A Location-Inventory-Pricing Supply Chain Network Design for Perishable Products Under Disruptions

Document Type : Research Paper


School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran


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.


Main Subjects

Article Title [فارسی]

ارائه یک مدل مکان‌یابی- موجودی ‌-قیمت‌گذاری ‌برای ‌شبکه ‌زنجیره‌ تامین ‌مواد ‌فسادپذیر با در نظر گرفتن امکان‌ بروز حوادث

Authors [فارسی]

  • طوبی اصغری
  • سروش آقامحمدی‌بوسجین
  • مسعود ربانی
دانشکده مهندسی صنایع،پردیس دانشکده های فنی، دانشگاه تهران
Abstract [فارسی]

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.

Keywords [فارسی]

  • مکان‌یابی- مجودی- قیمت‌گذاری
  • فسادپذیری
  • فراابتکاری
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 Research56, 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 Research271(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 Engineering101, 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 Research266(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 Engineering90, 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: Methodological45(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 Letters40(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 Research59(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 Engineering119, 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 Research52(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 Engineering95, 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 Studies11(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 Engineering108, 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 Research53(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 Studies10(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 Research22(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 Engineering69, 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 Research101, 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 Systems42, 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 Research65, 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 Engineering13(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 Transportation2019. 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 Studies10(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 Computation6(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 Studies8(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 Research259(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 Economics18(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 Engineering109, 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 Software105, 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 Management19(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 Computation217(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 Engineering31(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 Engineering109, 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 Review83, 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: Methodological83, 151-178. https://doi.org/10.1016/j.trb.2015.11.009
 [H1]City, Country: Publisher