Inventory Model Research Articles

Optimization of a Two-Warehouse EOQ Model for Non-Instantaneous Deteriorating Items with Polynomial Demand, Advance Payment, and Shortages

In this paper, a two-warehouse inventory model with polynomial time-dependent demand for noninstantaneous deteriorating items is taken into consideration. As advertisement plays a vital role in business, more advertisements are to be made by the retailer to increase demand in the competitive market. In order to meet the high demand, it is necessary to maintain two warehouses to store the goods in inventory: one owned warehouse with limited capacity and one rented warehouse with unlimited capacity. In order to increase sales, the retailer anticipates a discount from the manufacturer, and in turn, the retailer is equipped to pay a portion of the purchase price in advance. The model aims to optimize the overall profit by determining the appropriate order quantity and cycle length. Furthermore, numerical examples are provided for the validation of the proposed model.

Potential Controlling Factors and Landslide Susceptibility Features of the 2022 Ms 6.8 Luding Earthquake

On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides susceptibility mapping associated with this event based on an updated landslide inventory and logistic regression (LR) modeling. Firstly, we quantitatively assessed the importance of different controlling factors using the Jackknife and single-variable methods for modeling landslide occurrence. Subsequently, four landslide susceptibility assessment models were developed based on the LR model, and we evaluated the accuracy of the landslide susceptibility mappings using Receiver Operating Characteristic (ROC) curves and statistical measures. The results show that ground motion has the greatest influence on landslides in the entire study area, followed by elevation, while distance to rivers and topographic relief have little influence on the distribution of landslides. Compared to the NEE plate, PGA has a greater impact on landslides in the SWW plate. Moreover, the AUC value of the SWW plate significantly decreases for lithological types and aspect, indicating a more pronounced lithological control over landslides in the SWW plate. We attribute this phenomenon primarily to the occurrence of numerous landslides in Permian basalt and tuff in the SWW plate. Otherwise, the susceptibility results based on four models indicate that high-susceptibility areas predicted by different models are distributed along both sides of seismogenic faults and the Dadu Rivers. Landslide data have a significant impact on the model prediction results, and the model prediction accuracy based on the landslide data of the SWW plate is higher.

A Sustainable Inventory Model with Advertisement Effort for Imperfect Quality Items under Learning in Fuzzy Monsoon Demand

In this paper, we proposed a sustainable inventory model with a learning effect for imperfect quality items under different kinds of fuzzy environments like crisp, general fuzzy, cloudy fuzzy, and monsoon fuzzy. We divided the mathematical model into three parts under the learning effect according to the real-time fuzzy components (crisp, cloudy, and monsoon environments) of the demand rate of the items. We minimized the total inventory cost with respect to cycle length in each environment under the proposed assumptions. The non-linear optimization technique is applied for the algorithm and the solution method to find the decision variable. Finally, we compared the total inventory cost under different fuzzy environments and our finding is that the fuzzy monsoon environment is a more effective fuzzy environment than crisp and cloudy fuzzy environments. We have presented a numerical example for the validation of the proposed model and have shown the impact of the inventory input parameters on the cycle length and total inventory fuzzy cost. The managerial insights and future scope of this proposed study have been shown in the sensitivity analysis and conclusion. The limitations, application, future extension and scope, and social implementation have been shown in this research study.

Robust optimization approaches in inventory management: Part B - the comparative study

This comparative study, constituting Part B of our extensive investigation into Robust Optimization (RO) in inventory management, builds on the foundational insights from Part A’s survey. It conducts a thorough analysis of various RO formulations and algorithms, emphasizing their practical application, efficacy, and computational considerations in inventory management contexts. This study is meticulously structured to address the pivotal inquiries identified in Part A, encompassing the effective representation of uncertainty, the selection of optimal decision criteria, the influence of decision rules on inventory management performance, computational challenges, and the adaptability of these methods to evolving technological and market conditions. This research juxtaposes theoretical findings with empirical data, offering a comprehensive evaluation of the strengths, limitations, and practical implications of each robust inventory model discussed in the literature. This investigation not only complements the survey in Part A but also serves as a standalone, in-depth contribution to robust inventory management.

