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A Location-Inventory-Pricing Supply Chain Network Design for Perishable Products Under Disruptions | ||
Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
دوره 14، شماره 3، مهر 2021، صفحه 487-507 اصل مقاله (1 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22059/ijms.2020.287086.673754 | ||
نویسندگان | ||
Tooba Asghari؛ Soroush Aghamohamadi-Bosjin؛ Masoud Rabbani* | ||
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
چکیده | ||
In this study, we discuss a location-inventory-pricing model considering the capacity constraints of the warehouses, disruption, and multiple perishable products. We extend a model that assumes that warehouses may face disruption, failed warehouses cannot cover any service, and their customers are assigned to other warehouses. To decrease the risk of disruption, we examine the efficiency of markup pricing strategy and support services. The objective function of this MINLP is to maximize the total profit of warehouses. To solve this model, Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA) are used. To evaluate the recommended model, several sensitivity analyses are proposed. Finally, the results of numerical experiments implicate the high-performance of GOA in dealing with problems and achieving better results. According to the results, backup services and markup pricing strategies are very effective in reducing the damage caused by the disruption. | ||
کلیدواژهها | ||
Location-inventory؛ Perishability؛ Markup pricing؛ Disruption؛ Meta-heuristic algorithms | ||
عنوان مقاله [English] | ||
ارائه یک مدل مکانیابی- موجودی -قیمتگذاری برای شبکه زنجیره تامین مواد فسادپذیر با در نظر گرفتن امکان بروز حوادث | ||
نویسندگان [English] | ||
طوبی اصغری؛ سروش آقامحمدیبوسجین؛ مسعود ربانی | ||
دانشکده مهندسی صنایع،پردیس دانشکده های فنی، دانشگاه تهران | ||
چکیده [English] | ||
این پژوهش به بررسی یک مدل مکانیابی- مجودی- قیمتگذاری زنجیره تامین با در نظر گرفتن محدودیت ظرفیت انبارش و امکان خرابی مراکز انبارش به دلیل بروز حوادث و همچنین چند نوع کالای فسادپذیر، میپردازد. در این مدل فرض برآنست که با بروز حادثه، مرکز انبارش کاملا خراب میشود و مشتریان مربوط به مرکز خراب شده به دیگر مراکز تخصیص داده میشوند. بنابراین، برای کاهش ریسک موجود در مدل از استراتژیهای جایگزین از جمله استراتژی قیمت گذاری و سیستمهای سرویس دهی پشتیبان، استفاده شده است. در این مدل از روشهای فراابتکاری برای حل کردن مدل، اسفاده شده است و کارایی روشها با هم مقایسه شده است. در مرحله بعدی، جهت بررسی رفتار مدل، بر روی پارامترهای اساسی مدل تحلیل حساسیت اعمال گردیده است. | ||
کلیدواژهها [English] | ||
مکانیابی- مجودی- قیمتگذاری, فسادپذیری, فراابتکاری | ||
مراجع | ||
Asasi, M. S., Ahanch, M., & Amiri, M. S. (2017, December). A Grasshopper Optimization Algorithm to solve Optimal Distrbution System Reconfiguration and Distributed Generation Placement Problem. In 2017S IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI) Dec (Vol. 22). Tehran, Iran, pp. 0659-0666…[H1] https://doi.org/10.1109/KBEI.2017.8324880 Ahmadi-Javid, A., & Hoseinpour, P. (2015a). A location-inventory-pricing model in a supply chain distribution network with price-sensitive demands and inventory-capacity constraints. Transportation Research Part E: Logistics and Transportation Review, 82, 238-255. https://doi.org/10.1016/j.tre.2015.06.010 Ahmadi-Javid, A., & Hoseinpour, P. (2015b). Incorporating location, inventory and price decisions into a supply chain distribution network design problem. Computers & Operations Research, 56, 110-119. https://doi.org/10.1016/j.cor.2014.07.014 Ahmadi-Javid, A., Amiri, E., & Meskar, M. (2018). A profit-maximization location-routing-pricing problem: A branch-and-price algorithm. European Journal of Operational Research, 271(3), 866-881. https://doi.org/10.1016/j.ejor.2018.02.020 Ahmadzadeh, E., & Vahdani, B. (2017). A location-inventory-pricing model in a closed loop supply chain network with correlated demands and shortages under a periodic