Modeling and analysis of a new production methodology for achieving mass customization

Abstract

In this paper, we address a Stochastic-Demand Assembly Job Shop Scheduling Problem (SD-AJSSP) in the presence of the commonality of sub-assemblies across products. We propose a new production methodology, named Assemble-to-Order with Commonality of Sub-Assemblies (ATO-CS) to not only solve the SD-AJSSP, but also, achieve a successful implementation of a mass customisation system by collectively aiming to (1) keep the production costs low by leveraging upon commonality of sub-assemblies in products’ BOM and producing sub-assemblies on a mass scale during one of the two stages of production, (2) minimise the loss due to excess inventory build-up in anticipation of stochastic demand of products by postponing the production of certain apex sub-assemblies in products’ BOM until the actual demand is realised, and (3) reduce the time of the products’ delivery to customers. The ATO-CS method determines optimum production levels as well as schedules assembly operations/jobs over the machines at each stage of production, where the second stage is an assembly job shop and is shown to outperform commonly-used production methodologies. We also develop an algorithm for its implementation and show its efficacy over the use of the state-of-the-art commercial solver CPLEX® in obtaining a lower solution cost and smaller optimality gap.

KEYWORDS:

• Mass customisation
• production control
• optimisation
• stochastic demand
• job shop scheduling

Acknowledgement

Subhash Sarin’s work has partly been supported by National Science Foundation Research Grant No. CMMI-2034503.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of the study are available within the article.

Additional information

Funding

Subhash Sarin’s work has partly been supported by National Science Foundation [grant number CMMI-2034503].

Notes on contributors

Sanchit Singh

Sanchit Singh is a graduate of the Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia. He received his Ph.D. degree in 2019. Currently, he is working as an Operations Research Analyst for Amazon, India.

Subhash C. Sarin

Subhash C. Sarin currently holds the Paul T. Norton Endowed Professorship in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He has made research contributions in production scheduling, applied mathematical programming, and analysis of and designing algorithms for the operational control of manufacturing and logistics systems. He has published over hundred papers in the Industrial Engineering and Operations Research journals and over forty peer-reviewed full-length proceedings papers, and has co-authored three books in the production scheduling area. He has been recognised with several prestigious awards at the university, state, national, and international levels for his research work.