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Journal articles on the topic 'Optimal inventory management'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Jakšič, M., and J. C. Fransoo. "Optimal inventory management with supply backordering." International Journal of Production Economics 159 (January 2015): 254–64. http://dx.doi.org/10.1016/j.ijpe.2014.09.015.

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2

Agrawal, Narendra, and Stephen A. Smith. "Optimal inventory management using retail prepacks." European Journal of Operational Research 274, no. 2 (April 2019): 531–44. http://dx.doi.org/10.1016/j.ejor.2018.10.014.

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3

Tayi, Giri Kumar. "Optimal inventory cycle counting." Omega 13, no. 6 (January 1985): 535–39. http://dx.doi.org/10.1016/0305-0483(85)90041-6.

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4

Baradel, Nicolas, Bruno Bouchard, David Evangelista, and Othmane Mounjid. "Optimal inventory management and order book modeling." ESAIM: Proceedings and Surveys 65 (2019): 145–81. http://dx.doi.org/10.1051/proc/201965145.

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We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [12, 18, 19], the MM and the HFT define their trading strategy by optimizing the expected utility of terminal wealth, while the IB has a prescheduled task to sell or buy many shares of the considered asset. We derive the variational partial differential equations that characterize the value functions of the MM and HFT and explain how almost optimal control can be deduced from them. We then provide a first illustration of the interactions that can take place between these different market participants by simulating the dynamic of an order book in which each of them plays his own (optimal) strategy.

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5

Su, Shou-Mei, and Shy-Der Lin. "The Optimal Inventory Policy of Production Management." Engineering 05, no. 05 (2013): 9–13. http://dx.doi.org/10.4236/eng.2013.55a002.

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6

Wagner, Michael R. "Robust Inventory Management: An Optimal Control Approach." Operations Research 66, no. 2 (April 2018): 426–47. http://dx.doi.org/10.1287/opre.2017.1669.

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7

Stoikova, L. S. "Optimal inventory management under incomplete demand information." Cybernetics and Systems Analysis 27, no. 4 (1992): 573–80. http://dx.doi.org/10.1007/bf01130369.

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8

Raviv, Tal, and Ofer Kolka. "Optimal inventory management of a bike-sharing station." IIE Transactions 45, no. 10 (October 2013): 1077–93. http://dx.doi.org/10.1080/0740817x.2013.770186.

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9

Zhang, Mingyang, Xufeng Yang, Taichiu Edwin Cheng, and Chen Chang. "Inventory Management of Perishable Goods with Overconfident Retailers." Mathematics 10, no. 10 (May 17, 2022): 1716. http://dx.doi.org/10.3390/math10101716.

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In recent years, many retailers sell their products through not only offline but also online platforms. The sales of perishable goods on e-commerce platforms recorded phenomenal growth in 2020. However, some retailers are overconfident and order more products than the optimal ordering quantity, resulting in great losses due to product decay. In this paper, we apply the newsvendor model to analyze the impacts of overconfident behavior on the retailer’s optimal pricing and order quantity decisions and profit. Our model provides the overconfident retailer with a feasible and effective method to adjust optimal ordering and pricing decisions. Through numerical studies, we examine the retailer’s optimal decisions under the scenarios of complete rationality, over-estimation, and over-precision. We find that the over-estimation retailer always orders more products than the optimal order quantity, and the over-precision retailer always orders fewer products than the optimal order quantity. Under some conditions, overconfidence hurts the retailer’s revenue to a large extent. Therefore, it is beneficial for the overconfident retailer to adjust its order quantity according to our research findings.

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10

Sun, Pei Hong, Lei Tang, and Li Ying Tang. "Application of Optimal Control in Inventory Management of Production." Applied Mechanics and Materials 29-32 (August 2010): 2503–8. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2503.

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Countering the inventory management problem of manufacturing enterprise, according to the optimal control theory, considering the numbers of products as control variables and the stocks as the state variables, this essay establishes systemic real-time dynamic model, gives the objective function, and makes use of dynamic programming method to solve the optimal control and obtains the optimal inventory, which provides a theoretical foundation for the production and inventory management of manufacturing enterprise.

