Stéphane Dauzère-Pérès

AN INTEGRATED APPROACH FOR MODELING AND SOLVING THE GENERAL MULTIPROCESSOR JOB-SHOP SCHEDULING PROBLEM USING TABU SEARCH

The problem considered in this paper is an important extension of the classical job-shop scheduling problem, where the same operation can be performed on more than one machine. The problem is to assign each operation to a machine and to sequence the operations on the machines, such that the makespan of a set of jobs is minimized. We introduce an extended version of the disjunctive graph model, that is able to take into account the fact that operations have to be assigned to machines. This allows us to present an integrated approach, by defining a neighborhood structure for the problem where there is no distinction between reassigning or resequencing an operation. This neighborhood is proved to be connected. A tabu search procedure is proposed and computational results are provided.

Single item lot sizing problems

A state-of-the-art of a particular planning problem, the Single Item Lot Sizing Problem (SILSP), is given for its uncapacitated and capacitated versions. First classes of lot sizing problems are briefly surveyed. Various solution methods for the Uncapacitated Single Item Lot Sizing Problem (USILSP) are reviewed. Four different mathematical programming formulations of the classical problem are presented. Different extensions for real-world applications of this problem are discussed. Complexity results of the Capacitated Single Item Lot Sizing Problem (CSILSP) are given together with its different formulations and solution techniques.

A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations

In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.

Lot Sizing with Carbon Emission Constraints

This paper introduces new environmental constraints, namely carbon emission constraints, in multi-sourcing lot-sizing problems. These constraints aim at limiting the carbon emission per unit of product supplied with different modes. A mode corresponds to the combination of a production facility and a transportation mode and is characterized by its economical costs and its unitary carbon emission. Four types of constraints are proposed and analyzed in the single-item uncapacitated lot-sizing problem. The periodic case is shown to be polynomially solvable, while the cumulative, global and rolling cases are NP-hard. Perspectives to extend this work are discussed.

A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem

This paper considers the job-shop problem with release dates and due dates, with the objective of minimizing the total weighted tardiness. A genetic algorithm is combined with an iterated local search that uses a longest path approach on a disjunctive graph model. A design of experiments approach is employed to calibrate the parameters and operators of the algorithm. Previous studies on genetic algorithms for the job-shop problem point out that these algorithms are highly depended on the way the chromosomes are decoded. In this paper, we show that the efficiency of genetic algorithms does no longer depend on the schedule builder when an iterated local search is used. Computational experiments carried out on instances of the literature show the efficiency of the proposed algorithm.

Genetic algorithms to minimize the weighted number of late jobs on a single machine

The general one-machine scheduling problem is strongly NP-Hard when the objective is to minimize the weighted number of late jobs. Few methods exist to solve this problem. In an other paper, we developed a Lagrangean relaxation algorithm which gives good results on many instances. However, there is still room for improvement, and a metaheuristic might lead to better results. In this paper, we decided to use a genetic algorithm (GA). Although a GA is somewhat easy to implement, many variations exist, and we tested some of them to design the best GA for our problem. Three different engines to evaluate the fitness of a chromosome are considered, together with four types of crossover operators and three types of mutation operators. An improved GA is also proposed by applying local search on solutions determined from the chromosome by the engine. Numerical experiments on different tests of instances are reported. They show that starting from an initial population already containing a good solution is very effective. 2003 Elsevier B.V. All rights reserved.

Multi-resource shop scheduling with resource flexibility

The shop scheduling problem tackled in this paper integrates many features that can be found in practical settings. Every operation may need several resources to be performed, and furthermore, a resource may be selected in a given set of candidates resources. Finally, we also consider that an operation may have more than one predecessor and/or more than one successor on the routing. The problem is then to both assign operations to resources and sequence operations on the resources, in order to minimize the maximum completion time. A disjunctive graph representation of this problem is presented and a connected neighborhood structure is proposed. The latter can be used to derive a local search algorithm such as tabu search. Finally, some numerical experiments are presented and discussed.

Solving the discrete lotsizing and scheduling problem with sequence dependent set-up costs and set-up times using the Travelling Salesman Problem with time windows

Abstract In this paper we consider the Discrete Lotsizing and Scheduling Problem with sequence dependent set-up costs and set-up times (DLSPSD). DLSPSD contains elements from lotsizing and from job scheduling, and is known to be NP-Hard. An exact solution procedure for DLSPSD is developed, based on a transformation of DLSPSD into a Travelling Salesman Problem with Time Windows (TSPTW). TSPTW is solved by a novel dynamic programming approach due to Dumas et al. (1993). The results of a computational study show that the algorithm is the first one capable of solving DLSPSD problems of moderate size to optimality with a reasonable computational effort.

Lot Streaming in Job-Shop Scheduling

The issue in Lot Streaming is how to split lots into sublots in order to improve the makespan (or some other criterion). We present a model and an iterative procedure for a general job-shop environment. The procedure alternates between solving a lot-sizing problem with a given sequence of sublots on the machines, and a standard job-shop scheduling problem with fixed sublot sizes. We report the computational results on a significant sample of 120 job-shop and flow-shop scheduling problems (including the famous 10–10). In case of no setup, in a few iterations, the makespan approaches a lower bound using very few sublots, suggesting that the procedure yields a global optimum. As a by-product, this result somehow validates the capacitated lot-sizing models in which the detailed capacity constraints, induced by the sequencing of operations, are ignored.

A general approach for optimizing regular criteria in the job-shop scheduling problem

Even though a very large number of solution methods has been developed for the job-shop scheduling problem, a majority has been designed for the makespan criterion. In this paper, we propose a general approach for optimizing any regular criterion in the job-shop scheduling problem. The approach is a local search method that uses a disjunctive graph model and neighborhoods generated by swapping critical arcs. The connectivity property of the neighborhood structure is proved and a novel efficient method for evaluating moves is presented. Besides its generality, another prominent advantage of the proposed approach is its simple implementation that only requires to tune the range of one parameter. Extensive computational experiments carried out on various criteria (makespan, total weighted flow time, total weighted tardiness, weighted sum of tardy jobs, maximum tardiness) show the efficiency of the proposed approach. Best results were obtained for some problem instances taken from the literature.