Modified NSGA-II for a Bi-Objective Job Sequencing Problem (original) (raw)
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A two-stage genetic algorithm for multi-objective job shop scheduling problems
Journal of Intelligent Manufacturing, 2011
This paper presents a two-stage genetic algorithm (2S-GA) for multi-objective Job Shop scheduling problems. The 2S-GA is proposed with three criteria: Minimize makespan, Minimize total weighted earliness, and Minimize total weighted tardiness. The proposed algorithm is composed of two Stages: Stage 1 applies parallel GA to find the best solution of each individual objective function with migration among populations. In Stage 2 the populations are combined. The evolution process of Stage 2 is based on Steady-State GA using the weighted aggregating objective function. The algorithm developed can be used with one or two objectives without modification. The genetic algorithm is designed and implemented with the GALIB object library. The random keys representation is applied to the problem. The schedules are constructed using a permutation with m-repetitions of job numbers. Performance of the proposed algorithm is tested on published benchmark instances and compared with results from other published approaches for both the single objective and multi-objective cases. The experimental results show that 2S-GA is effective and efficient to solve job shop scheduling problem in term of solution quality.
A Bi-Objective Stage Shop Scheduling Problem with Modified NSGA-II and Modified MOPSO
Asian Journal of Research in Business Economics and Management, Volume 5(3), pp. 30-50 , 2015
Stage shop scheduling is an emergent area in the field of scheduling. This paper has proposed a biobjective stage shop scheduling problem with total completion time and total tardiness of jobs, as objectives. In order to solve the proposed formulated problem, modified NSGA-II (Nondominated Sorting Genetic Algorithm-II) and modified MOPSO (Multi-Objective Particle Swarm Optimization) have been proposed. A mutation algorithm for NSGA-II and a velocity updation algorithm based on circular motion of alleles (for NSGA-II) and circular motion of particles (for MOPSO) have also been introduced and have been embedded in the proposed algorithms. The experimental results show that NSGA-II performs better than MOPSO in some aspects, whereas MOPSO performs better than NSGA-II in some other aspects.
Universal Journal of Applied Mathematics, 2018
Many evolutionary algorithms have been used to solve multi-objective scheduling problems. NSGA-II is one of them that is based on the Pareto optimality concept and generally obtains good results. However, it is possible to improve its performance with some modifications. In this paper, two modified NSGA-II algorithms have been suggested for solving the multi-objective flexible job shop scheduling problem. The neighborhood structures defined for the problem are integrated into the algorithms to create better generations during the iterations. Also, their initial populations are created with an effective heuristic. In the first modified NSGA-II, after the creation of the offspring population, a neighbor of each individual in the parent population is constructed, and then one of them is selected according to the domination state of the solutions. Then the populations are merged to create a new population. In the second modified NSGA-II, only the solutions on the first and second fronts of the parent population and also their neighbors are merged with the offspring population. Other operators of the algorithms like the non-dominated sorting and calculating the crowding distances are as the classic NSGA-II. A comparison is done with a classic NSGA-II based on two metrics. The results show that as it is in the first modified NSGA-II, including neighbors of more individuals of the population provides better results because it increases diversity and intensity of the search. The performance of the second modified NSGA-II is almost similar to the NSGA-II. So, it can be concluded that although integrating the neighborhood structures can improve the performance of search, it is better to define that the structures should be applied to how many and which solutions, in otherwise the quality of search may not increase.
Evolutionary Multi-Objective Scheduling Procedures in Non-Standardized Production Processes
Dyna, 2012
Scheduling problems can be seen as multi-objective optimization problems (MOPs), involving the simultaneous satisfaction of several goals related to the optimal design, coordination, and management of tasks. The complexity of the goal functions and of the combinatorial methods used to find analytical solutions to them is quite high. The search for solutions (Pareto-optima) is better served by the use of genetic algorithms (GAs). In this paper, we analyze the performance of the non-dominated sorting genetic algorithm II ( ...
The problem investigated in this study involves an unrelated parallel machine scheduling problem with sequence-dependent setup times, different release dates, machine eligibility and precedence constraints. This problem has been inspired from a realistic scheduling problem in the shipyard. The optimization criteria are to simultaneously minimize mean weighted flow time and mean weighted tardiness. To formulate this complicated problem, a new mixed-integer programming model is presented. Considering the NP-complete characteristic of this problem, two famous meta-heuristics including a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective ant colony optimization (MOACO) which is a modified and adaptive version of BicriterionAnt algorithm are developed. Obviously, the precedence constraints increase the complexity of the scheduling problem in strong sense in order to generate feasible solutions, especially in parallel machine environment. Therefore a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. Due to the fact that appropriate design of parameter has a significant effect on the performance of algorithms, we calibrate the parameters of these algorithms by using new approach of Taguchi method. The performances of the proposed meta-heuristics are evaluated by a number of numerical examples. The results indicated that the suggested MOACO statistically outperformed the proposed NSGA-II in solving the test problems. In addition, the application of the proposed algorithms is justified by a real block erection scheduling problem in the shipyard.