Ordering and waste reuse decisions in a make-to-order system under demand uncertainty

Chung Chi Hsieh, Artya Lathifah

Research output: Contribution to journalArticlepeer-review

Abstract

This study considers a single-period make-to-order system in which a manufacturer produces two products: product 1 (2) with tighter (looser) specifications. Production of these two products involves a specific type of materials in a common process. Due to quality discrepancies, new materials purchased from the supplier are used in the production of both product types from which spent materials can be converted into recycled materials and used in the production of product 2. Based on expected total cost minimization, we investigate the manufacturer's decisions related to regular and expedited orders for new materials as well as the decisions related to reusing the manufacturing waste that jointly mitigate the risk of demand-and-supply mismatch through both analytical and numerical analyses. We find that at an exogenous recycling rate, waste reuse can complement the regular order for new materials in risk mitigation while lowering the manufacturer's expected total cost when the unit expedited ordering cost is significant and becomes larger. By contrast, waste reuse with endogenous recycling makes it possible to achieve both objectives even for small expedited ordering costs, through balancing the regular order quantity and the recycling effort. Furthermore, the regular order quantity typically is inversely related to the recycled materials used in production. However, the recycling effort is inversely related to the recycled materials as the unit expedited ordering cost varies and is in sync with the recycled materials when other model parameters vary. Finally, some additional insights obtained from numerical analyses are discussed.

Original languageEnglish
Pages (from-to)1290-1303
Number of pages14
JournalEuropean Journal of Operational Research
Volume303
Issue number3
DOIs
Publication statusPublished - 2022 Dec 16

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management
  • Industrial and Manufacturing Engineering

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