Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul

ERTUĞRUL AYYILDIZ, (Karadeniz Teknik Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, 61080, Ortahisar, Trabzon, Türkiye)
Alev TAŞKIN GÜMÜŞ (Yıldız Teknik Üniversitesi, Makine Fakültesi, Endüstri Mühendisliği Bölümü, 34349, Beşiktaş, İstanbul, Türkiye)
Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji
1 0
Yıl: 2022 Cilt: 10 Sayı: 4 Sayfa Aralığı: 925 - 939 Metin Dili: İngilizce DOI: 10.29109/gujsc.1145730 İndeks Tarihi: 23-03-2023

Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul

Öz:
Limited resources must be used effectively, precisely, and damage-free considering the increase in the consumption of petroleum and petroleum-derived products. Therefore, the accurate and effective distribution of petroleum and related problems with petroleum distribution have attracted much attention among practitioners and optimization working researchers over the years. The petroleum distribution problem, as a version of the vehicle routing problem (VRP), deals with the planning of petroleum distribution from the depot(s) to the petrol stations safely and quickly. In this study, the petrol station replenishment problem (PSRP) is handled and a case study is presented for a public company located in Istanbul. The problem is considered as a time- dependent VRP with time windows. To handle the proposed time-dependent problem in a more realistic way, variable tanker speeds are considered based on traffic density. In this study, a novel mixed integer mathematical model to solve time dependent PSRP with time windows is proposed. The optimum route is determined in which risks such as environment and marine pollution may occur in case of possible accidents, and these risks are minimized by the proposed mathematical model considering factors such as traffic, vehicle speed, road structure, the road's proximity to the sea, and living areas
Anahtar Kelime: Marine Pollution Petrol Station Replenishmen Problem Risk Minimization Time-dependent VRP

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] Febriandini IF, Yuniaristanto, Sutopo W (2020) Multi-Compartment Vehicle Routing Problem to Find the Alternative Distribution Route of Petroleum Product Delivery. In: IOP Conference Series: Materials Science and Engineering
  • [2] Qiu XH, Wang Z, Xue Q (2015) Investment in deepwater oil and gas exploration projects: a multi-factor analysis with a real options model. Pet Sci 12:525–533. https://doi.org/10.1007/s12182-015-0039-4
  • [3] Li H, Sun RJ, Dong KY, et al (2017) Selecting China’s strategic petroleum reserve sites by multi- objective programming model. Pet Sci 14:622–635. https://doi.org/10.1007/s12182-017-0175-0
  • [4] Mohtasham J (2015) Review Article-Renewable Energies. Energy Procedia 74:1289–1297. https://doi.org/10.1016/J.EGYPRO.2015.07.774
  • [5] Pan X, Teng F, Tian Y, Wang G (2015) Countries’ emission allowances towards the low-carbon world: A consistent study. Appl Energy 155:218–228. https://doi.org/10.1016/j.apenergy.2015.06.011
  • [6] Erkut E, Ingolfsson A (2000) Catastrophe avoidance models for hazardous materials route planning. Transp Sci 34:165–179. https://doi.org/10.1287/trsc.34.2.165.12303
  • [7] Kara BY, Verter V (2004) Designing a road network for hazardous materials transportation. Transp Sci 38:188–196. https://doi.org/10.1287/trsc.1030.0065
  • [8] Erkut E, Gzara F (2008) Solving the hazmat transport network design problem. Comput Oper Res 35:2234–2247. https://doi.org/10.1016/j.cor.2006.10.022
  • [9] Zografos KG, Androutsopoulos KN (2008) A decision support system for integrated hazardous materials routing and emergency response decisions. Transp Res Part C Emerg Technol 16:684–703. https://doi.org/10.1016/j.trc.2008.01.004
  • [10] Leonelli P, Bonvicini S, Spadoni G (2000) Hazardous materials transportation: A risk-analysis- based routing methodology. J Hazard Mater 71:283–300. https://doi.org/10.1016/S0304- 3894(99)00084-9
