طراحی شبکه در اتحاد استراتژیک تحت عدم قطعیت با رویکرد موازنه بین ریسک و عملکرد شبکه

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دانشجو دکتری، گروه مهندسی صنایع، مجتمع مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران.

2 استادیار، گروه مهندسی صنایع، مجتمع مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران.

3 دانشیار، گروه مهندسی صنایع، مجتمع مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران.

چکیده

هدف: هدف این مطالعه، ارائه یک مدل ریاضی جدید، برای طراحی شبکه تأمین تحت اتحاد استراتژیک و در نظر‌گیری مفاهیم مرتبط با اتحاد استراتژیک و همکاری بین اعضای زنجیره تأمین است. مدل پیشنهادی این پژوهش، نخستین مدل برای طراحی شبکه روبه‌جلو و عقب و تحت شرایط عدم قطعیت است.
روش: روش پژوهش، از نوع بنیادی و کاربردی است. در این پژوهش با استفاده از روش‌های بهینه‌سازی استوار تلاش شده است که یک مدل ریاضی کارا برای مقابله با عدم قطعیت ارائه شود. با توجه به پیچیدگی محاسباتی مدل ریاضی پیشنهادی، روش تجزیه بندرز استفاده شده و با به‌کارگیری سازوکار‌های شتاب‌دهی به روش بندرز، راه‌حلی کارا برای حل مدل ریاضی ارائه شده است.
یافته‌ها: نتایج تحقیق نشان‌دهنده کارایی اتحاد استراتژیک و همکاری در کاهش هزینه‌های شبکه تولید و توزیع کالاهاست. همچنین، کمّی‌سازی مفاهیم مرتبط با اتحاد استراتژیک در زنجیره تأمین و کارایی مدل پیشنهادی برای ایجاد فضای تصمیم‌گیری با توجه به برقراری رابطه تعادل بین ریسک و مزایای اتحاد استراتژیک، از یافته‌های دیگر این پژوهش بوده است.
نتیجه‌گیری: نتایج استفاده از داده‌های یک زنجیره تأمین آهن و فولاد، نشان‌دهنده کارایی مدل ریاضی برای ایجاد فضای تصمیم‌گیری مدیران و تصمیم‌گیران حوزه زنجیره تأمین آهن و فولاد است و همچنین نشان می‌دهد که روش ارائه‌شده، برای حل این مسئله عملکرد مناسبی دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Network Design in Strategic Alliance under Uncertainty with a Trade-off between Risk and Performance

نویسندگان [English]

  • Hamid Saffari 1
  • Morteza Abbasi 2
  • Jafar Gheidar Kheljanie 3
1 Ph.D. Candidate, Department of Industrial Engineering, Malik Ashtar University of Technology, Tehran, Iran.
2 Assistant Prof., Department of Industrial Engineering, Malek Ashtar University, Tehran, Iran.
3 Associate Prof., Department of Industrial Engineering, Malek Ashtar University, Tehran, Iran.
چکیده [English]

Objective: The purpose of this study is to present a new mathematical model to design a supply network by considering the strategic alliance and the relationships between the supply chain members under uncertainty. This study attempts to create a suitable decision-making environment for managers to optimize the network and make appropriate strategic decisions accordingly. Since the mathematical model of network design has computational complexity, providing a suitable solution method for the proposed model is another goal of this research.
Methods: As this paper is an applied study, a new mixed-integer linear programming model (MILP) has been presented. The robust optimization method has been used to deal with uncertainty risks such as the risk of changing sales and the return of products. In the mathematical model, strategic alliance levels and the level of risk for each of the partners are considered. The model has two objectives; minimizing the cost, and minimizing the risk of establishing a strategic alliance. The optimal location of the facilities, the selection of production methods, the capacity of facilities, the selection of colleagues, the level of strategic alliance, and their control level have been determined in the presented mathematical model. Considering the computational complexity of the mathematical model, The Benders decomposition method has been applied, and a solution for the proposed mathematical model has been developed and localized by using acceleration mechanisms.
Results: The results show the effectiveness of strategic alliances in reducing the costs of the production and distribution network of goods. The quantification of the concepts related to the strategic alliance in the supply chain, and the establishment of a trade-off between the risk and benefits of the strategic alliance are other research findings. Considering the different levels of strategic alliance and risk for each partner, the results of the current research show that a strategic alliance reduces the cost, and this cost reduction depends on the risk level of the partners. In addition, the computational results show the efficiency of the accelerated Benders decomposition algorithm for solving mathematical models in large-scale problems. In some problems that the Gams software is not able to provide the right answer in the appropriate time, the algorithm based on benders methods provided acceptable answers in a shorter time frame.
Conclusion: Applying the industry data shows the effectiveness of the model in creating a decision-making environment for managers and decision-makers. Also, the results show the appropriate performance of the solution method. Therefore, the finding of this research indicates a new research viewpoint in the field of network design under strategic alliance for the production and distribution of products.

