Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

Lin, Win-Chin

doi:10.1155/2018/8170294

Cited by 11 publications

(3 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Distributed scheduling has been extended for various production environments, like distributed flowshop with heterogeneous factories (H. Meng and Pan 2021), group scheduling (Q.-K. Pan et al 2021), hybrid flowshop (Hao et al 2019;Lei and Wang 2020;Y. Li, Li, Gao, Zhang, et al 2020;Ying and Lin 2018), and integrated assembly-production flowshop (Deng et al 2016;W.-C. Lin 2018;Wu et al 2018Wu et al , 2019. Various production settings and practical features have also been integrated into the distributed flowshop to facilitate its real-world applications; blocking conditions (W. Li et al 2019;Zhao et al 2020), limited buffer constraints (G. Zhang and Xing 2019), no-wait (Komaki and Malakooti 2017;S.-W. Lin and Ying 2016), no-idle (Ying et al 2017;Zhao et al 2021), customer order-priority (Meng et al 2019), time window constraints (Jing et al 2020), machine-breakdowns (Wang et al 2016), and preventive maintenance (Mao et al 2021) Neighborhood Descent to minimize the makespan.…”

Section: Literature Reviewmentioning

confidence: 99%

Meta-Lamarckian-based iterated greedy for optimizing distributed two-stage assembly flowshops with mixed setups

et al. 2022

View full text Add to dashboard Cite

Integrated scheduling of distributed manufacturing operations has implications for supply chain optimization and requires further investigations to facilitate its application area for various industry settings. This study extends the limited literature of the distributed two-stage production-assembly scheduling problems offering a twofold contribution. First, an original mathematical extension, the Distributed Two-Stage Assembly Flowshop Scheduling Problem with Mixed-Setup (DTSAFSP-MS) is investigated to integrate setup time constraints while addressing an overlooked scheduling assumption. Second, a novel extension to the Iterated Greedy algorithm is developed to solve this understudied scheduling problem. An extensive set of test instances is considered to evaluate the effectiveness of the developed solution algorithm comparing it with the current-best-performing algorithm in the literature. Results are supportive of the Meta-Lamarckian-based Iterated Greedy (MIG) as a strong benchmark algorithm for solving DTSAFSP-MS with the statistical tests confirming its meaningfully better performance compared to the state-of-the-art.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Meta-Lamarckian-based iterated greedy for optimizing distributed two-stage assembly flowshops with mixed setups

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Besides the above developments, our proposed branchand-bound algorithm adopts the depth first policy in the branching tree and assigns jobs in a forward manner starting from the first place to the last place [34,37]. In the branching nodes, we determine if the node should be cut by using Property 1 and Property 2.…”

Section: Some Heuristics and Some Variants Of The Csamentioning

confidence: 99%

A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times

Hsu

Lin

Duan

et al. 2020

Discrete Dynamics in Nature and Society

Self Cite

View full text Add to dashboard Cite

In many scheduling studies, researchers consider the processing times of jobs as constant numbers. This assumption sometimes is at odds with practical manufacturing process due to several sources of uncertainties arising from real-life situations. Examples are the changing working environments, machine breakdowns, tool quality variations and unavailability, and so on. In light of the phenomenon of scenario-dependent processing times existing in many applications, this paper proposes to incorporate scenario-dependent processing times into a two-machine flow-shop environment with the objective of minimizing the total completion time. The problem under consideration is never explored. To solve it, we first derive a lower bound and two optimality properties to enhance the searching efficiency of a branch-and-bound method. Then, we propose 12 simple heuristics and their corresponding counterparts improved by a pairwise interchange method. Furthermore, we set proposed 12 simple heuristics as the 12 initial seeds to design 12 variants of a cloud theory-based simulated annealing (CSA) algorithm. Finally, we conduct simulations and report the performances of the proposed branch-and-bound method, the 12 heuristics, and the 12 variants of CSA algorithm.

show abstract

“…In addition, a heuristic algorithm has been introduced to determine the upper bound. Lin (2018) investigated the problem of job shop with three machines, considering the effect of learning with the aim of minimizing the makespan. To solve the problem, he proposed a branch-and-bound algorithm with some dominance rules and a lower bound.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and two efficient branch and bound algorithms for job shop scheduling problem followed by an assembly stage

Daneshamooz

Hosseini

2021

View full text Add to dashboard Cite

Purpose Two-stage production systems including a processing shop and an assembly stage are widely used in various manufacturing industries. These two stages are usually studied independently which may not lead to ideal results. This paper aims to deal with a two-stage production system including a job shop and an assembly stage. Design/methodology/approach Some exact methods are proposed based on branch and bound (B&B) approach to minimize the total completion time of products. As B&B approaches are usually time-consuming, three efficient lower bounds are developed for the problem and variable neighborhood search is used to provide proper upper bound of the solution in each branch. In addition, to create branches and search new nodes, two strategies are applied including the best-first search and the depth-first search (DFS). Another feature of the proposed algorithms is that the search space is reduced by releasing the precedence constraint. In this case, the problem becomes equivalent to a parallel machine scheduling problem, and the redundant branches that do not consider the precedence constraint are removed. Therefore, the number of nodes and computational time are significantly reduced without eliminating the optimal solution. Findings Some numerical examples are used to evaluate the performance of the proposed methods. Comparison result to mathematical model (mixed-integer linear programming) validates the performance accuracy and efficiency of the proposed methods. In addition, computational results indicate the superiority of the DFS strategy with regard to CPU time. Originality/value Studies about the scheduling problems for two-stage production systems including job shop followed by an assembly stage traditionally present approximate method and metaheuristic algorithms to solve the problem. This is the first study that introduces exact methods based on (B&B) approach.

show abstract

Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

Cited by 11 publications

References 66 publications

Meta-Lamarckian-based iterated greedy for optimizing distributed two-stage assembly flowshops with mixed setups

Meta-Lamarckian-based iterated greedy for optimizing distributed two-stage assembly flowshops with mixed setups

A Robust Two-Machine Flow-Shop Scheduling Model with Scenario-Dependent Processing Times

Mathematical modeling and two efficient branch and bound algorithms for job shop scheduling problem followed by an assembly stage

Contact Info

Product

Resources

About