Robust closed-loop global supply chain network design under uncertainty: the case of the medical device industry

Abstract

Improving performance of global supply chains requires careful consideration of various factors including distance from markets, access to resources, exchange and tax rates, import tariffs, and trade regulations. In this paper, a comprehensive optimization model is proposed to maximise the after-tax profit of a closed-loop global supply chain for medical devices under uncertainty. The uncertainty of the decision-making environment is modelled using the budget of uncertainty concept in interval robust optimization. International financial issues due to the Economic Cooperation Organisation Trade Agreement as well as national regulations including transfer pricing limitations, exchange rates, tax rates, and import tariffs are considered. The proposed model considers various realistic assumptions pertaining to medical device supply chains such as multiple products, multiple periods, multiple echelons, and limited warehousing lifetime. In addition, reverse flows of perished and defective products are considered to address environmental concerns and customers’ requirements as well as to gain economic advantages. To tackle this problem, an efficient memetic algorithm is developed that incorporates adaptive variable neighbourhood search as its local search heuristic. Computational results demonstrate the efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context. In addition, several managerial insights are discussed based on the results.

Keywords:

• global supply chain network design
• green supply chain management
• robust optimization
• memetic algorithm
• adaptive variable neighbourhood search