Modeling a multi-objective green closed loop supply chain for tire industry

Abstract

The ever-increasing concerns of the growth in the volume of waste tires, reduction in natural resources and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. A CLSC is a network with a holistic view of the entire forward and reverse logistic system with the aim of transferring the EOL products up the stream from the end users for value recovery such as reuse, repair, remanufacturing, or recycling.There are many factors that are considered when designing a closed-loop supply-chain network, such as uncertainty in the parameters, costs, environmental impacts, social dimensions, governmental regulations, domestic and international competition, disruption and operational risks, reliability of the network, technological advancements, etc. In the real world, the CLSC network design is subject to a variety of uncertainties which can be random and fuzzy. Designing a reliable and environmentally conscious CLSC which considers the risks and uncertainties of the parameters in the network is necessary for a successful supply-chain network. The government regulations could have a significant impact on the supply chain network design, its management and the long/short-term investment decisions. Ever-changing restrictions, mandates and tax policies can affect corporation's competitive advantage, market share, and operational costs. Tariffs and caps imposed on imported goods, intended to limit the volume of imports, protect domestic employment and reduce competition among domestic industries. One of the challenges facing the recovery of EOL products is the uncertainty associated with the conditions of the EOL products after being returned. Designing intelligent products that can collect valuable information during their life cycles is something that has been studied in the past decade. This information can be used to extend the life span of the products and improve the value recovery of EOL products. In this dissertation, we take a closer look at the challenges facing the tire industry and obtain a solution that creates a win-win situation for companies in the tire network as well as the environment. We evaluate the available recovery options for EOL tires, identify the uncertain parameters in the logistics network and find the solution for dealing with these uncertainties using robust modeling for the logistics network. In the first model, we introduce a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty with the objective of reducing costs, reducing environmental impacts, and increasing the social responsibilities and the reliability of the network. Second, we study the impact of tariffs and government regulations on the profitability of the network. Third, we evaluate the impact of using sensor embedded tires on the design of CLSC network. The analysis is done using a novel Robust Fuzzy/ Possibilistic Stochastics Programming (RFSP) model that controls scenario-based uncertainty (disruption risks) and fuzzy-based uncertainty (operational risks) in the network simultaneously and provides more reliable results for the managers and the decision makers. We evaluate the possibility and feasibility of expanding the existing forward logistic network at different capacity levels to cope with the market demand under various scenarios as well as the feasibility of establishing a new reverse logistic network at different capacity levels to cope with the return rate of EOL tires. A case example is considered to illustrate the implementations and validation of the proposed models. --Author's abstract