A decision making framework for joint replenishment and delivery scheduling problems under mixed uncertainty

Guang Wang, Jian Zhou, Athanasios A. Pantelous, Yuanyuan Liu, Youwei Li

Research output: Contribution to journalArticleResearchpeer-review


Concerning the essence of risk, a joint replenishment and delivery scheduling problem with fuzzy cost-related parameters and random number of imperfect quality items is developed to make it suitable for the inherent uncertainties of procurement-shipment process. The mathematical modelling-based decision system is formulated as a chance-constrained programming with the idea of embedding decision makers’ risk tolerance. Following this notion, the model is translated into an equivalent non-linear counterpart and a neighbourhood heuristic search is designed based on the properties of the cost function. We introduce an integrated cross-entropy algorithm, incorporating the heuristic in the cross-entropy framework, to solve it. The numerical results demonstrate that ICE is quite effective in comparison to state-of-the-art algorithms. Our framework is helpful for decision makers to determine economically acceptable performance objectives in the presence of uncertain issues, and thus to build resilience in supply chain.

Original languageEnglish
Article number109835
Number of pages16
JournalComputers and Industrial Engineering
Publication statusPublished - Jan 2024


  • Chance-constrained programming
  • Cross-entropy algorithm
  • Joint replenishment
  • Mixed uncertainty
  • Supply chain resilience

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