Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
A modified distance method for multicriteria optimization, using genetic algorithms
Computers and Industrial Engineering
The vehicle routing problem
Multiobjective Scheduling by Genetic Algorithms
Multiobjective Scheduling by Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A genetic algorithm for the vehicle routing problem
Computers and Operations Research
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
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Logistics has received considerable attention recently due to the rapid technological development of electronics and the Internet. Normally, clients expect items to be delivered at times that are convenient for their own schedules. Therefore, a multi-objective problem (MOP) model that simultaneously considers the depot desires and clients expectations can better elucidate the real logistics operations than a single objective model. This study proposes a MOP model based on VRP, in which two objective functions are run to minimize the total delivering path distance, while maximize client satisfaction by fulfilling time-window requirements. Moreover, this study proposes a hybrid algorithm based on GA incorporating some greedy algorithms to solve the developed MOP model with discrete variables. Besides, the response surface methodology (RSM) from design of experiments is adopted to help determine the crossover and mutation rates in GA. Finally, an actual military application is employed to confirm the practicality of the proposed MOP model and hybrid algorithm.