نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی صنایع، دانشکده مهندسی، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران.

2 گروه مهندسی صنایع، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران.

3 گروه مدیریت صنعتی و فناوری اطلاعات، دانشگاه شهید بهشتی، تهران، ایران.

چکیده

هدف: افزایش جمعیت و به دنبال آن افزایش نیازهای انسانی و مشکلات مربوط به حوزه حمل‌ونقل، موجب شده است تا مدیران سازمان‌ها به دنبال راه‌حل‌هایی به‌منظور  افزایش سودآوری اقتصادی و کاهش هزینه‌ها برآیند. لذا در این مطالعه از مجموعه تصمیمات مکان‌یابی، مسیریابی وسایل نقلیه و مدیریت موجودی تحت عنوان ابزارهای اصلی مقابله با چنین مشکلاتی استفاده شده است.
روش‌شناسی پژوهش: مدل در سایز کوچک به کمک روش اپسیلون محدودیت با نرم‌افزار گمز حل و اعتبارسنجی شده است. ازآنجا که این مسأله جز مسائل NP-Hard به شمار می‌رود برای حل مسائل ابعاد بزرگ از الگوریتم‌های فرا ابتکاری ,MOPSO MOFF ,MOIWO, NSGA- II استفاده شده است.
یافته‌ها: نتایج حاصل از تمامی معیارهای مقایسه‌ای حاکی از برتری الگوریتم MOIWO نسبت به سایر الگوریتم‌ها و کارایی مناسب این روش‌ها در حل مدل ریاضی به‌خصوص در ابعاد بالا و زمان‌های کوتاه است می‌باشد.
اصالت/ارزش‌افزوده علمی: در این مطالعه طراحی شبکه حمل‌ونقل مواد خطرناک با در نظر گرفتن تصمیمات مربوط به مکان‌یابی، مسیریابی، موجودی مدنظر می‌باشد، برای این منظور یک مدل ریاضی جدید چندهدفه با اهداف کمینه‌نمودن هزینه،کمینه نمودن  زمان سفر و بیشینه‌سازی مسئولیت اجتماعی ارائه شده است.این مدل ریاضی قابلیت استفاده در حوزه‌های مختلف و ابعاد متفاوت را به همراه دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Presenting a Multi-objective mathematical Model of a location-routing-inventory problem for hazardous materials considering the concept elastic demand and queuing system

نویسندگان [English]

  • Parisa Bolhasani 1
  • Mohammad Fallah 1
  • Reza Tavakkoli-Moghaddam 2
  • Akbar Alam Tabriz 3

1 Department of Industrial Engineering, Faculty of Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

3 Department of Industrial Management and IT, Shahid Beheshti University, Tehran, Iran.

چکیده [English]

Purpose: Increasing the population, followed by increasing human needs and problems related to transportation, has led managers to seek for solutions by the goal of increasing economic profitability and reducing costs. Therefore, in this study, a set of location decisions, vehicle routing and inventory management has been used as the main tools to face with these problems.
Methodology: model has been solved and validated by Gams software using Epsilon method in small scale. Since this is one of the NP-Hard problems, meta-heuristic algorithms, MOPSO, MOFF, MOIWO, NSGA-II have been used to solve large-scale problems.
Finding: The results of all comparative criteria show the superiority of the MOIWO algorithm over other algorithms and the appropriate efficiency of these methods in solving the mathematical model, especially in high dimensions and short times.
Originality / Value: In this study, the design of hazardous materials transportation network is considered by considering the decisions related to location, routing, inventory. For this purpose, a new multi-objective mathematical model with the objectives of minimizing cost, minimizing travel time and maximum social responsibility is presented. This mathematical model can be used in different areas and different dimensions.

کلیدواژه‌ها [English]

