Pierre Dejax

An exact algorithm for the elementary shortest path problem with resource constraints: Application to some vehicle routing problems

In this article, we propose a solution procedure for the Elementary Shortest Path Problem with Resource Constraints (ESPPRC). A relaxed version of this problem in which the path does not have to be elementary has been the backbone of a number of solution procedures based on column generation for several important problems, such as vehicle routing and crew pairing. In many cases relaxing the restriction of an elementary path resulted in optimal solutions in a reasonable computation time. However, for a number of other problems, the elementary path restriction has too much impact on the solution to be relaxed or might even be necessary. We propose an exact solution procedure for the ESPPRC, which extends the classical label correcting algorithm originally developed for the relaxed (nonelementary) path version of this problem. We present computational experiments of this algorithm for our specific problem and embedded in a column generation scheme for the classical Vehicle Routing Problem with Time Windows.

Traveling Salesman Problems with Profits

Traveling salesman problems with profits (TSPs with profits) are a generalization of the traveling salesman problem (TSP), where it is not necessary to visit all vertices. A profit is associated with each vertex. The overall goal is the simultaneous optimization of the collected profit and the travel costs. These two optimization criteria appear either in the objective function or as a constraint. In this paper, a classification of TSPs with profits is proposed, and the existing literature is surveyed. Different classes of applications, modeling approaches, and exact or heuristic solution techniques are identified and compared. Conclusions emphasize the interest of this class of problems, with respect to applications as well as theoretical results.

Dynamic and stochastic models for the allocation of empty containers

The empty container allocation problem occurs in the context of the management of the land distribution and transportation operations of international maritime shipping companies. It involves dispatching empty containers of various types in response to requests by export customers and repositioning other containers to storage depots or ports in anticipation of future demands. We describe the problem and identify its basic structure and main characteristics. We then introduce two dynamic deterministic formulations for the single and multicommodity cases, which offer a general modeling framework for this class of problems, and which account for its specific characteristics: the space and time dependency of events, substitutions among container types, relationships with partner companies, imports and exports, massive equilibration flows, etc. Finally, we provide a mathematical formulation for handling, in the single commodity case, the uncertainty of demand and supply data that is characteristic of container allocation and distribution problems. Various modeling choices, data requirements, and algorithmic considerations related to the implementation of the models are also discussed.

Survey Paper---A Review of Empty Flows and Fleet Management Models in Freight Transportation

The transportation of freight, by any mode, usually generates a significant number of empty vehicle movements. Understanding and controlling this phenomenon is important for all levels of transportation and logistic system planning. In this paper, we propose a taxonomy of empty vehicle flow problems and models and review the existing literature on the subject. Major research trends and perspectives are identified and the advantages of a hierarchically integrated approach for the simultaneous management of empty and loaded freight vehicle movements are discussed.

A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal

The scheduling problem in a container terminal is characterized by the coordination of different types of equipment. In this paper, we present an integrated model to schedule the equipment. The objective is to minimize the makespan, or the time it takes to serve a given set of ships. The problem is formulated as a Hybrid Flow Shop Scheduling problem with precedence and Blocking constraints (HFSS-B). A tabu search algorithm is proposed to solve this problem. Certain mechanisms are developed and introduced into the algorithm to assure its quality and efficiency. The performance of the tabu search algorithm is analyzed from the computational point of view.

Models and Algorithms for Container Allocation Problems on Trains in a Rapid Transshipment Shunting Yard

Multimodal transport of containers can be an alternative to the road transportation but it requires to be competitive in terms of quality of service and price. In rail rail container terminals, new techniques are developed to facilitate rapid transfers of the containers between trains. In this article, we address the problem of the optimization of the operations management of rapid rail rail transshipment shunting yards. We are interested specifically in the optimization of containers allocation on tra ins (for the initial loading and their reloading after transshipment). We have developed a class of models with different levels of complexity and realism and we have proposed optimal and heuristic methods to solve them. The experimental results on realistic datasets are very promising in terms of the minimization of the container moves in a terminal as well as the use and sizing of the handling equipments.

The Profitable Arc Tour Problem: Solution with a Branch-and-Price Algorithm

In this article, we introduce a new arc routing problem that we call the profitable arc tour problem. This problem is defined on a graph in which profits and travel costs are associated with the arcs. The objective is to find a set of cycles in the graph that maximizes the collection of profit minus travel costs, subject to constraints limiting the number of times that profit is available on arcs and the maximal length of cycles. The problem is related both to constrained flow problems and to vehicle-routing problems. We tackle it from this standpoint and propose a branch-and-price algorithm for its solution. In the column-generation phase, the issue of the collection decisions while traveling through the arcs is addressed. In the branching phase, the fact that viewing solutions in terms of flow variables regularly induces an integer flow matrix leads us to introduce a branching method called the flow-splitting method. Finally, the relationships of this problem with constrained flow optimization are taken into account in an initial phase of the algorithm.

The Design, Planning, and Optimization of Reverse Logistics Networks

Reverse logistics is concerned with the return flows of products or equipment back from the consumer to the logistics network for reuse, recovery or recycling for environmental, economic or customer service reasons. In this paper, we review applications, case studies, models and techniques proposed for the design, planning and optimization of reverse logistics systems. We consider both cases of separate and integrated handling of original products and return flows throughout the logistics network. According to the hierarchical planning framework for logistics systems, the works are described in relation to their contribution to strategic, tactical or operational planning. Major contributions concern facility location, inventory management, transportation and production planning models. Directions for further research are indicated in all of these areas as well as for the general development of reverse logistics activities in a supply chain network.

Multiperiod Planning and Routing on a Rolling Horizon for Field Force Optimization Logistics

We address the problem of the planning and routing of technician visits to customers in the field, for maintenance or service logistics activities undertaken by utilities. The plans must be designed over a multiperiod, rolling horizon and updated daily. We have developed a memetic algorithm and a column generation/branch and bound heuristic in order to optimize an initial plan over a static horizon. We have then adapted our procedures to cope with a rolling horizon context, when a new plan has to be determined after the execution of each first daily period of the previous plan. We have tested our procedures on realistic data from the water distribution sector, and obtained good solutions in a reasonable amount of time. We show in particular the advantage of reutilization of partial solutions from the previous plan for the optimization of each new plan. Directions for further research are then indicated.

A 2-stage method for a field service routing problem with stochastic travel and service times

In this paper, we consider a specific variant of the field service routing problem. It consists in determining vehicle routes in a single period to serve two types of customers: mandatory and optional. Mandatory customers have to be served within a specified time window whereas optional customers may be served (or not) within the planning horizon. For more realism, we assume that service as well as travel times are stochastic and also that there are multiple depots. The objective is to visit as many optional customers as possible while minimizing the total travel time. To tackle this problem, we propose a 2-stage solution method: the planning stage and the execution stage. We decompose the planning stage into two phases: the design of a skeleton of mandatory customers and the insertion of optional customers in this skeleton. In the execution stage, we proceed to a real-time modification of the planned routes to face stochastic travel and service times and to enable time windows to be respected. HighlightsWe introduce the field service routing problem with stochastic travel and service times.We propose a two-stage method for solving this new problem.In the planning stage, we build routes with mandatory and then we insert optional customers.In the execution stage, we use dynamic programming algorithms to define the optimal policy to face stochasticity.