Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the use of genetic algorithms to solve location problems
Computers and Operations Research - Location analysis
A model and methodologies for the location problem with logistical components
Computers and Operations Research
Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
Computers and Industrial Engineering
New stochastic models for capacitated location-allocation problem
Computers and Industrial Engineering
Diversified SCM standard for the Japanese retail industry
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
An adaptive scheduling system with genetic algorithms for arranging employee training programs
Expert Systems with Applications: An International Journal
Automatic tropical cyclone eye fix using genetic algorithm
Expert Systems with Applications: An International Journal
A gestalt genetic algorithm: less details for better search
Proceedings of the 9th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
A genetic algorithm based heuristic to the multi-period fixed charge distribution problem
Applied Soft Computing
Computers and Electronics in Agriculture
International Journal of Computer Applications in Technology
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 12.05 |
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.