Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method
Abstract
:1. Introduction
2. Previous Review Papers
3. Research Methodology
3.1. Data Collection Methods
- What is the classification of LIRP based on the type of problem and the characteristics of variables?
- What are the model-based solutions for LIRP?
- What are the current trends of LIRP?
- What are the challenges and future work directions of LIRP in real-world applications?
- Papers retrieved must be published in proceedings of peer-reviewed international journals or international conferences in the English language.
- The topic must include (but is not limited to) at least one paper on location inventory path, covering logistics location, inventory decision, and route optimization decision.
- The article must have at least one essential attribute related to the title, keywords, abstract, and body content mentioned previously.
3.2. Descriptive Statistics
3.3. Category Classification
4. Review of Literature
4.1. Classification based on Problem Characteristics
4.1.1. LIRP Classification Based on Inventory Periodicity and Quantity of Productions
Single-Period Single-Product Problem (SPSPP)
Single-Period Multi-Product Problem (SPMPP)
Multi-Period Single-Product Problem (MPSPP)
Multi-Period Multi-Product Problem (MPMPP)
4.1.2. LIRP Classification Based on Echelons and Links
Single-Echelon Single-Link Problem (SESLP)
Multi-Echelon Single-Link Problem (MESLP)
Multi-Echelon Multi-Link Problem (MEMLP)
4.1.3. LIRP Classification Based on Number of Depots and Retailers
Single-Depot Multi-Retailer Problem (SDMRP)
Multi-Depot Multi-Retailer Problem (MDMRP)
4.2. Classification Based on Demand Data Types
4.3. Classification Based on Models and Solutions
- Solution Methods of MIP Model
- Solution Methods of MILP Model
- Solution Methods of MINLP Model
4.4. Classification Based on Applications
5. Current Trends
5.1. Descriptive Analysis
5.2. Overall Observations
6. Challenges and Future Work
6.1. LIRP with Multi-Period and Multi-Stage
6.2. LIRP with Multi-Echelon Multi-Link
6.3. LIRP with Green Closed-Loop Supply Chain
6.4. LIRP with Time Window and Shortage Allowance
6.5. LIRP with Out of Stock Inventory and Transport Disruption during COVID-19
6.6. LIRP in an Uncertain Environment
6.7. LIRP with Modern Heuristic Algorithms
6.8. LIRP in Healthcare Logistics
7. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
IRP | Inventory-routing problem |
LIP | Location-inventory problem |
LRP | Location-routing problem |
LIRP | Location-inventory-routing problem |
MIP | Mixed integer programming |
MILP | Mixed integer linear programming |
MINLP | Mixed integer nonlinear programming |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analysis |
CLSC | Closed-loop supply chain |
GSCM | Green supply-chain management |
TSCC | Total supply-chain cost |
ECLS | E-commerce logistics system |
HSC | Hazmat supply chain |
PPLN | Perishable-products logistics network |
CCLN | Cold-chain logistics network |
ESSC | Environmentally sustainable supply chain |
HUSC | Humanitarian supply chain |
HEL | Healthcare logistics |
MOP | Multi-objective programming |
CP | Capacity planning |
CS | Customer satisfaction |
DC | Distribution center |
RL | Reverse logistics |
MS | Multi-stages |
SC | Shortage cost |
TC | Transportation cost |
TP | Total profit |
TW | Time window |
CEEI | CO2 emission and environmental impacts |
HOFV | Homogeneous fleet of vehicles |
HEFV | Heterogeneous fleet of vehicles |
ICRP | Inventory control replenishment policy |
PSI | Positive social impacts |
SCR | Supply-chain risks |
TTD | Total traversed distance |
SPSPP | Single-period single-product problem |
SPMPP | Single-period multi-product problem |
MPSPP | Multi-period single-product problem |
MPMPP | Multi-period multi-product problem |
SESLP | Single-echelon single-link problem |
MESLP | Multi-echelon single-link problem |
MEMLP | Multi-echelon multi-link problem |
SDMRP | Single-depot multi-retailer problem |
MDMRP | Multi-depot multi-retailer problem |
References
- Archetti, C.; Bertazzi, L.; Laporte, G.; Speranza, M.G. A branch-and-cut algorithm for a vendor-managed inventory-routing problem. Transp. Sci. 2007, 41, 382–391. [Google Scholar] [CrossRef]
- Salhi, S.; Rand, G.K. The effect of ignoring routes when locating depots. Eur. J. Oper. Res. 1989, 39, 150–156. [Google Scholar] [CrossRef]
- Chen, Q.; Li, X.; Ouyang, Y. Joint inventory–location problem under the risk of probabilistic facility disruptions. Transp. Res. Part Methodol. 2011, 45, 991–1003. [Google Scholar] [CrossRef]
- Ahmadi Javid, A.; Azad, N. Incorporating location, routing and inventory decisions in supply chain network design. Transp. Res. Part Logist. Transp. Rev. 2010, 46, 582–597. [Google Scholar] [CrossRef]
- Li, Y.; Guo, H.; Wang, L.; Fu, J. A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under e-supply chain environment. Sci. World J. 2013, 2013, 125893. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yuchi, Q.; Wang, N.; He, Z.; Chen, H. Hybrid heuristic for the location-inventory-routing problem in closed-loop supply chain. Int. Trans. Oper. Res. 2021, 28, 1265–1295. [Google Scholar] [CrossRef]
- Perl, J.; Sirisoponsilp, S. Distribution networks: Facility location, transportation and inventory. Int. J. Phys. Distrib. Logist. Manag. 1988, 18, 18–26. [Google Scholar] [CrossRef]
- Jayaraman, V. Transportation, facility location and inventory issues in distribution network design. Int. J. Oper. Prod. Manag. 1998, 18, 471–494. [Google Scholar] [CrossRef]
- Nozick, L.K.; Turnquist, M.A. Integrating inventory impacts into a fixed-charge model for locating distribution centers. Transp. Res. Part E Logist. Transp. Rev. 1998, 34, 173–186. [Google Scholar] [CrossRef]
- Nozick, L.K.; Turnquist, M.A. Inventory, transportation, service quality and the location of distribution centers. Eur. J. Oper. Res. 2001, 129, 362–371. [Google Scholar] [CrossRef]
- Ambrosino, D.; Scutellà, M. Distribution network design: New problems and related models. Eur. J. Oper. Res. 2005, 165, 610–624. [Google Scholar] [CrossRef]
