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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

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-02313031
Contributor : Emlyon Business School <>
Submitted on : Friday, December 6, 2019 - 10:48:31 AM
Last modification on : Tuesday, January 26, 2021 - 10:46:02 PM

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  • HAL Id : hal-02313031, version 1

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Seyed Mohammad Javad Mirzapour Al-E-Hashem, Armand Baboli, Z. Sazvar. A stochastic aggregate production planning model in a green supply chain : Considering flexible lead times, nonlinear purchase and shortage cost functions. European Journal of Operational Research, 2013, pp.26-41 P. ⟨hal-02313031⟩

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