Solving the continuous flow-shop scheduling problem by metaheuristics

Fink, Andréas; Voß, Stefan

doi:10.1016/s0377-2217(02)00834-2

Cited by 125 publications

(71 citation statements)

References 42 publications

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“…Different genetic algorithms (GA) are applied by Chen and Neppalli [20], Aldowaisan and Allahverdi [21]. Among the other metaheuristics, one could refer the reader to particle swarm optimization (PSO) by Pan et al [22], simulated annealing (SA) by Fink and Voß [23], ant colony optimization (ACO) by Shyu et al [24] and tabu search (TS) by Grabowski and Pempera [25]. Khalili [26] proposed an iterated local search algorithm for flexible flow lines with sequence dependent setup times to minimize total weighted completion and also studied multiobjective no-wait hybrid flowshop scheduling problems to minimise both makespan and total tardiness [27].…”

Section: Literature Reviewmentioning

confidence: 99%

An electromagnetism-inspired method for a generalized flowshop problem

Khalili

2014

Manufacturing Rev.

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-There are two common simplifying assumptions made in the scheduling of operations. One is that machines are available continuously, and the other is that once a job completes one process it is immediately available for its next. Moreover, in flowshops, it is commonly assumed that jobs must visit all machines. In practice, however, these assumptions may not be justifiable in some applications. This paper deals with a generalized flow shop (GFS) problem with machine availability constraints, transportation times between stages, and machine skipping. The electromagnetism-like method (EM) has been successfully applied to some NP-hard problems and this has motivated us to apply and assess the effectiveness of an EM algorithm in the GFS scenarios. Simulated annealing (SA) and a number of other well-recognized heuristics to the given GFS scheduling problem that minimizes two independent objective functions, namely the total tardiness and the total completion time also has been applied. In order to evaluate the performance of the proposed EM and SA, a set of practical instances has been considered. The related results are analyzed in two parts; in term of the objective functions, and the observed effects on variables in our instances. Extensive experiments and statistical analyses demonstrate that our proposed EM is more efficient than SA and other heuristics with regards to the objective functions considered in this paper.

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Section: Literature Reviewmentioning

confidence: 99%

An electromagnetism-inspired method for a generalized flowshop problem

Khalili

2014

Manufacturing Rev.

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“…These benchmarks comprise of 12 different sets of problems ranging from The small and medium sized problems can be designated from 20 jobs and 5 machines to 50 jobs and 20 machines; in a total of 6 data sets and 60 problem instances. Each instance has 10 independent replications and the percentage relative difference (PRD) is computed as follows: (13) where C F&V is the referenced makespan provided by [4], and C DSOMA is the makespan found by the DSOMA algorithm. Furthermore, average percentage relative difference (APRD), maximum percentage relative difference (MaxPRD), minimum percentage relative difference (MinPRD) and the standard deviation (SD) of PRD are calculated.…”

Section: Experimentationmentioning

confidence: 99%

Discrete Self-Organising Migrating Algorithm for Flow Shop Scheduling With No Wait Makespan

Davendra¹,

Zelinka²,

Šenkeřík³

et al. 2011

AIP Conference Proceedings

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“…The Flow Shop Scheduling Problem (FSSP) addresses most famous machine scheduling problems of many manufacturing systems, assembly lines, and information service facilities [1,2]. Sometimes Flow Shops have no delay situations that occur in the production environment in many real-life situations where a job must be processed continuously, without any interruption, from beginning to end, in order to follow the technological order of a process, which leads to a variant with the added constraint of "no-wait" [3]. In order to maintain continuous processing of a job in No-Wait Flow Shop Scheduling (NWFSS), Fink and Vob [3] applied different kinds of metaheuristics and constructive heuristics, such as nearest neighbor, cheapest insertion, and the pilot method, along with steepest descent (SD), Iterated Steepest Descent (ISD), Simulated Annealing (SA), and Tabu Search (TS), and examined tradeoffs between solution quality and running time.…”

Section: Introductionmentioning

confidence: 99%

“…Gao et al [24] attempted to use the Enhanced Migrating Birds Algorithm (EMBO), based on neighborhood search heuristics, to avoid local optima. In order to improve the quality of solutions, Filho et al [25] came up with a novel Evolutionary Clustering Search (ECS) metaheuristic approach, and found it to have better results than the method of Fink and Vob [3], Discrete Particle Swarm Optimization (DPSO) [9]. Genetic Algorithm (GA) as a metaheuristic technique was quite popular for solving optimization problems, and, later, it was observed that the solution quality was improved using a hybridization technique.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

Bewoor

Prakash

Sapkal³

2017

Algorithms

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Abstract:The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO) metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO) algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH) heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard's benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.

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Solving the continuous flow-shop scheduling problem by metaheuristics

Cited by 125 publications

References 42 publications

An electromagnetism-inspired method for a generalized flowshop problem

An electromagnetism-inspired method for a generalized flowshop problem

Discrete Self-Organising Migrating Algorithm for Flow Shop Scheduling With No Wait Makespan

Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

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