Patrick Beullens

Closed-loop supply chain network equilibrium under legislation

This paper expands previous work dealing with oligopolistic supply chains to the field of closed-loop supply chains. The model presented has been formulated with the intent of examining issues surrounding the recent European Union directive regarding waste of electric and electronic equipment (WEEE). The network modelled consists of manufacturers and consumer markets engaged in a Cournot pricing game with perfect information. Closed-loop supply chain network equilibrium occurs when all players agree on volumes shipped and prices charged. Certain properties of the model are examined analytically. Numeric examples are included and have been solved using an extragradient method with constant step size. The equilibrium solution obtained provide interesting insights that lead into a number of areas for future research.

A guided local search heuristic for the capacitated arc routing problem

Abstract This paper presents a new local search algorithm for the capacitated arc routing problem (CARP). The procedure uses single vehicle moves and moves that operate on two routes, both derived from a node routing context but properly adapted to work well for arc routing problems. We combine the algorithm with the meta-heuristic guided local search and further use the mechanisms of neighbor lists and edge marking to improve the solution quality and to save computation time. Experiments on standard benchmark problems from the literature show that our algorithm outperforms the existing heuristics for the CARP. On a set of new test problems, the local search approach consistently produces high quality solutions and often detects an optimal solution within limited computation time.

Reverse Logistics Network Design

The different chapters of this book highlight manifold examples of reverse logistics programs. While these cases vary substantially with respect to products, actors, and underlying motivations, as discussed in Chapter 1, they share a number of fundamental managerial issues. One of these is the need for an appropriate logistics infrastructure.

Collection and Vehicle Routing Issues in Reverse Logistics

It is not yet known to what extent reverse logistics might increase the total amount of transportation in supply chains — partially since it will also reduce activities related to the use of new, extractive resources. It is clear, however, that the extra transportation will diminish the environmental benefits of closing the loop. Likewise, inefficient or ineffective transport activities limit the economic success of reprocessing products.

An integrated queuing and multi-objective bed allocation model with application to a hospital in China

In this paper, a multi-objective decision aiding model is introduced for allocation of beds in a hospital. The model is based on queuing theory and goal programming (GP). Queuing theory is used to obtain some essential characteristics of access to various departments (or specialities) within the hospital. Results from the queuing models are used to construct a multi-objective decision aiding model within a GP framework, taking account of targets and objectives related to customer service and profits from the hospital manager and all department heads. The paper describes an application of the model, dealing with a public hospital in China that had serious problems with loss of potential patients in some departments and a waste of hospital beds in others. The performance of the model and implications for hospital management are presented.

Exploring Variants of 2-Opt and 3-Opt for the General Routing Problem

The general routing problem (GRP) is the problem of finding a minimum length tour, visiting a number of specified vertices and edges in an undirected graph. In this paper, we describe how the well-known 2-opt and 3-opt local search procedures for node routing problems can be adapted to solve arc and general routing problems successfully. Two forms of the 2-opt and 3-opt approaches are applied to the GRP. The first version is similar to the conventional approach for the traveling salesman problem; the second version includes a dynamic programming procedure and explores a larger neighborhood at the expense of higher running times. Extensive computational tests, including ones on larger instances than previously reported in the arc routing literature, are performed with variants of both algorithms. In combination with the guided local search metaheuristic and the mechanisms of marking and neighbor lists, the procedures systematically detect optimal or high-quality solutions within limited computation time.

Automatic production planning of press brakes for sheet metal bending

This paper discusses the planning module of system that has been developed in a research project investigating the possibilities for automatic planning of press brakes. Only limited attention has been given to production-planning problems for bending processes in the sheet-metal industry. A well-considered production-planning module provides an important opportunity for cost saving and flow-time reduction. The module will support and may replace an experienced planner. Under certain assumptions, the planning problem can be modelled both ways as a Travelling Purchaser Problem (TPP) and as a Generalized Travelling Salesperson Problem (GTSP). A hierarchical decomposition approach is presented to solve the TPP, while the GTSP is solved using guided local search (GLS). These methods are compared based on a set of problems from a real production environment and perform well.

Analysis of collaborative savings and cost allocation techniques for the cooperative carrier facility location problem

Transport companies may cooperate to increase their efficiency levels by, for example, the exchange of orders or vehicle capacity. In this paper a new approach to horizontal carrier collaboration is presented: the sharing of distribution centres (DCs) with partnering organisations. This problem can be classified as a cooperative facility location problem and formulated as an innovative mixed integer linear programme. To ensure cooperation sustainability, collaborative costs need to be allocated fairly to the different participants. To analyse the benefits of cooperative facility location and the effects of different cost allocation techniques, numerical experiments based on experimental design are carried out on a UK case study. Sharing DCs may lead to significant cost savings up to 21.6%. In contrast to the case of sharing orders or vehicles, there are diseconomies of scale in terms of the number of partners and more collaborative benefit can be expected when partners are unequal in size. Moreover, results indicate that horizontal collaboration at the level of DCs works well with a limited number of partners and can be based on intuitively appealing cost sharing techniques, which may reduce alliance complexity and enforce the strength of mutual partner relationships.

A multi-agent based cooperative approach to scheduling and routing

In this paper, we propose a general agent-based distributed framework where each agent is implementing a different metaheuristic/local search combination. Moreover, an agent continuously adapts itself during the search process using a direct cooperation protocol based on reinforcement learning and pattern matching. Good patterns that make up improving solutions are identified and shared by the agents. This agent-based system aims to provide a modular flexible framework to deal with a variety of different problem domains. We have evaluated the performance of this approach using the proposed framework which embodies a set of well known metaheuristics with different configurations as agents on two problem domains, Permutation Flow-shop Scheduling and Capacitated Vehicle Routing. The results show the success of the approach yielding three new best known results of the Capacitated Vehicle Routing benchmarks tested, whilst the results for Permutation Flow-shop Scheduling are commensurate with the best known values for all the benchmarks tested.

Instance-specific multi-objective parameter tuning based on fuzzy logic

Finding good parameter values for meta-heuristics is known as the parameter setting problem. A new parameter tuning strategy, called IPTS, is proposed that is a novel instance-specific method to take the trade-off between solution quality and computational time into consideration. Two important steps in the method are an a priori statistical analysis to identify the factors that determine heuristic performance in both quality and time for a specific type of problem, and the transformation of these insights into a fuzzy inference system rule base which aims to return parameter values on the Pareto-front with respect to a decision maker’s preference.