scholarly journals A Two-Stage Three-Machine Flow Shop Assembly Problem Mixed with a Controllable Number and Sum-of-Processing Times-Based Learning Effect by Simulated Annealing Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Shang-Chia Liu
Keyword(s):  
Simulated Annealing ◽  
Processing Time ◽  
Learning Effect ◽  
Flow Shop ◽  
Service Industries ◽  
Two Stage ◽  
Processing Times ◽  
Assembly Scheduling ◽  
Assembly Problem ◽  
Annealing Algorithms

The two-stage assembly scheduling problem is widely used in industrial and service industries. This study focuses on the two-stage three-machine flow shop assembly problem mixed with a controllable number and sum-of-processing times-based learning effect, in which the job processing time is considered to be a function of the control of the truncation parameters and learning based on the sum of the processing time. However, the truncation function is very limited in the two-stage flow shop assembly scheduling settings. Thus, this study explores a two-stage three-machine flow shop assembly problem with truncated learning to minimize the makespan criterion. To solve the proposed model, we derive several dominance rules, lemmas, and lower bounds applied in the branch-and-bound method. On the other hand, three simulated annealing algorithms are proposed for finding approximate solutions. In both the small and large size number of job situations, the SA algorithm is better than the JS algorithm in this study. All the experimental results of the proposed algorithm are presented on small and large job sizes, respectively.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Win-Chin Lin
Keyword(s):  
Learning Effect ◽  
Flow Shop ◽  
Optimal Solutions ◽  
Work Experiences ◽  
Two Stage ◽  
Processing Times ◽  
Production Environments ◽  
Flow Shop Problem ◽  
Iterated Greedy ◽  
Machine Assembly

Two-stage production process and its applications appear in many production environments. Job processing times are usually assumed to be constant throughout the process. In fact, the learning effect accrued from repetitive work experiences, which leads to the reduction of actual job processing times, indeed exists in many production environments. However, the issue of learning effect is rarely addressed in solving a two-stage assembly scheduling problem. Motivated by this observation, the author studies a two-stage three-machine assembly flow shop problem with a learning effect based on sum of the processing times of already processed jobs to minimize the makespan criterion. Because this problem is proved to be NP-hard, a branch-and-bound method embedded with some developed dominance propositions and a lower bound is employed to search for optimal solutions. A cloud theory-based simulated annealing (CSA) algorithm and an iterated greedy (IG) algorithm with four different local search methods are used to find near-optimal solutions for small and large number of jobs. The performances of adopted algorithms are subsequently compared through computational experiments and nonparametric statistical analyses, including the Kruskal–Wallis test and a multiple comparison procedure.

Two-stage three-machine assembly scheduling problem with sum-of-processing-times-based learning effect

2019 ◽  
Vol 24 (7) ◽  
pp. 5445-5462 ◽  
Author(s):  
Yunqing Zou ◽  
Dujuan Wang ◽  
Win-Chin Lin ◽  
Jia-Yang Chen ◽  
Pay-Wen Yu ◽  
...  
Keyword(s):  
Learning Effect ◽  
Scheduling Problem ◽  
Two Stage ◽  
Processing Times ◽  
Assembly Scheduling ◽  
Machine Assembly

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

Proportionate Flow Shop Scheduling with Rejection

2017 ◽  
Vol 34 (04) ◽  
pp. 1750015 ◽  
Author(s):  
Shi-Sheng Li ◽  
De-Liang Qian ◽  
Ren-Xia Chen
Keyword(s):  
Processing Time ◽  
Flow Shop ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Flow Shop Scheduling ◽  
Total Weighted Completion Time ◽  
Processing Times ◽  
Proportionate Flow Shop ◽  
Total Penalty ◽  
Scheduling With Rejection

We consider the problem of scheduling [Formula: see text] jobs with rejection on a set of [Formula: see text] machines in a proportionate flow shop system where the job processing times are machine-independent. The goal is to find a schedule to minimize the scheduling cost of all accepted jobs plus the total penalty of all rejected jobs. Two variations of the scheduling cost are considered. The first is the maximum tardiness and the second is the total weighted completion time. For the first problem, we first show that it is [Formula: see text]-hard, then we construct a pseudo-polynomial time algorithm to solve it and an [Formula: see text] time for the case where the jobs have the same processing time. For the second problem, we first show that it is [Formula: see text]-hard, then we design [Formula: see text] time algorithms for the case where the jobs have the same weight and for the case where the jobs have the same processing time.

Metaheuristics for two-stage flow-shop assembly problem with a truncation learning function

2020 ◽  
pp. 1-24
Author(s):  
Chin-Chia Wu ◽  
Xingong Zhang ◽  
Ameni Azzouz ◽  
Wei-Lun Shen ◽  
Shuenn-Ren Cheng ◽  
...  
Keyword(s):  
Flow Shop ◽  
Two Stage ◽  
Learning Function ◽  
Assembly Problem

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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Ji-Bo Wang ◽  
Jian Xu ◽  
Jing Yang
Keyword(s):  
Pareto Front ◽  
Learning Effect ◽  
Flow Shop ◽  
Search Tree ◽  
Release Dates ◽  
Processing Times ◽  
Flow Shop Problem ◽  
Speed Up ◽  
Bicriterion Optimization ◽  
Time Subject

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.

A two-stage three-machine assembly scheduling problem with a truncation position-based learning effect

2019 ◽  
Vol 24 (14) ◽  
pp. 10515-10533 ◽  
Author(s):  
Ameni Azzouz ◽  
Po-An Pan ◽  
Peng-Hsiang Hsu ◽  
Win-Chin Lin ◽  
Shangchia Liu ◽  
...  
Keyword(s):  
Learning Effect ◽  
Scheduling Problem ◽  
Two Stage ◽  
Assembly Scheduling ◽  
Machine Assembly
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