- Liu, S.-C.; Lee, S.B. A two-phase heuristic method for the multi-depot location routing problem taking inventory control decisions into consideration. Int. J. Adv. Manuf. Technol. 2003, 22, 941–950. [Google Scholar] [CrossRef]
- Liu, S.-C.; Lin, C.C. A heuristic method for the combined location routing and inventory problem. Int. J. Adv. Manuf. Technol. 2005, 26, 372–381. [Google Scholar] [CrossRef]
- Farahani, R.Z.; Rashidi Bajgan, H.; Fahimnia, B.; Kaviani, M. Location-inventory problem in supply chains: A modelling review. Int. J. Prod. Res. 2014, 53, 3769–3788. [Google Scholar] [CrossRef]
- Drexl, M.; Schneider, M. A survey of variants and extensions of the location-routing problem. Eur. J. Oper. Res. 2014, 241, 283–308. [Google Scholar] [CrossRef]
- Mara, S.T.W.; Kuo, R.J.; Asih, A.M.S. Location-routing problem: A classification of recent research. Int. Trans. Oper. Res. 2021, 28, 2941–2983. [Google Scholar] [CrossRef]
- Coelho, L.C.; Cordeau, J.-F.; Laporte, G. Thirty years of inventory routing. Transp. Sci. 2013, 48, 472. [Google Scholar] [CrossRef] [Green Version]
- Roldán, R. Inventory routing problem with stochastic demand and lead time: State of the art. In Proceedings of the Joint Conference SOCO’14-CISIS’14, Advances in Intelligent Systems and Computing, Bilbao, Spain, 25–27 June 2014; Springer: Cham, Switzerland, 2014; pp. 1–10. [Google Scholar]
- Feshari, M.; Nazemi, A.; Sheikhtajian, S. A Library review study: Conceptual model for maritime inventory-routing problem. Int. J. Supply Oper. Manag. 2017, 4, 341–358. [Google Scholar]
- Roldán, R.F.; Basagoiti, R.; Coelho, L.C. A survey on the inventory-routing problem with stochastic lead times and demands. J. Appl. Log. 2017, 24, 15–24. [Google Scholar] [CrossRef]
- Malladi, K.T.; Sowlati, T. Sustainability aspects in inventory routing problem: A review of new trends in the literature. J. Clean. Prod. 2018, 197, 804–814. [Google Scholar] [CrossRef]
- Soysal, M.; Çimen, M.; Belbağ, S.; Toğrul, E. A review on sustainable inventory routing. Comput. Ind. Eng. 2019, 132, 395–411. [Google Scholar] [CrossRef]
- Cao, J. The inventory routing problem: A review. In Proceedings of the 20th COTA International Conference of Transportation Professionals, Xi’an, China, 14–16 August 2020; pp. 4488–4499. [Google Scholar]
- Thinakaran, N.; Jayaprakash, J.; Elanchezhian, C. Greedy algorithm for inventory routing problem in a supply chain—A review. Mater. Today Proc. 2019, 16, 1055–1060. [Google Scholar] [CrossRef]
- Harahap, A.Z.M.K. Deterministic inventory routing problem (DIRP): A literature review. Int. J. Supply Chain. Manag. 2017, 6, 284–288. [Google Scholar]
- Saragih, N.I.; Bahagia, S.N.; Syabri, I. A survey on location-routing-inventory problem. J. Adv. Res. Dyn. Control Syst. 2019, 11, 401–404. [Google Scholar]
- Page, M.J. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Syst. Rev. 2021, 10, 89. [Google Scholar] [CrossRef] [PubMed]
- Ting, K.H.; Lee, L.S.; Pickl, S.; Seow, H.-V. Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches. Appl. Sci. 2021, 11, 7996. [Google Scholar] [CrossRef]
- Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; The, P.G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, 339. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Neilson, A.; Indratmo Daniel, B.; Tjandra, S. Systematic review of the literature on big data in the transportation domain: Concepts and applications. Big Data Res. 2019, 17, 35–44. [Google Scholar] [CrossRef]
- De Armas, J.; Rodríguez-Pereira, J.; Vieira, B.; Ramalhinho, H. Optimizing assistive technology operations for aging populations. Sustainability 2021, 13, 6925. [Google Scholar] [CrossRef]
- Rayat, F.; Musavi, M.; Bozorgi-Amiri, A. Bi-objective reliable location-inventory-routing problem with partial backordering under disruption risks: A modified AMOSA approach. Appl. Soft Comput. 2017, 59, 622–643. [Google Scholar] [CrossRef]
- Daroudi, S.; Kazemipoor, H.; Najafi, E.; Fallah, M. The minimum latency in location routing fuzzy inventory problem for perishable multi-product materials. Appl. Soft Comput. 2021, 110, 107543. [Google Scholar] [CrossRef]
- Saragih, N.; Bahagia, S.; Suprayogi, S.; Syabri, I. Location-inventory-routing model with considering urban road networks. J. Ind. Eng. Manag. 2021, 14, 3557. [Google Scholar] [CrossRef]
- Song, Y.; Liu, Y.Q.; Sun, Q.; Chen, M.F.; Xu, H.T. A joint optimization model considering the product user’s risk preference for supply system disruption. Math. Probl. Eng. 2021, 2021, 5081753. [Google Scholar] [CrossRef]
- Ma, H.; Yang, X.; Zhang, D. The research into ILRIP for single-stage logistics distribution network under stochastic demand based on JITD. In Proceedings of the 7th International Conference on Service Systems and Service Management, ICSSSM’ 10, Tokyo, Japan, 28–30 June 2010; pp. 639–644. [Google Scholar]
- Hiassat, A.; Diabat, A. A location-inventory-routing-problem with perishable products. In Proceedings of the 41st International Conference on Computers and Industrial Engineering, Los Angeles, CA, USA, 23–25 October 2011; pp. 130–135. [Google Scholar]
- Liu, B.; Chen, H.; Li, Y.; Liu, X. A pseudo-parallel genetic algorithm integrating simulated annealing for stochastic location-inventory-routing problem with consideration of returns in e-commerce. Discret. Dyn. Nat. Soc. 2015, 2015, 586581. [Google Scholar] [CrossRef] [Green Version]
- Thi Phuong Nha, L.; Lee, T.-R. Model selection with considering the CO2 emission alone the global supply chain. J. Intell. Manuf. 2013, 24, 653–672. [Google Scholar]
- Sajjadi, S.R.; Cheraghi, S.H. Multi-products location routing problem integrated with inventory under stochastic demand. Int. J. Ind. Syst. Eng. 2011, 7, 454–476. [Google Scholar] [CrossRef]
- Guerrero, W.J.; Prodhon, C.; Velasco, N.; Amaya, C.A. Hybrid heuristic for the inventory location-routing problem with deterministic demand. Int. J. Prod. Econ. 2013, 146, 359–370. [Google Scholar] [CrossRef]
