A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data Envelopment Analysis: Theory, Methodology and Application
Data Envelopment Analysis: Theory, Methodology and Application
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
An improved model for vehicle routing problem with time constraint based on genetic algorithm
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software
Finding the most efficient DMUs in DEA: An improved integrated model
Computers and Industrial Engineering
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
A heuristic for the vehicle routing problem with due times
Computers and Industrial Engineering
Tuning Metaheuristics: A Machine Learning Perspective
Tuning Metaheuristics: A Machine Learning Perspective
Survey: The vehicle routing problem: A taxonomic review
Computers and Industrial Engineering
Design of evolutionary algorithms-A statistical perspective
IEEE Transactions on Evolutionary Computation
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This study proposes an alternative to the conventional empirical analysis approach for evaluating the relative efficiency of distinct combinations of algorithmic operators and/or parameter values of genetic algorithms (GAs) on solving the pickup and delivery vehicle routing problem with soft time windows (PDVRPSTW). Our approach considers each combination as a decision-making unit (DMU) and adopts data envelopment analysis (DEA) to determine the relative and cross efficiencies of each combination of GA operators and parameter values on solving the PDVRPSTW. To demonstrate the applicability and advantage of this approach, we implemented a number of combinations of GA's three main algorithmic operators, namely selection, crossover and mutation, and employed DEA to evaluate and rank the relative efficiencies of these combinations. The numerical results show that DEA is well suited for determining the efficient combinations of GA operators. Among the combinations under consideration, the combinations using tournament selection and simple crossover are generally more efficient. The proposed approach can be adopted to evaluate the relative efficiency of other meta-heuristics, so it also contributes to the algorithm development and evaluation for solving combinatorial optimization problems from the operational research perspective.