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11

Calvert, Ian. "The Optimal Inventory Replenishment Review Period." Journal of the Operational Research Society 37, no. 3 (March 1986): 319. http://dx.doi.org/10.2307/2582213.

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12

Calvert, Ian. "The Optimal Inventory Replenishment Review Period." Journal of the Operational Research Society 37, no. 3 (March 1986): 319. http://dx.doi.org/10.1057/jors.1986.51.

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13

Yu, Wenfang, Guisheng Hou, and Baogui Xin. "Decision-Making Optimization of Risk-Seeking Retailer Managed Inventory Model in a Water Supply Chain." Discrete Dynamics in Nature and Society 2021 (April 26, 2021): 1–18. http://dx.doi.org/10.1155/2021/9943753.

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Water retailer managed inventory is a classical and inevitable inventory management mode in present economic society. Stochastic models can more clearly explain demand uncertainty and are closely related to water supply chains. Risk preferences are widely valued in behavioral operation management. Related to the risk preferences in inventory management, the research on risk aversion is dominant, while risk-seeking is insufficient. Based on the model assumptions, the risk-seeking retailer’s optimal decision-making inventory model with stochastic demand in a water supply chain is studied. The risk-seeking retailer’s optimal inventory quantity, optimal inventory cost, supplier profit, retailer profit, and the profit of the entire water supply chain are derived. The validity of the equations is proved. The sensitivity analysis of the risk-seeking retailer’s optimal inventory decision-making is carried out. The risk level effects on the five dimensions, the retail price, wholesale price, unit shortage cost, unit inventory cost, and unit residual value, are displayed through numerical simulation. The optimal inventory quantity and optimal inventory cost of the risk-seeking retailer are obtained.

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14

ZHANG, JU-LIANG. "INTEGRATED DECISION ON PRICING, PROMOTION AND INVENTORY MANAGEMENT." Asia-Pacific Journal of Operational Research 29, no. 06 (December 2012): 1250038. http://dx.doi.org/10.1142/s0217595912500388.

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Firms often utilize promotion (such as coupons, advertisements, recruitment of excellent salespeople, and leafleting etc.) and dynamic adjustment of price to manage customers as well as proper production/inventory plan to satisfy the customers to get maximal profit in a firm. The decision on promotion and pricing and the decision on production/inventory must support each other. This paper addresses coordinated decision on pricing, promotion(non-price promotion) and inventory management. Specifically, we study a single item, periodic review model. The demand function is a linear demand function in which the market scale can be affected by the promotion conducted in that period. Unsatisfied demands are fully backlogged. We characterize the structure of the optimal policy that simultaneously determines the price, the promotion and the ordering quantity to maximize the total discounted profit with finite and infinite period problems. We show that the optimal replenishment policy is the quasi base stock list price target promotion policy, i.e., there exist a critical inventory level, a list price and a target promotion such that it is optimal to order up to the critical level, charge the list price and conduct the target promotion when the initial inventory is below the critical level and order nothing, conduct a higher promotion and charge a proper price to increase the demand otherwise. We also prove that the expected demand and the optimal promotion are increasing in the inventory, and price and promotion are complementary. Then we extend the problem to the case with capacity constraint. We show that the modified quasi base stock list price target promotion policy is optimal. For the joint decision problem on pricing, promotion and inventory control with positive fixed setup cost, we show that the optimal policy is (s, S, p, e) policy. That is, there exist two critical inventory levels stand St(st≤ St), a list price ptand a target promotion etin period t such that order up to St, charge price ptand conduct promotion etwhen the initial inventory is less than stand order nothing, charge a proper price and conduct a proper promotion otherwise.

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15

Yang, Jian, and Can Rong Zhang. "Study on the (s, S) Policy for the Manufacturing Enterprises Inventory Management." Advanced Materials Research 926-930 (May 2014): 3978–83. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3978.

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Using the inventory management in a manufacturing enterprise as the application background, this paper mainly studies on the optimal inventory policy for a part. The planning cycle of this problem is limited, and the demand in each planning cycle follows a discrete distribution. The objective is to minimize the inventory cost, namely, the total of transportation cost, inventory holding cost and penalty cost. Based on (s, S) policy, this paper puts forward two different inventory policies for the problem, and establishes two dynamic programming models accordingly. The numerical examples show that the optimal inventory policy significantly reduces the inventory cost.

