A Modified PSO to Optimize Manufacturers Production and Delivery

This paper presents a new approach to the solution of optimal Manufacturers Production and Delivery (MPD) scheduling problem, using improved particle swarm optimization (ISAPSO) technique. The producing and delivery system is highly complex and possesses nonlinear relationship of the problem variables, products storage, products transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved particle swarm optimization (PSO) technique is suggested that deals with an inequality constraint treatment mechanism to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed ISAPSO is applied on the quick response (QR) manufacturing supply chain system using making to order (MTO) strategy. Numerical results are compared with those obtained by PSO and ISAPSO approaches. The simulation results reveal that the proposed ISAPSO appears to be the best in terms of convergence speed, solution time and minimum cost to the MPD problems.

[1]  Tsan-Ming Choi,et al.  Mean-variance analysis of Quick Response Program ☆ , 2008 .

[2]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[3]  Yongqiang Wang,et al.  Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects , 2011, Expert Syst. Appl..

[4]  Idel Montalvo,et al.  Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems , 2010, Eng. Appl. Artif. Intell..

[5]  Susan A. Slotnick,et al.  Order acceptance with weighted tardiness , 2007, Comput. Oper. Res..

[6]  D. Y. Sha,et al.  A Multi-objective PSO for job-shop scheduling problems , 2009, 2009 International Conference on Computers & Industrial Engineering.

[7]  Ying Wang,et al.  Improved chaotic particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects , 2010 .

[8]  Walter O. Rom,et al.  Order acceptance using genetic algorithms , 2009, Comput. Oper. Res..

[9]  Purushothaman Damodaran,et al.  A branch and price solution approach for order acceptance and capacity planning in make-to-order operations , 2011, Eur. J. Oper. Res..

[10]  P. Daugherty,et al.  Lean Launch: Managing Product Introduction Risk Through Response‐Based Logistics , 1999 .

[11]  MA Shi-hua.  A Study on Safety Stock Management of Component Commonality Based on Lead Time , 2003 .

[12]  Feng Zou,et al.  A multi-objective endocrine PSO algorithm and application , 2011, Appl. Soft Comput..

[13]  Hirotaka Yoshida,et al.  A PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER AND VOLTAGE CONTROL CONSIDERING VOLTAGE STABILITY , 2000 .

[14]  Randolph W. Hall,et al.  Distribution Strategies that Minimize Transportation and Inventory Costs , 1985, Oper. Res..

[15]  Wooseung Jang,et al.  Production and allocation policies in a two-class inventory system with time and quantity dependent waiting costs , 2006, Comput. Oper. Res..

[16]  Suresh P. Sethi,et al.  Innovative Quick Response Programs: A Review , 2010 .

[17]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  Tharam S. Dillon,et al.  Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function , 2010 .

[19]  Songfeng Lu,et al.  Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization , 2010, Expert Syst. Appl..

[20]  A. Ferrari,et al.  Development of an optimal operation strategy in a sequential batch reactor (SBR) through mixed-integer particle swarm dynamic optimization (PSO) , 2010, Comput. Chem. Eng..

[21]  H. Shayeghi,et al.  Multi-stage fuzzy load frequency control using PSO , 2008 .

[22]  Mahdi Zarei,et al.  Solving a dynamic virtual cell formation problem by linear programming embedded particle swarm optimization algorithm , 2011, Appl. Soft Comput..

[23]  Ioannis G. Tsoulos,et al.  Enhancing PSO methods for global optimization , 2010, Appl. Math. Comput..

[24]  Jiangye Yuan,et al.  An improved WM method based on PSO for electric load forecasting , 2010, Expert Syst. Appl..

[25]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[26]  Pisal Yenradee,et al.  PSO-based algorithm for home care worker scheduling in the UK , 2007, Comput. Ind. Eng..

[27]  Xigang Yuan,et al.  An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints , 2007, Computers and Chemical Engineering.

[28]  Mehmet Barut,et al.  Revenue Management in Order-Driven Production Systems , 2005, Decis. Sci..

[29]  Doğan A. Serel,et al.  Optimal ordering and pricing in a quick response system , 2009 .

[30]  Dennis E. Blumenfeld,et al.  TRADE-OFF BETWEEN FREIGHT EXPEDITING AND SAFETY STOCK INVENTORY COSTS , 1984 .

[31]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[32]  Abdullah Al Mamun,et al.  A memetic model of evolutionary PSO for computational finance applications , 2009, Expert Syst. Appl..

[33]  B. Beamon.  Supply chain design and analysis:: Models and methods , 1998 .

[34]  Suresh P. Sethi,et al.  Coordination Mechanism for the Supply Chain with Leadtime Consideration and Price-Dependent Demand , 2009, Eur. J. Oper. Res..