Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The multi-objective uncapacitated facility location problem for green logistics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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It is strategically important to design efficient and environmentally friendly distribution networks. In this paper we propose a new methodology for solving the capacitated facility location problem (CFLP) based on combining an evolutionary multi-objective algorithm with Lagrangian Relaxation where financial costs and CO2 emissions are considered simultaneously. Two levels of decision making are required: 1) which facilities to open from a set of potential sites, and 2) which customers to assign to which open facilities without violating their capacity. We choose SEAMO2 (Simple Evolutionary Multi-objective Optimization 2) as our multi-objective evolutionary algorithm to determine which facilities to open, because of its fast execution speed. For the allocation of customers to open facilities we use a Lagrangian Relaxation technique. We test our approach on large problem instances with realistic qualities, and validate solution quality by comparison with extreme solutions obtained using CPLEX.