In a general flow-shop situation, where all the jobs must pass through all the machines in the same order, certain heuristic algorithms propose that the jobs with higher total process time should be given higher priority than the jobs with less total process time. Based on this premise, a simple algorithm is presented in this paper, which produces very good sequences in comparison with existing heuristics. The results of the proposed algorithm have been compared with the results from 15 other algorithms in an independent study by Park [13], who shows that the proposed algorithm performs especially well on large flow-shop problems in both the static and dynamic sequencing environments.

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

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Sequencing and scheduling: algorithms and complexity

We propose a hybrid algorithm for finding a set of nondominated solutions of a multi objective optimization problem. In the proposed algorithm, a local search procedure is applied to each solution (i.e., each individual) generated by genetic operations. Our algorithm uses a weighted sum of multiple objectives as a fitness function. The fitness function is utilized when a pair of parent solutions are selected for generating a new solution by crossover and mutation operations. A local search procedure is applied to the new solution to maximize its fitness value. One characteristic feature of our algorithm is to randomly specify weight values whenever a pair of parent solutions are selected. That is, each selection (i.e., the selection of two parent solutions) is performed by a different weight vector. Another characteristic feature of our algorithm is not to examine all neighborhood solutions of a current solution in the local search procedure. Only a small number of neighborhood solutions are examined to prevent the local search procedure from spending almost all available computation time in our algorithm. High performance of our algorithm is demonstrated by applying it to multi objective flowshop scheduling problems.

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

A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem

The majority of scheduling research assumes setup as negligible or part of the processing time. While this assumption simplifies the analysis and/or reflects certain applications, it adversely affects the solution quality for many applications which require explicit treatment of setup. Such applications, coupled with the emergence of production concepts like time-based competition and group technology, have motivated increasing interest to include setup considerations in scheduling problems. This paper provides a comprehensive review of the literature on scheduling problems involving setup times (costs). It classifies scheduling problems into batch and non-batch, sequence-independent and sequence-dependent setup, and categorizes the literature according to the shop environments of single machine, parallel machines, flowshops, and job shops. The suggested classification scheme organizes the scheduling literature involving setup considerations, summarizes the current research results for different problem types, and finally provides guidelines for future research.

A review of scheduling research involving setup considerations

This paper describes a simple algorithm for the solution of very large sequence problems without the use of a computer. It produces approximate solutions to the n job, m machine sequencing problem where no passing is considered and the criterion is minimum total elapsed time. Up to m-1 sequences may be found.

http://www1.ximb.ac.in/users/fac/surya/surya.nsf/23e5e39594c064ee852564ae004fa010/0f776dc7f3fe2550652574e2002240df/$file/n jobs and m- machine problem.pdf

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

This paper reviews the flowshop-sequencing research since 1954 that has been devoted to the static deterministic case in which n jobs are to be processed through a shop in which the processing times at each stage are fixed. The paper then comments on NP-completeness, the selection of criteria for optimization, and the lack of applications of this work in industry. Finally, it draws some conclusions about the possible future of sequencing research and the lessons that this area's work has to teach the rest of operations research.

The Lessons of Flowshop Scheduling Research

Types of industrial scheduling problems were investigated by personal visits to plants and by questionnaires mailed to scheduling departments. Information on problem sizes, job flow, optimization criteria and job similarity was obtained. Results indicate that most of the present procedures in theoretical research cannot handle average industrial problems. Also most commonly used objective criteria differ from industrial goals. There is a definite need for better communication between sequencing researchers and scheduling practioners.

Sequencing Research and the Industrial Scheduling Problem

A flowshop sequencing problem having an ordered processing time matrix is defined. Job and machine characteristics resulting in processing time relationships that have logical and practical bases are discussed. An optimizing solution procedure for a special class of ordered matrix problem is presented along with proof of optimality.

Flowshop Sequencing Problem with Ordered Processing Time Matrices

This paper examines a just-in-time (JIT) system with kanbans with three subassembly lines feeding a final assembly station. Variability in operation times exists and variability effects are reduced by increasing work in process levels or by unbalancing the subassembly lines through assignment of work content at each station. Of the several unbalancing methods that were analysed in this study, only the high-medium-low showed a consistent improvement in the output rate of the JIT production system. The output rates with unbalanced stations were always superior to the output rate of the perfectly balanced configurations used as controls. The extent of improvement over the output rate of balanced systems increased directly with the variability in operation times in final assembly and subassembly stations and inversely with the interstage buffer capacity allowed in the system.

Richard A. Dudek

Papers

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

The Lessons of Flowshop Scheduling Research

Sequencing Research and the Industrial Scheduling Problem

Flowshop Sequencing Problem with Ordered Processing Time Matrices

Increasing the production rate of a just-in-time production system with variable operation times