Inventory Model Research Articles

Optimal production plan in a production inventory model under the coexistence of both green and regular products: A study on Indian textile industry

The necessity to restrict environmental pollution worldwide has led to the development and implementation of green products. Various sectors within the industrial domain are now placing emphasis on the creation of environmentally friendly products in order to minimize their impact on the environment. The textile industry, both in India and globally, significantly contributes to environmental pollution through the release of untreated effluents into water and the emission of carbon into the atmosphere. Consequently, the popularity of green apparel is steadily increasing as a means to alleviate this crisis. It has been reported that, despite customer preference for green apparel, factors such as the cost of these products and a lack of consumer awareness regarding their importance deter a significant portion of potential buyers from making purchases. In order to stimulate demand for green apparel, the government could implement a subsidy scheme and initiate advertising campaigns highlighting the benefits of using such products. In the market, green apparel and regular apparel are viewed as substitutes for one another and coexist. As a result, there is a constant price competition between these two types of products. This research examines the optimal inventory decisions of manufacturers in a production inventory model where both green and regular products are present and product substitution is allowed. The demand for both types of products is influenced by their respective selling prices. Furthermore, the level of greenness and the efforts put into advertising green products have positive effects on generating demand for these products. The resulting objective function in this model is non-concave, and the quantum behaved particle swarm optimization technique is utilized to solve the corresponding optimization problem. Additionally, Mathematica software has also been employed to verify the efficiency of the proposed solution algorithm. To provide economic validation of the proposed model, a numerical example is presented. Finally, a sensitivity analysis of the key parameters is conducted to gain insights into their impact on the optimal solution. The results indicate that strategic and well-executed advertising campaigns, along with government subsidy schemes, can boost the market for green products and generate greater profits for manufacturers.

A dual-rate production inventory model for deteriorating items: Addressing shortages with variable production cycles

A dual-rate production inventory model for deteriorating items: Addressing shortages with variable production cycles

A Production inventory model for deteriorating items with quadratic time dependent demand with two levels of Production

A Production inventory model for deteriorating items with quadratic time dependent demand with two levels of Production

A sustainable manufacturing–remanufacturing inventory model with price and green sensitive demand for defective and usable items

A sustainable manufacturing–remanufacturing inventory model with two production phases is presented in this study. In this two phases model, the first phase is used for manufacturing whereas the second phase is used for both manufacturing and remanufacturing. Keeping in mind about environment and price of products, it is presumed that market demand rate is influenced by the greenness of item and unit selling price simultaneously. Remanufacturing is performed for non-serviceable items i.e., defective items which are produced from imperfect production and used items that are purchased from the market. But the products for which remanufacturing cost is more than manufacturing cost are scraped off. In the production process carbon releases from setting up, manufacture and holding of the perfect items. To mitigate the carbon emission carbon tax regulation is imposed. Next, concavity of the profit function is driven through the pseudo concavity. Profit is maximized by minimizing the economic, environmental, and social effect. The models are illustrated by a numerical example and a sensitivity analysis is also elaborated for fixed parameters. Special cases are provided to compare the result with previous literature. The results shows that manufacturer wishes to engage in remanufacturing as remanufacturing cost continuously increases with respect time. Another important finding is that the manufacturer can choose remanufacturing as the cost is lower than manufacturing for saving environment. Finally, mathematical findings are listed and managerial implications are recorded.

A Closed-Loop Supply Chain Inventory Model with Carbon Emissions and Green Technology Investment

This paper proposes an inventory model that integrates a manufacturer and a retailer in a supply chain system. The model employs environmentally friendly technology investments to reduce the emissions produced in the process. Furthermore, the demand at the retailer's end is unpredictable, and green investments affect the average demand levels. This research aims to identify the optimal delivery lot, the number of deliveries, the Safety Factor, the level of green technology, and the collection level to maximize the joint total profit. Numerical examples illustrate the model's practical application, and algorithms are developed to solve the problem. Sensitivity analysis is used to determine the key model parameters' effect on the model's behavior. Green investments have been shown to reduce emissions and increase returns on second-hand goods, thus enhancing the environmental efficiency of supply chains.

Pengoptimalan Persediaan Bahan Baku Utama Untuk Mengatasi Tantangan Industri Menggunakan Metode Just In Time (JIT) Dan Silver Meal Heuristic Pada PT. Indonesia Plafon Semesta

The inventory problem that still occurs at PT Indonesia Plafon Semesta is the accumulation of resin and calcium raw materials which results in considerable losses. PT Indofon began to experience a buildup of resin raw materials in August amounting to 16,100Kg and for calcium amounting to 15,200Kg so that each month it soared until its peak in October, namely a buildup of 23,500Kg of resin, and 23,200Kg of calcium. The use of the right method for inventory control problems is Just In Time , which is a management strategy used to optimize the production process and product delivery by buying and producing only when needed and at the right time. Silver Meal Heuristic, a heuristic approach used in inventory control to determine the optimal lot size. The quantity of raw material orders using JIT is relatively more than the silver meal, but with JIT the supplier only needs to send 4-5 times, meaning that the company can save costs. The quantity and frequency obtained from data processing using silver meal is more optimal. However, the cost of raw material inventory incurred is quite large. For a decrease in efficiency costs by implementing a policy of making purchases with Just In Time (JIT) and is expected to save on raw material inventory costs. So that these cost savings can be allocated by the company to other costs.

