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

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.

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Research on Flow Shop Scheduling Problem with Multi-customer and Fuzzy Due Date Based on Non-cooperative Games

2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop ◽

10.1109/kamw.2008.4810601 ◽

2008 ◽

Cited By ~ 3

Author(s):

Yanping Zhou ◽

Xingsheng Gu

Keyword(s):

Cooperative Games ◽

Flow Shop ◽

Flow Shop Scheduling ◽

Scheduling Problem ◽

Due Date ◽

Shop Scheduling ◽

Non Cooperative Games

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

New Trends and Issues Proceedings on Humanities and Social Sciences ◽

10.18844/prosoc.v4i10.3105 ◽

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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Stochastic Flow-Shop Scheduling with Lateness-Related Performance Measures

Probability in the Engineering and Informational Sciences ◽

10.1017/s0269964800002047 ◽

1991 ◽

Vol 5 (2) ◽

pp. 245-254 ◽

Cited By ~ 1

Author(s):

Chung-Yee Lee ◽

Chen-Sin Lin

Keyword(s):

Performance Measures ◽

Flow Shop ◽

Flow Shop Scheduling ◽

Stochastic Flow ◽

Due Date ◽

Scheduling Policies ◽

Shop Scheduling ◽

Processing Times

In this paper we consider stochastic flow-shop scheduling with reference to certain lateness-related performance measures. We show that for various assumptions on the distribution of job-processing times of a flow shop, certain scheduling policies following the stochastic analogy of the Earliest Due Date (EDD) rule yield optimal results.

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