Multi-objective Job-shop Problem Research Articles

Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

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.

A new dispatching rule based genetic algorithm for the multi-objective job shop problem

Hyper-heuristics or "methodologies to choose heuristics" are becoming increasingly popular given their suitability to solve hard real world combinatorial optimisation problems. Their distinguishing feature is that they operate in the space of heuristics or heuristic components rather than in the solution space. In Dispatching Rule Based Genetic Algorithms (DRGA) solutions are represented as sequences of dispatching rules which are called one at a time and used to sequence a number of operations onto machines. The number of operations that each dispatching rule in the sequence handles is a parameter to which DRGA is notoriously sensitive. This paper proposes a new hybrid DRGA which searches simultaneously for the best sequence of dispatching rules and the number of operations to be handled by each dispatching rule. The investigated DRGA uses the selection mechanism of NSGA-II when handling multi-objective problems. The proposed representation was used to solve different variants of the multi-objective job shop problem as well as the single objective problem with the sum of weighted tardiness objective. Our results, supported by the statistical analysis, confirm that DRGAs that use the proposed representation obtained better results in both the single and multi-objective environment overall and on each particular set of instances than DRGAs using the conventional dispatching rule representation and a GA that uses the more common permutation representation.

A genetic solution based on lexicographical goal programming for a multiobjective job shop with uncertainty

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly active schedules. Experimental results are presented for several problem instances, solved by the GA according to the proposed model, considering three objectives: makespan, tardiness and idleness. The results illustrate the potential of the proposed multiobjective model and genetic algorithm.

An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems

Scheduling for the flexible job-shop is very important in both fields of production management and combinatorial optimization. However, it is quite difficult to achieve an optimal solution to this ...