Operational and environmental performance measures in a multi-product closed-loop supply chain

Abstract

This paper investigates a number of operational and environmental performance measures, in particular those related to transportation operations, within a closed-loop supply chain. A mathematical model in the form of a linear programming formulation is used to model the problem, which captures the trade-offs between various costs, including those of emissions and of transporting commodities within the chain. Computational results are presented for a number of scenarios, using a realistic network instance.

Introduction

A supply chain refers to a broad set of activities associated with the transformation and flow of goods and services, including the flow of information, from the sources of materials to end-users (Bowersox and Closs, 1996). A typical supply chain primarily consists of a number of production facilities serving a number of market regions, and the transportation of goods from source to intermediate locations, and ultimately to end-users. Flow of material from source to end-users in a supply chain is via the so-called forward chain. Recent interest in supply chains lies in the recovery of products, which is typically achieved through processes such as repair, remanufacturing and recycling, which, combined with all the associated transportation and distribution operations, are collectively termed reverse chain activities. A supply chain in which forward and reverse supply chain activities are integrated is said to be one of a closed-loop, and research on such chains have given rise to the field of closed-loop supply chains (CLSCs).

One of the significant sources of greenhouse gas emissions and air pollution within a supply chain is transportation activity, which has harmful effects on human health and undesirable consequences, such as global warming. Most of the research devoted to the design of supply chains has focused on operational performance metrics, such as cost of production, purchasing and transportation, profit, and tax (Meixell and Gargeya, 2005) and has neglected the environmental aspects. Given recent concerns on the harmful consequences of supply chain activities on the environment, and transportation in particular, it has become necessary to specifically take into account environmental factors when planning and managing a supply chain. The list of environmental performance metrics of a supply chain is long, and includes emissions, energy use and recovery, spill and leak prevention and discharges (Hervani et al., 2005).

Green supply chain (GrSC) design extends the traditional definition of a supply chain by explicitly considering the following factors in the design process: (i) waste resulting from any processes within the chain, (ii) energy efficiency, (iii) greenhouse gas emissions, and (iv) legal environmental requirements. Among the listed factors, greenhouse gas emissions, and CO₂ in particular, are by far the most prominent with respect to their hazardous consequences on human health. One source of these emissions within a supply chain is the transportation activities that take place between various components in the chain.

In this study, we model and analyze a multi-product CLSC with an explicit focus on greenhouse gas emissions of the transportation activities, and the costs thereof, as well as product recovery. The model is in the form of a linear programming formulation and allows for the use of different modes of transport, each of which has its own emission rates and costs. The model also considers normal operational transportation costs, as well as various capacity limits on production and distribution. The contributions of this paper to the literature are twofold: (i) we consider environmental costs within a closed-loop supply chain network, mainly reflecting those of CO₂ emissions due to transporting material in forward and reverse logistics networks, and (ii) using the proposed model and a sample problem instance, we present results of computational experiments that shed light on the interactions of various performance indicators, primarily measured by cost, but also capturing the environmental aspects.

The rest of the paper is organized as follows: Section 2 presents a brief review of the literature on GrSC and CLSC. In Section 3, we describe a mathematical model for the CLSC. Results of computational experiments, using a sample instance are given in Section 4. Main conclusions are offered in Section 5.