A Production Inventory Model for Fractionally Time-Dependent Demand Rate with Weibull Deterioration and Partially Backlogged Items

A Production Inventory Model for Fractionally Time-Dependent Demand Rate with Weibull Deterioration and Partially Backlogged Items

MULTI-ITEM PROBABILISTIC INVENTORY MODELS CONSIDERING DISCOUNT, EXPIRATION, WAREHOUSE CAPACITY, AND CAPITAL CONSTRAINTS

In general, companies have inventory stored in warehouses to be used or sold in the future. To optimize inventory, companies require a model to determine the appropriate order quantity and optimal reorder timing to minimize total inventory costs. To stay within the set capacity, several factors need to be considered in inventory management, such as a product's shelf life, warehouse capacity, and capital. On the other hand, suppliers may offer discounts, and companies tend to take advantage of them. However, they must consider the warehouse capacity and capital availability. This paper constructs two probabilistic multi-item inventory models, considering discounts, expiration, warehouse capacity, and capital constraint. The first model considers all-unit discounts, while the second deals with incremental discounts. We consider three items to be managed and examine three replenishment policies, namely individual order, joint order, and a combined policy of individual and joint order, to minimize the total inventory cost. Sensitivity analysis is performed on the joint order policy to determine the influence of parameter values' changes on the reorder time and total inventory cost.

Two-Warehouse Inventory Model: Interval Valued Costs, Advanced Payment, Stock-Dependent Demand, and Partial Backlogging

A great deal of inventory costs are typically unpredictable because of the unpredictability of a competitive market. The fluctating demand from customer's and the unpredictability of the market economy make it difficult for researchers and operation research practitioners to appropriately replicate an inventory problem. In order to prevail this type of unpredictability circumstances, in this paper, we have represented the inventory parameters as interval. Using this concept, we progressed a two-warehouse inventory model for deteriorating items with a fixed Shelf life, partially backlogging shortages and interval-valued deterioration rate. In addition to this the parameters like ordering cost, purchase cost, shortage cost, deterioration cost for both the rented and owned warehouse except the backlogging parameter have been considered as interval-valued. The uncertainty in the inventory parameters motivated us to view them as interval-valued in the present research. Based on the assumptions, the cost function of this problem is a highly nonlinear constraint optimization problem. Mathematica is used to tackle this nonlinear optimization problem. A numerical example has been presented to demonstrate the computational result. Then, a sensitivity analysis has been performed to study the effect of changes of different parameters of the model on the optimal policy.

A Deterministic Inventory Model with Two Warehouses and a Finite Rate of Replacement for Deteriorating Products

Abstract: When demand is uniform, shortages are permitted, and the replenishment rate is finite, a deterministic inventory model for degrading items with two warehouses is created. It is anticipated that there may be differences in the rates of item deterioration between the two warehouses. An analysis of the model is conducted for the continuous release pattern situation. The variance in the ideal inventory level and optimum cost for changing shortage costs is illustrated with a numerical example and discussion of several unique instances

Linear and Nonlinear Time-Dependent Deteriorated Inventory Control Model

Linear and Nonlinear Time-Dependent Deteriorated Inventory Control Model

Investigation of inventory management practices at m. products bakery and confectionary limited

Managing stock effectively is important for any organization, running a bakery firm is no exception because without enough stock, services to the customers will come to an abrupt stop. M. Products Bakery and Confectionary Limited provides services for 24 hours and accordingly, needs to keep stock of certain materials and other supplies in order to fulfill its duties effectively. It is generally held opinion that where stock management is poor, it greatly affects the company’s operations as well. Hence this study investigated the Inventory management Practices, M. Products Bakery and Confectionary Limited taken as a case study. A sample of 50 respondents was selected for the study. Questionnaires, interviews and observation were used in attaining data from respondents. The study revealed that the company ensured lean inventory management systems and strategic supplier optimization and most importantly the use of inventory recording technique (stock valuation using FIFO, inventory and issue methods) to manage its inventory. The study also revealed that the company had a computerized system as an inventory management practice but it had no existing inventory model. The study revealed that Inventory Management Practices have impacted on the company’s operations, profitability and the ability of the company to meet customer demand. However, among the gaps within the inventory management practices at M. Products Bakery and Confectionary Limited was limited space, delayed orders from suppliers leading to insufficient inventories, poor quality products from suppliers, bureaucratic process in procurement, purchase of raw materials with early expiration date, increased damages due to poor handling during production, overstocking/under stocking, weak management system , lack of training of the employees and un qualified employees, use of manual inventory management system in some departments, scattered storage facilities and poor transportation mechanism (resulting into spoilage of materials), theft and attacks from rats which reduces the amount of inventory available for sale, reduced demand from some customers especially during the covid19 outbreak and heavy taxes from the government. It was therefore recommended that there is need to expand on its production facility and also adopt advanced warehouse techniques, management to emphasize the importance of inventory management, company should improve its relationship with its suppliers, adopt accurate forecasting techniques, should train more executives and employees and also adopt an advanced information system such as Electronic Data Interchange.