  • Sustainable supply chain
  • Hazardous material
  • Risk
  • MOIWO
Adarang, H., Bozorgi-Amiri, A., Khalili-Damghani, K., & Tavakkoli-Moghaddam, R. (2020). A robust bi-objective location-routing model for providing emergency medical services. Journal of Humanitarian Logistics and Supply Chain Management.
Alhaj, M. A., Svetinovic, D., & Diabat, A. (2016). Retracted: a carbon-sensitive two-echelon-inventory supply chain model with stochastic demand. Resources, conservation and recycling, 108, 82-87.
Ashtakala, B., & Eno, L. A. (1996). Minimum risk route model for hazardous materials. Journal of transportation engineering122(5), 350-357.
Baumol, W. J., & Wolfe, P. (1958). A warehouse-location problem. Operations research6(2), 252-263.
Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8(3), 256-279.
Dai, Z., Aqlan, F., Zheng, X., & Gao, K. (2018). A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & industrial engineering, 119, 338-352.
Darestani, S. A., & Hemmati, M. (2019). Robust optimization of a bi-objective closed-loop supply chain network for perishable goods considering queue system. Computers & industrial engineering, 136, 277-292.
Daskin, M. S., Coullard, C. R., & Shen, Z. J. M. (2002). An inventory-location model: Formulation, solution algorithm and computational results. Annals of operations research110(1), 83-106.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002). A fast and elitist multi objective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Dehghanian, F., & Mansour, S. (2009). Designing sustainable recovery network of end-of-life products using genetic algorithm. Resources, conservation and recycling53(10), 559-570.
Deng, S., Li, Y., Zhou, T., & Cao, Y. (2014, October). Study on recyclable reserve logistics network optimization based on e-commerce. 2014 International conference on management of e-commerce and e-government (pp. 337-340). IEEE.
DOT, U. (2000). Departmentwide program evaluation of the hazardous materials transportation programs (HMPE). Retrieved from http://www.phmsa.dot.gov/staticfiles/PHMSA/DownloadableFiles/Files/hmpe_report.pdf
Fahimnia, B., Sarkis, J., Dehghanian, F., Banihashemi, N., & Rahman, S. (2013). The impact of carbon pricing on a closed-loop supply chain: an Australian case study. Journal of cleaner production59, 210-225.
Ghasemi, A., Rayatpisheh, M. A., Haddadi, A., & Rayat, A. (2017).  PishehIdentifying and prioritizing the factors contributting to sustainability of food supply chain. Journal of environmental science and technology, 19(4), 369-382. (In Persian). DOI: 10.22034/JEST.2017.10738
Gross, D. (2008). Fundamentals of queueing theory. John Wiley & Sons.
Gzara, F. (2013). A cutting plane approach for bilevel hazardous material transport network design. Operations research letters, 41(1), 40-46.
Hassanzadeh, T., Faez, K., & Seyfi, G. (2012, February). A speech recognition system based on structure equivalent fuzzy neural network trained by firefly algorithm. 2012 international conference on biomedical engineering (ICoBE) (pp. 63-67). IEEE.
Hu, H., Li, X., Zhang, Y., Shang, C., & Zhang, S. (2019). Multi-objective location-routing model for hazardous material logistics with traffic restriction constraint in inter-city roads. Computers & industrial engineering, 128, 861-876.
Hum, S. H., Parlar, M., & Zhou, Y. (2018). Measurement and optimization of responsiveness in supply chain networks with queueing structures. European journal of operational research, 264(1), 106-118.
Javid, A. A., & Azad, N. (2010). Incorporating location, routing and inventory decisions in supply chain network design. Transportation research part E: logistics and transportation review46(5), 582-597.
Kalantari, M., Pishvaee, M., Yaghoubi, S. (2024). A multi objective model integrating financial and material flow in supply chain master planning. Journal of industrial management perspective, 5(3), 139-167.
Khodemani-Yazdi, M., Tavakkoli-Moghaddam, R., Bashiri, M., & Rahimi, Y. (2019). Solving a new bi-objective hierarchical hub location problem with an M∕ M∕ c queuing framework. Engineering applications of artificial intelligence, 78, 53-70.
Khosravi, S., & Akbari Jokar, M. R. (2014). Hub location problem considering an elastic demand. Proceedings of CIE44 & IMSS’14 (pp. 581-588). Istanbul, Turkey.
Li, Y., Guo, H., & Zhang, Y. (2018). An integrated location-inventory problem in a closed-loop supply chain with third-party logistics. International journal of production research, 56(10), 3462-3481.
Liu, S. C., & Lee, S. B. (2003). A two-phase heuristic method for the multi-depot location routing problem taking inventory control decisions into consideration. The international journal of advanced manufacturing technology22(11), 941-950.
Liu, Y., Dehghani, E., Jabalameli, M. S., Diabat, A., & Lu, C. C. (2020). A coordinated location-inventory problem with supply disruptions: A two-phase queuing theory–optimization model approach. Computers & industrial engineering, 142, 106326.
Mahmood Soltani, F., TavakoliMoghadam, R., Javadian, N., & MahmoodAbadi, A. (2020). Bi-objective modeling for a fuel distribution problem in a multi-distribution warehouse centers. Supply chain management, 15(42), 54-61. (In Persian). https://scmj.ihu.ac.ir/article_203521.html?lang=en
Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weeds colonization. Ecological informatics, 1(4), 355-366.
Mohammadi, A., Alem Tabriz, A., Pishvaee, M. (2018). Proposing model for master planning of sustainable supply chain with considering integration of physical and financial flow. Journal of industrial management perspective, 8(1), 39-62. (In Persian). https://jimp.sbu.ac.ir/article_87179.html?lang=en
