A Production Planning Optimization Model for Maximizing Battery Manufacturing Profitability

Literature Review

Previous approaches to battery production planning include simulation, network flow models, EOQ-type techniques, mathematical programming, and various heuristics. Turnquist (1991) described a software system for battery production planning developed by Cornell University for the Battery Strategic Business Unit of Delco Remy. The PC-based system includes three main components: (1) a forecasting module, (2) a multi-plant product allocations module, and (3) a scheduling module. The production scheduling module employs a network flow model to dynamically balance inventory and overtime costs given limited capacities and fluctuating demands for each plant.

Yenradeea (1994) combines simulation, the optimized production technology (OPT), and simple scheduling rules to schedule a four-stage battery production line. The OPT production plans successfully minimize inventory while maximizing the throughput rate. Using simulation experiments, these plans are shown to outperform both the push and the pull policies. Khadem and Ali (2008) develop a simulation model to optimize the cost effectiveness of a car battery manufacturer. The model is used to represent and analyze the dynamics of the battery assembly line, and also to make several recommendations for improving the line’s cycle time, productivity, and quality.