Jean-Yves Potvin

A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows

This paper describes a tabu search heuristic for the vehicle routing problem with soft time windows. In this problem, lateness at customer locations is allowed although a penalty is incurred and added to the objective value. By adding large penalty values, the vehicle routing problem with hard time windows can be addressed as well. In the tabu search, a neighborhood of the current solution is created through an exchange procedure that swaps sequences of consecutive customers (or segments) between two routes. The tabu search also exploits an adaptive memory that contains the routes of the best previously visited solutions. New starting points for the tabu search are produced through a combination of routes taken from different solutions found in this memory. Many best-known solutions are reported on classical test problems.

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.

Vehicle dispatching with time-dependent travel times

Abstract Most of the models for vehicle routing reported in the literature assume constant travel times. Clearly, ignoring the fact that the travel time between two locations does not depend only on the distance traveled, but on many other factors including the time of the day, impact the application of these models to real-world problems. In this paper, we present a model based on time-dependent travel speeds which satisfies the “first-in–first-out” property. An experimental evaluation of the proposed model is performed in a static and a dynamic setting, using a parallel tabu search heuristic. It is shown that the time-dependent model provides substantial improvements over a model based on fixed travel times.

Handbook of Metaheuristics

The first edition of the Handbook of Metaheuristics was published in 2003 under the editorship of Fred Glover and Gary A. Kochenberger. Given the numerous developments observed in the field of metaheuristics in recent years, it appeared that the time was ripe for a second edition of the Handbook. When Glover and Kochenberger were unable to prepare this second edition, they suggested that Michel Gendreau and Jean-Yves Potvin should take over the editorship, and so this important new edition is now available. Through its 21 chapters, this second edition is designed to provide a broad coverage of the concepts, implementations and applications in this important field of optimization. Original contributors either revised or updated their work, or provided entirely new chapters. The Handbook now includes updated chapters on the best known metaheuristics, including simulated annealing, tabu search, variable neighborhood search, scatter search and path relinking, genetic algorithms, memetic algorithms, genetic programming, ant colony optimization, multi-start methods, greedy randomized adaptive search procedure, guided local search, hyper-heuristics and parallel metaheuristics. It also contains three new chapters on large neighborhood search, artificial immune systems and hybrid metaheuristics. The last four chapters are devoted to more general issues related to the field of metaheuristics, namely reactive search, stochastic search, fitness landscape analysis and performance comparison.

The Vehicle Routing Problem with Time Windows Part II: Genetic Search

This paper is the second part of a work on the application of new search techniques for the vehicle routing problem with time windows. It describes GENEROUS, the GENEtic ROUting System, which is based on the natural evolution paradigm. Under this paradigm, a population of solutions evolves from one generation to the next by “mating” parent solutions to form new offspring solutions that exhibit characteristics inherited from their parents. For this vehicle routing application, a specialized methodology is devised for merging two vehicle routing solutions into a single solution that is likely to be feasible with respect to the time window constraints. Computational results on a standard set of test problems are reported, and comparisons are provided with other heuristics.

Parallel Tabu Search for Real-Time Vehicle Routing and Dispatching

An abundant literature about vehicle routing and scheduling problems is available in the scientific community. However, a large fraction of this work deals with static problems where all data are known before the routes are constructed. Recent technological advances now create environments where decisions are taken quickly, using new or updated information about the current routing situation. This paper describes such a dynamic problem, motivated from courier service applications, where customer requests with soft time windows must be dispatched in real time to a fleet of vehicles in movement. A tabu search heuristic, initially designed for the static version of the problem, has been adapted to the dynamic case and implemented on a parallel platform to increase the computational effort. Numerical results are reported using different request arrival rates, and comparisons are established with other heuristic methods.

Metaheuristics in Combinatorial Optimization

The emergence of metaheuristics for solving difficult combinatorial optimization problems is one of the most notable achievements of the last two decades in operations research. This paper provides an account of the most recent developments in the field and identifies some common issues and trends. Examples of applications are also reported for vehicle routing and scheduling problems.

Adaptive Memory Programming: A Unified View of Meta-Heuristics

The paper analyses recent developments of a number of memory-based metaheuristics such as taboo search, scatter search, genetic algorithms and ant colonies. Its shows that the implementations of these general solving methods are more and more similar. So, a unified presentation is proposed under the name of Adaptive Memory Programming (AMP). A number of methods recently developed for the quadratic assignment, vehicle routing and graph colouring problems are reviewed and presented under the adaptive memory programming point of view. AMP presents a number of interesting aspects such as a high parallelization potential and theability of dealing with real and dynamic applications.

Generating trading rules on the stock markets with genetic programming

Technical analysis is aimed at devising trading rules capable of exploiting short-term fluctuations on the financial markets. Recent results indicate that this market timing approach may be a viable alternative to the buy-and-hold approach, where the assets are kept over a relatively long time period. In this paper, we propose genetic programming as a means to automatically generate such short-term trading rules on the stock markets. Rather than using a composite stock index for this purpose, the trading rules are adjusted to individual stocks. Computational results, based on historical pricing and transaction volume data, are reported for 14 Canadian companies listed on the Toronto stock exchange market.

Diversion Issues in Real-Time Vehicle Dispatching

Recent technological advances in communication systems now allow the exploitation of realtime information for dynamic vehicle routing and scheduling. It is possible, in particular, to consider diverting a vehicle away from its current destination in response to a new customer request. In this paper, a strategy for assigning customer requests, which includes diversion, is proposed, and various issues related to it are presented. An empirical evaluation of the proposed approach is performed within a previously reported tabu search heuristic. Simulations compare the tabu search heuristic, with and without the new strategy, on a dynamic problem motivated from a courier service application. The results demonstrate the potential savings that can be obtained through the application of the proposed approach.