Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
The spatial and temporal consolidation of returned products in a closed-loop supply chain network
Computers and Industrial Engineering - Special issue: Logistics and supply chain management
A bi-objective reverse logistics network analysis for post-sale service
Computers and Operations Research
A memetic algorithm for the flexible flow line scheduling problem with processor blocking
Computers and Operations Research
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
Advances in Engineering Software
Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty
Computers and Industrial Engineering
Multiobjective memetic algorithms for time and space assembly line balancing
Engineering Applications of Artificial Intelligence
Multi objective outbound logistics network design for a manufacturing supply chain
Journal of Intelligent Manufacturing
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Logistics network design is a major strategic issue due to its impact on the efficiency and responsiveness of the supply chain. This paper proposes a model for integrated logistics network design to avoid the sub-optimality caused by a separate, sequential design of forward and reverse logistics networks. First, a bi-objective mixed integer programming formulation is developed to minimize the total costs and maximize the responsiveness of a logistics network. To find the set of non-dominated solutions, an efficient multi-objective memetic algorithm is developed. The proposed solution algorithm uses a new dynamic search strategy by employing three different local searches. To assess the quality of the novel solution approach, the quality of its Pareto-optimal solutions is compared to those generated by an existing powerful multi-objective genetic algorithm from the recent literature and to exact solutions obtained by a commercial solver.