A Production Planning Optimization Model for Maximizing Battery Manufacturing Profitability

A Production Planning Optimization Model for Maximizing Battery Manufacturing Profitability

Hesham K. Alfares (King Fahd University of Petroleum & Minerals, Saudi Arabia)
Copyright: © 2012 |Pages: 9
DOI: 10.4018/ijaie.2012010105
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

This paper presents an integer programming (IP) model for production planning, which is used to maximize the profitability of battery manufacturing in a mid-size company. Battery production is a complicated multi-stage process. The formation stage, during which the batteries are filled with acid and charged with electricity, is considered to be the bottleneck of this process. The IP model maximizes the total profit of batteries produced in the formation stage, subject to limited manufacturing resources as well as time limitations and demand restrictions. The IP model is able to accommodate a large variety of battery models and sizes, and also different charging circuit capacities and speeds. The model is formulated and optimally solved using Microsoft Excel Solver. Compared to the current manual production planning approach, the optimum IP-generated production plans lead to an average increase of 12% in daily profits.
Article Preview

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.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 4: 2 Issues (2017)
Volume 3: 2 Issues (2016)
Volume 2: 2 Issues (2014)
Volume 1: 2 Issues (2012)
View Complete Journal Contents Listing