The Research of Heuristic Algorithm for Flow Shop Scheduling Problem

2012 ◽  
Vol 605-607 ◽  
pp. 528-531
Author(s):  
Dan Tang ◽  
Hong Ping Shu
Keyword(s):  
Heuristic Algorithm ◽  
Processing Time ◽  
Flow Shop ◽  
Heuristic Method ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Average Quality ◽  
Simulation Experiments ◽  
Shop Scheduling ◽  
Better Than

For the flow shop scheduling problem which aims to minimize makespan, this paper gives a new derivation about its mathematical definition, and mining characteristics of the problem itself further. By which analysis, the new heuristic method proposed in the paper shorten the waiting time of each job as much as possible on the basis of reduce the processing time of the first machine and last job. The result of simulation experiments shows that, our new heuristic algorithm has good performance, and the average quality and stability of scheduling sequences generated by new method is significantly better than other heuristic algorithm which has the same complexity.

A Bee Colony Algorithm based Solver for Flow Shop Scheduling Problem

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Yosua Halim ◽  
Cecilia Esti Nugraheni
Keyword(s):  
Processing Time ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Food Sources ◽  
Scheduling Problem ◽  
Total Processing Time ◽  
Shop Scheduling ◽  
Bee Colony ◽  
Problem Instances ◽  
Parameter Values

Flow Shop Scheduling (FSS) is scheduled to involve n jobs and m machines in the same process sequence, where each machine processes precisely one job in a certain period. In FSS, when a machine is doing work, other machines cannot do the same job simultaneously. The solution to this problem is the job sequence with minimal total processing time.  Many algorithms can be used to determine the order in which the job is performed. In this paper, the algorithm used to solve the flow shop scheduling problem is the bee colony algorithm. The bee colony algorithm is an algorithm that applies the metaheuristic method and performs optimization according to the workings of the bee colony. To measure the performance of this algorithm, we conducted some experiments by using Taillard's Benchmark as problem instances. Based on experiments that have been carried out by changing the existing parameter values, the size of the bee population, the number of iterations, and the limit number of bees can affect the candidate solutions obtained. The limit is a control parameter for a bee when looking for new food sources. The more the number of bees, the more iterations, and the limit used, the better the final time of the sequence of work. The bee colony algorithm can reach the upper limit of the Taillard case in some cases in the number of machines 5 and 20 jobs. The more the number of machines and jobs to optimize, the worse the total processing time.

scholarly journals An Efficient Flow Shop Scheduling Problem With Makespan Objective

2021 ◽  
Vol 12 (4) ◽  
pp. 461-466
Author(s):  
S. Jayasankari, Et. al.
Keyword(s):  
Flow Shop ◽  
Manufacturing Industries ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Elapsed Time ◽  
Research Scholar ◽  
Better Than

Scheduling ‘n’ job in ‘m’ machine is a tedious task for the Research scholar to solve this type of problem.  Flow shop scheduling has its origin in the manufacturing industries in the early 1954.  In this paper, we have developed an algorithm with the objective is to minimize the total elapsed time (makespan). Examples are illustrated to demonstrate the proposed approach in detail.  Finally, the result obtained under the proposed method is compared with the existing methods available in the literature.  It is found that our algorithm performs better than the existing algorithm and the result have been incorporated in this article.

Alternative Heuristic Algorithm for Flow Shop Scheduling Problem

2013 ◽  
pp. 277-297
Author(s):  
Vladimír Modrák ◽  
R. Sudhakra Pandian ◽  
Pavol Semanco
Keyword(s):  
Heuristic Algorithm ◽  
Flow Shop ◽  
Idle Time ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Realistic Situation ◽  
Permutation Flow Shop

In this chapter an alternative heuristic algorithm is proposed that is assumed for a deterministic flow shop scheduling problem. The algorithm is addressed to an m-machine and n-job permutation flow shop scheduling problem for the objective of minimizing the make-span when idle time is allowed on machines. This chapter is composed in a way that the different scheduling approaches to solve flow shop scheduling problems are benchmarked. In order to compare the proposed algorithm against the benchmarked, selected heuristic techniques and genetic algorithm have been used. In realistic situation, the proposed algorithm can be used as it is without any modification and come out with acceptable results.

A Heuristic Algorithm for Flow Shop Scheduling Problem

2014 ◽  
Vol 643 ◽  
pp. 374-379
Author(s):  
Hua Wei Yuan ◽  
Yuan Wei Jing ◽  
Tao Ren
Keyword(s):  
Heuristic Algorithm ◽  
Completion Time ◽  
Numerical Experiments ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Large Size ◽  
Flow Shop Problem ◽  
Weighted Completion Time

This paper considers the m-machine flow shop problem to minimize weighted completion time. A heuristic algorithm is presented to deal with the problem for large size problem. At the end of the paper, some numerical experiments show the effectiveness of the heuristic.

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