- Karakostas, P.; Sifaleras, A.; Georgiadis, M.C. Adaptive variable neighborhood search solution methods for the fleet size and mix pollution location-inventory-routing problem. Expert Syst. Appl. 2020, 153, 113444. [Google Scholar] [CrossRef]
- Forouzanfar, F.; Tavakkoli-Moghaddam, R.; Bashiri, M.; Baboli, A.; Hadji Molana, S.M. New mathematical modeling for a location–routing–inventory problem in a multi-period closed-loop supply chain in a car industry. J. Ind. Eng. Int. 2017, 14, 537–553. [Google Scholar] [CrossRef] [Green Version]
- Yavari, M.; Enjavi, H.; Geraeli, M. Demand management to cope with routes disruptions in location-inventory-routing problem for perishable products. Res. Transp. Bus. Manag. 2020, 37, 100552. [Google Scholar] [CrossRef]
- Rahbari, M.; Razavi Hajiagha, S.H.; Amoozad Mahdiraji, H.; Riahi Dorcheh, F.; Garza-Reyes, J.A. A novel location-inventory-routing problem in a two-stage red meat supply chain with logistic decisions: Evidence from an emerging economy. Kybernetes 2021, 51, 1498–1531. [Google Scholar] [CrossRef]
- Wu, W.; Zhou, W.; Lin, Y.; Xie, Y.; Jin, W. A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption. Expert Syst. Appl. 2021, 166, 114034. [Google Scholar] [CrossRef]
- Ma, Z.; Dai, Y. Logistics for sustained economic development-infrastructure, information, integration. In Proceedings of the 2010 International Conference of Logistics Engineering and Management, Chengdu, China, 8–10 October 2010; pp. 2562–2568. [Google Scholar]
- Nekooghadirli, N.; Tavakkoli-Moghaddam, R.; Ghezavati, V.R.; Javanmard, S. Solving a new bi-objective location-routing-inventory problem in a distribution network by meta-heuristics. Comput. Ind. Eng. 2014, 76, 204–221. [Google Scholar] [CrossRef]
- Ghorbani, A.; Akbari Jokar, M.R. A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem. Comput. Ind. Eng. 2016, 101, 116–127. [Google Scholar] [CrossRef]
- Moradi Nasab, N.; Amin-Naseri, M.R. Designing an integrated model for a multi-period, multi-echelon and multi-product petroleum supply chain. Energy 2016, 114, 708–733. [Google Scholar] [CrossRef]
- Tavakkoli-Moghaddam, R.; Raziei, Z. A new bi-objective location-routing-inventory problem with fuzzy demands. IFAC-PapersOnLine 2016, 49, 1116–1121. [Google Scholar] [CrossRef]
- Zhalechian, M.; Tavakkoli-Moghaddam, R.; Zahiri, B.; Mohammadi, M. Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transp. Res. Part Logist. Transp. Rev. 2016, 89, 182–214. [Google Scholar] [CrossRef]
- Rafie-Majd, Z.; Pasideh, S.H.R.; Naderi, B. Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Comput. Chem. Eng. 2018, 109, 9–22. [Google Scholar] [CrossRef]
- Goodarzian, F.; Wamba, S.F.; Mathiyazhagan, K.; Taghipour, A. A new bi-objective green medicine supply chain network design under fuzzy environment: Hybrid metaheuristic algorithms. Comput. Ind. Eng. 2021, 160, 107535. [Google Scholar] [CrossRef]
- Hiassat, A.; Diabat, A.; Rahwan, I. A genetic algorithm approach for location-inventory-routing problem with perishable products. J. Manuf. Syst. 2017, 42, 93–103. [Google Scholar] [CrossRef]
- Karakostas, P.; Sifaleras, A.; Georgiadis, M.C. Variable neighborhood search-based solution methods for the pollution location-inventory-routing problem. Optim. Lett. 2022, 16, 211–235. [Google Scholar] [CrossRef]
- Wu, T.; Shi, L.; Geunes, J.; Akartunali, K. On the equivalence of strong formulations for capacitated multi-level lot sizing problems with setup times. J. Glob. Optim. 2012, 53, 615–639. [Google Scholar] [CrossRef] [Green Version]
- Hsieh, C.L.; Liao, S.H.; Ho, W.C. Incorporating location, routing and inventory decisions in dual sales channel—A hybrid genetic approach. In Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Bangkok, Thailand, 10–13 December 2013; pp. 452–456. [Google Scholar]
- Diabat, A.; Theodorou, E. A location–inventory supply chain problem: Reformulation and piecewise linearization. Comput. Ind. Eng. 2015, 90, 381–389. [Google Scholar] [CrossRef]
- Nakhjirkan, S.; Mokhatab Rafiei, F. An integrated multi-echelon supply chain network design considering stochastic demand: A genetic algorithm based solution. Promet. Traffic Traffico 2017, 29, 391–400. [Google Scholar] [CrossRef]
- Zheng, J.; Li, K.; Wu, D. Models for location inventory routing problem of cold chain logistics with NSGA-II algorithm. J. Donghua Univ. 2017, 34, 533–539. [Google Scholar]
- Nakhjirkan, S.; Rafiei, F.M.; Kashan, A.H. Developing an integrated decision making model in supply chain under demand uncertainty using genetic algorithm and network data envelopment analysis. Int. J. Math. Oper. Res. 2019, 14, 53–81. [Google Scholar] [CrossRef]
- Rahbari, M.; Razavi Hajiagha, S.H.; Raeei Dehaghi, M.; Moallem, M.; Riahi Dorcheh, F. Modeling and solving a five-echelon location–inventory–routing problem for red meat supply chain. Kybernetes 2020, 50, 66–99. [Google Scholar] [CrossRef]
- Li, K.; Li, D.; Wu, D. Multi-objective optimization for location-routing-inventory problem in cold chain logistics network with soft time window constraint. J. Eur. Des Syst. Autom. 2020, 53, 803–809. [Google Scholar] [CrossRef]
- Zhao, J.; Ke, G.Y. Incorporating inventory risks in location-routing models for explosive waste management. Int. J. Prod. Econ. 2017, 193, 123–136. [Google Scholar] [CrossRef]
- Jha, J.K.; Shanker, K. An integrated inventory problem with transportation in a divergent supply chain under service level constraint. J. Manuf. Syst. 2014, 33, 462–475. [Google Scholar] [CrossRef]
- Aghighi, A.; Malmir, B. Designing distribution networks of perishable products under stochastic demands and routs. In Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, Detroit, MI, USA, 23–25 September 2016; pp. 1008–1019. [Google Scholar]
- Wang, X. An integrated multi-depot location- inventory-routing problem for logistics distribution system planning of a chain enterprise. In Proceedings of the 2010 International Conference on Logistics Systems and Intelligent Management, Harbin, China, 9–10 January 2010; pp. 1427–1431. [Google Scholar]
