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Journal articles on the topic 'Inventory routing problem'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Yang, Xianfeng, and Lei Feng. "Inventory Routing Problem." Transportation Research Record: Journal of the Transportation Research Board 2378, no. 1 (January 2013): 32–42. http://dx.doi.org/10.3141/2378-04.

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2

Aydın, Nevin. "A Genetic Algorithm on Inventory Routing Problem." EMAJ: Emerging Markets Journal 3, no. 3 (March 5, 2014): 59–66. http://dx.doi.org/10.5195/emaj.2014.31.

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Inventory routing problem can be defined as forming the routes to serve to the retailers from the manufacturer, deciding on the quantity of the shipment to the retailers and deciding on the timing of the replenishments. The difference of inventory routing problems from vehicle routing problems is the consideration of the inventory positions of retailers and supplier, and making the decision accordingly. Inventory routing problems are complex in nature and they can be solved either theoretically or using a heuristics method. Metaheuristics is an emerging class of heuristics that can be applied to combinatorial optimization problems. In this paper, we provide the relationship between vendor-managed inventory and inventory routing problem. The proposed genetic for solving vehicle routing problem is described in detail.

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3

Kazemi, Seyed Mahmood, Masoud Rabbani, Reza Tavakkoli-Moghaddam, and Farid Abolhassani Shahreza. "Blood inventory-routing problem under uncertainty." Journal of Intelligent & Fuzzy Systems 32, no. 1 (January 13, 2017): 467–81. http://dx.doi.org/10.3233/jifs-152175.

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4

Archetti, Claudia, Nicola Bianchessi, Stefan Irnich, and M. Grazia Speranza. "Formulations for an inventory routing problem." International Transactions in Operational Research 21, no. 3 (February 5, 2014): 353–74. http://dx.doi.org/10.1111/itor.12076.

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5

Coelho, Leandro C., Jean-François Cordeau, and Gilbert Laporte. "The inventory-routing problem with transshipment." Computers & Operations Research 39, no. 11 (November 2012): 2537–48. http://dx.doi.org/10.1016/j.cor.2011.12.020.

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6

Lei, Jun Cheng, Yan Peng Wu, and Wen Fei Zeng. "Optimization Approach for Multi-Stork Inventory Routing Problem." Advanced Materials Research 268-270 (July 2011): 1637–40. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1637.

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Inventory routing problem is one of the key issues to achieve integrated management of logistics. Solving this problem effectively, we can improve vehicle utilization, and reduce distribution costs. This paper, concerning the problem in inventory routing of multi-variety, multi-vendor to multi-customers, proposed heuristic algorithm based on greedy rules. The core strategy of the algorithm is to choose circularly the current lowest unit cost routine ---Hamilton delivery routes. Simulation shows that the algorithm reduces the unloaded ratio of truck, raises the efficiency of truck delivery and saves transport costs.

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7

Diabat, Ali, Claudia Archetti, and Waleed Najy. "The Fixed-Partition Policy Inventory Routing Problem." Transportation Science 55, no. 2 (March 2021): 353–70. http://dx.doi.org/10.1287/trsc.2020.1019.

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In this paper, we formally introduce a variant of the inventory routing problem (IRP) that we call the fixed-partition policy IRP (FPP-IRP). In contrast to the classical IRP in which delivery routes are arbitrary, the FPP-IRP partitions customers into mutually exclusive clusters that are fixed throughout the optimization horizon, and distribution is performed separately for each cluster. By restricting the flexibility inherent in the classical IRP, the FPP-IRP attains many potential advantages. First, partitioning reduces the operational complexity of the system and allows a simpler organization of the distribution service. Second, it improves the robustness of the system by isolating disruptions to affected clusters. Third, it can fit the needs and requirements of specific applications in which consistency in the distribution policy, such as familiarity between customers and drivers and route invariance, is required. We present two fixed-partition policies for the IRP together with mathematical formulations and valid inequalities. We also present a worst-case analysis on the performance of these policies. Extensive computational results are presented to show the behavior of these policies and glean insights into their potential benefits.

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8

Aziz, Nur Arina Bazilah, and Choong Jing Yee. "Inventory Routing Problem with Carbon Emission Consideration." MATEMATIKA 35, no. 1 (April 1, 2019): 39–49. http://dx.doi.org/10.11113/matematika.v35.n1.1127.

