In today’s systems and networks, disruption is inevitable. Designing a reliable system to overcome probable facility disruptions plays a crucial role in planning and management. This article proposes a reliable capacitated facility joint inventory-location problem where location-independent disruption may occur in facilities. The system tries to satisfy customer’s demands and considers penalty costs for unmet customer demand. The article aims to minimize total costs such as establishing inventory, uncovered demand’s penalty, and transportation costs. While many articles in this area only use exact methods to solve the problem, this article uses a metaheuristic algorithm, the red deer algorithm, and the exact methods. Various numerical examples have shown the outstanding performance of the red deer algorithm compared to exact methods. Sensitivity analyses show the impacts of various parameters on the objective function and the optimal facility layouts. Lastly, managerial insights will be proposed based on sensitivity analysis.
Keywords: Joint inventory-location problem, customers satisfaction, independent disruption, red deer algorithm, reliable capacitated facility location problem
@article{RO_2022__56_5_3311_0,
author = {Delivand, Alireza Asadi and Moghadam, Shayan Shafiee and Jolai, Soroush and Aghsami, Amir and Jolai, Fariborz},
title = {A meta heuristic approach for reliable capacitated facility joint inventory-location problem with round-trip transportation under imperfect information of disruption in a {Fuzzy} environment},
journal = {RAIRO. Operations Research},
pages = {3311--3339},
year = {2022},
publisher = {EDP-Sciences},
volume = {56},
number = {5},
doi = {10.1051/ro/2022110},
mrnumber = {4481128},
zbl = {1502.90019},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2022110/}
}
TY - JOUR AU - Delivand, Alireza Asadi AU - Moghadam, Shayan Shafiee AU - Jolai, Soroush AU - Aghsami, Amir AU - Jolai, Fariborz TI - A meta heuristic approach for reliable capacitated facility joint inventory-location problem with round-trip transportation under imperfect information of disruption in a Fuzzy environment JO - RAIRO. Operations Research PY - 2022 SP - 3311 EP - 3339 VL - 56 IS - 5 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2022110/ DO - 10.1051/ro/2022110 LA - en ID - RO_2022__56_5_3311_0 ER -
%0 Journal Article %A Delivand, Alireza Asadi %A Moghadam, Shayan Shafiee %A Jolai, Soroush %A Aghsami, Amir %A Jolai, Fariborz %T A meta heuristic approach for reliable capacitated facility joint inventory-location problem with round-trip transportation under imperfect information of disruption in a Fuzzy environment %J RAIRO. Operations Research %D 2022 %P 3311-3339 %V 56 %N 5 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2022110/ %R 10.1051/ro/2022110 %G en %F RO_2022__56_5_3311_0
Delivand, Alireza Asadi; Moghadam, Shayan Shafiee; Jolai, Soroush; Aghsami, Amir; Jolai, Fariborz. A meta heuristic approach for reliable capacitated facility joint inventory-location problem with round-trip transportation under imperfect information of disruption in a Fuzzy environment. RAIRO. Operations Research, Tome 56 (2022) no. 5, pp. 3311-3339. doi: 10.1051/ro/2022110
[1] , and , Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters. Soc.-Econ. Planning Sci. 74 (2020) 100933. | DOI
[2] , and , An efficient approach for solving reliable facility location models. INFORMS J. Comput. 25 (2013) 720–729. | MR | DOI
[3] , and , A novel Markovian queueing-inventory model with imperfect production and inspection processes: a hospital case study. Comput. Ind. Eng. 162 (2021) 107772. | DOI
[4] , and , An integrated Markovian queueing-inventory model in a single retailer-single supplier problem with imperfect quality and destructive testing acceptance sampling. Adv. Ind. Eng. 55 (2021) 367–401.