- Yang, X.; Ma, H.; Zhang, D. Research into ILRIP for logistics distribution network of deteriorating item based on JITD. In Proceedings of the Communications in Computer and Information Science, Kaminoyama, Japan, 18–20 August 2010; pp. 152–160. [Google Scholar]
- Shuai, D.; Yanhui, L.; Lan, Y. Combine cost and time satisfaction into a multi-objective programming for integrated logistics system. In Proceedings of the 2011 International Conference on Computer Science and Service System (CSSS), Nanjing, China, 27–29 June 2011; pp. 283–287. [Google Scholar]
- Zhang, Y.; Qi, M.; Miao, L.; Liu, E. Hybrid metaheuristic solutions to inventory location routing problem. Transp. Res. Part E Logist. Transp. Rev. 2014, 70, 305–323. [Google Scholar] [CrossRef]
- Kechmane, L.; Nsiri, B.; Baalal, A. Optimization of a two-echelon location lot-sizing routing problem with deterministic demand. Math. Probl. Eng. 2018, 2018, 2745437. [Google Scholar] [CrossRef]
- Saif-Eddine, A.S.; El-Beheiry, M.M.; El-Kharbotly, A.K. An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem. Ain Shams Eng. J. 2019, 10, 63–76. [Google Scholar] [CrossRef]
- Saragih, N.I.; Bahagia, S.N.; Suprayogi Syabri, I. A heuristic method for location-inventory-routing problem in a three-echelon supply chain system. Comput. Ind. Eng. 2019, 127, 875–886. [Google Scholar] [CrossRef]
- Govindan, K.; Mina, H.; Esmaeili, A.; Gholami-Zanjani, S.M. An integrated hybrid approach for circular supplier selection and closed loop supply chain network design under uncertainty. J. Clean. Prod. 2020, 242, 118317. [Google Scholar] [CrossRef]
- Li, T.; Yang, W. Supply chain planning problem considering customer inventory holding cost based on an improved tabu search algorithm. Appl. Math. Nonlinear Sci. 2020, 5, 557–564. [Google Scholar] [CrossRef]
- Misni, F.; Lee, L.S. Modified harmony search algorithm for location-inventory-routing problem in supply chain network design with product returns. Malays. J. Math. Sci. 2021, 15, 1–20. [Google Scholar]
- Ahmadi-Javid, A.; Seddighi, A.H. A location-routing-inventory model for designing multisource distribution networks. Eng. Optim. 2012, 44, 637–656. [Google Scholar] [CrossRef]
- Deng, S.; Li, Y.; Zhou, T.; Cao, Y. Study on recyclable reserve logistics network optimization based on e-commerce. In Proceedings of the 2014 International Conference on Management of e-Commerce and e-Government, Shanghai, China, 31 October–2 November 2014; pp. 337–340. [Google Scholar]
- Guo, H.; Li, Y. Multiobjective location-inventory-routing problem taking returns into consideration. In Lecture Notes in Electrical Engineering; Springer: Berlin/Heidelberg, Germany, 2014; pp. 19–26. [Google Scholar]
- Guerrero, W.J.; Prodhon, C.; Velasco, N.; Amaya, C.A. A relax-and-price heuristic for the inventory-location-routing problem. Int. Trans. Oper. Res. 2015, 22, 129–148. [Google Scholar] [CrossRef]
- Deng, S.; Li, Y.; Guo, H.; Liu, B. Solving a closed-loop location-inventory-routing problem with mixed quality defects returns in e-commerce by hybrid ant colony optimization algorithm. Discret. Dyn. Nat. Soc. 2016, 2016, 6467812. [Google Scholar] [CrossRef] [Green Version]
- Fan, J.; Tian, X.F.; Deng, S.; Guo, H.; Zhang, Z.L. Multi-objective location-inventory-routing problem based on time-satisfaction degree. In Proceedings of the 6th International Conference on Logistics and Supply Chain Management, Changsha, China, 1–3 December 2016; pp. 295–303. [Google Scholar]
- Lerhlaly, S.; Lebbar, M.; Allaoui, H.; Ouazar, D.; Afifi, S. An integrated inventory location routing: Problem considering CO2 emissions. Contemp. Eng. Sci. 2016, 9, 303–314. [Google Scholar] [CrossRef]
- Riquelme-Rodríguez, J.-P.; Gamache, M.; Langevin, A. Location arc routing problem with inventory constraints. Comput. Oper. Res. 2016, 76, 84–94. [Google Scholar] [CrossRef]
- Yuchi, Q.; He, Z.; Yang, Z.; Wang, N. A location-inventory-routing problem in forward and reverse logistics network design. Discret. Dyn. Nat. Soc. 2016, 2016, 3475369. [Google Scholar] [CrossRef] [Green Version]
- Abou El Madj, B. An inventory location routing model with environmental considerations. In Proceedings of the MATEC Web of Conferences, Tianjin, China, 6–9 July 2017; p. 00002. [Google Scholar]
- Guo, H.; Li, C.; Zhang, Y.; Zhang, C.; Wang, Y. A nonlinear integer programming model for integrated location, inventory, and routing decisions in a closed-loop supply chain. Complexity 2018, 2018, 2726070. [Google Scholar] [CrossRef] [Green Version]
- Kaya, O.; Ozkok, D. A network design problem with location, inventory and routing decisions. In Proceedings of the GECCO 2018 Companion—Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion, Kyoto, Japan, 15–19 July 2018; Association for Computing Machinery, Inc.: New York, NY, USA, 2018; pp. 139–140. [Google Scholar]
- Sun, Q.; Chien, S.; Hu, D.W.; Ma, B.S. Optimizing the location-inventory-routing problem for perishable products considering food waste and fuel consumption. In Proceedings of the CICTP 2018: Intelligence, Connectivity, and Mobility—Proceedings of the 18th COTA International Conference of Transportation Professionals, Beijing, China, 5–8 July 2018; American Society of Civil Engineers (ASCE): Reston, VA, USA, 2018; pp. 482–491. [Google Scholar]
- Chen, C.; Tian, Z.; Yao, B. Optimization of two-stage location-routing-inventory problem with time-windows in food distribution network. Ann. Oper. Res. 2019, 273, 111–134. [Google Scholar]
- Misni, F.; Lee, L.S.; Seow, H.-V. Hybrid harmony search-simulated annealing algorithm for location-inventory-routing problem in supply chain network design with defect and non-defect items. Appl. Sci. 2020, 10, 6625. [Google Scholar] [CrossRef]
- Aloui, A.; Mrabti, N.; Hamani, N.; Delahoche, L. Towards a collaborative and integrated optimization approach in sustainable freight transportation. IFAC PapersOnLine 2021, 54, 671–676. [Google Scholar] [CrossRef]
- Nasr, N.; Niaki, S.T.A.; Hussenzadek Kashan, A.; Seifbarghy, M. An efficient solution method for an agri-fresh food supply chain: Hybridization of lagrangian relaxation and genetic algorithm. Environ. Sci. Pollut. Res. 2021 online ahead of print.
