Multiple allocation hub covering flow problem under uncertainty

ŞENER N., FEYZİOĞLU O.

ANNALS OF OPERATIONS RESEARCH, cilt.320, sa.2, ss.975-997, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 320 Sayı: 2
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s10479-022-04553-2
  • Dergi Adı: ANNALS OF OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Computer & Applied Sciences, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.975-997
  • Anahtar Kelimeler: Hub location, Network design, Inter-hub discount factor, L-shaped algorithm, Simulated data, Stochastic optimization, LOCATION PROBLEM, DEMAND, FORMULATION, ALGORITHMS, FACILITY, NETWORK
  • Galatasaray Üniversitesi Adresli: Evet

Özet

The hub covering problem can be viewed as a combination of the set covering and the hub location problem. In a traditional hub covering problem, all parameters are assumed to be deterministic. However, neglecting uncertainties associated with demand and transportation costs may lead to inferior solutions. Thus, we introduce three novel stochastic optimization models to formulate amultiple allocation hub covering problem in which deterministic transportation cost and demand assumptions are relaxed. Further, we also reflect scale economy to the models via inter-hub transportation cost discount factor. The L-Shaped Algorithm is applied to solve the sculpted problems. To test the efficiency of the proposed models, several test instances are generated and solved based on simulated data. The effects of the change of coverage radius, inter-hub discount factor, and hub opening cost are also examined. Results of this study validate that modeling the hub covering problem as a stochastic optimization model yields up 13.05% efficiency when compared to the expectation of the expected value method.