  • [11] Samanlioglu F (2013) A multi-objective mathematical model for the industrial hazardous waste location-routing problem. Eur J Oper Res 226:332–340. https://doi.org/10.1016/j.ejor.2012.11.019
  • [12] Saat MR, Werth CJ, Schaeffer D, et al (2014) Environmental risk analysis of hazardous material rail transportation. J Hazard Mater 264:560–569. https://doi.org/10.1016/j.jhazmat.2013.10.051
  • [13] Bonvicini S, Antonioni G, Morra P, Cozzani V (2015) Quantitative assessment of environmental risk due to accidental spills from onshore pipelines. Process Saf Environ Prot 93:31–49. https://doi.org/10.1016/j.psep.2014.04.007
  • [14] Lam C, Zhou W (2016) Statistical analyses of incidents on onshore gas transmission pipelines based on PHMSA database. Int J Press Vessel Pip 145:29–40. https://doi.org/10.1016/j.ijpvp.2016.06.003
  • [15] Zarei E, Azadeh A, Khakzad N, et al (2017) Dynamic safety assessment of natural gas stations using Bayesian network. J Hazard Mater 321:830–840. https://doi.org/10.1016/j.jhazmat.2016.09.074
  • [16] Ma C (2018) Network optimisation design of Hazmat based on multi-objective genetic algorithm under the uncertain environment. Int J Bio-Inspired Comput 12:236–244. https://doi.org/10.1504/IJBIC.2018.096482
  • [17] Yang Q, Chin KS, Li YL (2018) A quality function deployment-based framework for the risk management of hazardous material transportation process. J Loss Prev Process Ind 52:81–92. https://doi.org/10.1016/j.jlp.2018.02.001
  • [18] Hu H, Li J, Li X (2018) A credibilistic goal programming model for inventory routing problem with hazardous materials. Soft Comput 22:5803–5816. https://doi.org/10.1007/s00500-017-2663-y
  • [19] Hu H, Li X, Zhang Y, et al (2019) Multi-objective location-routing model for hazardous material logistics with traffic restriction constraint in inter-city roads. Comput Ind Eng 128:861–876. https://doi.org/10.1016/j.cie.2018.10.044
  • [20] Ghaderi A, Burdett RL (2019) An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network. Transp Res Part E Logist Transp Rev 127:49–65. https://doi.org/10.1016/j.tre.2019.04.011
  • [21] Li YL, Yang Q, Chin KS (2019) A decision support model for risk management of hazardous materials road transportation based on quality function deployment. Transp Res Part D Transp Environ 74:154–173. https://doi.org/10.1016/j.trd.2019.07.026
  • [22] Chiou SW (2020) A resilience-based signal control for a time-dependent road network with hazmat transportation. Reliab Eng Syst Saf 193:106570. https://doi.org/10.1016/j.ress.2019.106570
  • [23] Hu H, Li J, Li X, Shang C (2020) Modeling and Solving a Multi-Period Inventory Fulfilling and Routing Problem for Hazardous Materials. J Syst Sci Complex 1–23. https://doi.org/10.1007/s11424-019-8176-2
  • [24] Ziaei Z, Jabbarzadeh A (2021) A multi-objective robust optimization approach for green location-routing planning of multi-modal transportation systems under uncertainty. J Clean Prod 291:. https://doi.org/10.1016/J.JCLEPRO.2020.125293
  • [25] Zhou Z, Ha M, Hu H, Ma H (2021) Half open multi-depot heterogeneous vehicle routing problem for hazardous materials transportation. Sustain 13:1–17. https://doi.org/10.3390/su13031262
  • [26] Ayyildiz E, Taskin Gumus A (2021) Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul. Environ Sci Pollut Res 28:35798– 35810. https://doi.org/10.1007/s11356-021-13223-y
  • [27] Men J, Chen G, Zhou L, Chen P (2022) A pareto-based multi-objective network design approach for mitigating the risk of hazardous materials transportation. Process Saf Environ Prot 161:860–875. https://doi.org/10.1016/j.psep.2022.03.048
  • [28] Erkut E, Ingolfsson A (2005) Transport risk models for hazardous materials: Revisited. Oper Res Lett 33:81–89. https://doi.org/10.1016/j.orl.2004.02.006
  • [29] Chang TS, Nozick LK, Turnquist MA (2005) Multiobjective path finding in stochastic dynamic networks, with application to routing hazardous materials shipments. Transp Sci 39:383–399. https://doi.org/10.1287/trsc.1040.0094