- Misni, F.; Lee, L.S.; Jaini, N.I. Multi-objective hybrid harmony search-simulated annealing for location-inventory-routing problem in supply chain network design of reverse logistics with CO2 emission. J. Physics: Conf. Ser. 2021, 1988, 012054. [Google Scholar] [CrossRef]
- Tang, J.; Ji, S.; Jiang, L. The design of a sustainable location-routing-inventory model considering consumer environmental behavior. Sustainability 2016, 8, 211. [Google Scholar] [CrossRef] [Green Version]
- Ahmadi-Javid, A.; Amiri, E.; Meskar, M. A profit-maximization location-routing-Pricing Problem: A Branch-and-Price Algorithm. Eur. J. Oper. Res. 2018, 271, 866–881. [Google Scholar] [CrossRef]
- Tavana, M.; Abtahi, A.-R.; Di Caprio, D.; Hashemi, R.; Yousefi-Zenouz, R. An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations. Socio-Econ. Plan. Sci. 2018, 64, 21–37. [Google Scholar] [CrossRef]
- Vahdani, B.; Veysmoradi, D.; Noori, F.; Mansour, F. Two-stage multi-objective location-routing-inventory model for humanitarian logistics network design under uncertainty. Int. J. Disaster Risk Reduct. 2018, 27, 290–306. [Google Scholar] [CrossRef]
- Fatemi Ghomi, S.M.T.; Asgarian, B. Development of metaheuristics to solve a transportation inventory location routing problem considering lost sale for perishable goods. J. Model. Manag. 2019, 14, 175–198. [Google Scholar] [CrossRef]
- Karakostas, P.; Sifaleras, A.; Georgiadis, M.C. A general variable neighborhood search-based solution approach for the location-inventory-routing problem with distribution outsourcing. Comput. Chem. Eng. 2019, 126, 263–279. [Google Scholar] [CrossRef]
- Pourhejazy, P.; Kwon, O.K.; Lim, H. Integrating sustainability into the optimization of fuel logistics networks. KSCE J. Civ. Eng. 2019, 23, 1369–1383. [Google Scholar] [CrossRef]
- Aloui, A.; Hamani, N.; Delahoche, L. An integrated optimization approach using a collaborative strategy for sustainable cities freight transportation: A Case study. Sustain. Cities Soc. 2021, 75, 103331. [Google Scholar] [CrossRef]
- Aydemir-Karadag, A. Bi-objective adaptive large neighborhood search algorithm for the healthcare waste periodic location inventory routing problem. Arab. J. Sci. Eng. 2022, 47, 3861–3876. [Google Scholar] [CrossRef] [PubMed]
- Morales Chavez, M.M.; Costa, Y.; Sarache, W. A three-objective stochastic location-inventory-routing model for agricultural waste-based biofuel supply chain. Comput. Ind. Eng. 2021, 162, 107759. [Google Scholar] [CrossRef]
- Rahbari, M.; Arshadi Khamseh, A.; Sadati-Keneti, Y.; Jafari, M.J. A risk-based green location-inventory-routing problem for hazardous materials: NSGA II, MOSA, and multi-objective black widow optimization. Environ. Dev. Sustain. 2022, 24, 2804–2840. [Google Scholar] [CrossRef]
- Rabbani, M.; Mokarrari, K.R.; Akbarian-saravi, N. A multi-objective location inventory routing problem with pricing decisions in a sustainable waste management system. Sustain. Cities Soc. 2021, 75, 103319. [Google Scholar] [CrossRef]
- Zarrat Dakhely Parast, Z.; Haleh, H.; Avakh Darestani, S.; Amin-Tahmasbi, H. Green reverse supply chain network design considering location-routing-inventory decisions with simultaneous pickup and delivery. Sustain. Supply Chain. Netw. Des. 2021. [Google Scholar] [CrossRef] [PubMed]
- Zhu, A.; Wen, Y.; Kaplan, M. Green logistics location-routing optimization solution based on improved GA a1gorithm considering low-carbon and environmental protection. J. Math. 2021, 2021, 6101194. [Google Scholar] [CrossRef]
- Monroy, A.G.A.; Díaz, H.L. A parallel programming approach to the solution of the location-inventory and multi-echelon routing problem in the humanitarian supply chain. Transp. Res. Procedia 2021, 58, 495–502. [Google Scholar] [CrossRef]
- Shafiee Moghadam, S.; Aghsami, A.; Rabbani, M. A hybrid NSGA-II algorithm for the closed-loop supply chain network design in e-commerce. RAIRO—Oper. Res. 2021, 55, 1643–1674. [Google Scholar] [CrossRef]
- Seyedhosseini, S.M.; Bozorgi-Amiri, A.; Daraei, S. An integrated location-routing-inventory problem by considering supply disruption. iBusiness 2014, 06, 29–37. [Google Scholar] [CrossRef] [Green Version]
- Ghani, N.E.A.; Shariff, S.S.R.; Zahari, S.M. Optimization of location routing inventory problem with transshipment. In AIP Conference Proceedings; American Institute of Physics Inc.: College Park, MA, USA, 2015; p. 050043. [Google Scholar]
- Angazi, H. An integrated location inventory routing model in supply chain network designing under uncertainty. Decis. Sci. Lett. 2016, 5, 551–568. [Google Scholar] [CrossRef]
- Shariff, S.S.R.; Kamal, N.S.; Omar, M.; Moin, N.H. Location routing inventory problem with transshipment points using p-center. J. Ind. Eng. Manag. Sci. 2016, 1, 59–72. [Google Scholar]
- Gholamian, M.R.; Heydari, M. An inventory model with METRIC approach in location-routing-inventory problem. Adv. Prod. Eng. Manag. 2017, 12, 115–126. [Google Scholar] [CrossRef]
- Habibi, F.; Asadi, E.; Sadjadi, S.J. Developing a location-inventory-routing model using METRIC approach in inventory policy. Uncertain Supply Chain. Manag. 2017, 5, 337–358. [Google Scholar] [CrossRef]
