Design a Mathematical Planning Approach to Optimize the Supply Chain Taking Into Account Uncertainties In Distributors

• Autorzy: Chetthamrongchai Paitoon , Dayupay Johnry , Alshiqi Sevdie , Alghazali Tawfeeq Abdulameer Hashim , Iswanto A. Heri , Cavaliere Luigi Pio Leonardo , Al-Nussairi Ahmed Kateb Jumaah , Mohmmed Karrar Hatif , Kadhim Mustafa M.

Identyfikatory

DOI

10.2478/fcds-2022-0022

• Języki publikacji: EN

Abstrakty

EN

With the globalization of markets and increasing competition in global markets, the attempts of organizations to survive in this market has increased and has resulted in the emergence of the philosophy of Supply Chain Management. There is uncertainty in the reliability of supply chain facilities for reasons such as natural disasters, terrorist attacks, labor errors, and weather conditions. Therefore, when making strategic decisions, the system will continue to operate with minimal damage. Over the course of this study, the uncertainty of supplier layers in the supply chain has been modeled. To meet that aim, the issue of supply chain, including producers, warehouses, suppliers and consumers are considered. To calculate the cost of breakdowns due to the non-functioning of distributors, the scenario-building method has been utilized. Finally, the desired model is solved with Gomez software and the results are presented. The result of the study demonstrate the efficiency of this model in the facility location decision-making in supply chains.

Słowa kluczowe

EN

capacity levels; network design; probability of occurrence; scenario building; supply chain

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2022
• Tom: Vol. 47, No. 4
• Strony: 409--420
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Chetthamrongchai Paitoon

• Faculty of Business Administration, Kasetsart University, Thailand

• https://orcid.org/0000-0002-9744-4358

autor

Dayupay Johnry

• Graduate School Department, Cebu Technological University - Moalboal, Philippines

• https://orcid.org/0000-0002-4522-4775

autor

Alshiqi Sevdie

• University of Prishtina “Hasan Prishtina”, Economic Faculty, Kosova

• https://orcid.org/0000-0002-1110-3078

autor

Alghazali Tawfeeq Abdulameer Hashim

• English department, The Islamic University, Najaf, Iraq

• https://orcid.org/0000-0003-0131-3992

autor

Iswanto A. Heri

• Public Health Department, Faculty of Health Science, University of Pembangunan Nasional Veteran Jakarta, Jakarta, Indonesia

• https://orcid.org/0000-0002-1102-0176

autor

Cavaliere Luigi Pio Leonardo

• Department of Economics, University of Foggia, Foggia, Italy

• https://orcid.org/0000-0003-3169-567X

autor

Al-Nussairi Ahmed Kateb Jumaah

• Al-Manara College For Medical Sciences, Iraq

• https://orcid.org/0000-0003-2347-8878

autor

Mohmmed Karrar Hatif

• Scientific research Center, Al-Ayen University, Thi-Qar, Iraq

• https://orcid.org/0000-0002-3140-8477

autor

Kadhim Mustafa M.

• Department of Dentistry, Kut University College, Kut Wasit, 52001, Iraq
• College of technical engineering, The Islamic University, Najaf, Iraq
• Department of Pharmacy, Osol Aldeen University College, Baghdad, Iraq

