• Vol 10, No 1 (2019)
  • Industrial Engineering

Reverse Logistics Modeling Considering Environmental and Manufacturing Costs: A Case Study of Battery Recycling in Indonesia

Ilyas Masudin, Thomy Eko Saputro, Genial Arasy, Ferry Jie


Cite this article as:
Masudin, I., Saputro, T.E., Arasy, G., Jie, F., 2019. Reverse Logistics Modeling Considering Environmental and Manufacturing Costs: A Case Study of Battery Recycling in Indonesia. International Journal of Technology. Volume 10(1), pp. 189-199
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Ilyas Masudin Department of Industrial Engineering, University of Muhammadiyah Malang, Jl. Raya Tlogomas 246 Malang, 65144, Indonesia
Thomy Eko Saputro Department of Industrial Engineering, University of Muhammadiyah Malang, Jl. Raya Tlogomas 246 Malang, 65144, Indonesia
Genial Arasy Department of Industrial Engineering, University of Muhammadiyah Malang, Jl. Raya Tlogomas 246 Malang, 65144, Indonesia
Ferry Jie School of Business & Law, Edith Cowan University, 270 Joondalup Dr, Joondalup Westren Australia, 6027, Australia
Email to Corresponding Author

Abstract
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This article models a reverse logistics network for battery recycling with consideration of environmental and manufacturing costs. The model is developed for a reverse flow multi-echelon supply chain, from end customers to the remanufacturing process. Linear programming is used to formulate mathematical models and LINGO® is applied to solve the problem of determining optimal orders for and sales of recycled batteries, lead alloy and plastics, as well as the optimal level of safety stock (service level) for the recycling centers along the reverse logistics network.  The number of battery orders from unused battery collectors, and the sales of lead alloy and plastics to the remanufacturing process considering transportation, environmental cost, disassembly cost and inventory costs, are found optimally in different periods. The study also indicates that there is a correlation between the associated costs and inventory decisions and total profit in recycling centers.

Recycling; Remanufacturing; Reverse logistics

Introduction

Reverse logistics (RL), which refers to a series of activities starting from the level of customer collection of products and ending with product remanufacturing processes,   has received much attention recently in term of approaches and network models (Sarkis, 2001; Soto Zuluaga, 2005; Wang, 2015). Reverse logistics has become an issue of increased concern in environmental studies since the operation of the manufacturing process, particularly for hazardous materials, impacts negatively on all the parties in a supply chain if it is not appropriately organized. Employing good logistics management throughout the material flow, with the involvement of planning, managing and controlling the flow of waste until its disposal, can alleviate the risk of hazardous material. In the outbound side of green supply chain management, reverse logistics, or environmental distribution, is an approach to improve firms’ environmental performance (Rao, 2002).

The optimization model for dealing with transportation and routing problems by controlling the risk of hazardous waste from the perspective of reverse distribution planning has developed rapidly. However, most of the reverse distribution planning models only focus on production planning (Guide Jr, 2000; Park, 2005). Regardless of such focus, this study exclusively deals with the production planning and inventory control which are integrated into the developed model. Products such as batteries, which contain hazardous material, can have a negative effect on the environment (Kusrini et al., 2015). This study develops a linear programming model for multi-period RL in order to determine the optimal number of batteries that should beproduced and the number which should be stored as inventory. The objective of the proposed model is to minimize total logistics cost, including those of purchase, inventory and transportation, with respect to the environmental risk cost. The study develops RL models for batteries in a multi-echelon distribution system with consideration of environmental manufacturing costs, from the point of supply to the point of collection and then delivery to the remanufacturing point. 


Conclusion

Most researchers in developing a mathematical model of RL networks have only considered the costs associated with transportation and disassembly costs, while RL network models of recycled batteries have been paid little attention in terms of the inclusion of environmental implications. This study has developed a mathematical model using linear programming for the RL network of battery recycling with multi-period planning. The proposed model takes into account various parameters, including the holding cost of batteries, lead alloy and plastics. The results indicate that the parameters associated with transportation, disassembly and inventory decisions, such as holding costs and service levels, impact significantly on profit.

The results of the sensitivity analysis show that the manufacturing process in the recycling centers is interconnected with the supply and demand from the collection centers and factories respectively. The development of mathematical models of RL in further research needs to consider the forward logistic network for closed loop supply chain purposes. Integration of both flows of the logistic network would improve the performance of the model, as well as the level of implication of associated parameter changes to the broader supply chain.

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