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16

Davison, Matthew, Yuri Lawryshyn, and Volodymyr Miklyukh. "Optimal inventory policy through dual sourcing." Computational Management Science 17, no. 2 (June 2020): 327–55. http://dx.doi.org/10.1007/s10287-020-00371-8.

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17

Dimitrov, Stanko, and Oben Ceryan. "Optimal inventory decisions when offering layaway." International Journal of Production Research 57, no. 4 (September 25, 2018): 1161–75. http://dx.doi.org/10.1080/00207543.2018.1502484.

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18

Wang, Yun Rui. "Enterprise Inventory Management and Simulation." Advanced Materials Research 933 (May 2014): 874–78. http://dx.doi.org/10.4028/www.scientific.net/amr.933.874.

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Inventory is an important link in supply chain, according to characteristics and objectives of inventory control, the reasonable safety inventory, order point, the highest inventory and inventory strategy were determined based on historical sales data. Using Witness simulation software, the optimal strategy was selected from nine inventory strategies by running simulation model for one year. If goods shortage was allowing, the seventh schemes (72000,158100) was better ,the minimum total cost was 888158 yuan ; considering allowing a small amount of shortage, the cost of eighth schemes ( 82000,158100) was least of 1095167; the third scenarios (92000,138100) was selected if without shortage, and the minimum total cost was 1254909 yuan. This shows, the application of computer simulation to assist in the management of inventory is scientific and feasible, it can help enterprises to save the order and operation cost.

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19

Aardal, Karen, Orjan Jonsson, and Henrik Jonsson. "Optimal Inventory Policies with Service-Level Constraints." Journal of the Operational Research Society 40, no. 1 (January 1989): 65. http://dx.doi.org/10.2307/2583078.

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20

Aardal, Karen, Örjan Jonsson, and Henrik Jönsson. "Optimal Inventory Policies with Service-Level Constraints." Journal of the Operational Research Society 40, no. 1 (January 1989): 65–73. http://dx.doi.org/10.1057/jors.1989.6.

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21

Khmelnitsky, Eugene, and Gonen Singer. "An optimal inventory management problem with reputation-dependent demand." Annals of Operations Research 231, no. 1 (April 22, 2014): 305–16. http://dx.doi.org/10.1007/s10479-014-1600-z.

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22

Goonatilake, Rohitha, and Sofia C. Maldonado. "Essentials of Novel Inventory Management Systems." Engineering Management Research 7, no. 1 (February 12, 2018): 31. http://dx.doi.org/10.5539/emr.v7n1p31.

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Keeping inventories adequately maintained to meet the demands of future sales or uses in business enterprises determines the extent of business’s success. Hence, a successful inventory management is necessary, in particular, guided by the formulating of deterministic, stochastic, and probabilistic models. This essentially describes optimal inventory policies in terms of when and how to replenish the inventory. In this paper, we discuss several basic considerations used in determining inventory models that address the needs of current manufacturing and business enterprises. Theories and essentials supported by the novel approaches are integrated to reflect the representation of many actual situations. Multi-product and multi-facility models are given additional consideration at the end.

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23

Qiu, Yuzhuo, Jun Qiao, and Panos M. Pardalos. "Optimal production, replenishment, delivery, routing and inventory management policies for products with perishable inventory." Omega 82 (January 2019): 193–204. http://dx.doi.org/10.1016/j.omega.2018.01.006.

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24

Ruiz, Cesar, Edward Pohl, and Haitao Liao. "Bayesian degradation modelling for spare parts inventory management." IMA Journal of Management Mathematics 32, no. 1 (May 13, 2020): 31–49. http://dx.doi.org/10.1093/imaman/dpaa008.

