scholarly journals A Heuristic Approach to Flow Shop Scheduling Problem in Which Processing Times Are Associated with Their Respective Probabilities with No-Idle Constraint

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Deepak Gupta ◽  
Harminder Singh
Keyword(s):  
Processing Time ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Numerical Illustration ◽  
Fixed Sequence ◽  
Shop Scheduling ◽  
General Flow ◽  
Processing Times

This paper is an attempt to study general flow shop scheduling problem in which processing time of jobs is associated with probabilities under no-idle constraint. The objective of this paper is to develop a heuristic algorithm to flowshop scheduling so that no machine remains idle during working for any given sequence of jobs. The proposed algorithm is simple, and easy to understand and provides an important tool in many practical situations for minimizing the expected hiring cost of the machines for a fixed sequence of job processing. A numerical illustration is also given to justify the proposed algorithm.

scholarly journals Minimization of Utilization Time for Specially Structured n × 3 Scheduling Model with Jobs in a String of Disjoint Job Blocks

2019 ◽  
Vol 8 (12) ◽  
pp. 3554-3558
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Numerical Illustration ◽  
Optimal Sequence ◽  
Shop Scheduling ◽  
Elapsed Time ◽  
Processing Times ◽  
Scheduling Model ◽  
Utilization Time

The present paper investigates n×3 specially structured flow shop scheduling model with processing of jobs on given machines in a string of disjoint job blocks and with probabilities associated to the processing times of jobs. The objective is to minimize utilization time of second and third machine and also minimize the total elapsed time for processing the jobs for n×3 specially structured flow shop scheduling problem. The algorithm developed in this paper is quite straightforward and easy to understand and also present an essential way out to the decision maker for attaining an optimal sequence of jobs. The algorithm developed in this paper is validated by a numerical illustration.

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.

GA-BASED IMPROVEMENT HEURISTIC FOR MINIMIZING MAKESPAN IN A GENERAL FLOW-SHOP SCHEDULING PROBLEM

2021 ◽  
pp. 1109-1115
Author(s):  
Brahma Datta Shukla, Pragya Singh Tomar
Keyword(s):  
Lower Bound ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
General Flow ◽  
Constructive Heuristic ◽  
Permutation Flow Shop ◽  
Constructive Heuristics ◽  
Flow Shop Problem

The study proposes an evolutionary algorithm-based improvement heuristic for the permutation flow-shop problem. The method uses a constructive heuristic to arrive at a good first solution. The GA-based improvement heuristic is used in conjunction with CDS, Gupta's algorithm, and Palmer's Slope Index, which are all well-known constructive heuristics. The method is put to the test on a series of ten issues that vary from 4 to 25 tasks and 4 to 30 machines. The outcomes are also compared to some of the most well-known lower-bound options

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.

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