کلیدواژه‌ها [English]

  • Accelerated Benders decomposition
  • Colleague selection
  • Risk management
  • Strategic alliance
  • Supply chain management

مراجع

منابع
اختیاری، مصطفی؛ زندیه، مصطفی؛ عالم تبریز، اکبر؛ ربیعه، مسعود (1398). ارائه یک مدل برنامه ریزی دوسطحی برای زنجیره تأمین چند مرحله‌ای با تأکید بر قابلیت اطمینان در شرایط عدم قطعیت. مدیریت صنعتی، 11 (2)، 117- 206.
حسینی دهشیری، سید جلال الدین؛ امیری، مقصود؛ الفت، لعیا؛ پیشوایی، میرسامان (1401). رویکرد برنامه‌ریزی فازی استوار جدید به‌منظور طراحی شبکه زنجیرۀ تأمین حلقه بسته. مدیریت صنعتی، 14(3)، 421-457.‎
خلیلی، سید محمد؛ پویا، علیرضا؛ کاظمی، مصطفی؛ فکور ثقیه، امیرمحمد (1401). طراحی یک شبکه زنجیرۀ تأمین بنزین پایدار و تاب‌آور تحت شرایط عدم قطعیت اختلال (مطالعه موردی: شبکه زنجیرۀ تأمین بنزین استان خراسان رضوی). مدیریت صنعتی، 14(1)، 27-79.‎
سیبویه، علی؛ آذر، عادل؛ زندیه، مصطفی (1401). ارائه مدل دومرحله‏ای احتمالی استوار برای طراحی زنجیرۀ تأمین خون تاب‌آور با درنظرگرفتن اختلال زلزله و بیماری واگیردار. مدیریت صنعتی، 13(4)، 664-703‎.
محمدی، امیرسالار؛ عالم تبریز، اکبر؛ پیشوایی، میرسامان (1398). طراحی شبکه زنجیرۀ تأمین سبز حلقه‏ بسته همراه با تصمیم‌های مالی در شرایط عدم قطعیت. مدیریت صنعتی، 10(1)، 61-84.
موسوی، مهسا؛ جمالی، غلامرضا؛ قربان پور، احمد (1400). ارائه مدل بهینه‌سازی شبکه زنجیرۀ تأمین سبز- تاب‌آوردر صنایع سیمان، مدیریت صنعتی، 13(2)، 222- 245.
یوسفی زنوز، رضا؛ حقیقی راد، فرزاد؛ ذاکری تبار، سجاد (1400). طراحی شبکه زنجیرۀ تأمین حلقه بسته در فضای عدم قطعیت. فصلنامه مدیریت راهبردی در سیستم های صنعتی، 15(54)، 197-218.‎
 