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Abstract Decision makers in various sectors, such as manufacturing and transportation, strive to minimize downtime costs. Often, brief-planned stoppage times allow for changes in shifts and line configurations and longer periods are scheduled for major repairs. It is quite important to proactively make use of these downtimes to reduce the costs of unexpected downtimes due to failures. Among many aspects, the availability of spare parts significantly affects the operational costs of such systems. Current sensor technologies enable the condition monitoring of critical components and degradation-based spare parts management. This paper focuses on Bayesian degradation modelling for spare parts inventory management for a new system. We propose a stochastic dynamic program to minimize the expected spare parts inventory cost for a fixed planning horizon. A numerical example illustrates the value of Bayesian analysis in this management setting. The proposed methodology finds the optimal time between long stoppages and optimal spare parts order quantity when the prior information about the degradation process is accurate. The methodology can be used to analyse the sensitivity of the optimal solution to changes in the accuracy and bias of the prior distributions of the model parameters, the cost structure and the number of machines in the system.

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25

Fox, Edward J., Richard Metters, and John Semple. "Optimal Inventory Policy with Two Suppliers." Operations Research 54, no. 2 (April 2006): 389–93. http://dx.doi.org/10.1287/opre.1050.0229.

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26

Arrow, Kenneth J. "The Genesis of “Optimal Inventory Policy”." Operations Research 50, no. 1 (February 2002): 1–2. http://dx.doi.org/10.1287/opre.50.1.1.17785.

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27

Anjani, Andan, and Adirizal Nizar. "Inventory management and cost efficiency." International Journal of Research in Business and Social Science (2147- 4478) 10, no. 2 (March 21, 2021): 217–27. http://dx.doi.org/10.20525/ijrbs.v10i2.1042.

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This research aims to observe current inventory management applied by one of the medical equipment distributors specializing in eye health devices, and provide recommendations for an optimal inventory management system to achieve cost efficiency afterward. The method used in this research is quantitative, focusing on processing and analyzing numerical data obtained from the company to calculate safety stock and the number of orders. In addition, ABC classification is also used in data processing to group items based on their value. Items belonging to class A (having a value of 80% for the company) will be used in the data processing. The next step is to conduct forecasting simulations for demand forecasts. The results of the forecast will be used for calculating the safety stock and order quantity. The output from the results of data processing and analysis in this study shows 67 items included in class A, which will then be processed using Minitab software for forecasting. Based on comparing the four forecasting methods, the one with the lowest error value is Trend Analysis. If the company can apply the storage method according to the fixed-time period model calculation, there is a potential savings of US $ 717,133 or 63% of the total overstock

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28

Dong, Li Li. "The Study on the Model of Vendor Management Inventory in Supply Chain." Applied Mechanics and Materials 644-650 (September 2014): 6105–8. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.6105.

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In order to reduce inventory cost effectively, the vendor management inventory (VMI) is studied in this paper. The inventory problem of supply chain is described, the model of VMI is established, and the optimal solution is obtained. The research in this paper can enrich the supply chain management theory, and help enterprises improve inventory management level.

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29

Chen, Youhua Frank, Ye Lu, and Minghui Xu. "Optimal inventory control policy for periodic-review inventory systems with inventory-level-dependent demand." Naval Research Logistics (NRL) 59, no. 6 (July 24, 2012): 430–40. http://dx.doi.org/10.1002/nav.21498.

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30

LEE, T. S., and WEI SHIH. "Optimal forecast biasing in theoretical inventory models." International Journal of Production Research 27, no. 5 (May 1989): 809–30. http://dx.doi.org/10.1080/00207548908942589.

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31

Clark, Andrew J., and Herbert Scarf. "Optimal Policies for a Multi-Echelon Inventory Problem." Management Science 50, no. 12_supplement (December 2004): 1782–90. http://dx.doi.org/10.1287/mnsc.1040.0265.

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32

Emsermann, Markus, and Burton Simon. "Optimal Control of an Inventory with Simultaneous Obsolescence." Interfaces 37, no. 5 (October 2007): 445–54. http://dx.doi.org/10.1287/inte.1070.0304.

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33

Idayani, Darsih, and Subchan Subchan. "Optimal Control of Multi-Supplier Inventory Management with Lead Time." International Journal of Computing Science and Applied Mathematics 6, no. 1 (February 21, 2020): 23. http://dx.doi.org/10.12962/j24775401.v6i1.5040.

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34

Chang, Li-Fen, Shou-Mei Su, and Shy-Der Lin. "Optimal Inventory Policy of Production Management: A Present Value Framework." Engineering 05, no. 06 (2013): 556–60. http://dx.doi.org/10.4236/eng.2013.56067.

