Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning

This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining ef...

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Bibliographic Details
Published in:Journal of intelligent manufacturing Vol. 29; no. 3; pp. 585 - 601
Main Authors: Zhang, S., Wong, T. N.
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2018
Springer Nature B.V
Subjects:
Advanced manufacturing technologies
Algorithms
Ant colony optimization
Business and Management
Control
Graphical representations
Heuristic
Heuristic methods
Job shops
Machines
Manufacturing
Mechatronics
Parameters
Process planning
Processes
Production
Production scheduling
Robotics
Scheduling
Tuning
Integrated process planning and scheduling
Ant colony optimization
Algorithm parameter tuning
ISSN:0956-5515, 1572-8145
Online Access:Get full text
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Summary:This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. In accordance with the characteristics of the IPPS problem, the mechanism of ACO algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. Using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for ACO parameter tuning for IPPS problems provided. The results show that with the specific modifications made on ACO algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-014-1023-3