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Inventory Routing Problem (IRP) has been continuously developed and improved due to pressure from global warming issue particularly related to greenhouse gases (GHGs) emission. The burning of fossil fuel for transportations such as cars, trucks, ships, trains, and planes primarily emits GHGs. Carbon dioxide (CO2) from burning of fossil fuel to power transportation and industrial process is the largest contributor to global GHGs emission. Therefore, the focus of this study is on solving a multi-period inventory routing problem (MIRP) involving carbon emission consideration based on carbon cap and offset policy. Hybrid genetic algorithm (HGA) based on allocation first and routing second is used to compute a solution for the MIRP in this study. The objective of this study is to solve the proposed MIRP model with HGA then validate the effectiveness of the proposed HGA on data of different sizes. Upon validation, the proposed MIRP model and HGA is applied on real-world data. The HGA is found to be able to solve small size and large size instances effectively by providing near optimal solution in relatively short CPU execution time.

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9

Ramkumar, N., P. Subramanian, T. T. Narendran, and K. Ganesh. "A hybrid heuristic for inventory routing problem." International Journal of Electronic Transport 1, no. 1 (2011): 45. http://dx.doi.org/10.1504/ijet.2011.043113.

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10

Bard, Jonathan F., and Narameth Nananukul. "The integrated production–inventory–distribution–routing problem." Journal of Scheduling 12, no. 3 (August 20, 2008): 257–80. http://dx.doi.org/10.1007/s10951-008-0081-9.

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11

Nambirajan, Ramkumar, Abraham Mendoza, Subramanian Pazhani, T. T. Narendran, and K. Ganesh. "CAR: heuristics for the inventory routing problem." Wireless Networks 26, no. 8 (January 23, 2020): 5783–808. http://dx.doi.org/10.1007/s11276-020-02259-6.

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12

Stacey, Jennifer, Malini Natarajarathinam, and Charles Sox. "The storage constrained, inbound inventory routing problem." International Journal of Physical Distribution & Logistics Management 37, no. 6 (July 17, 2007): 484–500. http://dx.doi.org/10.1108/09600030710763396.

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13

Song, Jin-Hwa, and Kevin C. Furman. "A maritime inventory routing problem: Practical approach." Computers & Operations Research 40, no. 3 (March 2013): 657–65. http://dx.doi.org/10.1016/j.cor.2010.10.031.

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14

Riquelme-Rodríguez, Juan-Pablo, Michel Gamache, and André Langevin. "Location arc routing problem with inventory constraints." Computers & Operations Research 76 (December 2016): 84–94. http://dx.doi.org/10.1016/j.cor.2016.06.012.

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15

Sedaghat, Atefe, Masood Rabbani, and Hamed Farrokhi. "A Sustainable Transportation Location Inventory Routing Problem." COMPUTATIONAL RESEARCH PROGRESS IN APPLIED SCIENCE & ENGINEERING 8, no. 3 (2022): 1–11. http://dx.doi.org/10.52547/crpase.8.3.2813.

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16

Ab Halim, Huda Zuhrah, Nureffa Natasha Mohd Azliana, Nuridawati Baharom, Nur Fatihah Fauzi, Nurizatul Syarfinas Ahmad Bakhtiar, and Nur Izzati Khairudin. "Green Inventory Routing Problem using Hybrid Genetic Algorithm." Journal of Computing Research and Innovation 6, no. 4 (September 20, 2021): 10–19. http://dx.doi.org/10.24191/jcrinn.v6i4.254.

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Carbon dioxide (CO2) is known as one of the largest sources of global warming. One of the ways to curb CO2 emissions is by considering the environmental aspect in the supply chain management. This paper analyses the influence of carbon emissions on the Inventory Routing Problem (IRP). The IRP network consists of a depot, an assembly plant and multiple suppliers. The deterministic demands vary and are determined by the assembly plant. Fixed transportation cost, fuel consumption cost and inventory holding cost are used to evaluate the system’s total cost in which fuel consumption cost is determined by fuel consumption rate, distance, and fuel price. Backordering and split pick-up are not allowed. The main purpose of this study is to analyze the distribution network especially the overall costs of the supply chain by considering the CO2 emissions as well. The problem is known as Green Inventory Routing Problem (GIRP). The mixed-integer linear programming of this problem is adopted from Cheng et al. wherein this study a different Hybrid Genetic Algorithm is proposed at mutation operator. As predicted, GIRP has a higher total cost as it considered fuel consumption cost together with the transportation and inventory costs. The results showed the algorithm led to different sequences of routings considering the carbon dioxide emission in the objective function.

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17

Sun, Bin Feng. "Research of Inventory Routing Problem Based on Combination Auction." Applied Mechanics and Materials 37-38 (November 2010): 960–63. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.960.