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35

Cheng, Longsheng, Ching-Shih Tsou, Ming-Chang Lee, Li-Hua Huang, Dingwei Song, and Wei-Shan Teng. "Tradeoff Analysis for Optimal Multiobjective Inventory Model." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/619898.

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Deterministic inventory model, the economic order quantity (EOQ), reveals that carrying inventory or ordering frequency follows a relation of tradeoff. For probabilistic demand, the tradeoff surface among annual order, expected inventory and shortage are useful because they quantify what the firm must pay in terms of ordering workload and inventory investment to meet the customer service desired. Based on a triobjective inventory model, this paper employs the successive approximation to obtain efficient control policies outlining tradeoffs among conflicting objectives. The nondominated solutions obtained by successive approximation are further used to plot a 3D scatterplot for exploring the relationships between objectives. Visualization of the tradeoffs displayed by the scatterplots justifies the computation effort done in the experiment, although several iterations needed to reach a nondominated solution make the solution procedure lengthy and tedious. Information elicited from the inverse relationships may help managers make deliberate inventory decisions. For the future work, developing an efficient and effective solution procedure for tradeoff analysis in multiobjective inventory management seems imperative.

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36

Lutfiana, Lina. "ANALISIS MANAJEMEN PERSEDIAAN UMKM JAZID BASTOMI BATIK DI PURWOREJO." Jurnal Ekonomi Syariah, Akuntansi dan Perbankan (JESKaPe) 4, no. 1 (July 27, 2020): 55–66. http://dx.doi.org/10.52490/jeskape.v4i1.689.

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Abstract This study aims to know more effective inventory management in UMKM Jazid Bastomi Batik. With descriptive qualitatif approach, researcher analyze inventory management done in UMKM Jazid Bastomi Batik. Observation and interview and documentation data results show inventory management of UMKM Jazid Bastomi Batik optimal yet. This is showing a minimum inventory, because limited inventory. For that, reseacher use safety stock technique, EOQ, and ROP to analyze inventory. It can improve effective and efficient inventory management. Keywords: Inventory, Inventory Management, Safety Stock, EOQ, ROP Abstrak Studi ini bertujuan untuk mengetahui manajemen persediaan yang paling efektif pada UMKM Jazid Bastomi Batik. Dengan pendekatan kualitatif deskriptif, peneliti menganalisis manajemen persediaan di UMKM Jazid Bastomi Batik.Data hasil observasi wawancara, dan dokumentasi menunjukkan bahwa manajemen persediaan pada UMKM Jazid Bastomi Batik belum optimal. Hal ini ditunjukkan dengan minimnya persediaan yang ada, karena terdapat keterbatasan dalam mengelola persediaan. Untuk itu, peneliti menggunakan teknik safety stock, EOQ, dan ROP untuk menganalisis persediaan yang dapat menunjang pengelolaan persediaan secara efektif dan efisien. Kata kunci: Persediaan, Manajemen Persediaan, Safety Stock, EOQ, ROP

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37

Gao, Long, Douglas J. Thomas, and Michael B. Freimer. "Optimal Inventory Control with Retail Pre-Packs." Production and Operations Management 23, no. 10 (March 21, 2014): 1761–78. http://dx.doi.org/10.1111/poms.12189.

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38

Xu, Jianjun, and Lanlan Cao. "Optimal in-store inventory policy for omnichannel retailers in franchising networks." International Journal of Retail & Distribution Management 47, no. 12 (December 9, 2019): 1251–65. http://dx.doi.org/10.1108/ijrdm-09-2018-0199.