- Asadi, E.; Habibi, F.; Nickel, S.; Sahebi, H. A bi-objective stochastic location-inventory-routing model for microalgae-based biofuel supply chain. Appl. Energy 2018, 228, 2235–2261. [Google Scholar] [CrossRef]
- Habibi, F.; Asadi, E.; Sadjadi, S.J. A location-inventory-routing optimization model for cost effective design of microalgae biofuel distribution system: A case study in Iran. Energy Strategy Rev. 2018, 22, 82–93. [Google Scholar] [CrossRef]
- Momenikiyai, M.; Ebrahimnejad, S.; Vahdani, B. A bi-objective mathematical model for inventory-distribution-routing problem under risk pooling effect: Robust meta-heuristic approach. Econ. Comput. Econ. Cybern. Stud. Res. 2018, 52, 257–274. [Google Scholar]
- Fallah-Tafti, A.; Vahdatzad, M.A.; Sadegheiyeh, A. A comprehensive mathematical model for a location-routing-inventory problem under uncertain demand: A numerical illustration in cash-in-transit sector. Int. J. Eng. Trans. B Appl. 2019, 32, 1634–1642. [Google Scholar]
- Manavizadeh, N.; Shaabani, M.; Aghamohamadi, S. Designing a green location routing inventory problem considering transportation risks and time window: A case study. J. Ind. Syst. Eng. 2019, 12, 27–56. [Google Scholar]
- Rabbani, M.; Heidari, R.; Yazdanparast, R. A stochastic multi-period industrial hazardous waste location-routing problem: Integrating NSGA-II and monte carlo simulation. Eur. J. Oper. Res. 2019, 272, 945–961. [Google Scholar] [CrossRef]
- Zheng, X.; Yin, M.; Zhang, Y. Integrated optimization of location, inventory and routing in supply chain network design. Transp. Res. Part B Methodol. 2019, 121, 1–20. [Google Scholar] [CrossRef]
- Biuki, M.; Kazemi, A.; Alinezhad, A. An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. J. Clean. Prod. 2020, 260. [Google Scholar] [CrossRef]
- Kaya, O.; Ozkok, D. A blood bank network design problem with integrated facility location, inventory and routing decisions. Netw. Spat. Econ. 2020, 20, 757–783. [Google Scholar] [CrossRef]
- Karimkhani, S.Z.; Mina, H.; Biuki, M.; Govindan, K. A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Ann. Oper. Res. 2020, 295, 425–452. [Google Scholar] [CrossRef]
- Aghighi, A.; Goli, A.; Malmir, B.; Tirkolaee, E.B. The stochastic location-routing-inventory problem of perishable products with reneging and balking. J. Ambient. Intell. Humaniz. Comput. 2021, 1–20. [Google Scholar] [CrossRef]
- Ji, S.; Tang, J.; Sun, M.; Luo, R. Multi-objective optimization for a combined location-path-inventory system considering carbon-capped differences. J. Ind. Manag. Optim. 2022, 18, 1949–1977. [Google Scholar] [CrossRef]
- Josiah, T.; Suhada, A.; Chetthamrongchai, P.; Purbasari, H. Optimization of the location, inventory and routing of capacity vehicles with interval uncertainty. Ind. Eng. Manag. Syst. 2021, 20, 654–661. [Google Scholar] [CrossRef]
- Liu, A.; Zhu, Q.; Xu, L.; Lu, Q.; Fan, Y. Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model. Transp. Res. Part E Logist. Transp. Rev. 2021, 150, 102319. [Google Scholar] [CrossRef]
- Mahjoob, M.; Fazeli, S.S. Green supply chain network design with emphasis on inventory decisions. Sustain. Oper. Comput. 2021, 2, 214–229. [Google Scholar] [CrossRef]
- Harati, S.; Roghanian, E.; Hafezalkotob, A.; Shojaie, A.A. A robust two-stage stochastic location-routing-inventory model for perishable items. Teh. Vjesn. Tech. Gaz. 2021, 28, 1989–1995. [Google Scholar]
- Shu, B.; Pei, F.; Zheng, K.; Yu, M. LIRP optimization of cold chain logistics in satellite warehouse mode of supermarket chains. J. Intell. Fuzzy Syst. 2021, 41, 4825–4839. [Google Scholar] [CrossRef]
- Yang, Y.; Zhang, J.; Sun, W.; Pu, Y. Research on NSGA-III in location-routing-inventory problem of pharmaceutical logistics intermodal network. J. Intell. Fuzzy Syst. 2021, 41, 699–713. [Google Scholar] [CrossRef]
- Tavakkoli-Moghaddam, R.; Forouzanfar, F.; Ebrahimnejad, S. Incorporating location, routing, and inventory decisions in a bi-objective supply chain design problem with risk-pooling. J. Ind. Eng. Int. 2013, 9, 19. [Google Scholar] [CrossRef] [Green Version]
- Chen, D.; Chen, D.; Sun, G.; Liu, G. Combined location routing and inventory problem of e-commerce distribution system with fuzzy random demand. Int. J. Hybrid Inf. Technol. 2014, 7, 429–442. [Google Scholar] [CrossRef]
- Lin, R.-H. An integrated model for supplier selection under a fuzzy situation. Int. J. Prod. Econ. 2012, 138, 55–61. [Google Scholar] [CrossRef]
- Gholipour, S.; Ashoftehfard, A.; Mina, H. Green supply chain network design considering inventory-location-routing problem: A fuzzy solution approach. Int. J. Logist. Syst. Manag. 2020, 35, 436–452. [Google Scholar] [CrossRef]
- Khalili Nasr, A.; Tavana, M.; Alavi, B.; Mina, H. A novel fuzzy multi-objective circular supplier selection and order allocation model for sustainable closed-loop supply chains. J. Clean. Prod. 2021, 287, 124994. [Google Scholar] [CrossRef]
- Tavana, M.; Tohidi, H.; Alimohammadi, M.; Lesansalmasi, R. A location-inventory-routing model for green supply chains with low-carbon emissions under uncertainty. Environ. Sci. Pollut. Res. 2021, 28, 50636–50648. [Google Scholar] [CrossRef]
