Cycle cost considerations in a continuous review inventory control model

Konur D., Yildirim G.

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, cilt.72, sa.4, ss.800-821, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 72 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/01605682.2019.1700189
  • Dergi Adı: JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, IBZ Online, International Bibliography of Social Sciences, Periodicals Index Online, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.800-821
  • Anahtar Kelimeler: Inventory, stochastic demand, cycle cost, multi-objective, ECONOMIC ORDER QUANTITY, TERMS SUPPLIER CREDIT, SERVICE LEVEL CONSTRAINT, LEAD-TIME DEMAND, TRADE CREDIT, PERMISSIBLE DELAY, EPQ MODEL, Q POLICY, ENVIRONMENTAL CONSIDERATIONS, NEWSVENDOR PROBLEM
  • Gazi Üniversitesi Adresli: Hayır

Özet

In this study, the continuous review order-quantity-re-order point (Q, R) model is analysed with cycle cost considerations. First, we formulate the maximum cycle cost of a given (Q, R) policy using a distribution-free approach. Then, two approaches are introduced to minimize the maximum cycle cost: (i) adjusting R of a given (Q, R) policy and (ii) designing a new (Q, R) policy. Optimum inventory control decisions are characterized for each approach. A set of numerical studies is presented to compare the outcomes of both approaches to three long-term cost minimization approaches, namely the cost minimizing (Q, R) policy, the distribution-free minmax (Q, R) policy, and the distribution-free (Q, R) policy based on the maximum entropy principle. Our numerical results demonstrate the viability of the two approaches introduced and discuss implications of penalty costs and lead time demand's coefficient of variation. Later, we formulate a bi-objective model with the objectives of expected cost and maximum cycle cost minimizations and propose a bi-directional method to approximate the set of Pareto efficient solutions. Numerical examples are presented to illustrate the algorithm and demonstrate the Pareto front.