  • [30] Zheng B (2010) Multi-Objective Vehicle Routing Problem in Hazardous Material Transportation. In: ICLEM 2010. American Society of Civil Engineers, Reston, VA, pp 3136–3142
  • [31] Tang J, Ma Y, Guan J, Yan C (2013) A Max-Min Ant System for the split delivery weighted vehicle routing problem. Expert Syst Appl 40:7468–7477. https://doi.org/10.1016/j.eswa.2013.06.068
  • [32] Cantú JMV, Solano AB, Leyva EAL, Acosta ML (2017) Optimization of territories and transport routes for hazardous materials in a distribution network. J Ind Eng Manag 10:604–622. https://doi.org/10.3926/jiem.2107
  • [33] Zhang M, Wang N, He Z, et al (2019) Bi-Objective Vehicle Routing for Hazardous Materials Transportation with Actual Load Dependent Risks and Considering the Risk of Each Vehicle. IEEE Trans Eng Manag 66:429–442. https://doi.org/10.1109/TEM.2018.2832049
  • [34] Wichapa N, Khokhajaikiat P (2018) Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm. Int J Ind Eng Comput 9:75–98. https://doi.org/10.5267/j.ijiec.2017.4.003
  • [35] Caceres-Cruz J, Arias P, Guimarans D, et al (2014) Rich vehicle routing problem: Survey. ACM Comput Surv 47:1–28. https://doi.org/10.1145/2666003
  • [36] Cornillier F, Boctor FF, Laporte G, Renaud J (2008) An exact algorithm for the petrol station replenishment problem. J Oper Res Soc 59:607–615. https://doi.org/10.1057/palgrave.jors.2602374
  • [37] Cornillier F, Boctor FF, Laporte G, Renaud J (2008) A heuristic for the multi-period petrol station replenishment problem. Eur J Oper Res 191:295–305. https://doi.org/10.1016/j.ejor.2007.08.016
  • [38] Ng WL, Leung SCH, Lam JKP, Pan SW (2008) Petrol delivery tanker assignment and routing: A case study in Hong Kong. J Oper Res Soc 59:1191–1200. https://doi.org/10.1057/palgrave.jors.2602464
  • [39] Cornillier F, Laporte G, Boctor FF, Renaud J (2009) The petrol station replenishment problem with time windows. Comput Oper Res 36:919–935. https://doi.org/10.1016/j.cor.2007.11.007
  • [40] Surjandari I, Rachman A, Dianawati F, Wibowo RP (2011) Oil fuel delivery optimization for multi product and multi depot: the case of petrol station replenishment problem (PSRP). In: International Conference on Graphic and Image Processing (ICGIP 2011). SPIE, p 82853Q
  • [41] Boctor FF, Renaud J, Cornillier F (2011) Trip packing in petrol stations replenishment. Omega 39:86–98. https://doi.org/10.1016/j.omega.2010.03.003
  • [42] Cornillier F, Boctor F, Renaud J (2012) Heuristics for the multi-depot petrol station replenishment problem with time windows. Eur J Oper Res 220:361–369. https://doi.org/10.1016/j.ejor.2012.02.007
  • [43] Triki C (2013) Solution methods for the periodic petrol station replenishment problem. J Eng Res 10:69–77. https://doi.org/10.24200/tjer.vol10iss2pp69-77
  • [44] Carotenuto P, Giordani S, Massari S, Vagaggini F (2015) Periodic capacitated vehicle routing for retail distribution of fuel oils. In: Transportation Research Procedia. Elsevier, pp 735–744
  • [45] Benantar A, Ouafi R, Boukachour J (2016) A petrol station replenishment problem: new variant and formulation. Logist Res 9:1–18. https://doi.org/10.1007/s12159-016-0133-z
  • [46] Carotenuto P, Giordani S, Massari S, Vagaggini F (2018) A multi-depot periodic vehicle routing model for petrol station replenishment. In: Advances in Intelligent Systems and Computing. Springer Verlag, pp 421–437
  • [47] Carotenuto P, Giordani S, Celani D (2017) Planning Retail Distribution of Fuel Oils. In: Transportation Research Procedia. Elsevier B.V., pp 484–491
  • [48] Al-Hinai N, Triki C (2020) A two-level evolutionary algorithm for solving the petrol station replenishment problem with periodicity constraints and service choice. Ann Oper Res 286:325–350. https://doi.org/10.1007/s10479-018-3117-3
  • [49] Yahyaoui H, Kaabachi I, Krichen S, Dekdouk A (2018) Two metaheuristic approaches for solving the multi-compartment vehicle routing problem. Oper Res 1–24. https://doi.org/10.1007/s12351-018-0403-4
