Éric D. Taillard

Benchmarks for basic scheduling problems

Abstract In this paper, we propose 260 randomly generated scheduling problems whose size is greater than that of the rare examples published. Such sizes correspond to real dimensions of industrial problems. The types of problems that we propose are: the permutation flow shop, the job shop and the open shop scheduling problems. We restrict ourselves to basic problems: the processing times are fixed, there are neither set-up times nor due dates nor release dates, etc. Then, the objective is the minimization of the makespan.

PROBABILISTIC DIVERSIFICATION AND INTENSIFICATION IN LOCAL SEARCH FOR VEHICLE ROUTING

This article presents a probabilistic technique to diversify, intensify, and parallelize a local search adapted for solving vehicle routing problems. This technique may be applied to a very wide variety of vehicle routing problems and local searches. It is shown that efficient first-level tabu searches for vehicle routing problems may be significantly improved with this technique. Moreover, the solutions produced by this technique may often be improved by a postoptimization technique presented in this article, too. The solutions of nearly forty problem instances of the literature have been improved.

A user's guide to tabu search

We describe the main features of tabu search, emphasizing a perspective for guiding a user to understand basic implementation principles for solving combinatorial or nonlinear problems. We also identify recent developments and extensions that have contributed to increasing the efficiency of the method. One of the useful aspects of tabu search is the ability to adapt a rudimentary prototype implementation to encompass additional model elements, such as new types of constraints and objective functions. Similarly, the method itself can be evolved to varying levels of sophistication. We provide several examples of discrete optimization problems to illustrate the strategic concerns of tabu search, and to show how they may be exploited in various contexts. Our presentation is motivated by the emergence of an extensive literature of computational results, which demonstrates that a well-tuned implementation makes it possible to obtain solutions of high quality for difficult problems, yielding outcomes in some settings that have not been matched by other known techniques.

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.

Paper: Robust taboo search for the quadratic assignment problem

An adaptation of taboo search to the quadratic assignment problem is discussed in this paper. This adaptation is efficient and robust, requiring less complexity and fewer parameters than earlier adaptations. In order to improve the speed of our taboo search, two parallelization methods are proposed and their efficiencies shown for a number of processors proportional to the size of the problem. The best published solutions to many of the biggest problems have been improved and every previously best solution (probably optimal) of smaller problems has been found. In addition, an easy way of generating random problems is proposed and good solutions of these problems, whose sizes are between 5 and 100, are given.

Some efficient heuristic methods for the flow shop sequencing problem

Abstract In this paper the best heuristic methods known up to now are compared to solve the flow shop sequencing problem and we improve the complexity of the best one. Next, this problem is applied to taboo search, a new technique to solve combinatorial optimization problems, and computational experiments are reported. Finally a parallel taboo search algorithm is presented and experimental results show that this heuristic allows very good speed-up.

Parallel iterative search methods for vehicle routing problems

This paper presents two partition methods that speed up iterative search methods applied to vehicle routing problems including a large number of vehicles. Indeed, using a simple implementation of taboo search as an iterative search method, every best-known solution to classical problems was found. The first partition method (based on a partition into polar regions) is appropriate for Euclidean problems whose cities are regularly distributed around a central depot. The second partition method is suitable for any problem and is based on the arborescence built from the shortest paths from any city to the depot. Finally, solutions that are believed to be optimum are given for problems generated on a grid.

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.

Parallel Taboo Search Techniques for the Job Shop Scheduling Problem

We apply the global optimization technique called taboo search to the job shop scheduling problem and show that our method is typically more efficient than the shifting bottleneck procedure, and also more efficient than a recently proposed simulated annealing implementation. We also identify a type of problem for which taboo search provides an optimal solution in a polynomial mean time in practice, while an implementation of the shifting bottleneck procedure seems to take an exponential amount of computation time. Included are computational results that establish new best solutions for a number of benchmark problems from the literature. Finally, we give a fast parallel algorithm that provides good solutions to very large problems in a very short computation time. INFORMS Journal on Computing , ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

COMPARISON OF ITERATIVE SEARCHES FOR THE QUADRATIC ASSIGNMENT PROBLEM.

Abstract This paper compares some of the most efficient heuristic methods for the quadratic assignment problem. These methods are known as strict taboo search, robust taboo search, reactive taboo search and genetic hybrids. It is shown that the efficiency of these methods strongly depends on the problem type and that no one method is better than all the others. A fast method for tuning the short term memory parameters of taboo searches is proposed and its validity is experimentally verified on long searches. A new type of quadratic assignment problem occurring in the design of grey patterns is proposed and it is shown how to adapt and improve the existing iterative searches for this specific problem. Finally, the usual way of implementing approximations of strict taboo search is discussed and better approximations are proposed.