[5] , Improved approximation of the general soft-capacitated facility location problem. RAIRO: Oper. Res. 41 (2007) 83–93. | MR | Zbl | Numdam | DOI
[6] , , and , Reliable -median facility location problem: two-stage robust models and algorithms. Transp. Res. Part B: Methodol. 64 (2014) 54–72. | DOI
[7] , , and , A dynamic closed-loop location-inventory problem under disruption risk. Comput. Ind. Eng. 90 (2015) 414–428. | DOI
[8] , and , An integrated FTA-DFMEA approach for reliability analysis and product configuration considering warranty cost. Prod. Eng. 9 (2015) 635–646. | DOI
[9] and , An optimal put option contract for a reverse supply chain: case of remanufacturing capacity uncertainty. Ann. Oper. Res. (2021) 1–24. DOI: . | DOI | MR | Zbl
[10] and , Decision-making in a fuzzy environment. Manage. Sci. 17 (1970) B-141. | MR | Zbl | DOI
[11] , , , and , Dynamic model for system-level strategic intermodal facility investment planning. Transp. Res. Record 2548 (2016) 24–34. | DOI
[12] , and , Location and reliability problems on a line: impact of objectives and correlated failures on optimal location patterns. Omega 41 (2013) 766–779. | DOI
[13] , and , Maximum coverage capacitated facility location problem with range constrained drones. Transp. Res. Part C: Emerg. Technol. 99 (2019) 1–18. | DOI
[14] , and , Joint inventory-location problem under the risk of probabilistic facility disruptions. Transp. Res. Part B: Methodol. 45 (2011) 991–1003. | DOI
[15] , and , Reliable facility location design under the risk of disruptions. Oper. Res. 58 (2010) 998–1011. | MR | Zbl | DOI
[16] , and , An inventory-location model: formulation, solution algorithm and computational results. Ann. Oper. Res. 110 (2002) 83–106. | MR | Zbl | DOI
[17] , , and , Green supply chain management through call option contract and revenue-sharing contract to cope with demand uncertainty. Cleaner Logistics Supply Chain 2 (2021) 100010. | DOI
[18] , and , Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput. 24 (2020) 14637–14665. | DOI
[19] and , Genetic Algorithm and Engineering Design. John Wiley & Sons, Inc., New York (1997).
[20] and , Contracts between an e-retailer and a third party logistics provider to expand home delivery capacity. Comput. Ind. Eng. 163 (2022) 107763. | DOI
[21] , and , A genetic algorithm approach for location-inventory-routing problem with perishable products. J. Manuf. Syst. 42 (2017) 93–103. | DOI
[22] and , Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets Syst. 111 (2000) 3–28. | MR | Zbl | DOI
[23] , , and , Optimizing a bi-objective reliable facility location problem with adapted stochastic measures using tuned-parameter multi-objective algorithms. Knowl.-Based Syst. 95 (2016) 45–57. | DOI
[24] , , and , Solving the simple plant location problem by genetic algorithm. RAIRO: Oper. Res. 35 (2001) 127–142. | MR | Zbl | Numdam | DOI
[25] and , A continuum approximation approach to reliable facility location design under correlated probabilistic disruptions. Transp. Res. Part B: Methodol. 44 (2010) 535–548. | DOI
[26] , and , A supporting station model for reliable infrastructure location design under interdependent disruptions. Transp. Res. Part E: Logistics Transp. Rev. 60 (2013) 80–93. | DOI
[27] , , and , Facility location decisions with random disruptions and imperfect estimation. Manuf. Serv. Oper. Manage. 15 (2013) 239–249. | DOI
[28] and , Locating multiple types of charging facilities for battery electric vehicles. Transp. Res. Part B: Methodol. 103 (2017) 30–55. | DOI
[29] , , , and , A coordinated location-inventory problem with supply disruptions: a two-phase queuing theory–optimization model approach. Comput. Ind. Eng. 142 (2020) 106326. | DOI
[30] , , and , Testing facility location and dynamic capacity planning for pandemics with demand uncertainty. Eur. J. Oper. Res. (2021). DOI: . | DOI | MR | Zbl
[31] , and , Reliable facility location design under uncertain correlated disruptions. Manuf. Serv. Oper. Manage. 17 (2015) 445–455. | DOI
[32] , , , and , Multi-stakeholders’ assessment of bike sharing service quality based on DEMATEL–VIKOR method. Int. J. Logistics Res. App. 22 (2019) 449–472. | DOI
