Multi-matrix Real-coded Genetic Algorithm for Minimising Total Costs in Logistics Chain Network

The importance of supply chain and logistics management has been widely recognised. Effective management of the supply chain can reduce costs and lead times and improve responsiveness to changing customer demands. This paper proposes a multi-matrix real-coded Generic Algorithm (MRGA) based optimisation tool that minimises total costs associated within supply chain logistics. According to finite capacity constraints of all parties within the chain, Genetic Algorithm (GA) often produces infeasible chromosomes during initialisation and evolution processes. In the proposed algorithm, chromosome initialisation procedure, crossover and mutation operations that always guarantee feasible solutions were embedded. The proposed algorithm was tested using three sizes of benchmarking dataset of logistic chain network, which are typical of those faced by most global manufacturing companies. A half fractional factorial design was carried out to investigate the influence of alternative crossover and mutation operators by varying GA parameters. The analysis of experimental results suggested that the quality of solutions obtained is sensitive to the ways in which the genetic parameters and operators are set.

[1]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[2]  Haldun Aytug,et al.  Use of genetic algorithms to solve production and operations management problems: A review , 2003 .

[3]  Pupong Pongcharoen,et al.  Stochastic Optimisation Timetabling Tool for university course scheduling , 2008 .

[4]  M. Gen,et al.  Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach , 2002 .

[5]  Chuanwen Jiang,et al.  A matrix real-coded genetic algorithm to the unit commitment problem , 2006 .

[6]  Christopher Earl,et al.  A Typology of UK Engineer-to-Order Companies , 2001 .

[7]  P. Pongcharoena,et al.  Determining optimum Genetic Algorithm parameters for scheduling the manufacturing and assembly of complex products , 2002 .

[8]  Reza Zanjirani Farahani,et al.  A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain , 2008 .

[9]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[10]  Christian Hicks,et al.  A Genetic Algorithm tool for optimising cellular or functional layouts in the capital goods industry , 2006 .

[11]  Pupong Pongcharoen,et al.  Exploration of Genetic Parameters and Operators through Travelling Salesman Problem , 2007 .

[12]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[13]  Philip M. Kaminsky,et al.  Designing and managing the supply chain : concepts, strategies, and case studies , 2007 .

[14]  Mikael Rönnqvist,et al.  Supply chain modelling of forest fuel , 2004, Eur. J. Oper. Res..

[15]  S. Chopra,et al.  Supply Chain Management: Strategy, Planning & Operation , 2007 .

[16]  S. S. Chaudhry *,et al.  Application of genetic algorithms in production and operations management: a review , 2005 .

[17]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[18]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[19]  Christian Hicks,et al.  Applying designed experiments to optimize the performance of genetic algorithms used for scheduling complex products in the capital goods industry , 2001 .

[20]  Pupong Pongcharoen,et al.  The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure , 2004, Eur. J. Oper. Res..

[21]  A. S. Thoke,et al.  International Journal of Electrical and Computer Engineering 3:16 2008 Fault Classification of Double Circuit Transmission Line Using Artificial Neural Network , 2022 .