Efficient algorithms for the capacitated concentrator location problem
Computers and Operations Research
Journal of Global Optimization
The single machine ready time scheduling problem with fuzzy processing times
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
Vehicle routing scheduling for cross-docking in the supply chain
Computers and Industrial Engineering - Special issue: Logistics and supply chain management
Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management
Information Sciences: an International Journal
Computers and Industrial Engineering
Supply chain modeling in uncertain environment with bi-objective approach
Computers and Industrial Engineering
Fuzzy optimization for supply chain planning under supply, demand and process uncertainties
Fuzzy Sets and Systems
Distribution planning decisions using interactive fuzzy multi-objective linear programming
Fuzzy Sets and Systems
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
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This paper proposes a fuzzy bi-objective mixed integer linear programming formulation and solution methodology for a multi-echelon, multi-product and multi-period supply chain planning. The supply chain is a network of suppliers, plants, distribution centers, cross-docks and retailers. Products are delivered to retailers through cross-docks or directly from manufacturing plants. Cross-docking as an efficient logistic strategy is an intermediate level that involves receiving products from various resources distribution centers, sorting and then shipping them to their destinations retailers. The aim of this paper is to present a model that minimizes the total cost and develops a just-in-time JIT distribution for the supply chain. The proposed model integrates procurement, production and distribution plans in the tactical level under fuzzy supply, production and demand by considering cross-docking and direct shipments simultaneously. Triangular fuzzy numbers are adapted to represent fuzzy parameters. Moreover, the fuzzy chance-constrained programming is applied to transform the fuzzy model into an auxiliary crisp model.