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Purpose The purpose of this paper is to characterize the optimal ordering and allocation policy for a store replenishment decision in the context of an omnichannel retailer in a franchise network. The authors further show that a myopic policy is optimal, which circumvents the curse of dimensionality for the multi-period inventory model and help store managers optimize their decisions about the amount of inventory to stock for both online and offline demands and the percentage of inventory to reserve for online orders. Design/methodology/approach This research is trigged by several managerial studies which suggest reserving a certain percentage of the in-store inventory for online orders as a good store inventory allocation practice for omnichannel retailers in a franchise network. The authors used an analytical model to develop this practice by clarifying how store managers can decide on the amount of inventory to replenish and the percentage to reserve for online orders. Findings This study develops a finite horizon, periodic review inventory model to identify an optimal and dynamic replenishment and allocation policy. The analysis uncovers the system’s fundamental structural property concavity. The research shows that, due to this property, the optimal replenishment policy is a base-stock policy. The latter is due to the base stock level being independent of the initial inventory at hand, and the optimal allocation level being non-decreasing on the base-stock level. Research limitations/implications This study contributes to the literature on store inventory management for omnichannel retailers in a franchise network by investigating their optimal store inventory ordering and allocation policy. Nevertheless, the zero-lead time and zero-setup cost assumptions limit the findings. Practical implications Insights into an optimal store inventory policy may guide franchisee store managers to decide on the amount of inventory to replenish and the percentage to reserve for online orders. Originality/value The originality of this paper lies in its focus on in-store inventory management for omnichannel retailers in a franchise network. The findings are helpful for franchisor retailers to implement the omnichannel strategy at the level of in-store inventory management. Beyond using incentive systems, the franchisor should leverage legitimate powers by mentioning a relevant measure in their contracts with their franchisee to minimize their channel conflicts and ensure their customers have seamless shopping experiences.

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39

Mubiru, Kizito Paul. "Joint Replenishment Problem in Drug Inventory Management of Pharmacies under Stochastic Demand." Brazilian Journal of Operations & Production Management 15, no. 2 (June 1, 2018): 302–10. http://dx.doi.org/10.14488/bjopm.2018.v15.n2.a12.

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In today’s fast-paced and competitive market place, organizations need every edge available to them to ensure success in planning and managing inventory of items with demand uncertainty. In such an effort, cost effective methods in determining optimal replenishment policies are paramount. In this paper, a mathematical model is proposed that optimize inventory replenishment policies of a periodic review inventory system under stochastic demand; with particular focus on malaria drugs in Ugandan pharmacies. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for drugs that treat malaria. Using weekly equal intervals, the decisions of whether or not to replenish additional units of drugs were made using discrete time Markov chains and dynamic programming over a finite period planning horizon. Empirical data was collected from two pharmacies in Uganda. The customer transactions of drugs were taken on a weekly basis; where data collected was analyzed and tested to establish the optimal replenishment policy and inventory costs of drugs. Results from the study indicated the existence of an optimal state-dependent replenishment policy and inventory costs of drugs at the respective pharmacies.

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40

Ma, Le, and Yi Chai. "Modeling and Analysis of the Manufacturers Inventory in Closed-Loop Supply Chain." Applied Mechanics and Materials 541-542 (March 2014): 1507–12. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.1507.

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In this paper, the closed-loop supply chain inventory management as the research object, based on traditional inventory theory forward and reverse supply chain will be presented by combining deterministic closed-loop supply chain manufacturer's optimal inventory policy, determined by calculating the optimal production lot and do sensitivity analysis.

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41

Chou, Chien Chang. "A New Fuzzy Backorder Inventory Model for the Decision Making of Material Inventory in the Manufacture System." Key Engineering Materials 431-432 (March 2010): 106–9. http://dx.doi.org/10.4028/www.scientific.net/kem.431-432.106.

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Inventory management is one of important issues in the manufacture system. Thus, this paper proposes a new fuzzy approach to solve the inventory management problems in the manufacture system. In the past, few papers discussed the square roots of fuzzy number. The square roots of fuzzy number can be applied to solve the optimal inventory quantity problems in the fuzzy manufacture system. This paper first proposed the square roots of fuzzy number. Finally, the square roots of fuzzy number are applied to obtain the optimal order quantity for the fuzzy backorder inventory model in the manufacture system.

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42

Lin, Jennifer, Henry Chao, and Peterson Julian. "A demand independent inventory model." Yugoslav Journal of Operations Research 23, no. 1 (2013): 129–35. http://dx.doi.org/10.2298/yjor120127021l.

