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Planning the location of facilities in the supply chain using the firefly meta-innovation algorithm

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Analysis of supply chain location issues and decision-making regarding the location of facilities in the supply chain is one of the most important issues in the decision-making of governments, organizations and companies. Undoubtedly, the correct location of facilities has very important effects on economic benefits, providing appropriate services and customer satisfaction. Supply chain issue is one of the most widely used issues in today's competitive world and location issues are among the most used issues in designing supply chain networks to improve and reduce costs and increase competitiveness. The facilities under consideration include warehouses and distribution centers, which have been solved with the aim of reducing transportation costs. And then the two methods are compared. The problem is solved in small, medium and large dimensions and finally it was concluded that the firefly algorithm had a better performance than the genetic algorithm.
Rocznik
Strony
433--454
Opis fizyczny
Bibliogr. 12 poz., rys., tab.
Twórcy
  • Department of Industrial Management, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
Bibliografia
  • [1] Pourghader Chobar, A., Adibi, M.A., Kazemi, A., A novel multi-objective model for hub location problem considering dynamic demand and environmental issues. Journal of Industrial Engineering and Management Studies, 8, 1, 2021, 1-31.
  • [2] Ghasemi, P., Goodarzian, F., Gunasekaran, A., Abraham, A., A bi-level mathematical model for logistic management considering the evolutionary game with environmental feedbacks. The International Journal of Logistics Management, 2021.
  • [3] Lotfi, R., Mardani, N., Weber, G.W., Robust bi‐level programming for renewable energy location. International Journal of Energy Research, 45, 5, 2021, 7521-7534.
  • [4] Chobar, A.P., Adibi, M.A., & Kazemi, A. (2022). Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms. Environment, Development and Sustainability, 1-28.
  • [5] Rahmaty, M., Daneshvar, A., Salahi, F., Ebrahimi, M., & Chobar, A. P. (2022). Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks. Discrete Dynamics in Nature and Society, 2022.
  • [6] Jahangiri, S., Abolghasemian, M., Pourghader Chobar, A., Nadaffard, A., & Mottaghi, V. (2021). Ranking of key resources in the humanitarian supply chain in the emergency department of iranian hospital: a real case study in COVID-19 conditions. Journal of applied research on industrial engineering, 8 (Special Issue), 1-10.
  • [7] Sohrabi, R., Pouri, K., Sabk Ara, M., Davoodi, S.M., Afzoon, E., & Pourghader Chobar, A. (2021). Applying sustainable development to economic challenges of small and medium enterprises after implementation of targeted subsidies in Iran. Mathematical Problems in Engineering, 2021.
  • [8] Goli, A., Tirkolaee, E.B., Malmir, B., Bian, G.B., & Sangaiah, A.K. (2019). A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing, 101(6), 499-529.
  • [9] Abdolazimi, O., Shishebori, D., Goodarzian, F., Ghasemi, P., Appolloni, A., Designing a new mathematical model based on ABC analysis for inventory control problem: A real case study. RAIRO-Operations Research, 55, 4, 2021, 2309-2335.
  • [10] Rezaei Kallaj, M., Abolghasemian, M., Moradi Pirbalouti, S., Sabk Ara, M., Pourghader Chobar, A., Vehicle Routing Problem in Relief Supply under a Crisis Condition considering Blood Types. Mathematical Problems in Engineering, 2021.
  • [11] Khalili-Damghani, K., Tavana, M., Ghasemi, P., A stochastic bi-objective simulation–optimization model for cascade disaster location-allocation-distribution problems. Annals of Operations Research, 309, 1, 2022, 103-141.
  • [12] Babaeinesami, A., Tohidi, H., Ghasemi, P., Goodarzian, F., Tirkolaee, E.B., A closed-loop supply chain configuration considering environmental impacts: a self-adaptive NSGA-II algorithm. Applied Intelligence, 2022, 1-19.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-616efad1-9296-474d-99ff-35f108d0ae1e
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