review system. Computers & Chemical Engineering, 101, 148-166. https://doi.org/10.1016/j.compchemeng.2017.02.027 Amiri-Aref, M., Klibi, W., & Babai, M. Z. (2018). The multi-sourcing location inventory problem with stochastic demand. European Journal of Operational Research, 266(1), 72-87. https://doi.org/10.1016/j.ejor.2017.09.003 Asl-Najafi, J., Zahiri, B., Bozorgi-Amiri, A., & Taheri-Moghaddam, A. (2015). A dynamic closed-loop location-inventory problem under disruption risk. Computers & Industrial Engineering, 90, 414-428. https://doi.org/10.1016/j.cie.2015.10.012 Chen, Q., Li, X., & Ouyang, Y. (2011). Joint inventory-location problem under the risk of probabilistic facility disruptions. Transportation Research Part B: Methodological, 45(7), 991-1003. https://doi.org/10.1016/j.trb.2011.04.004 Chen, X., & Hu, P. (2012). Joint pricing and inventory management with deterministic demand and costly price adjustment. Operations Research Letters, 40(5), 385-389. https://doi.org/10.1016/j.orl.2012.05.011 Chen, X., Zhou, S. X., & Chen, Y. (2011). Integration of inventory and pricing decisions with costly price adjustments. Operations Research, 59(5), 1144-1158. https://doi.org/10.1287/opre.1110.0946 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. https://doi.org/10.1016/j.cie.2018.04.007 Dehghani, E., Pishvaee, M. S., & Jabalameli, M. S. (2018). A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions. RAIRO-Operations Research, 52(4), 1147-1173. https://doi.org/10.1051/ro/2018012 Etebari, F., & Dabiri, N. (2016). A hybrid heuristic for the inventory routing problem under dynamic regional pricing. Computers & Chemical Engineering, 95, 231-239. https://doi.org/10.1016/j.compchemeng.2016.09.018 Fahimi, K., Seyedhosseini, S. M., & Makui, A. (2018). Dynamic competitive supply chain network design with price dependent demand and Huff utility function. Iranian Journal of Management Studies, 11(2), 271-305. https://doi.org/10.22059/IJMS.2018.241299.672813 Farahani, M., Shavandi, H., & Rahmani, D. (2017). A location-inventory model considering a strategy to mitigate disruption risk in supply chain by substitutable products. Computers & Industrial Engineering, 108, 213-224. https://doi.org/doi.org/10.1016/j.cie.2017.04.032 Farahani, R. Z., Rashidi Bajgan, H., Fahimnia, B., & Kaviani, M. (2015). Location-inventory problem in supply chains: A modelling review. International Journal of Production Research, 53(12), 3769-3788. https://doi.org/10.1080/00207543.2014.988889 Ghasemy Yaghin, R., Fatemi Ghomi, S. M. T., & Torabi, S. A. (2017). Incorporating return on inventory investment into joint lot-sizing and price discriminating decisions: A fuzzy chance constraint programming model. Iranian Journal of Management Studies, 10(4), 929-959. https://doi.org/10.22059/IJMS.2017.230829.672615 Guerrero, W. J., Prodhon, C., Velasco, N., & Amaya, C. A. (2015). A relax‐and‐price heuristic for the inventory‐location‐routing problem. International Transactions in Operational Research, 22(1), 129-148. https://doi.org/10.1111/itor.12091 Gzara, F., Nematollahi, E., & Dasci, A. (2014). Linear location-inventory models for service parts logistics network design. Computers & Industrial Engineering, 69, 53-63. https://doi.org/10.1016/j.cie.2013.12.014 Hamdan, B., & Diabat, A. (2019). A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research, 101, 130-143. https://doi.org/10.1016/j.cor.2018.09.001 Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of Manufacturing Systems, 42, 93-103. https://doi.org/10.1016/j.jmsy.2016.10.004 Kaya, O., & Urek, B. (2016). A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research, 65, 93-103. https://doi.org/10.1016/j.cor.2015.07.005 Kuhnle, A., & Lanza, G. (2019). Investigation of closed-loop supply chains with product refurbishment as integrated location-inventory problem. Production Engineering, 13(3-4), 293-303. https://doi.org/10.1007/s11740-019-00885-4 