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This paper is an extension of Deng et al. (2007) that was published in the European Journal of Operational Research. We have generalized their model from ramp type demand to arbitrary positive demand while theoretically discovering an important phenomenon: the optimal solution is actually independent of the demand as pointed out by Wou (2010), Hung (2011) and Lin (2011). We extend their inventory models in which the deteriorated rate is any non-negative function and backlog rate is inversely linearly related to the waiting time. Our findings will provide a new inventory system to help decision makers decide the optimal ordering quantity and replenishment policy.

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43

Scarf, Herbert E. "Optimal inventory policies when sales are discretionary." International Journal of Production Economics 93-94 (January 2005): 111–19. http://dx.doi.org/10.1016/j.ijpe.2004.06.010.

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44

Li, Xiaoming. "Optimal inventory policies in decentralized supply chains." International Journal of Production Economics 128, no. 1 (November 2010): 303–9. http://dx.doi.org/10.1016/j.ijpe.2010.07.028.

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45

Cadenillas, Abel, Peter Lakner, and Michael Pinedo. "Optimal Control of a Mean-Reverting Inventory." Operations Research 58, no. 6 (December 2010): 1697–710. http://dx.doi.org/10.1287/opre.1100.0835.

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46

Mulya, Yudhia. "Inventory simulation-optimization model for small business." International Journal of Business, Economics, and Social Development 1, no. 2 (June 26, 2020): 55–60. http://dx.doi.org/10.46336/ijbesd.v1i2.36.

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Simulation-optimization inventory models are widely used in inventory management studies, including for perishable good. A good inventory management is required to save inventory cost. We use simulation approach to take into consideration of uncertain demand and lead time to obtain a better result of optimal order quantity that minimizes inventory cost. Simulation result shows that simulation-optimization models generates lower total inventory cost. The simulation provides information of objectives in inventory management: (1) how much to order and (2) when to order with minimum cost which gives valuable information in business decision making.

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47

Jiang, Qingsong, Wei Xing, Ruihuan Hou, and Baoping Zhou. "An Optimization Model for Inventory System and the Algorithm for the Optimal Inventory Costs Based on Supply-Demand Balance." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/508074.

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In order to investigate the inventory optimization of circulation enterprises, demand analysis was carried out firstly considering supply-demand balance. Then, it was assumed that the demand process complied with mutually independent compound Poisson process. Based on this assumption, an optimization model for inventory control of circulation enterprises was established with the goal of minimizing the average total costs in unit time of inventory system. In addition, the optimal computing algorithm for inventory costs was presented. Meanwhile, taking the agricultural enterprises in Aksu, Xinjiang, China, for example, the researchers conducted numerical simulation and sensitivity analysis. Through constantly adjusting and modifying the parameters values in model, the optimal stock and the optimal inventory costs were obtained. Therein, the numerical results showed that the uncertainty of lead time greatly influenced the optimal inventory strategy. Besides, it was demonstrated that the research results provided a valuable reference for the agricultural enterprises in terms of optimal management for inventory system.

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48

Singha, Kanokwan, Jirachai Buddhakulsomsiri, and Parthana Parthanadee. "Optimal Cycle Service Level for Continuous Stocked Items with Limited Storage Capacity." KnE Life Sciences 4, no. 2 (March 1, 2018): 82. http://dx.doi.org/10.18502/kls.v4i2.1659.

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This paper involves determining an optimal cycle service level for continuously stocked items that explicitly considers storage space capacity. Inventory management is under a continuous review policy. The total inventory management cost consisting of ordering cost, inventory holding cost, shortage cost, and over-capacity cost. Shortage items are assumed to be backlogged. A numerical example is provided to demonstrate the method. Keywords: Continuous Review; Cycle Service Level; Storage Space Capacity; Over-Capacity Cost

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Kharvi, Suresha, and T. P. M. Pakkala. "An Optimal Inventory Policy for Subsequent Price Reduction Problem." International Journal of Operations and Quantitative Management 26, no. 2 (December 23, 2020): 147. http://dx.doi.org/10.46970/2020.26.2.4.

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Chakravarty, Amiya K. "An Optimal Heuristic for Coordinated Multi-Item Inventory Replenishments." Journal of the Operational Research Society 36, no. 11 (November 1985): 1027. http://dx.doi.org/10.2307/2582435.

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