Pareto Optimality of Production Schedules in the Stage of Populations Selection of the MOIA Immune Algorithm

2014 ◽  
Vol 657 ◽  
pp. 869-873 ◽  
Author(s):  
Iwona Paprocka ◽  
Krzysztof Kalinowski
Keyword(s):  
Pareto Optimality ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Pareto Frontier ◽  
Immune Algorithm ◽  
Immune Memory ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Job Shop Scheduling Problem ◽  
Selection Of

In the paper, the problem of achieving Pareto optimal solutions set with application of the elaborated Multi Objectives Immune Algorithm is presented. The Pareto frontier provides a variety of compromise solutions for contradictive criteria to a decision maker. We propose the application of the selection based on the Pareto optimality to maintain solutions with great diversity in an immune memory. Stimulation and suppression mechanisms are used to control the diversity of generated solutions. Computer simulations are done for a job shop scheduling problem.

scholarly journals An improved memetic algorithm for the flexible job shop scheduling problem with transportation times

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1518-1528
Author(s):  
Guohui Zhang ◽  
Jinghe Sun ◽  
Xixi Lu ◽  
Haijun Zhang
Keyword(s):  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Memetic Algorithm ◽  
Optimal Solutions ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Annealing Algorithm ◽  
Transportation Times ◽  
Improved Memetic Algorithm

In the practical production, the transportation of jobs is existed between different machines. These transportation operations directly affect the production cycle and the production efficiency. In this study, an improved memetic algorithm is proposed to solve the flexible job shop scheduling problem with transportation times, and the optimization objective is minimizing the makespan. In the improved memetic algorithm, an effective simulated annealing algorithm is adopted in the local search process, which combines the elite library and mutation operation. All the feasible solutions are divided into general solutions and local optimal solutions according to the elite library. The general solutions are executed by the simulated annealing algorithm to improve the quality, and the local optimal solutions are executed by the mutation operation to increase the diversity of the solution set. Comparison experiments with the improved genetic algorithm show that the improved memetic algorithm has better search performance and stability.

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

2014 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
JarosÅ‚aw Rudy ◽  
Dominik Żelazny
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Pareto Frontier ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Real World Applications ◽  
Multi Agent ◽  
Disjoint Sets

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.

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