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A stochastic aggregate production planning model in a green supply chain: Considering flexible lead times, nonlinear purchase and shortage cost functions

Mirzapour Al-E-Hashem, S. M. J ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1016/j.ejor.2013.03.033
  3. Publisher: 2013
  4. Abstract:
  5. In this paper we develop a stochastic programming approach to solve a multi-period multi-product multi-site aggregate production planning problem in a green supply chain for a medium-term planning horizon under the assumption of demand uncertainty. The proposed model has the following features: (i) the majority of supply chain cost parameters are considered; (ii) quantity discounts to encourage the producer to order more from the suppliers in one period, instead of splitting the order into periodical small quantities, are considered; (iii) the interrelationship between lead time and transportation cost is considered, as well as that between lead time and greenhouse gas emission level; (iv) demand uncertainty is assumed to follow a pre-specified distribution function; (v) shortages are penalized by a general multiple breakpoint function, to persuade producers to reduce backorders as much as possible; (vi) some indicators of a green supply chain, such as greenhouse gas emissions and waste management are also incorporated into the model. The proposed model is first a nonlinear mixed integer programming which is converted into a linear one by applying some theoretical and numerical techniques. Due to the convexity of the model, the local solution obtained from linear programming solvers is also the global solution. Finally, a numerical example is presented to demonstrate the validity of the proposed model
  6. Keywords:
  7. Aggregate production planning ; Demand uncertainty ; Green principles ; Nonlinear shortage cost ; Quantity discount ; Supply chain management ; Shortage cost ; Aggregates ; Gas emissions ; Greenhouse gases ; Stochastic programming ; Waste management ; Stochastic models
  8. Source: European Journal of Operational Research ; Volume 230, Issue 1 , 2013 , Pages 26-41 ; 03772217 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S037722171300266X