Abstract
In this paper the job shop scheduling problem (JSP) with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP) were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO) and genetic algorithm (GA) for MOJSP. Both of those methods employ certain technique, taken from the multi-criteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.
Highlights
Companies are always interested in maintaining competitive position in fast-changing market, this is usually done by optimizing the business and production processes
There are 80 instances divided into 8 instance sizes, computation results were combined into groups
This paper proposes new look at scheduling model widely used in real production problems
Summary
Companies are always interested in maintaining competitive position in fast-changing market, this is usually done by optimizing the business and production processes. Access to effective and fast optimization methods is extremely important. This phenomenon results in constant development of new approaches to optimization of various practical problems. The so-called job shop scheduling problem (JSP) represents a class of widely studied cases based on ideas derived from production engineering and has been classified as a NP-hard problem [5]. Most of the currently used single objective models are adaptable to real world applications, but modern production scheduling problems need further advancements. Multi-objective scheduling is the result of natural evolution of models and solution methods, oriented on practice, since scheduling decisions usually have to take into account several economic indexes simultaneously.
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