A note on “Two-machine flow-shop scheduling with rejection” and its link with flow-shop scheduling and common due date assignment

2012 ◽  
Vol 39 (12) ◽  
pp. 3244-3246 ◽  
Author(s):  
V. T'kindt ◽  
F. Della Croce
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Due Date ◽  
Due Date Assignment ◽  
Common Due Date ◽  
Shop Scheduling ◽  
Scheduling With Rejection

Two-machine Flow Shop Scheduling with a Common Due Date to Maximize Total Early Work

2021 ◽  
Author(s):  
Xin Chen ◽  
Qian Miao ◽  
Bertrand M.T. Lin ◽  
Malgorzata Sterna ◽  
Jacek Blazewicz
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Due Date ◽  
Common Due Date ◽  
Shop Scheduling

Bi-objective hybrid flow shop scheduling with common due date

2019 ◽  
Author(s):  
Zhi Li ◽  
Ray Y. Zhong ◽  
Ali Vatankhah Barenji ◽  
J. J. Liu ◽  
C. X. Yu ◽  
...  
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Due Date ◽  
Common Due Date ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

New results on two-machine flow-shop scheduling with rejection

2015 ◽  
Vol 31 (4) ◽  
pp. 1493-1504 ◽  
Author(s):  
Liqi Zhang ◽  
Lingfa Lu ◽  
Shisheng Li
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Shop Scheduling ◽  
Scheduling With Rejection

A New Hybrid Particle Swarm Optimization for Solving Flow Shop Scheduling Problem with Fuzzy due Date

2011 ◽  
Vol 189-193 ◽  
pp. 2746-2753 ◽  
Author(s):  
Hai Bo Tang ◽  
Chun Ming Ye ◽  
Liang Fu Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Due Date ◽  
Swarm Optimization ◽  
Evolutionary Mechanism ◽  
Shop Scheduling ◽  
Evolution Algorithm

Coping with the characteristic of flow shop scheduling problem with uncertain due date, fuzzy arithmetic on fuzzy numbers is applied to describe the problem, and then a new hybrid algorithm model which integrate particle swarm optimization into the evolutionary mechanism of the knowledge evolution algorithm is presented to solve the problem. By the evolutionary mechanism of knowledge evolution algorithm, we can exploit the global search ability. By the operating characteristic of PSO, we can enhance the local search ability. The algorithm is tested with MATLAB simulation. The result, compared with Genetic algorithm and modified particle swarm optimization, shows the feasibility and effectiveness of the proposed algorithm.

Genetic algorithm applied to the flow shop scheduling problem under effects of fuzzy learning and deterioration with a common fuzzy due date

2018 ◽  
Vol 4 (10) ◽  
pp. 306-316
Author(s):  
M. Duran Toksari
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Real Life ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Due Dates ◽  
Due Date ◽  
Shop Scheduling ◽  
Completion Times ◽  
Fuzzy Learning

This research is to develop an approximate solution for a flow shop scheduling problem under the effects of fuzzy learning and deterioration with a common fuzzy due date by applying genetic algorithm technique. Real life is complex and filled with ambiguity and uncertainty. Due dates may not be always determined by a decision maker because of their biased approach and past experiences. Therefore, due dates may be defined in forms of any fuzzy set to encode decision maker’s biased approaches and satisfaction levels for completion times of jobs. The objective function of the problem in this research is to maximise decision maker’s sum of satisfaction levels with respect to completion times of jobs on a flow shop scheduling environment by applying genetic algorithm technique. Keywords: Flow shop, fuzzy due date, genetic algorithm, learning effect, deterioration effect.

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