Multiobjective optimization with messy genetic algorithms
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Computers and Operations Research
The Best Shape for a Crossdock
Transportation Science
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Vehicle routing scheduling for cross-docking in the supply chain
Computers and Industrial Engineering - Special issue: Logistics and supply chain management
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Positioning of goods in a cross-docking environment
Computers and Industrial Engineering
Computers and Industrial Engineering
Minimizing makespan in two-stage hybrid cross docking scheduling problem
Computers and Operations Research
Expert Systems with Applications: An International Journal
Truck scheduling at zero-inventory cross docking terminals
Computers and Operations Research
Multiple crossdocks with inventory and time windows
Computers and Operations Research
Scheduling trucks in cross-docking systems: Robust meta-heuristics
Computers and Industrial Engineering
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
Ant colony optimization algorithm to solve for the transportation problem of cross-docking network
Computers and Industrial Engineering
Vehicle routing with cross-docking in the supply chain
Expert Systems with Applications: An International Journal
A bounded dynamic programming approach to schedule operations in a cross docking platform
Computers and Industrial Engineering
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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The importance and applicability of cross-docking systems have grown rapidly in recent years. As these systems play a key role, particularly, in distribution networks, the launch of multi-objective approaches can contribute to solve the real-world cases and problems of such systems, in which many different and even conflicting objectives are considered. Hence, this paper addresses three famous multi-objective algorithms including non-dominated sorting genetic algorithm-II (NSGA-II), strength Pareto evolutionary algorithm-II (SPEA-II), and sub-population genetic algorithm-II (SPGA-II) to solve the cross-docking scheduling problem, in which product items are unloaded from inbound trailers in the receiving dock and then are categorized and loaded onto outbound trailers in the shipping dock. Since the time aspect of such activities is so determining and crucial, objective functions are considered as the total operational time (makespan) and the total lateness of all outbound trailers. Furthermore, In order to appraise the performance of these algorithms, four criteria are proposed and compared with each other to demonstrate the strengths of each applied algorithm.