Robust optimization approaches in inventory management: Part A—the survey

This work, the first part (Part A) of a comprehensive study, presents a survey on Robust Optimization (RO) in inventory management, highlighting its role in addressing uncertainties. This survey reviews various modeling frameworks, types of uncertainties, decision-making criteria, and decision rules essential to RO. The subsequent part (Part B) offers a comparative study, analyzing robust inventory models and highlighting key analytical and numerical contributions. This survey critically evaluates the effectiveness of RO in managing model uncertainty, enhancing decision-making processes, deriving structural insights, and boosting computational efficiency. Additionally, it discusses the limitations and challenges of applying RO to inventory management. While acknowledging the foundational role of traditional inventory literature in establishing essential theories, optimal policies, and efficient algorithms, this article addresses a significant gap by focusing on inventory management through the lens of RO methodology. Together with the comparative study, these works encapsulate the current state of robust inventory management, shedding light on future research directions and ongoing challenges in this evolving field.

Optimal analysis of time-price discount-advertisement dependent demand with credit policy for inventory models

Optimal analysis of time-price discount-advertisement dependent demand with credit policy for inventory models

Optimisation of a multilevel logistics network for prepositioned warehouses under an omni-channel retail model

A new omni-channel retailing mode, prepositioned warehouses, which developed rapidly in China during the COVID-19 pandemic, was introduced, and a three-level logistics network optimisation model for agricultural products was presented. This study takes into account the characteristics of the omni-channel retail model, which combines the two delivery methods of vehicle delivery and consumer in-store purchase, adds the freshness penalty incurred by fresh agricultural products in the storage process of the prepositioned warehouse, and establishes a mathematical optimisation model of the three-stage logistics network of the prepositioned warehouse that meets the characteristics of omni-channel retailing on the basis of the classical EOQ inventory model. In this work, the shortest delivery time and the smallest total cost are set as the optimisation objectives, and the multi-objective problem is transformed into a single objective through normalisation and linear weighting methods to obtain a mixed-integer optimisation model. This paper solves the problems of site selection for prepositioned warehouses, merchandise replenishment strategies, and optimisation of delivery routes based on data from real cases. Additionally, through sensitivity analysis, the management inspiration of the enterprise is obtained.

Data Farming the Parameters of Simulation-Optimization Solvers

The performance of a simulation-optimization algorithm, a.k.a. a solver, depends on its parameter settings. Much of the research to date has focused on how a solver’s parameters affect its convergence and other asymptotic behavior. While these results are important for providing a theoretical understanding of a solver, they can be of limited utility to a user who must set up and run the solver on a particular problem. When running a solver in practice, good finite-time performance is paramount. In this paper, we explore the relationship between a solver’s parameter settings and its finite-time performance by adopting a data farming approach. The approach involves conducting and analyzing the outputs of a designed experiment wherein the factors are the solver’s parameters and the responses are assorted performance metrics measuring the solver’s speed and solution quality over time. We demonstrate this approach with a study of the ASTRO-DF solver when solving a stochastic activity network problem and an inventory control problem. Through these examples, we show that how some of the solver’s parameters are set greatly affects its ability to achieve rapid, reliable progress and gain insights into the solver’s inner workings. We discuss the implications of using this framework for tuning solver parameters, as well as for addressing related questions of interest to solver specialists and generalists.

Tackling inventory losses of small accessories in engineering manufacturing: a case study of Euro Manufacturing

Learning outcomes After completion of the case study, the students will be able to understand ABC analysis and develop a systematic approach using PDCA, analyze processes, technology, employee training and supplier relationships when analyzing shrink and developing solutions, evaluate how technology improves production inventory control and visibility and recognize the importance of fostering a culture of employee accountability and ownership to minimize inventory loss and improve overall operational efficiency. Case overview/synopsis On June 2, 2023, sitting in his office in Karachi, Pakistan, Khan Aamir, the manager of store and inventory at Euro Manufacturing, found himself immersed in a cloud of confusion. The incessant loss of inventory items, particularly the nut bolts and small accessories, had become a perplexing challenge. To address these losses and provide a cycle count report to the director of supply chain, Aamir, manager of store and inventory, was given the responsibility to take action. He was looking for a comprehensive approach to address the current problems and prevent further losses in the future. This case study examines the various reasons for the losses, including theft, inadequate inventory control methods, human error and problems with suppliers. It highlights the importance of established procedures, the use of technology (such as barcode scanning, radio-frequency identification tagging and inventory management software) and the cultivation of a culture of accountability among employees. Complexity academic level This case study is developed for class discussion in the course of operations management or supply chain management. This case study is suitable for use with undergrad students. This case study can be taught in a module on operations management or supply chain management, as part of a broader course in business management or industrial engineering. Supplementary materials Teaching notes are available for educators only. Subject code CSS: 9: Operations and logistics.

