scholarly journals A Flow Shop Scheduling Problem with Transportation Time and Separated Setup Time of Jobs

2012 ◽  
Vol 38 ◽  
pp. 1327-1332 ◽  
Author(s):  
S. Agrawal ◽  
A. Gautam ◽  
D.K. Chauhan ◽  
L.M. Tiwari ◽  
S. Kapoor
Keyword(s):  
Flow Shop ◽  
Setup Time ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Transportation Time ◽  
Shop Scheduling

scholarly journals Minimizing Hiring Cost of the Machine Under No Idle Constraints in N*2 Flow Shop Scheduling with Transportation and Set Up Time.

2019 ◽  
Vol 9 (2) ◽  
pp. 5227-5229
Keyword(s):  
Flow Shop ◽  
Setup Time ◽  
Idle Time ◽  
Flow Shop Scheduling ◽  
Start Time ◽  
Transportation Time ◽  
Shop Scheduling ◽  
Set Up

The present paper deals with a problem of n*2 flow shop scheduling where setup time of the machines are separated from their dispensation time and transportation time for moving from first machine to second machine is also given. Our aim is to get the most favorable jobs sequence which can make machines idle time equal to zero so that rental cost can be minimized. In order to ensure no idle constraint, we have developed an algorithm in which we have delayed the start time of machine B, so that machine B works continuously without any idle time. A simple mathematical example is also given to clear the concept.

Diversified teaching-learning-based optimization for fuzzy two-stage hybrid flow shop scheduling with setup time

2021 ◽  
pp. 1-15
Author(s):  
Deming Lei ◽  
Bingjie Xi
Keyword(s):  
Flow Shop ◽  
Setup Time ◽  
Distributed Scheduling ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Distributed scheduling has attracted much attention in recent years; however, distributed scheduling problem with uncertainty is seldom considered. In this study, fuzzy distributed two-stage hybrid flow shop scheduling problem (FDTHFSP) with sequence-dependent setup time is addressed and a diversified teaching-learning-based optimization (DTLBO) algorithm is applied to optimize fuzzy makespan and total agreement index. In DTLBO, multiple classes are constructed and categorized into two types according to class quality. Different combinations of global search and neighborhood search are used in two kind of classes. A temporary class with multiple teachers is built based on Pareto rank and difference index and evolved in a new way. Computational experiments are conducted and results demonstrate that the main strategies of DTLBO are effective and DTLBO has promising advantages on solving the considered problem.

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