Mohtashami, Z., Aghsami, A., & Jolai, F. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of cleaner production, 242, 118452.
Nadizadeh, A., & Nasab, H. H. (2014). Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm. European journal of operational research238(2), 458-470.
Nekooghadirli, N., Tavakkoli-Moghaddam, R., Ghezavati, V. R., & Javanmard, A. S. (2014). Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics. Computers & industrial engineering76, 204-221.
Noguchi, H., Hienuki, S., & Fuse, M. (2020). Network theory-based accident scenario analysis for hazardous material transport: A case study of liquefied petroleum gas transport in japan. Reliability engineering & system safety, 203, 107107.
Pan, S., Ballot, E., & Fontane, F. (2013). The reduction of greenhouse gas emissions from freight transport by pooling supply chains. International journal of production economics, 143(1), 86-94.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems206, 1-20.
Rabbani, M., Heidari, R., & Yazdanparast, R. (2019). A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and Monte Carlo simulation. European journal of operational research, 272(3), 945-961.
Rayatpisha, S., Ahmay, R., & Abbasi, M. (2019). Using a combined approach of qualitative & multi-criteria decision making (mcdm) approach in order to presentation of sustainable supply chains model in petrochemical industry. Industrial management studies, 16(51), 145-180. (InPersian). DOI: 10.22054/JIMS.2019.3955.1556
Saeedi, S., Mohammadi, M., & Torabi, S. (2015). A De Novo programming approach for a robust closed-loop supply chain network design under uncertainty: An M/M/1 queueing model. International journal of industrial engineering computations, 6(2), 211-228.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation2(3), 221-248.
Talebzadehhosseini, S. (2015). Measuring sustainability performance of supply chain management practices using fuzzy inference (Doctoral dissertation, The University of Regina (Canada)). http://hdl.handle.net/10294/6831
Tavakoli Moghadam, R., & Otrodi, F. (2013). Vehicle routing problem with time-windows and weighted nodes for perishable food delivery. 2nd national conference on indurtrial engineering & systems. Isfahan. https://en.civilica.com/doc/251372/
Tavakoli Moghadam, R., Alinaghian, M., Noroozi, N., & Salamatbakhsh, A. (2011). Solving a newvehicle routing problem considering safety in hazardous materials transportation: areal-case study. Quarterly journal of transportation engineering, 2(3), 223-237. (In Persian). DOR: 20.1001.1.20086598.1390.2.3.2.7
Toth, P., & Vigo, D. (2002). An overview of vehicle routing problems. The vehicle routing problem, 1-26.
Tsao, Y. C., Thanh, V. V., Lu, J. C., & Yu, V. (2018). Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming. Journal of cleaner production, 174, 1550-1565.
Vafaeenezhad, T., Tavakkoli-Moghaddam, R., & Cheikhrouhou, N. (2019). Multi-objective mathematical modeling for sustainable supply chain management in the paper industry. Computers & industrial engineering, 135, 1092-1102.
Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., & Baboli, A. (2012). Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/c queuing model. Transportation research part E: logistics and transportation review, 48(6), 1152-1168.
Validi, S., Bhattacharya, A., & Byrne, P. J. (2015). A solution method for a two-layer sustainable supply chain distribution model. Computers & operations research, 54, 204-217.
Varsei, M., & Polyakovskiy, S. (2017). Sustainable supply chain network design: A case of the wine industry in Australia. Omega66, 236-247.
Watson-Gandy, C. D. T., & Dohrn, P. J. (1973). Depot location with van salesmen—a practical approach. Omega1(3), 321-329.
Yang, X. S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
Yousefi Nejad Attari, M., Karbasi, V., & Sharifi, S. (2020). Presenting a bi-objective random planning model for nursing services. Iranian journal of supply chain management, 21(65), 73-88. (In Persian). https://scmj.ihu.ac.ir/article_205263.html
Yousefi-Babadi, A., Tavakkoli-Moghaddam, R., Bozorgi-Amiri, A., & Seifi, S. (2017). Designing a reliable multi-objective queuing model of a petrochemical supply chain network under uncertainty: a case study. Computers & chemical engineering, 100, 177-197.
Zahiri, B., Tavakkoli-Moghaddam, R., & Pishvaee, M. S. (2014). A robust possibilistic programming approach to multi-period location–allocation of organ transplant centers under uncertainty. Computers & industrial engineering, 74, 139-148.
Zhalechian, M., Tavakkoli-Moghaddam, R., & Rahimi, Y. (2017). A self-adaptive evolutionary algorithm for a fuzzy multi-objective hub location problem: An integration of responsiveness and social responsibility. Engineering applications of artificial intelligence, 62, 1-16.
Zhalechian, M., Tavakkoli-Moghaddam, R., & Rahimi, Y. (2017). A self-adaptive evolutionary algorithm for a fuzzy multi-objective hub location problem: An integration of responsiveness and social responsibility. Engineering applications of artificial intelligence, 62, 1-16.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation research part E: logistics and transportation review, 89, 182-214.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M. (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation research part E: logistics and transportation review, 89, 182-214.
Zhang, S., Lee, C. K. M., Wu, K., & Choy, K. L. (2016). Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels. Expert systems with applications, 65, 87-99.