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The Inventory Routing Problem, once taken as a centralized optimization problem, integrated the inventory management and vehicle routing simultaneously, which leads IRP with distributed feature. The components were mapped into agents and coordinated by combination auction to generate the optimal delivery strategy. An experiment was conducted to show the distributed approach of multi-agent system might solve the centralized problems.

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18

Gholamian, M. R., and M. Heydari. "An inventory model with METRIC approach in location-routing-inventory problem." Advances in Production Engineering & Management 12, no. 2 (May 31, 2017): 115–26. http://dx.doi.org/10.14743/apem2017.2.244.

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19

Widyadana, Gede, Krisna Wahyudi, and Nyoman Sutapa. "An inventory routing problem for deteriorating items with dynamic demand and spoilage rate." Yugoslav Journal of Operations Research 31, no. 2 (2021): 153–64. http://dx.doi.org/10.2298/yjor200516037w.

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Inventory routing problems (IRP) are among important tools to be used for implementing vendor manage inventory. Many researchers try to develop methods for solving inventory routing problem, however, only a few developed methods for inventory routing problems for spoilage items. In reality, many items are deteriorated and spoiled during transportation and storage period. In this paper, we developed a model and methods to solve the inventory routing problem for deteriorating items with dynamic demand and spoilage rate, i.e., demand varies and items spoil during planning periods. Those cases are more realistic since many commodities such as fruits and vegetables have dynamic demand and spoilage rate. A Genetic Algorithm and Particle Swarm Optimization are developed to solve the problem with various demands in a specific planning period since the problem is Np-hard. A numerical example and sensitivity analysis are conducted to verify the model, and to get management insight it. The result is interesting and support general hypothesis that dynamic demands result in higher inventory cost than the static demands, and the increasing demand results in increasing inventory cost. Also, the results show that increasing demand and deteriorating rates significantly affect the total cost, therefore, the developed model is important and significantly useful to be used for solving IRP with dynamic demand and spoilage items

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20

Vadseth, Simen T., Henrik Andersson, and Magnus Stålhane. "An iterative matheuristic for the inventory routing problem." Computers & Operations Research 131 (July 2021): 105262. http://dx.doi.org/10.1016/j.cor.2021.105262.

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21

Zojaji, Amir, Kiarash Soltaniani, Lars Magnus Hvattum, and Sebastián Urrutia. "Cyclic solutions to a maritime inventory routing problem." Maritime Transport Research 3 (2022): 100074. http://dx.doi.org/10.1016/j.martra.2022.100074.

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22

Pei Yingmei, Ye Chunming, and Zuo Cuihong. "Application of GRASP in the Inventory Routing Problem." International Journal of Advancements in Computing Technology 4, no. 23 (December 31, 2012): 507–14. http://dx.doi.org/10.4156/ijact.vol4.issue23.60.

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23

FU, Cheng-hong, and Zhuo FU. "Inventory routing problem and its recent development: Review." Journal of Computer Applications 30, no. 2 (March 22, 2010): 453–57. http://dx.doi.org/10.3724/sp.j.1087.2010.00453.

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24

Li, Yu n., and Shu Ping Yi. "The inventory-routing problem in field supply logistics." International Journal of Applied Systemic Studies 8, no. 3 (2018): 181. http://dx.doi.org/10.1504/ijass.2018.096098.

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25

Li, Yu Lin, and Shu Ping Yi. "The inventory-routing problem in field supply logistics." International Journal of Applied Systemic Studies 8, no. 3 (2018): 181. http://dx.doi.org/10.1504/ijass.2018.10017369.

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26

Shirokikh, V. A., and E. A. Lezhnina. "Coalition-Formation Problem for Cooperative Inventory Routing Game." Automation and Remote Control 80, no. 7 (July 2019): 1358–67. http://dx.doi.org/10.1134/s0005117919070129.

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27

Siswanto, Nurhadi, Stefanus Eko Wiratno, Ahmad Rusdiansyah, and Ruhul Sarker. "Maritime inventory routing problem with multiple time windows." Journal of Industrial & Management Optimization 13, no. 5 (2017): 1–27. http://dx.doi.org/10.3934/jimo.2018091.

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28

Ekici, Ali, Okan Örsan Özener, and Gültekin Kuyzu. "Cyclic Delivery Schedules for an Inventory Routing Problem." Transportation Science 49, no. 4 (November 2015): 817–29. http://dx.doi.org/10.1287/trsc.2014.0538.

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29

Agra, Agostinho, Marielle Christiansen, Lars Magnus Hvattum, and Filipe Rodrigues. "Robust Optimization for a Maritime Inventory Routing Problem." Transportation Science 52, no. 3 (June 2018): 509–25. http://dx.doi.org/10.1287/trsc.2017.0814.