References
Agarwal, R., & Ergun, Ö. (2008). Mechanism design for a multicommodity flow game in service network alliances. Operations Research Letters, 36(5), 520-524.
Aloui, A., Hamani, N., Derrouiche, R., & Delahoche, L. (2021). Assessing the benefits of horizontal collaboration using an integrated planning model for two-echelon energy efficiency-oriented logistics networks design. International Journal of Systems Science: Operations & Logistics, 1-22.
Arslan, O., Archetti, C., Jabali, O., Laporte, G., & Speranza, M. G. (2020). Minimum cost network design in strategic alliances. Omega, 96, 102079.
Azad, N., & Hassini, E. (2019). A benders decomposition method for designing reliable supply chain networks accounting for multi mitigation strategies and demand losses, Transportation Science, 53(5), 1287-1312.
Ballot, E., & Fontane, F. (2010). Reducing transportation CO2 emissions through pooling of supply networks: perspectives from a case study in French retail chains. Production Planning & Control, 21(6), 640-650.
Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems, Numerische mathematic, 4(1), 238-252.
Chiesa, V. (2001). R&D strategy & organisation: Managing technical change in dynamic contexts. World Scientific.
Das, T. K., & Teng, B. S. (1998). Resource and risk management in the strategic alliance making process. Journal of management, 24(1), 21-42.
Das, T. K., & Teng, B. S. (2000). A resource-based theory of strategic alliances. Journal of management, 26(1), 31-61.
Difrancesco, R. M., Meena, P., & Tibrewala, R. (2021). Buyback and risk-sharing contracts to mitigate the supply and demand disruption risks. European Journal of Industrial Engineering, 15(4), 550-581.
Dogan, K., & Goetschalckx, M. (1999). A primal decomposition method for the integrated design of multi-period production-distribution systems. Iie Transactions, 31(11), 1027-1036.
Ekhteari, M., Zandieh, M., Alam Tabriz, A., Rabia, M. (2018). Presenting a two-level planning model for a multi-stage supply chain with an emphasis on reliability in uncertainty. Industrial Management Journal, 11 (2), 117-206. (in Persian)
Foroozesh, N., Karimi, B., & Mousavi, S. M. (2022). Green-resilient supply chain network design for perishable products considering route risk and horizontal collaboration under robust interval-valued type-2 fuzzy uncertainty: A case study in food industry. Journal of Environmental Management, 307, 114470.
Groothedde, B., Ruijgrok, C., & Tavasszy, L. (2005). Towards collaborative, intermodal hub networks: A case study in the fast moving consumer goods market. Transportation Research Part E: Logistics and Transportation Review, 41(6), 567-583.
Guo, Y., Yu, J., Allaoui, H., & Choudhary, A. (2022). Lateral collaboration with cost-sharing in sustainable supply chain optimisation: A combinatorial framework. Transportation Research Part E: Logistics and Transportation Review, 157, 102593
Habibi, M. K., Allaoui, H., & Goncalves, G. (2018). Collaborative hub location problem under cost uncertainty. Computers & Industrial Engineering, 124, 393-410.
He, Q., Meadows, M., Angwin, D., Gomes, E., & Child, J. (2020). Strategic alliance research in the era of digital transformation: Perspectives on future research. British Journal of Management, 31(3), 589-617.
Hosseini Dahshiri, S.J., Amiri, M., Olfat, L. & Pishvaee, M. (2022). A new robust fuzzy programming approach to design a closed-loop supply chain network. Industrial Management Journal, 14(3), 421-457. (in Persian)
Houghtalen, L., Ergun, Ö. & Sokol, J. (2011). Designing mechanisms for the management of carrier alliances. Transportation Science, 45(4), 465-482.
Jeihoonian, M., Zanjani, M. K., and Gendreau, M. (2016). Accelerating Benders decomposition for closed-loop supply chain network design: Case of used durable products with different quality levels. European Journal of Operational Research, 251(3), 830-845.
Kale, P., & Singh, H. (2007). Building Firm Capabilities through Learning: The Role of the Alliance Learning Process in Alliance Capability and Firm-Level Alliance Success. Strategic Management Journal, 28, 981-1000.
Khalili, S.M., Poya, A., Kazemi, M., Fakkur Thaqih, A.M.. (2022). Designing a sustaiable and resilient gasoline supply chain network under disruption uncertainty (case study: Gasoline supply chain network of Razavi Khorasan). Industrial Management Journal, 14(1), 27-79. (in Persian)
Krajewska, M. A., Kopfer, H., Laporte, G., Ropke, S., & Zaccour, G. (2008). Horizontal cooperation among freight carriers: request allocation and profit sharing. Journal of the Operational Research Society, 59(11), 1483-1491.
Kuyzu, G. (2017). Lane covering with partner bounds in collaborative truckload transportation procurement. Computers & Operations Research, 77, 32-43.
Liu, K., Zhou, Y., & Zhang, Z. (2010). Capacitated location model with online demand pooling in a multi-channel supply chain. European journal of operational research, 207(1), 218-231.
Mafini, C., & Muposhi, A. (2017). Predictive analytics for supply chain collaboration, risk management and financial performance in small to medium enterprises. Southern African Business Review, 21(1), 311-338.
Mardan, E., Govindan, K., Mina, H., and Gholami-Zanjani, S. M. (2019). An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem, Journal of cleaner production, 235, 1499-1514.
