Comparison of cooperative and classical evolutionary algorithms for global supply chain optimisation

This paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and two approaches were used for global optimisation: a classical evolutionary approach and a cooperative coevolutionary approach. The latter approach produced higher quality solutions due to its use of communication between silos. Additionally, a second problem was presented involving an existing Australian multi-factory sheet steel business.

[1]  Mitsuo Gen,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation , 1996 .

[2]  David E. Goldberg,et al.  Alleles, loci and the traveling salesman problem , 1985 .

[3]  Kenneth A. De Jong,et al.  Understanding cooperative co-evolutionary dynamics via simple fitness landscapes , 2005, GECCO '05.

[4]  John E. Beasley,et al.  Route first--Cluster second methods for vehicle routing , 1983 .

[5]  Jeffrey K. Bassett,et al.  An Analysis of Cooperative Coevolutionary Algorithms A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University , 2003 .

[6]  Lawrence Davis,et al.  Applying Adaptive Algorithms to Epistatic Domains , 1985, IJCAI.

[7]  Mitchell A. Potter,et al.  EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES , 2006 .

[8]  Wathiq N. Abdullah,et al.  Solving Job-Shop Scheduling Problem Using Genetic Algorithm Approach , 2011 .

[9]  A. E. Eiben Genetic algorithms + data structures = evolution programs: Z. Michalewicz. Springer, Berlin, 1996, 3rd revised and extended edition (1st edition appeared in 1992), 387 pp. (hardcover), 68 figures, 36 tables, price DM 58 , 1997 .

[10]  Jan Karel Lenstra,et al.  Job Shop Scheduling by Simulated Annealing , 1992, Oper. Res..

[11]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[12]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[13]  Christian Prins,et al.  A simple and effective evolutionary algorithm for the vehicle routing problem , 2004, Comput. Oper. Res..

[14]  Ronald L. Rivest,et al.  Introduction to Algorithms, Second Edition , 2001 .

[15]  Kenneth A. De Jong,et al.  Sequential versus Parallel Cooperative Coevolutionary Algorithms for Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[16]  Frederick Ducatelle,et al.  Ant colony optimization and local search for bin packing and cutting stock problems , 2004, J. Oper. Res. Soc..

[17]  Kenneth A. De Jong,et al.  Relationships between internal and external metrics in co-evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[18]  Penousal Machado,et al.  Vehicle Routing Problem: Doing It The Evolutionary Way , 2002, GECCO.

[19]  Richard K. Belew,et al.  Methods for Competitive Co-Evolution: Finding Opponents Worth Beating , 1995, ICGA.

[20]  Clarence H. Martin,et al.  Integrated Production, Distribution, and Inventory Planning at Libbey-Owens-Ford , 1993 .

[21]  Uzay Kaymak,et al.  Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete , 2007, Eur. J. Oper. Res..

[22]  Risto Miikkulainen,et al.  Efficient Reinforcement Learning through Symbiotic Evolution , 1996, Machine Learning.

[23]  Karl-Dirk Kammeyer,et al.  Parameter Study for Differential Evolution Using a Power Allocation Problem Including Interference Cancellation , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[24]  John H. Holland,et al.  Properties of the Bucket Brigade , 1985, ICGA.

[25]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

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

[27]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[28]  Paolo Toth,et al.  The vehicle routing problem , 2001 .

[29]  David W. Coit,et al.  Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach , 1996, Comput. Oper. Res..

[30]  Turan Paksoy,et al.  A genetic algorithm approach for multi-objective optimization of supply chain networks , 2006, Comput. Ind. Eng..

[31]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[32]  Mitchell A. Potter,et al.  The design and analysis of a computational model of cooperative coevolution , 1997 .

[33]  Jae Young Choi,et al.  A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights , 1995, Comput. Oper. Res..

[34]  Nicos Christofides,et al.  The vehicle routing problem , 1976, Revue française d'automatique, informatique, recherche opérationnelle. Recherche opérationnelle.

[35]  Phil Husbands,et al.  Simulated Co-Evolution as the Mechanism for Emergent Planning and Scheduling , 1991, International Conference on Genetic Algorithms.

[36]  Mitsuo Gen,et al.  A genetic algorithm approach to the bi-criteria allocation of customers to warehouses , 2003 .

[37]  Xin Yao,et al.  A new evolutionary approach to cutting stock problems with and without contiguity , 2002, Comput. Oper. Res..

[38]  George B. Dantzig,et al.  The Truck Dispatching Problem , 1959 .

[39]  Kenneth A. De Jong,et al.  The Coevolution of Antibodies for Concept Learning , 1998, PPSN.