- Lofberg, J. YALMIP: A toolbox for modeling and optimization in MATLAB. In Proceedings of the 2004 IEEE International Conference on Robotics and Automation, Taipei, Taiwan, 2–4 September 2004; pp. 284–289. [Google Scholar]
- Prodhon, C.; Prins, C. A survey of recent research on location-routing problems. Eur. J. Oper. Res. 2014, 238, 1–17. [Google Scholar] [CrossRef]
- Liu, Y.-K.; Liu, B. Fuzzy random variables: A scalar expected value operator. Fuzzy Optim. Decis. Mak. 2003, 2, 143–160. [Google Scholar] [CrossRef]
- Kwakernaak, H. Fuzzy random variables—I. definitions and theorems. Inf. Sci. 1978, 15, 1–29. [Google Scholar] [CrossRef]
- Liu, B. Theory and Practice of Uncertain Programming. Stud. Fuzziness Soft Comput. 2009, 239, 1–205. [Google Scholar]
Reference | Highlights | Year | Number of Papers |
---|---|---|---|
[14] | An extensive review of the existing literature on the LIP model. Provided significant insights and identified potential research topics for future research. | 1976–2013 | 142 |
[15] | A classification of problem variants and extension of LRP. Conveyed the main ideas of each paper. | 2006–2014 | 154 |
[16] | Proposed a new taxonomy to capture some recently emerging issues in LRP. Provided analysis of publication intensity, problem characteristics, solution methods, and applications. | 2014–2019 | 222 |
[17] | Categorized IRPs with respect to their structural variants and with respect to availability. | 1987–2012 | 130 |
[18] | Proposed information management of IRP. Provided the relationship between inventory policy and demand information. Summarized requirement modeling and used optimization methods to find suitable solutions. | 2006–2014 | 41 |
[19] | Presented an overview of the conceptual framework of marine IRP. | 2010–2017 | 60 |
[20] | Reviewed IRP studying random demand and random lead times with a focus on their multi-warehouse aspects. Reviewed some characteristics and solutions of multi-warehouse IRP. | 2003–2017 | 66 |
[21] | Reviewed research on IRP that considered a novel classification for sustainable development. Introduced practical aspects and incorporate sustainability into the model. Emphasized scarcity and the direction of future study. | 2010–2018 | 75 |
[22] | First literature review of alternative IRP. Pointed out that the existing literature is not helpful enough for the decision-making process of legislators. | 1984–2018 | 329 |
[23] | Classified according to the models and the algorithms of IRP. Classified according to time horizon and structure. | 1983–2013 | 41 |
[24] | Summarized the comparison of three algorithms for solving a certain IRP. | 1997–2014 | 26 |
[25] | Reviewed the IRP for determining the demand rate of single-depot multi-retailer. | 1985–2017 | 14 |
[26] | Investigated the technical status of LIRP based on model components. | 2003–2018 | 11 |
Type of Publication | Year | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Total | |
Journal | 1 | 1 | 2 | 4 | 5 | 3 | 11 | 8 | 9 | 11 | 12 | 30 | 97 |
Conference Proceedings | 4 | 2 | 0 | 1 | 2 | 1 | 2 | 1 | 2 | 0 | 0 | 0 | 15 |
Total | 5 | 3 | 2 | 5 | 7 | 4 | 13 | 9 | 11 | 11 | 12 | 30 | 112 |
Publisher | Number of Papers | Year | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | ||
Elsevier | 42 | 2 | 0 | 0 | 1 | 3 | 1 | 5 | 3 | 6 | 6 | 4 | 11 |
Springer | 15 | 1 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 2 | 2 | 6 |
Hindawi | 8 | 0 | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 2 | 0 | 0 | 2 |
IEEE Xplore | 7 | 2 | 1 | 0 | 1 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
Emerald Insight | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
Taylor & Francis | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Growing Science | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Wiley Online | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
IOS Press | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
MDPI | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
EDP Sciences | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
Others | 25 | 0 | 1 | 0 | 0 | 2 | 1 | 2 | 4 | 3 | 2 | 4 | 6 |
Total | 112 | 5 | 3 | 2 | 5 | 7 | 4 | 13 | 9 | 11 | 11 | 12 | 30 |
Country | Year | Total | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | ||
China | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 2 | 0 | 3 | 0 | 7 | 16 |
Iran | 0 | 1 | 0 | 0 | 1 | 0 | 2 | 0 | 2 | 1 | 3 | 7 | 17 |
France | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 3 |
Indonesia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 2 |
Canada | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
Denmark | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
Malaysia | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Morocco | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
Taiwan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Turkey | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
Total | 1 | 2 | 0 | 1 | 2 | 1 | 5 | 3 | 2 | 5 | 5 | 17 | 44 |
Problem Characteristics | Demand Data Types | Models/Solutions | Applied Fields | ||
---|---|---|---|---|---|
Period-Product | Echelons-Links | Depots-Retailers | |||