An Inventory Model for Deteriorating Items with Stock-Dependent Demand under Fuzzy Lead Time and Partial Backlogging

For inventory management purposes, lead time refers to the amount of time it takes for a purchase order to be completed. Its effect is an important phenomenon in inventory management system. It also plays a significant role when lead time unknown to the decision makers. This paper deals with an inventory model for deteriorating items under fuzzy lead time. The stock dependent demand with partially backlogged shortages are considered in the proposed model. The total inventory costs for both crisp model and fuzzy model are derived. The fuzzy lead time is assumed to be triangular and trapezoidal numbers. The signed distance methods (SD) is used for defuzzification purpose. The developed model is validated with the help of numerical illustration under both crisp and fuzzy scenario. A pictorial presentation is furnished to explain the behaviour of the total inventory costs towards lead-crisp, lead-triangular and lead-trapezoidal values. Lastly a sensitivity analysis is performed to judge the sensitive behaviour of the total cost towards changes of the cost parameters.

Optimal profit in two-level trade credit eoq model with default risk and reminder cost under finite time horizon having time-dependent demand and deterioration

Trade credit is a type of promotional activity that generally increases demand and revenue but also invites default risk due to dishonest customers. Due to default risk, revenue is lost, and to overcome this, an arrangement is made to remind the defaulters. A retailer dealing with a perishable item wants to exhaust the stock quickly after a certain deterioration level. Demand and deterioration of the products are normally dynamic. The business period of seasonal products is uncertain. Considering these facts, we formulate and analyze a two-level trade credit inventory model, where the wholesaler and retailer give credit periods to their corresponding downstream customers. After a certain level of deterioration, the retailer increases the credit period for the customers for early stock exhaustion, and to reduce default risk, a reminder cost is introduced. These activities increase the profit. The mathematical models under different circumstances are formulated for different time horizons. Some existing results are deduced. The models are numerically solved using a parametric study and the Generalised Reduced Gradient method through LINGO 19.0 software. Some lemmas and theorems are deduced to establish the analytical outcomes. Trade-offs between the number of the business cycle, trade credit, and reminder cost against optimum profit are separately demonstrated. The results with and without reminder cost are compared, and it is shown that the model with reminder cost fetches more profit. Profit under different uncertain environments are evaluated, and they differ marginally. Some beneficial impacts are discussed.

Emission reduction investment for green products of a manufacturing system via optimal control theory and artificial hummingbird algorithm

Now, due to the global industrialization, carbon emission from the industrial section imposes a severe impact on the current environment. Due to the increasing awareness of environmental and sustainability issues, green products, also known as eco-friendly, include a considerable impact on the today’s market economy. Keeping mind of these two important issues of the current scenario, a production inventory model is developed in which time-dependent production rate is considered. In this work, the level of greenness of the products, selling price and time, influences the customers’ demand rate. Again, two different cases are considered due to the business assimilation of defectively manufactured items: rework and salvage policies. Additionally, the company uses pollution reduction technologies to lower emissions from the manufacturing firms in order to lower emission taxes. The Euler-Lagrange equation is used to solve an optimal control problem that arises in the model as time-dependent production rate is taken into the account. The optimisation problem corresponding to the proposed model is solved with the help of artificial hummingbird algorithm (AHA) as the system's average profit functions for both of the considered scenarios are highly nonlinear in nature w. r. to its decision variables. Further, the results obtained from AHA are compared with the help of other well-known metaheuristics. Finally, sensitivity analysis is conducted to arrive at meaningful conclusions and implications for the competent manager of the manufacturing firm.

A Three-Warehouse Inventory Model for Deteriorating Items to Study the Effective Preservation Cost and Green Investment Under Partial Substitution

A Three-Warehouse Inventory Model for Deteriorating Items to Study the Effective Preservation Cost and Green Investment Under Partial Substitution

Collaborative system model construction and simulation optimization of e-commerce enterprise supply chain

ABSTRACT With the rise and rapid development of the e-commerce industry, the pressure borne by the supply chain of e-commerce enterprises is increasing, which greatly increases the complexity and uncertainty of inventory management in the e-commerce supply chain. The study integrates system dynamics and collaborative supply chain inventory management to construct a new model. The study first uses the concept of system dynamics to optimize the supply chain inventory management system; then the collaborative supply chain is used to construct a system model by combining the optimized management system. The results show that the collaborative supply chain inventory management model constructed by the study can better accelerate the product turnover rate, improve the overall operational efficiency and market competitiveness of the supply chain, and can provide new support for the development of e-commerce enterprises.

