the MIP solver) is affected by the size of the problem instances and other features of the problem. The paper concludes by making some recommendations on the type of method that could be more suitable for different problem sizes.
Wasakorn Laesanklang, Dario Landa-Silva, J. Arturo Castillo-Salazar


Weitere Informationen

Premium Partner