Multi-Objective Flexible Job-Shop Scheduling Method Study Based on The Integration Algorithm of GA-BP

2013 ◽  
Vol 7 (2) ◽  
pp. 109-116 ◽  
Author(s):  
Defan Zhou ◽  
Jingang Jiang ◽  
Chunling Luo ◽  
Yao Chen
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Integration Algorithm ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Scheduling Method

Approach for Multi-Objective Flexible Job Shop Scheduling

2012 ◽  
Vol 542-543 ◽  
pp. 407-410 ◽  
Author(s):  
Hong Jie Hui
Keyword(s):  
Petri Net ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Scheduling Method ◽  
Parikh Vector

A multi-objective scheduling method based on the controlled Petri net and GA is proposed to the flexible job shop scheduling problem (FJSP). Function objectives of the proposed method are to minimize the completion time and the total expense and workload of machines. Firstly, a Parikh vector based approach for Petri net controller is introduced, and based on this method, the Petri net model is constructed for FSP with machine breaking down. Then, the genetic algorithm (GA) is applied based on the controlled Petri net model and Pareto. Finally, simulation results based on an example show that the method is efficient.

scholarly journals Genetic Programming with Delayed Routing for Multi-Objective Dynamic Flexible Job Shop Scheduling

2020 ◽  
Author(s):  
Binzi Xu ◽  
Yi Mei ◽  
Yan Wang ◽  
Zhicheng Ji ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Dynamic Environment ◽  
Accurate Information ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Conflicting Objectives

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e. the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this paper, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and more accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multi-objective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

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