On the stochastic inventory problem under order capacity constraints

Rossi R., Chen Z., TARIM Ş. A.

European Journal of Operational Research, cilt.312, sa.2, ss.541-555, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 312 Sayı: 2
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.ejor.2023.06.045
  • Dergi Adı: European Journal of Operational Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, EconLit, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.541-555
  • Anahtar Kelimeler: Inventory, Modified multi-(s,S) policy, Order capacity, Stochastic lot sizing
  • Hacettepe Üniversitesi Adresli: Evet

Özet

We consider the single-item single-stocking location stochastic inventory system under a fixed ordering cost component. A long-standing problem is that of determining the structure of the optimal control policy when this system is subject to order quantity capacity constraints; to date, only partial characterisations of the optimal policy have been discussed. An open question is whether a policy with a single continuous interval over which ordering is prescribed is optimal for this problem. Under the so-called “continuous order property” conjecture, we show that the optimal policy takes the modified multi-(s,S) form. Moreover, we provide a numerical counterexample in which the continuous order property is violated, and hence show that a modified multi-(s,S) policy is not optimal in general. However, in an extensive computational study, we show that instances violating the continuous order property do not surface, and that the plans generated by a modified multi-(s,S) policy can therefore be considered, from a practical standpoint, near-optimal. Finally, we show that a modified (s,S) policy also performs well in this empirical setting.