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30

Chien, T. William, Anantaram Balakrishnan, and Richard T. Wong. "An Integrated Inventory Allocation and Vehicle Routing Problem." Transportation Science 23, no. 2 (May 1989): 67–76. http://dx.doi.org/10.1287/trsc.23.2.67.

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31

Kleywegt, Anton J., Vijay S. Nori, and Martin W. P. Savelsbergh. "The Stochastic Inventory Routing Problem with Direct Deliveries." Transportation Science 36, no. 1 (February 2002): 94–118. http://dx.doi.org/10.1287/trsc.36.1.94.574.

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32

Campbell, Ann Melissa, and Martin W. P. Savelsbergh. "A Decomposition Approach for the Inventory-Routing Problem." Transportation Science 38, no. 4 (November 2004): 488–502. http://dx.doi.org/10.1287/trsc.1030.0054.

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33

Archetti, Claudia, and M. Grazia Speranza. "The inventory routing problem: the value of integration." International Transactions in Operational Research 23, no. 3 (November 30, 2015): 393–407. http://dx.doi.org/10.1111/itor.12226.

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34

Zhang, Ying, Mingyao Qi, Lixin Miao, and Erchao Liu. "Hybrid metaheuristic solutions to inventory location routing problem." Transportation Research Part E: Logistics and Transportation Review 70 (October 2014): 305–23. http://dx.doi.org/10.1016/j.tre.2014.07.010.

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35

Jafarian, Ahmad, Nasrin Asgari, Seyed Sina Mohri, Elham Fatemi-Sadr, and Reza Zanjirani Farahani. "The inventory-routing problem subject to vehicle failure." Transportation Research Part E: Logistics and Transportation Review 126 (June 2019): 254–94. http://dx.doi.org/10.1016/j.tre.2019.04.009.

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36

Bertazzi, Luca, Adamo Bosco, Francesca Guerriero, and Demetrio Laganà. "A stochastic inventory routing problem with stock-out." Transportation Research Part C: Emerging Technologies 27 (February 2013): 89–107. http://dx.doi.org/10.1016/j.trc.2011.06.003.

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37

Zhao, Qiu-Hong, Shou-Yang Wang, and K. K. Lai. "A partition approach to the inventory/routing problem." European Journal of Operational Research 177, no. 2 (March 2007): 786–802. http://dx.doi.org/10.1016/j.ejor.2005.11.030.

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38

Archetti, Claudia, Luca Bertazzi, Alain Hertz, and M. Grazia Speranza. "A Hybrid Heuristic for an Inventory Routing Problem." INFORMS Journal on Computing 24, no. 1 (February 2012): 101–16. http://dx.doi.org/10.1287/ijoc.1100.0439.

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39

Archetti, Claudia, Natashia Boland, and M. Grazia Speranza. "A Matheuristic for the Multivehicle Inventory Routing Problem." INFORMS Journal on Computing 29, no. 3 (August 2017): 377–87. http://dx.doi.org/10.1287/ijoc.2016.0737.

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40

Riquelme-Rodríguez, Juan-Pablo, Michel Gamache, and André Langevin. "Periodic capacitated arc-routing problem with inventory constraints." Journal of the Operational Research Society 65, no. 12 (December 2014): 1840–52. http://dx.doi.org/10.1057/jors.2013.159.

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41

Guerrero, William J. "Hybrid heuristics for the inventory-location-routing problem." 4OR 12, no. 3 (June 3, 2014): 299–300. http://dx.doi.org/10.1007/s10288-014-0261-8.

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42

A. Guimarães, Thiago, Leandro C. Coelho, Cleder M. Schenekemberg, and Cassius T. Scarpin. "The two-echelon multi-depot inventory-routing problem." Computers & Operations Research 101 (January 2019): 220–33. http://dx.doi.org/10.1016/j.cor.2018.07.024.

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43

Karoonsoontawong, Ampol, and Avinash Unnikrishnan. "Inventory Routing Problem with Route Duration Limits and Stochastic Inventory Capacity Constraints." Transportation Research Record: Journal of the Transportation Research Board 2378, no. 1 (January 2013): 43–53. http://dx.doi.org/10.3141/2378-05.

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44

Díaz-Parra, Ocotlán, Jorge A. Ruiz-Vanoye, Beatriz Bernábe Loranca, Alejandro Fuentes-Penna, and Ricardo A. Barrera-Cámara. "A Survey of Transportation Problems." Journal of Applied Mathematics 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/848129.

