Solving the integrated cell formation and worker assignment problem using particle swarm optimization and linear programming

Outbound container storage location assignment problem (OCSLAP) could be defined as how a series of outbound containers should be stacked in the yard according to certain assignment rules so that the outbound process could be facilitated. Considering the NP-hard nature of OCSLAP, a novel particle swarm optimization (PSO) method is proposed. The contributions of this paper could be outlined as follows: First, a neighborhood-based mutation operator is introduced to enrich the diversity of the population to strengthen the exploitation ability of the proposed algorithm. Second, a mechanism to transform the infeasible solutions into feasible ones through the lowest stack principle is proposed. Then, in the case of trapping into the local solution in the search process, an intermediate disturbance strategy is implemented to quickly jump out of the local solution, thereby enhancing the global search capability. Finally, numerical experiments have been done and the results indicate that the proposed algorithm achieves a better performance in solving OCSLAP.

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A novel approach to solving weapon-target assignment problem based on hybrid particle swarm optimization algorithm

Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology ◽

10.1109/emeit.2011.6023352 ◽

2011 ◽

Cited By ~ 3

Author(s):

Zhu Bo ◽

Zou Feng-xing ◽

Wei Jia-hua

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Assignment Problem ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Hybrid Particle ◽

Hybrid Particle Swarm Optimization ◽

Novel Approach ◽

Target Assignment

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IMPROVED PARTICLE SWARM OPTIMIZATION FOR NON-LINEAR PROGRAMMING PROBLEM WITH BARRIER METHOD

International Journal of Students Research in Technology & Management ◽

10.18510/ijsrtm.2017.5410 ◽

2017 ◽

Vol 5 (4) ◽

pp. 72-80

Author(s):

Raju Prajapati ◽

Om Prakash Dubey ◽

Randhir Kumar

Keyword(s):

Linear Programming ◽

Particle Swarm Optimization ◽

Programming Language ◽

Particle Swarm ◽

Barrier Method ◽

Lagrangian Multipliers ◽

Swarm Optimization ◽

Non Linear Programming ◽

Non Linear ◽

Linear Programming Problems

The Non-Linear Programming Problems (NLPP) are computationally hard to solve as compared to the Linear Programming Problems (LPP). To solve NLPP, the available methods are Lagrangian Multipliers, Sub gradient method, Karush-Kuhn-Tucker conditions, Penalty and Barrier method etc. In this paper, we are applying Barrier method to convert the NLPP with equality constraint to an NLPP without constraint. We use the improved version of famous Particle Swarm Optimization (PSO) method to obtain the solution of NLPP without constraint. SCILAB programming language is used to evaluate the solution on sample problems. The results of sample problems are compared on Improved PSO and general PSO.

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