Optimal lot sizing, process quality improvement and setup cost reduction
Operations Research
Integrated value chains and their implications from a business and technology standpoint
Decision Support Systems - Special issue for business to business electronic commerce, issues and solutions
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
A combined model of network design and production/distribution planning for a supply network
Computers and Industrial Engineering - Supply chain management
An optimal production run time with imperfect production processes and allowable shortages
Computers and Operations Research
An integrated model for supplier selection decisions in configuration changes
Expert Systems with Applications: An International Journal
Vendor selection in outsourcing
Computers and Operations Research
Computers and Industrial Engineering
Computers and Industrial Engineering
A three-phase integrated model for product configuration change problems
Expert Systems with Applications: An International Journal
Supply chain modeling in uncertain environment with bi-objective approach
Computers and Industrial Engineering
Applying the linear particle swarm optimization to a serial multi-echelon inventory model
Expert Systems with Applications: An International Journal
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
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This paper focuses on developing a decision methodology for the production and distribution planning of a multi-echelon unbalanced supply chain. In the supply chain system discussed here, multiple products, production loss, transportation loss, quantity discount, production capacity, and starting-operation quantity are considered simultaneously, and the system pattern is ascertained with based on appropriate partners and suitable transportation quantities. To make a quality decision in supply chain planning, we first propose an optimization mathematical model which integrates cost and time criteria. Then, a particle swarm optimization (PSO) solving method is proposed for obtaining acceptable results is called MEDPSO. The MEDPSO introduces the maximum possible quantity strategy into the basic procedure of PSO to generate the initial feasible population in a timely fashion and provides an exchange and disturbance mechanism to prevent particle lapse into the local solution. Finally, one case and two simulated supply chain structures are proposed to illustrate the effectiveness of the MEDPSO method by comparing the results of classical GA and PSO in solving multi-echelon unbalanced supply chain planning problems with quantity discount.