Neutrosophic Inventory Management: A Cost-Effective Approach

Abstract Classical inventory models (IM) serve as quantitative tools for determining the optimal order quantities, timing of orders, and safety stock levels for specific inventory items or item groups. Zadeh (1965. Fuzzy sets. Information and Control, 8, 338–353) introduced fuzzy theory and Dubois and Parade (1988. Fuzzy logic in expert systems: The role of uncertainty management. Fuzzy Sets and Systems, 28, 3–17) presented the study of fuzzy inventory model, which, however, exhibits limitations in effectively handling uncertainty, inaccuracies, and imprecise data. In 1999, Smarandache presented the idea of neutrosophic set theory to handle uncertainty. Using trapezoidal neutrosophic numbers, this study extends the idea of neutrosophic sets to inventory management, concentrating on resolving the uncertainty associated with holding costs, ordering costs, and shortage costs. First time within the literature of the neutrosophic set, our new method not only addresses existing problems but can also tackle other issues that no other authors have successfully resolved so far. Additionally, we conduct a comparative analysis of our proposed model against existing models in this article. Based on this comparative study, our findings assert the superior performance of our proposed model in relation to some of the existing models. In conclusion, we wrap up our research by presenting graphical, logical, and tabular comparisons with the existing methods.

Методи обґрунтування запасів запасних частин у системі управління транспортним процесом

The article formulates directions and analyzes the possibilities of substantiating and choosing the most optimal variant of applying mathematical models for forecasting the number and range of spare parts. The provided recommendations allow to take into account the multi-brand fleets of motor transport enterprises and trends for extrapolation in the short term, identified by the results of observations in real operating conditions. The main reasons for the occurrence of prolonged vehicle downtime due to technical malfunctions are considered. Traditional methods of planning spare parts inventories based on the use of reliability theory and results of mileage forecasting, standard time to failure; inventory management theory and statistical methods for forecasting random processes; methods of operations research; economic and mathematical methods; stochastic methods of transformation of random variables are analyzed. The article formalizes the grouping of factors that affect the size of stocks and the range of spare parts in the operation of rolling stock of enterprises, depending on the state of the system of organization of work of services for maintenance and repair of vehicles; structure of the fleet and technical characteristics of vehicles; level of development of the production base; availability, interest and qualification of personnel; operating conditions of automotive equipment. It is proposed to improve the logistics of spare parts inventory management and to formulate measures to improve the performance of carriers based on an assessment of the total costs incurred by the enterprise to maintain the reliability of rolling stock in operation and taking into account the amount of lost profit due to vehicle downtime in the technical service units. At the same time, it is recommended to use the mathematical apparatus of fuzzy set theory for decision-making, which allows formalizing the parameters of vaguely defined expectations of consumers of motor transport services and reducing the subjectivity of decision-making when solving problems of optimizing spare parts stocks in the transport process management system based on the analysis of the structure of consumer properties and service quality indicators.

Multistage supply chain inventory model for controllable deterioration and imperfect production with carbon emissions regulations under Stackelberg game approach

Multistage supply chain inventory model for controllable deterioration and imperfect production with carbon emissions regulations under Stackelberg game approach

A Study of an EOQ Model of Deteriorated Items with Pentagonal Dense Fuzzy Demand Rate

In this project work, we deal with an economic order quantity inventory model of deteriorating items under non-random uncertain demand. Here we consider the customers screen the fresh items during the selling period. After a certain period of time, the deteriorated items are sold at a discounted price. Firstly, we solve the crisp model, and then the model is converted into a fuzzy environment. Here we consider the pentagonal dense fuzzy, trapezoidal dense fuzzy, and triangular dense fuzzy for a comparative study. We have taken the numerical result using LINGO 18.0 software. Finally, sensitivity analysis and graphical illustration have been given to check the validity of the model.

Simple heuristics for the joint inventory and pricing models with fixed replenishment costs

This study considers the joint inventory and pricing problem of a firm selling a single item over a multi-period planning horizon. A fixed replenishment cost is incurred whenever the replenishment decision is made. Period demands are considered to be non-stationary, stochastic, and price-dependent. The literature shows that the optimal policy in such a system is of an (s, S, p)-type. The implementation of this type of policy might be challenging in practice due to the prevailing computational difficulty in determining the optimal policy parameters. This study, therefore, aims to develop computationally efficient heuristic approaches for the joint inventory and pricing problem. The proposed heuristics provide different levels of flexibility in making inventory and pricing decisions. Furthermore, they are essentially built on the concept of the replenishment cycle and do not require solving a stochastic dynamic program. Our numerical study demonstrates the profit efficiency of the proposed heuristic approaches against the optimal policy. The results also indicate that firms lacking the technical ability to employ a dynamic pricing strategy might still gain significant profit improvement by only using a dynamic inventory control policy.