• https://orcid.org/0000-0002-7749-4999

Bibliografia

• [1]. Campbell J.F., Integer programming formulations of discrete hub location problems, European Journal of Operational Research, 72, 2, 1994, 387-405.
• [2]. Chen C.L., Wang B.W., Lee W.C., Multiobjective optimization for a multienterprise supply chain network, Industrial & Engineering Chemistry Research, 42,9, 2003, 1879-1889.
• [3]. Goli A., Malmir B., A covering tour approach for disaster relief locating and routing with fuzzy demand, International Journal of Intelligent Transportation Systems Research, 18, 1, 2020,140-152.
• [4]. Handfield R., Warsing D., Wu X., (Q, r) Inventory policies in a fuzzy uncertain supply chain environment, European Journal of Operational Research, 197, 2, 2009, 609-619.
• [5]. Huang M., Dong L., Kuang H., Jiang Z.Z., Lee L.H., Wang, X., Supply chain network design considering customer psychological behavior-a 4PL perspective, Computers & Industrial Engineering, 159, 2021, 107484
• [6]. Langevin A., Riopel D., (Eds.), Logistics systems: design and optimization (Vol. 2). Springer Science & Business Media, 2005
• [7]. Murthy D.N.P., Solem O., Roren T., Product warranty logistics: Issues and challenges, European Journal of Operational Research, 156, 1, 2004, 110-126.
• [8]. Pahlevan S.M., Hosseini S.M.S., Goli A., Sustainable supply chain network design using products’ life cycle in the aluminum industry, Environmental Science and Pollution Research, 2021, 1-25.
• [9]. Peidro D., Mula J., Poler R., Verdegay J.L., Fuzzy optimization for supply chain planning under supply, demand and process uncertainties, Fuzzy sets and systems, 160, 18, 2009, 2640-2657.
• [10]. Peidro D., Mula J., Jiménez M., del Mar Botella M., A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment, European Journal of Operational Research, 205, 1, 2010, 65-80.
• [11]. Petrovic D., Roy R., Petrovic R., Modelling and simulation of a supply chain in an uncertain environment, European journal of operational research, 109, 2, 1998, 299-309.
• [12]. Pike D.F., Cohen M.A., Performance Characteristic Stochastic Integrated Production Distribution System, European Journal of operational research, 68, 1993, 23-48.
• [13]. Scaparra M.P., Church R.L., A bilevel mixed-integer program for critical infrastructure protection planning, Computers & Operations Research, 35, 6, 2008, 1905-1923.
• [14]. Shen Z.J.M., Daskin M.S., Trade-offs between customer service and cost in integrated supply chain design, Manufacturing & service operations management, 7, 3, 2005, 188-207.
• [15]. Snyder L.V., Daskin M.S., Reliability models for facility location: the expected failure cost case, Transportation science, 39, 3, 2005, 400-416.
• [16]. Li Y., Li X., Shu J., Song M., Zhang K., A general model and efficient algorithms for reliable facility location problem under uncertain disruptions, INFORMS Journal on Computing, 34, 1, 2022, 407-426.
• [17]. Tsiakis P., Shah N., Pantelides C.C., Design of multi-echelon supply chain networks under demand uncertainty, Industrial & engineering chemistry research, 40, 16, 2001, 3585-3604.
• [18]. Wang J., Shu Y.F., Fuzzy decision modeling for supply chain management, Fuzzy Sets and systems, 150, 1, 2005, 107-127.
• [19]. Yolmeh A., Saif U., Closed-loop supply chain network design integrated with assembly and disassembly line balancing under uncertainty: an enhanced decomposition approach, International Journal of Production Research, 59, 9, 2021, 2690-2707.
• [20]. Rubashkina Y., Galeotti M., Verdolini E., Environmental regulation and competitiveness: Empirical evidence on the Porter Hypothesis from European manufacturing sectors, Energy Policy, 83, 2015, 288-300.
• [21]. Baghizadeh K., Zimon D., Jum’a L., Modeling and optimization sustainable forest supply chain considering discount in transportation system and upplier selection under uncertainty, Forests, 12, 8, 2021, 964.
• [22]. RezaHoseini A., Noori S., Ghannadpour S.F., Integrated scheduling of suppliers and multi-project activities for green construction supply chains under uncertainty, Automation in Construction, 122, 2021, 103485.
• [23]. Nguyen T.D., Nguyen-Quang T., Venkatadri U., Diallo C., Adams M., Mathematical programming models for fresh fruit supply chain optimization: a review of the literature and emerging trends, AgriEngineering, 3, 3, 2021, 519-541.