Particle Swarm Optimization Algorithm for the Prepack Optimization Problem

Packing problem is one of the most well-known problems in inventory control situations. There are some methods for solving such problems one of which is the meta-heuristic algorithm. The PSO method is a procedure that solves problems with a heuristic procedure. In this study, some realistic assumptions are considered and each particle has its speed. Then, these particles transfer in the solution space, and after every iteration, fitness function for each particle is calculated. We intend to solve the packaging problem by using PSO in this investigation. It is considered that the operable cost in this problem is considerable. In this case, we have the instance of the warehouse that should manage a wide range of various shops, requiring a given group of items. Consequently, this paper is solved a Mixed-integer linear programming problem for the pre-pack optimization problem and results shows the presented method could reduce the optimal amount in reasonable time effort.

[1]  Michele Monaci,et al.  Algorithms for packing and scheduling problems , 2003, 4OR.

[2]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[3]  Kashif Ishaque,et al.  An Improved Particle Swarm Optimization (PSO)–Based MPPT for PV With Reduced Steady-State Oscillation , 2012, IEEE Transactions on Power Electronics.

[4]  Louis-Martin Rousseau,et al.  The PrePack Optimization Problem , 2014, CPAIOR.

[5]  Fevrier Valdez,et al.  Modular Neural Networks architecture optimization with a new nature inspired method using a fuzzy combination of Particle Swarm Optimization and Genetic Algorithms , 2014, Information Sciences.

[6]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications , 2008, Natural Computing.

[7]  Issarachai Ngamroo,et al.  Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control , 2015 .

[8]  Claude-Guy Quimper,et al.  Integration of AI and OR Techniques in Constraint Programming , 2016, Lecture Notes in Computer Science.

[9]  Andrea Lodi,et al.  Two-dimensional packing problems: A survey , 2002, Eur. J. Oper. Res..

[10]  Po-Hung Chen,et al.  Two-Level Hierarchical Approach to Unit Commitment Using Expert System and Elite PSO , 2012, IEEE Transactions on Power Systems.

[11]  Paulo Henrique da F Silva,et al.  Blending PSO and ANN for Optimal Design of FSS Filters With Koch Island Patch Elements , 2010, IEEE Transactions on Magnetics.

[12]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part I: background and development , 2007, Natural Computing.

[13]  Ganapati Panda,et al.  A Particle-Swarm-Optimization-Based Decentralized Nonlinear Active Noise Control System , 2012, IEEE Transactions on Instrumentation and Measurement.

[14]  Luís Gouveia,et al.  Solving the variable size bin packing problem with discretized formulations , 2008, Comput. Oper. Res..

[15]  Francisco Jurado,et al.  Corrigendum to “Optimization of distributed generation systems using a new discrete PSO and OPF” [Electr. Power Syst. Res. (2012) 84 (1) 174–180] , 2015 .

[16]  Jidong Wang,et al.  Optimal capacity allocation of standalone wind/solar/battery hybrid power system based on improved particle swarm optimisation algorithm , 2013 .

[17]  Sofiene Kachroudi,et al.  Predictive Driving Guidance of Full Electric Vehicles Using Particle Swarm Optimization , 2012, IEEE Transactions on Vehicular Technology.

[18]  Nanbo Jin,et al.  Hybrid Real-Binary Particle Swarm Optimization (HPSO) in Engineering Electromagnetics , 2010, IEEE Transactions on Antennas and Propagation.

[19]  Ying Lin,et al.  Particle Swarm Optimization With an Aging Leader and Challengers , 2013, IEEE Transactions on Evolutionary Computation.

[20]  Daniele Vigo,et al.  An Exact Approach for the Vehicle Routing Problem with Two-Dimensional Loading Constraints , 2007, Transp. Sci..

[21]  N. K. Jain,et al.  A Review of Particle Swarm Optimization , 2018, Journal of The Institution of Engineers (India): Series B.

[22]  Kim-Fung Man,et al.  Computational Optimization Algorithms for Antennas and RF/Microwave Circuit Designs: An Overview , 2012, IEEE Transactions on Industrial Informatics.

[23]  Raymond Chiong,et al.  Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms , 2015, Inf. Sci..

[24]  Narendra G. Bawane,et al.  Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery , 2015, Appl. Soft Comput..

[25]  Yasunori Mitani,et al.  Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach , 2012, IEEE Transactions on Smart Grid.