Total flow time minimization in no-wait job shop using a hybrid discrete group search optimizer

Deng, Guanlong; Zhang, Zhiwang; Jiang, Tianhua; Zhang, Shuning

doi:10.1016/j.asoc.2019.05.007

Cited by 19 publications

(4 citation statements)

References 43 publications

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“…(Vermeulen, 2011) develop an exact solution method using ILP (Integer Linear Programming) and a combination of binary search with CP (Constraint Programming) to find an optimal makespan. Deng et al (2019) formulate the no-wait job shop problem with a total flow time criterion based on time difference and decomposes the problem into timetabling and sequencing sub-problems. They proposed by adopting favorable features of the iterated greedy algorithm, the population-based iterated greedy (PBIG) algorithm for the sequencing sub-problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

BOUGLOULA

2023

Management and Production Engineering Review

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This work is interested to optimize the job shop scheduling problem with a no wait constraint. This constraint occurs when two consecutive operations in a job must be processed without any waiting time either on or between machines. The no wait job shop scheduling problem is a combinatorial optimization problem. Therefore, the study presented here is focused on solving this problem by proposing strategy for making Jaya algorithm applicable for handling optimization of this type of problems and to find processing sequence that minimizes the makespan (Cmax). Several benchmarks are used to analyze the performance of this algorithm compared to the best-known solutions.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

BOUGLOULA

2023

Management and Production Engineering Review

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“…Given the importance of scheduling in the design of decision support systems [4], case-specific formulations, and effective solution algorithms are of high significance to help modernize the production systems. Recent studies developed state-of-the-art solution approaches [5][6][7][8][9] to facilitate the industry-scale application of the NWSPs, which are rather complex. Many other studies extended the NWSP, for example through including setup times [10], machine maintenance [11], no-idle-time situation [12], and distributed production environment [13], among the other examples (see [14]), to address case-specific industrial needs.…”

Section: Introductionmentioning

confidence: 99%

Improved Beam Search for Optimizing No-Wait Flowshops With Release Times

et al. 2020

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Production management of perishable goods is highly complex and requires well-informed decisions in corresponding stages. In such production environments, scheduling problems with time constraints are of high relevance to ensure the timely flow of the work-in-process material and goods. This study introduces the no-wait flowshop scheduling problem with release times (NWFSP-RT) to help advance decision support systems in the food production industry. For this purpose, an original mixed-integer linear programming (MILP) formulation is proposed for minimizing the makespan. Since Beam Search (BS) algorithm has been successfully applied to solve various scheduling problems, a BS algorithm, and an improved variant, the local search-based Beam Search (BSLS) algorithms are developed to solve the NWFSP-RT problem. Extensive numerical analysis is conducted to analyze the performance of the algorithms in solving this highly intractable extension of the scheduling problems. We showed that BSLS effectively avoids early convergence and local optimality while dismissing non-promising search directions within a partial enumeration solution approach. The statistical analysis confirmed that the improved BS algorithm performs better in terms of solution quality. Applications of the developed heuristic are worthwhile research topics to pursue in solving other complex optimization problems. INDEX TERMS Flowshop scheduling, no-wait, release time, Beam Search.

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“…Most of previous cases discussed about job-shop scheduling problem is happen when all machine is real on job shop layout like similar machine installed into same location/area with variance product, machine and not uniform for setup and running time when load on different machine for different product. (Deng, Zhang, Jiang, & Zhang, 2019). (Benttaleb, Hnaien, & Yalaoui, 2018), was consider a two-machine on job-shop scheduling problem were one machine is assumed unavailable during production run.…”

Section: Introductionmentioning

confidence: 99%

“…(Benttaleb, Hnaien, & Yalaoui, 2018), was consider a two-machine on job-shop scheduling problem were one machine is assumed unavailable during production run. In (Deng et al, 2019), tried to solve job-shop scheduling problem through no-wait job to minimize total flow time.…”

Section: Introductionmentioning

confidence: 99%

A Study Review of Completion Multi Job on Job Shop Scheduling Technique To Minimize Make Span

Irwan

2020

PROFIS

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In this paper, the development of flexibility job shop scheduling problem was many discussed before globally. The concept job shop scheduling had discussion like minimize total make span, throughput approaching, lot streaming, no-wait job shop, and many more. Also, into job shop scheduling always consider deadline or due date for job load into machine available with target at the end how to optimize machine capacity and minimum make span.

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Total flow time minimization in no-wait job shop using a hybrid discrete group search optimizer

Cited by 19 publications

References 43 publications

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

Optimizing the Job Shop Scheduling Problem with a no Wait Constraint by Using the Jaya Algorithm Approach

Improved Beam Search for Optimizing No-Wait Flowshops With Release Times

A Study Review of Completion Multi Job on Job Shop Scheduling Technique To Minimize Make Span

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