A heuristic for no-wait flow shop scheduling

Sapkal, Sagar U.; Laha, Dipak

doi:10.1007/s00170-013-4924-y

Cited by 31 publications

(9 citation statements)

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“…Ding et al [20] generated an initial sequence by arranging jobs in non-increasing order of their standard deviations (STD). Sapkal and Laha [21] presented an efficient heuristic method for F m |prmu, no − wait|T F T which generated the initial sequence of jobs based on bottleneck machines. Edy Bertolissi [22] proposed a heuristic for F m |prmu, no − wait|T F T constructed the seed by selecting the smallest pair of partial flow-times (e.g.…”

Section: Related Workmentioning

confidence: 99%

An Iterated Greedy Heuristic for Mixed No-Wait Flowshop Problems

Wang

Ruíz

et al. 2018

IEEE Trans. Cybern.

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The mixed no-wait flowshop problem with both wait and no-wait constraints has many potential real-life applications. The problem can be regarded as a generalization of the traditional permutation flowshop and the no-wait flowshop. In this paper, we study, for the first time, this scheduling setting with makespan minimization. We first propose a mathematical model and then we design a speed-up makespan calculation procedure. By introducing a varying number of destructed jobs, a modified iterated greedy algorithm is proposed for the considered problem which consists of four components: 1) initialization solution construction; 2) destruction; 3) reconstruction; and 4) local search. To further improve the intensification and efficiency of the proposal, insertion is performed on some neighbor jobs of the best position in a sequence during the initialization, solution construction, and reconstruction phases. After calibrating parameters and components, the proposal is compared with five existing algorithms for similar problems on adapted Taillard benchmark instances. Experimental results show that the proposal always obtains the best performance among the compared methods.

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Section: Related Workmentioning

confidence: 99%

An Iterated Greedy Heuristic for Mixed No-Wait Flowshop Problems

Wang

Ruíz

et al. 2018

IEEE Trans. Cybern.

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“…To meet this constraint, a job may be delayed at the beginning. So, in order to solve this type of problems, a delay matrix (δ) needs to be calculated [17].…”

Section: No-wait Flow Shop Scheduling Problem (Nwfssp)mentioning

confidence: 99%

“…To compare the proposed heuristic with the existing heuristics, we carried out the experimentation by running each instance independently 10 times; for each replication we used "Average Relative Percentage Deviation" (ARPD) as a performance measure, which is popular in the scheduling literature [9,10,14,16,17]. ARPD is given by:…”

Section: Computational and Statistical Evaluationmentioning

confidence: 99%

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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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“…As Figure 3 displays, there are four exchange intervals [1,2], [2,6], [6,7] and [7,10]. If the first interval exchanges with the fourth interval, a new feasible solution x can be obtained.…”

Section: Eg3mentioning

confidence: 99%