Li, Z., & Hai, J. (2019). A capacitated location-inventory model with demand selection. Journal of Advanced Transportation, 2019. https://doi.org/10.1155/2019/2143042 Nemati, Y., Madhoushi, M., & Safaei Ghadikolaei, A. (2017). Towards supply chain planning integration: Uncertainty analysis using fuzzy mathematical programming approach in a plastic forming company. Iranian Journal of Management Studies, 10(2), 335-364. https://doi.org/10.22059/IJMS.2017.218842.672334 Neve, A. G., Kakandikar, G. M., & Kulkarni, O. (2017). Application of grasshopper optimization algorithm for constrained and unconstrained test functions. International Journal of Swarm Intelligence and Evolutionary Computation, 6(3), 1-7. https://doi.org/10.4172/2090-4908.1000165 Orand, S. M., Mirzazadeh, A., Ahmadzadeh, F., & Talebloo, F. (2015). Optimization of the inflationary inventory control model under stochastic conditions with Simpson approximation: Particle swarm optimization approach. Iranian Journal of Management Studies, 8(2), 203-220. https://doi.org/10.22059/IJMS.2015.52631 Puga, M. S., & Tancrez, J. S. (2017). A heuristic algorithm for solving large location–inventory problems with demand uncertainty. European Journal of Operational Research, 259(2), 413-423. https://doi.org/10.1016/j.ejor.2016.10.037 Punyim, P., Karoonsoontawong, A., Unnikrishnan, A., & Xie, C. (2018). Tabu search heuristic for joint location-inventory problem with stochastic inventory capacity and practicality constraints. Networks and Spatial Economics, 18(1), 51-84. https://doi.org/10.1007/s11067-017-9357-y Rafie-Majd, Z., Pasandideh, S. H. R., & Naderi, B. (2018). Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Computers & Chemical Engineering, 109, 9-22. https://doi.org/10.1016/j.compchemeng.2017.10.013 Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software, 105, 30-47. https://doi.org/10.1016/j.advengsoft.2017.01.004 Saremi S., Mirjalili S., Mirjalili S., & Song Dong J. (2020). Grasshopper Optimization Algorithm: Theory, literature review, and application in hand posture estimation. In: Mirjalili S., Song Dong J., & Lewis A. (Eds.), Nature-inspired optimizers: Studies in computational intelligence, vol. 811 (pp. 107-122). Springer, Cham. https://doi.org/10.1007/978-3-030-12127-3_7 Smith, S. A., & Agrawal, N. (2017). Optimal markdown pricing and inventory allocation for retail chains with inventory dependent demand. Manufacturing & Service Operations Management, 19(2), 290-304. https://doi.org/10.1287/msom.2016.0609 Taleizadeh, A. A., Niaki, S. T. A., & Barzinpour, F. (2011). Multiple-buyer multiple-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead-time: A harmony search algorithm. Applied Mathematics and Computation, 217(22), 9234-9253. https://doi.org/10.1016/j.amc.2011.04.001 Tavakkoli-Moghaddam, R., Yadegari, M., & Ahmadi, G. (2018). Closed-loop supply chain inventory-location problem with spare parts in a multi-modal repair condition. International Journal of Engineering, 31(2), 346-356. https://doi.org/10.5829/ije.2018.31.02b.20 Vahdani, B., Soltani, M., Yazdani, M., & Mousavi, S. M. (2017). A three level joint location-inventory problem with correlated demand, shortages and periodic review system: Robust meta-heuristics. Computers & Industrial Engineering, 109, 113-129. https://doi.org/10.1016/j.cie.2017.04.041 Zhang, Y., Qi, M., Lin, W. H., & Miao, L. (2015). A metaheuristic approach to the reliable location routing problem under disruptions. Transportation Research Part E: Logistics and Transportation Review, 83, 90-110. https://doi.org/10.1016/j.tre.2015.09.001 Zhang, Y., Snyder, L. V., Qi, M., & Miao, L. (2016). A heterogeneous reliable location model with risk pooling under supply disruptions. Transportation Research Part B: Methodological, 83, 151-178. https://doi.org/10.1016/j.trb.2015.11.009 [H1]City, Country: Publisher | ||
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