[33] and , Designing a reliable and dynamic multimodal transportation network for biofuel supply chains. Transp. Sci. 51 (2017) 494–517. | DOI
[34] , and , A hybrid NSGA-II algorithm for the closed-loop supply chain network design in e-commerce. RAIRO: Oper. Res. 55 (2021) 1643. | MR | Numdam | DOI
[35] , and , Reliable single-allocation hub location problem with disruptions. Transp. Res. Part E: Logistics Transp. Rev. 123 (2019) 90–120. | DOI
[36] , , and , Reliable logistics networks design with facility disruptions. Transp. Res. Part B: Methodol. 45 (2011) 1190–1211. | DOI
[37] , and , Approximation of discrete spatial data for continuous facility location design. Integr. Comput.-Aided Eng. 21 (2014) 311–320. | DOI
[38] , , , and , Optimizing a bi-objective location-allocation-inventory problem in a dual-channel supply chain network with stochastic demands. RAIRO: Oper. Res. 55 (2021) 3245–3279. | MR | Zbl | Numdam | DOI
[39] and , Firm innovation and supply chain resilience: a dynamic capability perspective. Int. J. Logistics Res. App. 23 (2020) 254–269. | DOI
[40] , , , , and , Off-site construction three-echelon supply chain management with stochastic constraints: a modelling approach. Buildings 12 (2022) 119. | DOI
[41] and , Reliable capacitated facility location problem with service levels. EURO J. Transp. Logistics 7 (2018) 315–341. | DOI
[42] , and , The reliable facility location problem: formulations, heuristics, and approximation algorithms. INFORMS J. Comput. 23 (2011) 470–482. | MR | Zbl | DOI
[43] , and , A joint location-inventory model. Transp. Sci. 37 (2003) 40–55. | DOI
[44] , and , Facility location-network design problem: reliability and investment budget constraint. J. Urban Planning Dev. 140 (2014) 04014005. | DOI
[45] and , Reliability models for facility location: the expected failure cost case. Transp. Sci. 39 (2005) 400–416. | DOI
[46] , , , , and , OR/MS models for supply chain disruptions: a review. IIE Trans. 48 (2016) 89–109. | DOI
[47] , , , , , and , A stakeholder oriented approach to the optimization of transports of people with disabilities. In: Supply Chain Forum: An International Journal. Vol. 21. Taylor & Francis (2020) 93–102. | DOI
[48] , and , Reliable hub network design: formulation and solution techniques. Transp. Sci. 51 (2017) 358–375. | DOI
[49] , Uncapacitated and capacitated facility location problems. In: Foundations of Location Analysis. Springer, New York, NY (2011) 25–37. | Zbl | DOI
[50] , and , EOQ formula when inventory cost is fuzzy. Int. J. Prod. Econ. 45 (1996) 499–504. | DOI
[51] , and , Reliable location-routing design under probabilistic facility disruptions. Transp. Sci. 50 (2016) 1128–1138. | DOI
[52] , and , Planning facility location under generally correlated facility disruptions: use of supporting stations and quasi-probabilities. Transp. Res. Part B: Methodol. 122 (2019) 115–139. | DOI
[53] , , , , and , A reliability model for facility location design under imperfect information. Transp. Res. Part B: Methodol. 81 (2015) 596–615. | DOI
[54] , , and , A reliable facility location design model with site-dependent disruption in the imperfect information context. PloS One 12 (2017) e0177104. | DOI
[55] , and , Reliable facility location design with round-trip transportation under imperfect information part II: a continuous model. Transp. Res. Part B: Methodol. 124 (2019) 44–59. | DOI
[56] , , and , Reliable facility location design with round-trip transportation under imperfect information Part I: a discrete model. Transp. Res. Part E: Logistics Transp. Rev. 133 (2020) 101825. | DOI
[57] , , and , A heterogeneous reliable location model with risk pooling under supply disruptions. Transp. Res. Part B: Methodol. 83 (2016) 151–178. | DOI
[58] , , and , Traffic equilibrium and charging facility locations for electric vehicles. Networks Spatial Econ. 17 (2017) 435–457. | Zbl | DOI | MR
[59] and , Customer satisfaction of bicycle sharing: studying perceived service quality with SEM model. Int. J. Logistics Res. App. 22 (2019) 437–448. | DOI
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