A New Model for Lean and Green Closed-Loop Supply Chain Optimization

Abstract

The dynamics of Supply Chain Management (SCM) have changed over the years; new paradigms are added into the SCM as a response to changes related with increasing environmental concerns and pressures. Therefore, lean and green practices in a company belong to its most important factors to enhance the company’s performance. In this study a new model, inspired by an automotive supply chain, is proposed for lean and green closed-loop supply chain management. In this model, we deal with lean and green drivers to set the objectives of the decision makers (DMs) as follows; (1) Construction: The amount of emitted CO2 depends on the size of the potential facilities; (2) Production: Higher equipment or techniques in a production system means a higher environmental investment and leads to lower CO2 emissions; (3) Handling: The usage of a proper forklift is an important decision to increase the productivity and reduce CO2 emissions; (4) Transportation: Three different options in the transportation process which DMs can choose are considered: Small-sized, medium-sized, and heavy-sized trucks. All of the truck types differ in transportation cost and CO2 emissions with respect to the engines. The benefits of the heavy-sized trucks are obvious: Less transportation cost with bigger lot size deliveries but more environmental pollution as well; (v) On Time Deliveries: Lean manufacturing needs suppliers to comply with delivery times, which directly affect the buyer’s manufacturing lead times, operational performances and competitiveness. Thus, late deliveries of suppliers aimed to be minimized. Under these circumstances, there are few trade-offs which need to be optimized simultaneously. The developed model consists of six different objectives: minimization of transportation cost, purchasing and operational cost, fixed facility cost, environmental cost, handling cost and late deliveries. In order to validate the proposed model, a numerical example is implemented and analyzed by using fuzzy weighted additive method where the weights are determined via Fuzzy Analytic Hierarchy Process (Fuzzy AHP) method. © Springer Nature Switzerland AG 2019.