This paper addresses a multi-product multi-period Inventory Routing Problem (IRP) where multiple capacitated vehicles distribute products from multiple suppliers to a single plant to meet the given demand of each product over a finite planning horizon.
The demand associated with each product is assumed to be deterministic and time varying. In this supply chain, the products are assumed to be ready for collection at the supplier site when the vehicle arrives. A transshipment option is considered as a possible solution to increase the performance of the supply chain and shows the impact of this solution on the environment. A green logistic issue is also incorporated into the model by considering the interrelationship between the transportation cost and the greenhouse gas emission level. The proposed model is a mixed-integer linear program and solved by CPLEX. We provide a numerical study showing the applicability of the model and underlining the impact of the transshipment option on improved supply chain performance.
Because global warming is recognized as one of the greatest challenges of this century, Greenhouse gas (GHG) emissions are increasingly becoming a focus of attention. Global warming results from increased GHG concentrations in the atmosphere. In response to this challenge, a number of organizations are applying ‘green’ principles such as using environmentally friendly raw materials and recycled paper for packaging and reducing their use of fossil fuels. These green principles have been expanded to many areas, including supply chains (Chung and Wee, 2008, Zhu et al., 2008, Lin et al., 2011 and Wang et al., 2012). Adding the ‘green’ concept to the ‘supply chain’ concept creates a new paradigm where the supply chain has a direct relation to the environment (e.g., Diabat and Govindan, 2011, Wang et al., 2011, Zhu and Sarkis, 2011 and Eltayeb et al., 2011).
Globalization, with its increasing industrial trend towards outsourcing, has caused transportation to become the most visible sector that has increased GHG emissions over the last two decades. Transportation activities are therefore one of the primary contributors to global warming (Fig. 1), leading to the recent expansion of green logistics investigating as a subset of the green supply chain (Srivastava, 2007, Sheu, 2008, Bai and Sarkis, 2010, Yeh and Chuang, 2011 and Mirzapour et al., 2013). A comprehensive review of the studies on green logistics can be found in Dekker et al. (2012). Logistics are now widely recognized as value-adding components in organizations. The primary objective of logistics is to coordinate activities such as freight transport, storage, inventory management and materials handling. One of the well-known topics typically addressed in this regard is the Inventory Routing Problem (IRP).
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Fig. 1.
Total GHG emissions by sector in the EU-27, 2011. (European Environment Agency, 2013).
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The IRP in a supply chain simultaneously determines the optimal inventory levels, delivery routes, and vehicle scheduling based on the minimal cost criterion (Moin et al., 2011). In the past, this cost has been assessed solely in economic terms. Due to the increasing of environmental concerns, companies must better account for the external costs of logistics associated with global warming such as air pollution, noise, vibrations and accidents (Quariguasi et al., 2009). This study attempts a novel approach of reducing GHG emissions in IRPs to achieve a balance between economic and environmental objectives.
In Section 2, we review previous studies on the IRP in existing literature. We describe the Inventory Routing Problem under study in Section 3; its mathematical formulation is then provided in Section 4. A numerical study, as well as the managerial insights, is provided in Section 5 and Section 6 concludes the paper and proposes further research in this field.
In this study, a novel mathematical model was presented to address a multi-product multi-period Inventory Routing Problem in a many-to-one supply chain network. The proposed model exhibited two distinct features. First, a transshipment option was considered as a possible solution to reduce travel distances. Under this policy, a vehicle provided a specific product for the assembly plant, either directly from the supplier which manufactured the product or from the temporary storage of the other suppliers resulting from previous trips. Second, various vehicle types with different capacities and GHG emission indices were considered. These features enabled the model to select the appropriate transportation mode (as well as the transportation route) to reduce the total supply chain costs and improve the environmental health criteria (lowering GHG emissions). The results show that the model is straightforward to use in practice; a sensitivity analysis was performed to prove that the model could present more constructive solutions from a “green logistics” point of view. Promising areas for further research include applying the proposed model to other supply chain structures and other kinds of products (e.g., deteriorating items), developing multi-objective models with respect to green logistics and developing models under uncertain conditions.