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
Study on multi-stage logistic chain network: a spanning tree-based genetic algorithm approach
Computers and Industrial Engineering - Supply chain management
Trade-offs Between Customer Service and Cost in Integrated Supply Chain Design
Manufacturing & Service Operations Management
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A steady-state genetic algorithm for multi-product supply chain network design
Computers and Industrial Engineering
Integrated multistage logistics network design by using hybrid evolutionary algorithm
Computers and Industrial Engineering
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
A memetic algorithm for bi-objective integrated forward/reverse logistics network design
Computers and Operations Research
Multi-criteria mathematical model for designing the distribution network of a consumer goods company
Computers and Industrial Engineering
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Journal of Intelligent Manufacturing
A simulation modeling framework for multiple-aisle automated storage and retrieval systems
Journal of Intelligent Manufacturing
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Outbound logistics network (OLN) in the downstream supply chain of a firm plays a dominant role in the success or failure of that firm. This paper proposes the design of a hybrid and flexible OLN in multi objective context. The proposed distribution network for a manufacturing supply chain consists of a set of customer zones (CZs) at known locations with known demands being served by a set of potential manufacturing plants, a set of potential central distribution centers (CDCs), and a set of potential regional distribution centers (RDCs). Three variants of a single product classified based on nature of demand are supplied to CZs through three different distribution channels. The decision variables include number of plants, CDCs, RDCs, and quantities of each variant of product delivered to CZs through a designated distribution channel. The goal is to design the network with multiple objectives so as to minimize the total cost, maximize the unit fill rates, and maximize the resource utilization of the facilities in the network. The problem is formulated as a mixed integer linear programming problem and a multiobjective genetic algorithm (MOGA) called non-dominated sorting genetic algorithm--II (NSGA-II) is employed to solve the resulting NP-hard combinatorial optimization problem. Computational experiments conducted on randomly generated data sets are presented and analyzed showing the effectiveness of the solution algorithm for the proposed network.