A Hybrid Discrete Firefly Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems with Maintenance Activity

2014 ◽  
Vol 575 ◽  
pp. 922-925 ◽  
Author(s):  
S. Karthikeyan ◽  
P. Asokan ◽  
M. Chandrasekaran
Keyword(s):  
Firefly Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Maintenance Activity ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Availability Constraints ◽  
Discrete Firefly Algorithm

This paper presents a novel hybrid discrete firefly algorithm (HDFA) for solving the multi-objective flexible job shop scheduling problem with non fixed availability constraints (FJSP-nfa) due to maintenance activity. Three minimization objectives-the maximum completion time, the workload of the critical machine and the total workload of all machines are considered simultaneously. In this study, the discrete firefly algorithm is adopted to solve the problem, in which the machine assignment and operation sequence are processed by constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. In addition the decoding mechanism considering the maintenance activity is presented. A neighbourhood based local search is hybridized to enhance the exploitation capability. Representative benchmark problems are solved in order to evaluate and study the performance of the proposed algorithm.

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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