Title: A new multi-objective/product green supply chain considering quality level reprocessing cost

Authors: Parvaneh Loni; Alireza Arshadi Khamseh; Seyyed Hamid Reza Pasandideh

Addresses: Industrial Engineering Department, Kharazmi University, Tehran, Iran ' Industrial Engineering Department, Faculty of Engineering, Kharazmi University, No. 49, Mofateh Ave. Near to Engelab Ave., 15719-14911 Tehran, Iran ' Industrial Engineering Department, Faculty of Engineering, Kharazmi University, No. 49, Mofateh Ave. Near to Engelab Ave., 15719-14911 Tehran, Iran

Abstract: Green supply chain is among the hottest recent research subjects in supply chain management which not only optimises the costs and service levels during a time period all over the chain, but also considers effect of emission of CO₂ greenhouse gas on the overall value of supply chain for sustainable development. Therefore, in this paper, a bi-objective, multi-stage, multi-product quadratic optimisation model is proposed by taking into account the quality level of the purchased materials plus their reprocessing extra costs to a system in stochastic mode and the environmental costs of CO₂ emission. Here we also consider both straight and step by step transportation in supply chain network design. Since the problem is NP-hard, a priority-based genetic algorithm model is proposed. In this paper, three methods including BOM, LP-metric and elastic BOM are applied to solve small data, whereas, elastic BOM offers larger solution space and more justified solutions. Hence, for solving large data, three priority-based genetic algorithms corresponding to MODM techniques are utilised. Then, by using TOPSIS for small and ANOVA for medium and large problems, the optimum procedure for balancing the existing costs and CO₂ emission in the chain is selected.

Keywords: green supply chain; priority-based genetic algorithm; pb-GA; MODM techniques; CO₂ emission.

DOI: 10.1504/IJSOM.2018.091437

International Journal of Services and Operations Management, 2018 Vol.30 No.1, pp.1 - 22

Received: 10 Jan 2016
Accepted: 09 Aug 2016
Published online: 01 May 2018 *

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