The scheduling of deliveries in a production-distribution system with multiple buyers

Jiafu Tang, Kai Leung Yung, Iko Kaku, Jianbo Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


In a real production and distribution business environment with one supplier and multiple heterogeneous buyers, the differences in buyers' ordering cycles have influence on production arrangements. Consequently, the average inventory level (AIL) at the supplier's end is affected by both the production policy and the ordering policy, typically by the scheduling of deliveries. Consequently, the average inventory holding cost is most deeply affected. In this paper, it is proposed that the scheduling of deliveries be formulated as a decision problem to determine the time point at which deliveries are made to buyers in order to minimize the supplier's average inventory. A formulation of the average inventory level (AIL) in a production cycle at the supplier's end using a lot-for-lot policy is developed. Under the lot-for-lot policy, the scheduling of deliveries (SP) is formulated as a nonlinear programming model used to determine the first delivery point for each buyer with an objective to minimize the sum of the product of the individual demand quantity and the first delivery time for each buyer. Thus, the SP model determines not only the sequence of the first deliveries to individual buyers, but also the time when the deliveries are made. An 6 Ann Oper Res (2008) 161: 5-23 iterative heuristic procedure (IHP) is developed to solve the SP model assuming a given sequence of buyers. Six sequence rules are considered and evaluated via simulation.
Original languageEnglish
Pages (from-to)5-23
Number of pages19
JournalAnnals of Operations Research
Issue number1
Publication statusPublished - 1 Jul 2008


  • Buyer-vendor coordination
  • Heuristics procedure
  • Lot-for-lot-policy
  • Scheduling of deliveries

ASJC Scopus subject areas

  • Management Science and Operations Research
  • General Decision Sciences


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