Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

Shiau, Yau-Ren; Tsai, Ming-Shua; Lee, Wen‐Chiung; Cheng, T.C.E.

doi:10.1016/j.cie.2015.05.032

Cited by 31 publications

(9 citation statements)

References 35 publications

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“…Liu and Feng (2014) considered two machine no-wait flow shop scheduling problem with consideration of learning effect and convex resource dependent processing times. Shiau et al (2015) studied two machine flow shop scheduling problem by considering learning effects where the objective function was minimization of total completion time. They presented a branch-and-bound and genetic algorithms for solving the proposed problem.…”

Section: Literature Reviewmentioning

confidence: 99%

M-machine, no-wait flowshop scheduling with sequence dependent setup times and truncated learning function to minimize the makespan

Azizi¹,

Jabbari²,

Kheirkhah³

2016

10.5267/j.ijiec

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Recently, learning effects have been studied as an interesting topic for scheduling problems, however, most researches have considered single or two-machine settings. Moreover, learning factor has been considered for job times instead of setup times and the same learning effect has been used for all machines. This paper studies the m-machine no-wait flowshop scheduling problem considering truncated learning effect in no-wait flowshop environment. In this problem, setup time is a function of job position in the sequence with a learning truncation parameter and each machine has its own learning effect. In this paper, a mixed integer linear programming is proposed for the problem to solve such problem. This problem is NP-hard so an improved genetic algorithm (GA) and a simulated annealing (SA) algorithm are developed to find near optimal solutions. The accuracy and efficiency of the proposed procedures are tested against different criteria on various instances. Numerical experiments approve that SA outperforms in most instances.

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Section: Literature Reviewmentioning

confidence: 99%

M-machine, no-wait flowshop scheduling with sequence dependent setup times and truncated learning function to minimize the makespan

Azizi¹,

Jabbari²,

Kheirkhah³

2016

10.5267/j.ijiec

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“…Mor and Mosheiov (2014) study polynomial time solution algorithms for the problem on a proportionate flow shop with two agents. Shiau, Tsai, Lee, and Cheng (2015) present some GAs two-agent two-machine flow shop with learning effects. Lei (2015a, b) propose a variable neighborhood search (VNS) and a two-phase neighbor-hood search for two-agent scheduling in flow shop and hybrid flow shop respectively to simultaneously minimize objectives of two agents under the given upper bound.…”

Section: Introductionmentioning

confidence: 99%

A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents

Lei

Guo

2015

Expert Systems with Applications

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“…More recently, Xu et al [11], Eren [12], Wu and Lee [13], Rudek [14], Kuo et al [15], Lee and Chung [16], Sun et al [17,18], Cheng et al [19], Li et al [20], Wang and Zhang [21], J. B. Wang and J. J. Wang [22], Lai et al [23], Liu and Feng [24], Wang and Zhang [21], Wu et al [25], Shiau et al [26], Lu [27], Qin et al [28], He [29], and Wang et al [30] considered flow shop scheduling problems with learning effects, but without release dates. Xu et al [11] considered the flow shop scheduling problems with position-dependent learning effect, i.e., if job J j is in position l of a schedule, the actual processing time p ijl of job J j on machine M i is p ijl = p ij a − bl and p ijl = p ij l α , where p ij denotes the normal processing time of job J j on M i and a > 0, b ≥ 0, and α ≤ 0 are the learning rates.…”

Section: Introductionmentioning

confidence: 99%

“…For several regular objective functions, they presented approximate algorithms. Shiau et al [26] considered the flow shop scheduling problems with general position-dependent learning effect, i.e., if job J j is in position l of a schedule, the actual processing time p ijl of job J j on machine M i is p ijl = p ij g l , where 1 = g 1 ≥ g 2 ≤ ⋯ ≥ g n is a nonincreasing function. Sun et al [18] considered the total weighted completion time minimization flow shop scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Bicriterion Optimization for Flow Shop with a Learning Effect Subject to Release Dates

Wang

Yang

2018

Complexity

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This paper investigates a two-machine flow shop problem with release dates in which the job processing times are variable according to a learning effect. The bicriterion is to minimize the weighted sum of makespan and total completion time subject to release dates. We develop a branch-and-bound (B&B) algorithm to solve the problem by using a dominance property, several lower bounds, and an upper bound to speed up the elimination process of the search tree. We further propose a multiobjective memetic algorithm (MOMA), enhanced by an initialization strategy and a global search strategy, to obtain the Pareto front of the problem. Computational experiments are also carried out to examine the effectiveness and the efficiency of the B&B algorithm and the MOMA algorithm.

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Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

Cited by 31 publications

References 35 publications

M-machine, no-wait flowshop scheduling with sequence dependent setup times and truncated learning function to minimize the makespan

M-machine, no-wait flowshop scheduling with sequence dependent setup times and truncated learning function to minimize the makespan

A shuffled frog-leaping algorithm for hybrid flow shop scheduling with two agents

Bicriterion Optimization for Flow Shop with a Learning Effect Subject to Release Dates

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