Jean-François Cordeau

A unified tabu search heuristic for vehicle routing problems with time windows

This paper presents a unified tabu search heuristic for the vehicle routing problem with time windows and for two important generalizations: the periodic and the multi-depot vehicle routing problems with time windows. The major benefits of the approach are its speed, simplicity and flexibility. The performance of the heuristic is assessed by comparing it to alternative methods on benchmark instances of the vehicle routing problem with time windows. Computational experiments are also reported on new randomly generated instances for each of the two generalizations.

A tabu search heuristic for periodic and multi‐depot vehicle routing problems

We propose a tabu search heuristic capable of solving three well-known routing problems: the periodic vehicle routing problem, the periodic traveling salesman problem, and the multi-depot vehicle routing problem. Computational experiments carried out on instances taken from the literature indicate that the proposed method outperforms existing heuristics for all three problems.

A Survey of Optimization Models for Train Routing and Scheduling

The aim of this paper is to present a survey of recent optimization models for the most commonly studied rail transportation problems. For each group of problems, we propose a classification of models and describe their important characteristics by focusing on model structure and algorithmic aspects. The review mainly concentrates on routing and scheduling problems since they represent the most important portion of the planning activities performed by railways. Routing models surveyed concern the operating policies for freight transportation and railcar fleet management, whereas scheduling models address the dispatching of trains and the assignment of locomotives and cars. A brief discussion of analytical yard and line models is also presented. The emphasis is on recent contributions, but several older yet important works are also cited.

A guide to vehicle routing heuristics

Several of the most important classical and modern heuristics for the vehicle routing problem are summarized and compared using four criteria: accuracy, speed, simplicity and flexibility. Computational results are reported.

The dial-a-ride problem: models and algorithms

Abstract The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for n users who specify pickup and delivery requests between origins and destinations. The aim is to plan a set of m minimum cost vehicle routes capable of accommodating as many users as possible, under a set of constraints. The most common example arises in door-to-door transportation for elderly or disabled people. The purpose of this article is to review the scientific literature on the DARP. The main features of the problem are described and a summary of the most important models and algorithms is provided.

A tabu search heuristic for the static multi-vehicle dial-a-ride problem

This article describes a tabu search heuristic for the dial-a-ride problem with the following characteristics. Users specify transportation requests between origins and destinations. They may provide a time window on their desired departure or arrival time. Transportation is supplied by a fleet of vehicles based at a common depot. The aim is to design a set of least cost vehicle routes capable of accommodating all requests. Side constraints relate to vehicle capacity, route duration and the maximum ride time of any user. Extensive computational results are reported on randomly generated and real-life data sets.

A Branch-and-Cut Algorithm for the Dial-a-Ride Problem

In the dial-a-ride problem, users formulate requests for transportation from a specific origin to a specific destination. Transportation is carried out by vehicles providing a shared service. The problem consists of designing a set of minimum-cost vehicle routes satisfying capacity, duration, time window, pairing, precedence, and ride-time constraints. This paper introduces a mixed-integer programming formulation of the problem and a branch-and-cut algorithm. The algorithm uses new valid inequalities for the dial-a-ride problem as well as known valid inequalities for the traveling salesman, the vehicle routing, and the pick-up and delivery problems. Computational experiments performed on randomly generated instances show that the proposed approach can be used to solve small to medium-size instances.

Models and Tabu Search Heuristics for the Berth-Allocation Problem

In the berth-allocation problem (BAP) the aim is to optimally schedule and assign ships to berthing areas along a quay. The objective is the minimization of the total (weighted) service time for all ships, defined as the time elapsed between the arrival in the harbor and the completion of handling. Two versions of the BAP are considered: the discrete case and the continuous case. The discrete case works with a finite set of berthing points. In the continuous case ships can berth anywhere along the quay. Two formulations and a tabu search heuristic are presented for the discrete case. Only small instances can be solved optimally. For these sizes the heuristic always yields an optimal solution. For larger sizes it is always better than a truncated branch-and-bound applied to an exact formulation. A heuristic is also developed for the continuous case. Computational comparisons are performed with the first heuristic and with a simple constructive procedure.

New Heuristics for the Vehicle Routing Problem

This chapter reviews some of the best metaheuristics proposed in recent years for the Vehicle Routing Problem. These are based on local search, on population search and on learning mechanisms. Comparative computational results are provided on a set of 34 benchmark instances.

The Dial-a-Ride Problem (DARP): Variants, modeling issues and algorithms

Abstract.The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for n users who specify pick-up and drop-off requests between origins and destinations. The aim is to plan a set of m minimum cost vehicle routes capable of accommodating as many users as possible, under a set of constraints. The most common example arises in door-to-door transportation for elderly or disabled people. The purpose of this article is to review the scientific literature on the DARP. The main features of the problem are described and classified and some modeling issues are discussed. A summary of the most important algorithms is provided.