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Open AccessJournal ArticleDOI

Some efficient heuristic methods for the flow shop sequencing problem

Éric D. Taillard
- 05 Jul 1990 - 
- Vol. 47, Iss: 1, pp 65-74
TLDR
In this paper, the best heuristic methods known up to now are compared to solve the flow shop sequencing problem and they improve the complexity of the best one, and a parallel taboo search algorithm is presented and experimental results show that this heuristic allows very good speed-up.
About: 
This article is published in European Journal of Operational Research.The article was published on 1990-07-05 and is currently open access. It has received 811 citations till now. The article focuses on the topics: Flow shop scheduling & Incremental heuristic search.

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Book

Tabu Search

Fred Glover, +1 more
TL;DR: This book explores the meta-heuristics approach called tabu search, which is dramatically changing the authors' ability to solve a host of problems that stretch over the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservation and scores of other problems.
Journal ArticleDOI

Benchmarks for basic scheduling problems

Éric D. Taillard
- 22 Jan 1993 - 
TL;DR: This paper proposes 260 randomly generated scheduling problems whose size is greater than that of the rare examples published, and the objective is the minimization of the makespan.
Journal ArticleDOI

A multi-objective genetic local search algorithm and its application to flowshop scheduling

Hisao Ishibuchi, +1 more
TL;DR: A hybrid algorithm for finding a set of nondominated solutions of a multi objective optimization problem that uses a weighted sum of multiple objectives as a fitness function to randomly specify weight values whenever a pair of parent solutions are selected.
Book ChapterDOI

Iterated local search

Helena R. Lourenço, +2 more
TL;DR: Iterated Local Search (ILS) as mentioned in this paper is a general purpose metaheuristic for finding good solutions of combinatorial optimization problems, which is based on building a sequence of (locally optimal) solutions by perturbing the current solution and applying local search to that modified solution.
Journal ArticleDOI

A user's guide to tabu search

Fred Glover, +2 more
- 01 May 1993 - 
TL;DR: This presentation demonstrates that a well-tuned implementation of tabu search 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.
References
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Journal ArticleDOI

Tabu Search—Part II

Fred Glover
- 01 Aug 1989 - 
TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.
Book

Introduction to sequencing and scheduling

A. J. Clewett
TL;DR: In this article, the authors present an introduction to Sequencing and Scheduling in the context of the Operational Research Society (ORS) and the International Journal of Distributed Sensor Networks (ILS).
Journal ArticleDOI

A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem

Muhammad Nawaz, +2 more
- 01 Jan 1983 - 
TL;DR: A simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics, and performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.
Journal ArticleDOI

An Evaluation of Flow Shop Sequencing Heuristics

David G. Dannenbring
- 01 Jul 1977 - 
TL;DR: Three previously unreported heuristics are included in the study, one of which turns out to be superior to the other ten heuristic tested and is presented in this paper.
Journal ArticleDOI

A new heuristic method for the flow shop sequencing problem

Marino Widmer, +1 more
- 25 Jul 1989 - 
TL;DR: In this paper, a new heuristic method is presented for solving the m -machine, n -job flow shop scheduling problem, which is composed of two phases: the first finds an initial sequence using an analogy with the travelling salesman problem and the second tries to improve this solution using taboo search techniques.
Related Papers (5)

A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem

Muhammad Nawaz, +2 more
- 01 Jan 1983 - 

Benchmarks for basic scheduling problems

Éric D. Taillard
- 22 Jan 1993 - 

Optimal two- and three-stage production schedules with setup times included

Selmer Martin Johnson
- 01 Mar 1954 - 

The Complexity of Flowshop and Jobshop Scheduling

Michael Randolph Garey, +2 more
- 01 May 1976 - 

A Heuristic Algorithm for the n Job, m Machine Sequencing Problem

Herbert G. Campbell, +2 more
- 01 Jun 1970 - 
Frequently Asked Questions (6)
Q1. What contributions have the authors mentioned in the paper "Some efficient heuristic methods for the flow shop sequencing problem" ?

The authors compare in this paper the best heuristic methods known up to now to solve the flow shop sequencing problem and they improve the complexity of the best one. Next, the authors apply to this problem taboo search, a new technique to solve combinatorial optimization problems, and they report computational experiments. Finally a parallel taboo search algorithm is presented and experimental results show that this heuristics allows very good speed-up. 

In very few words, a transputer is a microprocessor equipped with 4 bidirectional communication links, especially designed to create multiprocessor networks with distributed memory (MIMD machines); the synchronizations between processors are made by the messages. 

With this technique, the authors could work 1.92 and 1.99 times faster with 2 processors than with only one, for problems of 10 machines and respectively 10 and 40 jobs. 

7 the evolution of the makespan as a function of the total number of iterations (stopping condition 1) and this evolution, function of the number of iterations without improving the best solution. 

taboo search needs great calculation times; in order to reduce them,we have presented two methods of parallelization that can be simultaneously applied. 

Another approach of parallelization is the following: the authors have remarked that thetime needed to evaluate the value of the makespans of the neighbourhood is almost the entire calculation time; in order to speed up the algorithm, one has to reduce this calculation time.