AI SearchA genetic algorithm for a location-routing problem
Call Number: AIT Thesis no.ISE-99-47 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. ISE-99-47Publication details: Bangkok : Asian Institute of Technology, 1999Description: 65 p.: illSubject(s): Online resources: Dissertation note: Thesis (M.Sc.) - Asian Institute of Technology Summary: In distribution system, the strategic and tactical decisions of the locations of depots and the routes of allocated customers are addressed in Location-Routing Problem. Since the traditional optimization method takes a long computational time to solve the problem. An alternative method, a genetic algorithm is proposed in this study. Firstly, the permutation encoding and decoding interpret solutions as clustering and routing simultaneously. Secondly, the initialization procedure originates feasible solutions. Then, the combined roulette wheel and rank selection chooses two parents to crossover by a new technique called group crossover. It saves the fixed and variable costs and generates feasible offspring. After crossover, the swap mutation is also applied. Subsequently, the algorithms are written in C++ program. The GA performance is evaluated by the experiment varying problem sizes and comparing the results with those of optimization package called CPLEX. The values of GA parameters are also studied for their effects on the quality of solutions. It is found that their appropriate values depend on the problem sizes. In general, GA performs well as it generates heuristic solutions with a less computational time compared with that of CPLEX. Moreover, GA can find solutions in large problems that CPLEX cannot solve in a limited time.
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Thesis (M.Sc.) - Asian Institute of Technology
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science, School of Advanced Technologies
In distribution system, the strategic and tactical decisions of the locations of depots and the routes of allocated customers are addressed in Location-Routing Problem. Since the traditional optimization method takes a long computational time to solve the problem. An alternative method, a genetic algorithm is proposed in this study. Firstly, the permutation encoding and decoding interpret solutions as clustering and routing simultaneously. Secondly, the initialization procedure originates feasible solutions. Then, the combined roulette wheel and rank selection chooses two parents to crossover by a new technique called group crossover. It saves the fixed and variable costs and generates feasible offspring. After crossover, the swap mutation is also applied. Subsequently, the algorithms are written in C++ program. The GA performance is evaluated by the experiment varying problem sizes and comparing the results with those of optimization package called CPLEX. The values of GA parameters are also studied for their effects on the quality of solutions. It is found that their appropriate values depend on the problem sizes. In general, GA performs well as it generates heuristic solutions with a less computational time compared with that of CPLEX. Moreover, GA can find solutions in large problems that CPLEX cannot solve in a limited time.
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