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This paper aims at being a guide to understand the different types of transportation problems by presenting a survey of mathematical models and algorithms used to solve different types of transportation modes (ship, plane, train, bus, truck, Motorcycle, Cars, and others) by air, water, space, cables, tubes, and road. Some problems are as follows: bus scheduling problem, delivery problem, combining truck trip problem, open vehicle routing problem, helicopter routing problem, truck loading problem, truck dispatching problem, truck routing problem, truck transportation problem, vehicle routing problem and variants, convoy routing problem, railroad blocking problem (RBP), inventory routing problem (IRP), air traffic flow management problem (TFMP), cash transportation vehicle routing problem, and so forth.

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45

Liu, Gia-Shie, and Kuo-Ping Lin. "A decision support system of green inventory-routing problem." Industrial Management & Data Systems 119, no. 1 (February 4, 2019): 89–110. http://dx.doi.org/10.1108/imds-11-2017-0533.

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Purpose The purpose of this paper is to develop a decision support system to consider geographic information, logistics information and greenhouse gas (GHG) emission information to solve the proposed green inventory routing problem (GIRP) for a specific Taiwan publishing logistics firm. Design/methodology/approach A GIRP mathematical model is first constructed to help this specific publishing logistics firm to approximate to the optimal distribution system design. Next, two modified Heuristic-Tabu combination methods that combine savings approach, 2-opt and 1-1 λ-interchange heuristic approach with two modified Tabu search methods are developed to determine the optimum solution. Findings Several examples are given to illustrate the optimum total inventory routing cost, the optimum delivery routes, the economic order quantities, the optimum service levels, the reorder points, the optimum common review interval and the optimum maximum inventory levels of all convenience stores in these designed routes. Sensitivity analyses are conducted based on the parameters including truck loading capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs and unit transport costs to support optimal distribution system design regarding the total inventory routing cost and GHG emission level. Originality/value The most important finding is that GIRP model with reordering point inventory control policy should be applied for the first replenishment and delivery run and GIRP model with periodic review inventory control policy should be conducted for the remaining replenishment and delivery runs based on overall simulation results. The other very important finding concerning the global warming issue can help decision makers of GIRP distribution system to select the appropriate type of truck to deliver products to all retail stores located in the planned optimal delivery routes depending on GHG emission consumptions.

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46

Guo, Hao, Congdong Li, Ying Zhang, Chunnan Zhang, and Yu Wang. "A Nonlinear Integer Programming Model for Integrated Location, Inventory, and Routing Decisions in a Closed-Loop Supply Chain." Complexity 2018 (July 22, 2018): 1–17. http://dx.doi.org/10.1155/2018/2726070.

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Facility location, inventory management, and vehicle routing are three important decisions in supply chain management, and location-inventory-routing problems consider them jointly to improve the performance and efficiency of today’s supply chain networks. In this paper, we study a location-inventory-routing problem to minimize the total cost in a closed-loop supply chain that has forward and reverse logistics flows. First, we formulate this problem as a nonlinear integer programming model to optimize facility location, inventory control, and vehicle routing decisions simultaneously in such a system. Second, we develop a novel heuristic approach that incorporates simulated annealing into adaptive genetic algorithm to solve the model efficiently. Last, numerical analysis is presented to validate our solution approach, and it also provides meaningful managerial insight into how to improve the closed-loop supply chain under study.

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47

Wong, Lily, and Noor Hasnah Moin. "Ant Colony Optimization For Split Delivery Inventory Routing Problem." Malaysian Journal of Computer Science 30, no. 4 (December 15, 2017): 333–48. http://dx.doi.org/10.22452/mjcs.vol30no4.5.

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48

Chugunov, E. S., and V. V. Zakharov. "Heuristic Method for Solving Multi-Product Inventory Routing Problem." Informatsionno-upravliaiushchie sistemy (Information and Control Systems) 6, no. 79 (December 2015): 105–11. http://dx.doi.org/10.15217/issn1684-8853.2015.6.105.

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49

Gaur, Vishal, and Marshall L. Fisher. "A Periodic Inventory Routing Problem at a Supermarket Chain." Operations Research 52, no. 6 (December 2004): 813–22. http://dx.doi.org/10.1287/opre.1040.0150.

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50

Lei, Lei, Shuguang Liu, Andrzej Ruszczynski, and Sunju Park. "On the integrated production, inventory, and distribution routing problem." IIE Transactions 38, no. 11 (November 2006): 955–70. http://dx.doi.org/10.1080/07408170600862688.

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