Efficient approximation scheme for job assignment in a multi-factory environment

ABSTRACT

As manufacturing environments are getting increasingly decentralized, while the customer diversity of requirements is continuously growing, it becomes important for manufacturers to optimize complex production processes across multiple factories. We propose a dynamic algorithm based on a fully polynomial approximation scheme (FPTAS) to schedule jobs between a main factory and another set of sub-factories. The decision maker will balance workload across the two sets of factories, while considering each job’s specific properties such as complexity, due-date, profit earned if completed on time. We validated the algorithm applicability in real life, using data provided by a company that is involved in building development. Our results suggest that our algorithm has the potential to assist decision makers in efficiently assigning jobs across multiple processors. To the best of our knowledge, the current paper is the first to propose and design a rapid and efficient FPTAS approximation for a multi-factory setting.

KEYWORDS:

• Multi-factory
• FPTAS
• Decision making
• scheduling

Acknowledgments

We wish to thank the construction development company and its CEO for providing the data used in this paper and checking its application in practice in their divisions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Notes on contributors

Guy Wachtel

Guy Wachtel is a researcher in the domains of Logistics Management, Operation Research, Applied Mathematics and Algorithms. He received his Bachelor’s and Master’s degrees in Logistical Management from Bar-Ilan University in Israel, and holds his Ph.D. in Management from the same university. The topic of his PhD thesis: Applications and development of approximation algorithms. His latest research interests include efficient algorithms in scheduling for efficient decision-making in healthcare and industry, as well as machine learning and simulation-based decision models for efficient evacuation strategies.

Amir Elalouf

Amir Elalouf is a Senior Faculty Member and the Head of the technology management program of the Department of Management, Bar Ilan University, Ramat Gan, Israel.  He holds a B.Sc in mathematics and computer science and a PhD in Operation Research from Ben Gurion University, Israel. Dr. Elalouf’s main interest and expertise is in modeling and algorithms for complex problems and in developing solution methods for practical management problems. For the latter he has received three grants to support his research programs.