Adopting co-evolution and constraint-satisfaction concept on genetic algorithms to solve supply chain network design problems

With the rapid globalization of markets, integrating supply chain technology has become increasingly complex. That is, most supply chains are no longer limited to a particular region. Because the numbers of branch nodes of supply chains have increased, products and raw materials vary and resource constraints differ. Thus, integrating planning mechanisms should include the capacity to respond to change. In the past, mathematical programming and a general heuristics algorithm were used to solve globalized supply chain network design problems. When mathematical programming is used to solve a problem and the number of decision variables is too high or constraint conditions are too complex, computation time is long, resulting in low efficiency, and can easily become trapped in partial optimum solution. When a general heuristics algorithm is used and the number of variables and constraints is too high, the degree of complexity increases. This usually results in an inability of people to think about resource constraints of enterprises and obtain an optimum solution. Therefore, this study uses genetic algorithms with optimum search features. This work combines the co-evolutionary mode, which is in accordance with various criteria and evolves dynamically, and constraint-satisfaction mode capacity to narrow the search space, which helps in finding rapidly a solution that, solves supply chain integration network design problems. Additionally, via mathematical programming, a simple genetic algorithm, co-evolutionary genetic algorithm, constraint-satisfaction genetic algorithm and co-evolutionary constraint genetic algorithm are used to compare the experiments result and processing time to confirm the performance of the proposed method. (C) 2010 Elsevier Ltd. All rights reserved.