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 article describes a heuristic algorithm which will yield an optimal or near optimal minimal makespan solution to a specific type of sequencing/scheduling problem defined as the dependent sequencing problem which includes the pure job-shop and flow-shop sequencing problems. The name ‘ dependent sequencing ’ was selected for this model since it reflects the possibility of interdependence between activities of the technological processing order graph (GTPO) not present in ‘ pure ’ job -shop or f low-shop sequencing models. There are some approaches to this class of problem which provide optimal solutions, but their time to solutions and/or theoretical approaches make them difficult to understand and apply. Recent articles in the literature have recommended that research in the sequencing/ scheduling area be directed toward developing practical approaches which can be easily understood and applied. The dependent shop algorithm utilizes extensions of PERT/CPM techniques which are widely known and therefor...

The dependent shop sequencing algorithm A general algorithm for solving flow shop, job shop, and dependent shop sequencing problems

A simulation and analysis of variance study was conducted of a production line system operating in a near Just-In-Time (JIT) mode. The objective was to investigate the effect of four system parameters on the production line idle time and the carrying time of inventory. The system consisted of a production line producing one unit of product each production cycle to a JIT demand, each cycle was defined as one unit of time. The demand arrived stochastically during each cycle. The system was supplied by a supplier delivering materials with a stochastic arrival time of up to two cycles. The four system parameters were (1) operating inventory order quantity, representing a policy of ordering which permitted supply arrival for more than one cycle of production requirement, (2) stochastic demand arrival of up to one cycle, (3) safety stock of up to 3 units (Kanbans) of production, and (4) stochastic arrival of supply of up to three cycles.

Richard A. Dudek

Papers

The dependent shop sequencing algorithm A general algorithm for solving flow shop, job shop, and dependent shop sequencing problems

A Study of JIT Application under Stochastic Demand and Supply Arrival