Masrurul, M. (2012). An overview of strategic alliance: competitive advantages in alliance constellations. Advances in management, 5, 12.
Moghaddam, M., & Nof, S. Y. (2016). Real-time optimization and control mechanisms for collaborative demand and capacity sharing. International Journal of Production Economics, 171, 495-506.
Mohammadi, A., Alam Tabriz, A. & Pishvaee, M. (2018). Designing a closed-loop green supply chain network with financial decisions under uncertainty. Industrial Management Journal, 10(1), 61-84. (in Persian)
Mousavi, M., Jamali, Gh., Gurbanpour, A. (2021). Presenting the optimization model of green-resilient supply chain cement industries, Industrial Management Journal, 13(2), 222-245. (in Persian)
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.
Oh, S. C., Min, H., & Ahn, Y. H. (2021). Inventory risk pooling strategy for the food distribution network in Korea. European Journal of Industrial Engineering, 15(4), 439-462.
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.
Pan, S., Ballot, E., Fontane, F., & Hakimi, D. (2014). Environmental and economic issues arising from the pooling of SMEs’ supply chains: case study of the food industry in western France. Flexible Services and Manufacturing Journal, 26(1), 92-118.
Philsoophian, M., Akhavan, P., & Abbasi, M. (2021). Strategic alliance for resilience in supply chain: A bibliometric analysis. Sustainability, 13(22), 12715.
Rajabion, L., Mokhtari, A. S., Khordehbinan, M. W., Zare, M., & Hassani, A. (2019). The role of knowledge sharing in supply chain success: Literature review, classification and current trends. Journal of Engineering, Design and Technology, 17(6), 1222-1249.
Ryan-Charleton, T., Gnyawali, D. R., & Oliveira, N. (2022). Strategic alliance outcomes: Consolidation and new directions. Academy of Management Annals, 16(2), 719-758.
Saeed, N. (2013). Cooperation among freight forwarders: Mode choice and intermodal freight transport. Research in Transportation Economics, 42(1), 77-86.
Saffari, H., Makui, A., Mahmoodian, V., & Pishvaee, M. S. (2015). Multi-objective robust optimization model for social responsible closed-loop supply chain solved by non-dominated sorting genetic algorithm. Journal of Industrial and Systems Engineering, 8(3), 42-58.
Saharidis, G. K., Boile, M., and Theofanis, S. (2011). Initialization of the Benders master problem using valid inequalities applied to fixed-charge network problems, Expert Systems with Applications, 38) 6(, 6627-6636.
Sambasivan, M., Siew-Phaik, L., Mohamed, Z. A., & Leong, Y. C. (2013). Factors influencing strategic alliance outcomes in a manufacturing supply chain: role of alliance motives, interdependence, asset specificity and relational capital. International Journal of Production Economics, 141(1), 339-351.
Setyadi, T. (2022). Strategic alliances, competitive advantages, and bandwagon effect in the perum perhutani wood industry. International Journal of Science and Environment (IJSE), 2(2), 47-53.
Shahbaz, M. S., Sohu, S., Khaskhelly, F. Z., Bano, A., & Soomro, M. A. (2019). A novel classification of supply chain risks. Engineering, Technology & Applied Science Research, 9(3), 4301-4305.
Siboyeh, A., Azar, A. & Zandiyeh, M. (2022). Presenting a robust two-stage probabilistic model for the design of a resilient blood supply chain considering earthquake and infectious disease disruption. Industrial Management Journal, 13(4), 703-664. (in Persian)
Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990-4012.
Sugiono, A., Rahayu, A., & Wibowo, L. A. (2022). Environmental ncertainty factor, incoterm and implication for a strategic alliance in freight forwarder companies’ case study in Indonesia. Asian Journal of Logistics Management, 1(1), 1-15.
Tang, X., Lehuédé, F., & Péton, O. (2016). Location of distribution centers in a multi-period collaborative distribution network. Electronic Notes in Discrete Mathematics, 52, 293-300.
Üster, H., Easwaran, G., Akçali, E., and Çetinkaya, S. (2007). Benders decomposition with alternative multiple cuts for a multi-product closed-loop supply chain network design model. Naval research logistics (NRL), 54 (8), 890-907.
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.
Verdonck, L., Caris, A. N., Ramaekers, K., & Janssens, G. K. (2013). Collaborative logistics from the perspective of road transportation companies. Transport Reviews, 33(6), 700-719.
Wang, Y., Zhang, S., Guan, X., Peng, S., Wang, H., Liu, Y., & Xu, M. (2020). Collaborative multi-depot logistics network design with time window assignment. Expert Systems with Applications, 140, 112910.
Williams, L. R., Esper, T. L., & Ozment, J. (2022). The electronic supply chain: Its impact on the current and future structure of strategic alliances, partnerships and logistics leadership. International Journal of Physical Distribution & Logistics Management, 32(8), 703-719.
Yousefi Zenouz, R., Haghigi Rad, F., Zakari Tabar, S. (2021). Designing a closed-loop supply chain network in uncertainty. Journal of Strategic Management in Industrial Systems, 15(54), 197-218. (in Persian)
مدیریت صنعتی
دوره 15، شماره 1
1402
صفحه 112-149

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