Reverse Logistics Modeling Considering Environmental and Manufacturing Costs: A Case Study of Battery Recycling in Indonesia
Published at : 28 Jan 2019
Volume : IJtech
Vol 10, No 1 (2019)
DOI : https://doi.org/10.14716/ijtech.v10i1.2164
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
| 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 |
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
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
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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