Boonsothonsatit, K., Kara, S., Kayis, B., Ibbotson, S., 2015. Development of a Generic decision support system based on multi-Objective Optimisation for Green supply chain network design (GOOG). Journal of Manufacturing Technology and Management, 26(7)
Abstract
Purpose – This paper explains the development stages of a Generic decision support system which is based on multi-Objective Optimisation for Green supply chain network design (GOOG). It aims to support decision makers to effectively design their supply chain networks from-cradle-to-gate using three key objectives: the lowest cost and environmental impact as well as the shortest lead time by incorporating the decision maker’s inputs
Design/methodology/approach – GOOG is developed by the integration of Fuzzy Goal Programming (FGP) and weighted max-min operator which is used to trade-off conflicting objectives and overcome fuzziness in specifying target values of individual objectives. It is solved by using exact algorithm and is successfully validated in the industry. It aims to suggest the best-fitted partners and manufacturing plant locations, their order allocations, and appropriate transportation modes and lot-sizes, covering issues from-cradle-to-gate.
Findings – Lead time which was overlooked in previous green supply chain network design is used together with cost and environmental impact as objectives by incorporating decision maker’s preferences. Validation of the model shows that GOOG provided values close to the set of the relative objective weights provided by the manufacturing company.
Research limitations/implications – GOOG has generic features so it needs to be further validated in several industries under various scenarios and strategic decisions. Further validation may lead to enhancing its scope to include multi-echelon supply chain networks as well as inclusion of carbon tax and/or ETS. It can also be extended to cover the entire product life cycle (from-cradle-to-gate stages).
Practical implications – GOOG could be used as a decision support tool for selections of supply chain partners and manufacturing plant locations, their allocations of orders, and choosing of transportation modes and lot-sizes, whilst aiming to fulfill the decision makers’ objectives of minimizing product cost, lead time and environmental impact.
Originality/value – The contribution of this paper to body of knowledge is three-folded. GOOG is a unique decision support tool which aims to support decision makers to effectively design their networks by including several variables in order to achieve the objectives set. The option of inputting the relative weightings by manufacturers for contradicting objectives offer flexibility to the decision makers and increase the practicality of the model.
Keywords: green supply chain, partner selection, manufacturing plant location selection, decision support system