An Efficient Algorithm Applied to Optimized Billing Sequencing

Abstract

This paper addresses the Optimized Billing Sequencing (OBS) problem to maximize billing of the order portfolio of a typical Distribution Center (DC). This is a new problem in the literature, and the search for the best billing mix has generated demands for better optimization methods for DCs. Therefore, the objective of this paper is to provide an effective algorithm that presents quick and optimized solutions for higher-complexity OBS levels. This algorithm is called Iterative Greedy Algorithm (IGA-OBS), and its performance is compared to the genetic algorithm (GA-OBS) by Pinto and Nagano. Performance evaluations were carried out after intense computational experiments for problems with different complexity levels. The results demonstrate that the GA-OBS is limited to medium-size instances, whereas the IGA-OBS is better adapted to reality, providing OBS with solutions with satisfactory time and quality. The IGA-OBS enables managers to make decisions in a more agile and consistent way in terms of the trade-off between the level of customer service and the maximization of the financial result of DCs. This paper fills a gap in the literature, makes innovative contributions, and provides suggestions for further research aimed at developing more suitable optimization methods for OBS.

Keywords

• iterative greedy algorithm
• genetic algorithm
• maximize billing
• distribution center.