Single-Single | Single-Single | Single-Multi | Deterministic | MIP | ECLS |
Single-Multi | Multi-Single | Multi-Multi | Variable | MILP | HSC |
Multi-Single | Multi-Multi | Stochastic | MINLP | PPLN | |
Multi-Multi | Fuzzy | Exact Algorithm | CCLN | ||
Heuristic and Metaheuristic | ESSC | ||||
Mixed Exact and Heuristic and Metaheuristic | HUSC | ||||
Other Approaches | HEL | ||||
Others |
MPMPP | MESLP | MDMRP | Total |
---|---|---|---|
✓ | ✓ | ✓ | 23 |
✓ | ✓ | 6 | |
✓ | ✓ | 13 | |
✓ | ✓ | 12 | |
✓ | 9 | ||
✓ | 5 | ||
✓ | 37 |
Type of Problem | SPSPP | SPMPP | MPSPP | MPMPP | SESLP | MESLP | MEMLP | SDMRP | MDMRP |
---|---|---|---|---|---|---|---|---|---|
Single-objective | 6 | 2 | 19 | 28 | 3 | 21 | 4 | 3 | 46 |
Multi-objective | 2 | 1 | 5 | 23 | 1 | 25 | 2 | 1 | 39 |
Total | 8 | 3 | 24 | 51 | 4 | 46 | 6 | 4 | 85 |
Model Type | MIP | MILP | MINLP | Total |
---|---|---|---|---|
Single-objective | 22 | 22 | 20 | 64 |
Multi-objective | 12 | 18 | 18 | 48 |
Total | 34 | 40 | 38 | 112 |
Demand Data Type | Deterministic | Variable | Stochastic | Fuzzy | Total |
---|---|---|---|---|---|
Single-objective | 29 | 8 | 26 | 1 | 64 |
Multi-objective | 8 | 13 | 20 | 7 | 48 |
Total | 37 | 21 | 46 | 8 | 112 |
Demand Data Types | Year | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Total | |
Deterministic | 1 | 2 | 1 | 3 | 3 | 2 | 7 | 3 | 4 | 1 | 4 | 6 | 37 |
Variable | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 3 | 4 | 2 | 9 | 21 |
Stochastic | 4 | 1 | 0 | 2 | 3 | 2 | 4 | 5 | 4 | 6 | 4 | 11 | 46 |
Fuzzy | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 2 | 4 | 8 |
Total | 5 | 3 | 2 | 5 | 7 | 4 | 13 | 9 | 11 | 11 | 12 | 30 | 112 |
Demand Data Types | MIP | MILP | MINLP | Total |
---|---|---|---|---|
Deterministic | 10 | 17 | 10 | 37 |
Variable | 8 | 7 | 6 | 21 |
Stochastic | 15 | 9 | 22 | 46 |
Fuzzy | 1 | 7 | 0 | 8 |
Total | 34 | 40 | 38 | 112 |
Type of Problem | SPSPP | SPMPP | MPSPP | MPMPP | SESLP | MESLP | MEMLP | SDMRP | MDMRP |
---|---|---|---|---|---|---|---|---|---|
Deterministic | 1 | 1 | 10 | 11 | 1 | 11 | 1 | 1 | 29 |
Variable | 0 | 0 | 6 | 13 | 2 | 11 | 0 | 0 | 13 |
Stochastic | 6 | 1 | 8 | 21 | 1 | 19 | 5 | 3 | 35 |
Fuzzy | 1 | 1 | 0 | 6 | 0 | 5 | 0 | 0 | 8 |
Total | 8 | 3 | 24 | 51 | 4 | 46 | 6 | 4 | 85 |
Model Type of Problem | SPSPP | SPMPP | MPSPP | MPMPP | SESLP | MESLP | MEMLP | SDMRP | MDMRP |
---|---|---|---|---|---|---|---|---|---|
MIP | 4 | 0 | 8 | 11 | 2 | 8 | 1 | 3 | 24 |
MILP | 0 | 1 | 9 | 24 | 2 | 23 | 1 | 0 | 29 |
MINLP | 4 | 2 | 7 | 16 | 0 | 15 | 4 | 1 | 32 |
Total | 8 | 3 | 24 | 51 | 4 | 46 | 6 | 4 | 85 |
Solution Approach | Year | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Total | |
Exact Algorithm only | 1 | 1 | 0 | 2 | 0 | 2 | 5 | 1 | 2 | 2 | 3 | 7 | 26 |
Heuristic and Metaheuristic | 4 | 2 | 1 | 3 | 6 | 1 | 8 | 6 | 8 | 9 | 7 | 17 | 72 |
Exact, Heuristic and Metaheuristic | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 4 | 10 |
Other Approaches | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 2 | 4 |
Total | 5 | 3 | 2 | 5 | 7 | 4 | 14 | 8 | 11 | 11 | 12 | 30 | 112 |
Solution Approach | SPSPP | SPMPP | MPSPP | MPMPP | SESLP | MESLP | MEMLP | SDMRP | MDMRP |
---|---|---|---|---|---|---|---|---|---|
Exact Algorithm only | 0 | 1 | 10 | 12 | 0 | 15 | 2 | 1 | 17 |
Heutistic and Metaheuristic | 7 | 1 | 11 | 31 | 3 | 27 | 3 | 3 | 56 |
Exact, Heuristic and Metaheuristic | 1 | 0 | 2 | 6 | 1 | 4 | 1 | 0 | 8 |
Others | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 4 |
Total | 8 | 3 | 24 | 51 | 4 | 46 | 6 | 4 | 85 |
Solution Approach | MIP | MILP | MINLP | Total |
---|---|---|---|---|
Exact Algorithm | 4 | 18 | 4 | 26 |
Heutistic and Metaheuristic | 27 | 18 | 27 | 72 |
Exact, Heuristic and Metaheuristic | 2 | 3 | 5 | 10 |
Other Approaches | 1 | 1 | 2 | 4 |
Total | 34 | 40 | 38 | 112 |
Type of Application | Year | Total | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | ||
ECLS | 0 | 0 | 1 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 7 |
HSC | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 2 | 2 | 2 | 2 | 4 | 15 |
PPLN | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 3 | 4 | 10 | 21 |
CCL | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 2 | 5 |
ESSC | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 1 | 2 | 6 | 13 |
HUSC | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 3 |
HEL | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 2 | 5 |
Others | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 5 |
Total | 1 | 1 | 1 | 3 | 1 | 1 | 7 | 6 | 8 | 7 | 12 | 26 | 74 |
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Liu, L.; Lee, L.S.; Seow, H.-V.; Chen, C.Y. Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method. Sustainability 2022, 14, 15853. https://doi.org/10.3390/su142315853
Liu L, Lee LS, Seow H-V, Chen CY. Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method. Sustainability. 2022; 14(23):15853. https://doi.org/10.3390/su142315853
Chicago/Turabian StyleLiu, Lihua, Lai Soon Lee, Hsin-Vonn Seow, and Chuei Yee Chen. 2022. "Logistics Center Location-Inventory-Routing Problem Optimization: A Systematic Review Using PRISMA Method" Sustainability 14, no. 23: 15853. https://doi.org/10.3390/su142315853