  • [50] Gromov VA, Kuznietzov KA, Pigden T (2019) Decision support system for light petroleum products supply chain. Oper Res 19:219–236. https://doi.org/10.1007/s12351-016-0290-5
  • [51] Wei XT, Liao Q, Zhang HR, et al (2021) MILP formulations for highway petrol station replenishment in initiative distribution mode. Pet Sci 18:994–1010. https://doi.org/10.1007/S12182-021-00551-4
  • [52] Pan X, Teng F, Wang G (2014) Sharing emission space at an equitable basis: Allocation scheme based on the equal cumulative emission per capita principle. Appl Energy 113:1810–1818. https://doi.org/10.1016/j.apenergy.2013.07.021
  • [53] Kocak İ, Boran K (2019) Türkiye’deki İllerin Elektrik Tüketim Etkinliklerinin Veri Zarflama Analizi İle Değerlendirilmesi. Politek Derg 22:351–365. https://doi.org/10.2339/POLITEKNIK.444371
  • [54] Huang X, Wang X, Pei J, et al (2018) Risk assessment of the areas along the highway due to hazardous material transportation accidents. Nat Hazards 93:1181–1202. https://doi.org/10.1007/s11069-018-3346-4
  • [55] Xing Y, Chen S, Zhu S, et al (2020) Exploring risk factors contributing to the severity of hazardous material transportation accidents in China. Int J Environ Res Public Health 17:. https://doi.org/10.3390/ijerph17041344
  • [56] Gendreau M, Ghiani G, Guerriero E (2015) Time-dependent routing problems: A review. Comput Oper Res 64:189–197. https://doi.org/10.1016/j.cor.2015.06.001
  • [57] Yandex (2018) Veriler ve Raporlar — Yandex İstanbul için 3 Yıllık Trafik Analizi. https://yandex.com.tr/company/press_center/infographics/istanbul_traffic. Accessed 16 Jul 2020
  • [58] Republic of Turkey-General Directorarate of Highways (2020) Hız Sınırları. https://www.kgm.gov.tr/Sayfalar/KGM/SiteTr/Trafik/HizSinirlari.aspx. Accessed 27 Jul 2020
APA AYYILDIZ E, Taskin A (2022). Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. , 925 - 939. 10.29109/gujsc.1145730
Chicago AYYILDIZ ERTUGRUL,Taskin Alev Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. (2022): 925 - 939. 10.29109/gujsc.1145730
MLA AYYILDIZ ERTUGRUL,Taskin Alev Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. , 2022, ss.925 - 939. 10.29109/gujsc.1145730
AMA AYYILDIZ E,Taskin A Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. . 2022; 925 - 939. 10.29109/gujsc.1145730
Vancouver AYYILDIZ E,Taskin A Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. . 2022; 925 - 939. 10.29109/gujsc.1145730
IEEE AYYILDIZ E,Taskin A "Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul." , ss.925 - 939, 2022. 10.29109/gujsc.1145730
ISNAD AYYILDIZ, ERTUGRUL - Taskin, Alev. "Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul". (2022), 925-939. https://doi.org/10.29109/gujsc.1145730
APA AYYILDIZ E, Taskin A (2022). Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 10(4), 925 - 939. 10.29109/gujsc.1145730
Chicago AYYILDIZ ERTUGRUL,Taskin Alev Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji 10, no.4 (2022): 925 - 939. 10.29109/gujsc.1145730
MLA AYYILDIZ ERTUGRUL,Taskin Alev Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, vol.10, no.4, 2022, ss.925 - 939. 10.29109/gujsc.1145730
AMA AYYILDIZ E,Taskin A Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji. 2022; 10(4): 925 - 939. 10.29109/gujsc.1145730
Vancouver AYYILDIZ E,Taskin A Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji. 2022; 10(4): 925 - 939. 10.29109/gujsc.1145730
IEEE AYYILDIZ E,Taskin A "Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul." Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 10, ss.925 - 939, 2022. 10.29109/gujsc.1145730
ISNAD AYYILDIZ, ERTUGRUL - Taskin, Alev. "Mixed Integer Programming Formulation for Time-Dependent Petrol Station Replenishment Problem: A Real-Life Case in İstanbul". Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji 10/4 (2022), 925-939. https://doi.org/10.29109/gujsc.1145730
Sayfa Oluşturulma Tarihi
24-04-2024 6:10

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları