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Multiproduct multiple-buyer single-vendor supply chain problem with stochastic demand, variable lead-time, and multi-chance constraint

Taleizadeh, A. A ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1016/j.eswa.2011.11.001
  3. Publisher: 2012
  4. Abstract:
  5. In this paper, a multi-product multi-chance constraint joint single-vendor multi-buyers inventory problem is considered in which the demand follows a uniform distribution, the lead-time is assumed to vary linearly with respect to the lot size, and the shortage in combination of backorder and lost-sale is assumed. Furthermore, the orders are placed in multiple of packets, there is a limited space available for the vendor, there are chance constraints on the vendor service rate to supply the products, and there is a limited budget for each buyer to purchase the products. While the elements of the buyers' cost function are holding, shortage, order and transportation costs, the set up and holding costs are assumed for the vendor. The goal is to determine the re-order point and the order quantity of each product for each buyer such that the chain total cost is minimized. We show the model of this problem to be a mixed integer nonlinear programming type and in order to solve it a particle swarm optimization (PSO) approach is used. To justify the results of the proposed PSO algorithm, a genetic algorithm (GA) is applied as well to solve the problem. Then, the quality of the results and the CPU times of reaching the solution are compared through three numerical examples that are given to demonstrate the applicability of the proposed methodology in real world inventory control problems. The comparison results show the PSO approach has better performances than the GA method
  6. Keywords:
  7. Changeable lead-time ; Genetic algorithm ; Inventory control ; Multiproduct multi-constraint ; Partial backordering ; Particle swarm optimization ; Supply chain ; Backorders ; Chance constraint ; Comparison result ; Constraint joints ; CPU time ; Holding costs ; Inventory control problems ; Inventory problem ; Leadtime ; Limited space ; Lot size ; Mixed-integer nonlinear programming ; Multi-products ; Numerical example ; Order quantity ; Particle swarm ; PSO algorithms ; Service rates ; Stochastic demand ; Total costs ; Transportation cost ; Uniform distribution ; Costs ; Genetic algorithms ; Integer programming ; Nonlinear programming ; Particle swarm optimization (PSO) ; Quality control ; Sales ; Supply chain management ; Supply chains ; Problem solving
  8. Source: Expert Systems with Applications ; Volume 39, Issue 5 , 2012 , Pages 5338-5348 ; 09574174 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0957417411015314