A Replenishment Policy for an Inventory Model with Price-Sensitive Demand with Linear and Quadratic Back Order in a Finite Planning Horizon

A Replenishment Policy for an Inventory Model with Price-Sensitive Demand with Linear and Quadratic Back Order in a Finite Planning Horizon

Sustainability in a Reverse Logistics Inventory Model with Carbon Emission, Reliability and Credit Financing Policy

Sustainability in a Reverse Logistics Inventory Model with Carbon Emission, Reliability and Credit Financing Policy

An Inventory Model for Deteriorating Items with Price Dependent Demand Under Fuzzy and Neutrosophic Fuzzy Environment

This paper represents an inventory model of deteriorating items under fuzzy and neutrosophic environment without allowing shortages in inventory. Demand is price dependent and is compared between crisp, fuzzy and neutrosophic fuzzy demand. Realistically it is observed that although the cycle time of any supply chain system is uncertain but uncertainty is mainly dependent on the selling price. So, here we have compared the model using crisp, fuzzy and neutrosophic fuzzy demand. The result is illustrated with the help of numerical example under fuzzy and neutrosophic fuzzy demand. KEYWORDS: Inventory, Fuzzy and Neutrosophic Fuzzy Price dependent Demand.

Uncertain random optimal control model for deteriorating inventory with the finite horizon

This paper examines a deteriorating inventory model with combined pricing and production during a limited selling time in an uncertain random setting. Demand is influenced by market size and price, which are portrayed in terms of random variables. The uncertainty factors affecting the inventory are represented by multiple Liu processes. Inventories deteriorate by a certain percentage and back-orders for inventories are permitted. This study aims to determine the optimal level of production rate and pricing that maximizes the producer’s total expected profit through the application of uncertain random optimal control theory, which is based on uncertain theory and probability theory. Numerical examples are used to compare joint pricing dynamic production model with dynamic production model under an optimal static price, highlighting the benefits of implementing joint pricing dynamic production model. Furthermore, it is evident that an increase in deterioration rate results in decreased on-hand inventory, yet higher production rate and price. It should also be noted that different levels of probability expectation of demand can have an impact on production inventory problem.

A comprehensive inventory management model with weibull distribution deterioration, ramp-type demand, carbon emission reduction, and shortages

This study examines the challenges of two warehouses operating under Last-In-First-Out (LIFO) order policies in inventory management, including unpredictable demand patterns, the decay of Weibull distribution, and the need to reduce carbon emissions by adopting green technology. The research addresses various shortage circumstances using advanced inventory modeling techniques to manage ramp-type demand and Weibull distribution deterioration. Additionally, it aims to reduce carbon emissions by incorporating environmentally friendly technologies. By combining advanced inventory modeling with green technology, businesses can effectively manage unpredictable market situations while actively contributing to the global initiative of reducing carbon emissions. It also takes into account various potential backlog scenarios. The proposed model also considers no, partial, and complete shortages and their combinations. This research aims to determine the optimal cycle duration for retailers to increase their total profit while simultaneously investing in green technology. A numerical illustration is provided, and a sensitivity analysis is performed in MATLAB on the optimal solutions concerning parameters to demonstrate applicability.

Optimal decisions for green products with advanced payment scheme and selling price‐dependent demand under interval uncertainty

In the current competitive market, green products are becoming more and more in demand across all customer demographics due to environmental contamination. Consequently, this has a direct impact on the notable rise in product demand. Conversely, a product cannot be used or repurposed if its maximum useful life has already elapsed. Therefore, the product's maximum lifespan plays a crucial role in inventory management. Advance payment is a critical component of the supplier–retailer relationship for a seasonal product with high demand. By integrating these issues, an inventory model for perishable goods is created, wherein the demand for the good is impacted by both its selling price and its green level. The model also includes linearly time‐dependent holding costs. According to this model, product greenness has an increasing effect on per‐unit costs. Different numerical examples are studied for supporting this model. Optimality of the objective function is shown graphically as well as numerically with the help of MATHEMATICA software. Finally, a fruitful conclusion is reached after performing a sensitivity analysis and visually depicting the impact of changing various factors.

Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit

Background: Efficient inventory management is critical for sustainability in supply chains. However, maintaining adequate inventory levels becomes challenging in the face of unpredictable demand patterns. Furthermore, the need to disseminate demand-related information throughout a company often relies on cloud services. However, this method sometimes encounters issues such as limited bandwidth and increased latency. Methods: To address these challenges, our study introduces a system that incorporates a machine learning algorithm to address inventory-related uncertainties arising from demand fluctuations. Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. The system is integrated with fog computing to facilitate the rapid dissemination of information throughout the company. Results: In experiments, we compare the proposed system with the conventional demand estimation approach based on historical data and observe that the proposed system consistently outperformed the conventional approach. Conclusions: This research introduces an inventory management system based on a novel deep learning architecture that integrates the attention mechanism with cloud computing to address the Newsvendor problem. Experiments demonstrate the better accuracy of this system in comparison to existing methods. More studies should be conducted to explore its applicability to other demand modeling scenarios.

Different type of pay later facility for green product with selling price dependent demand using grey wolf optimizer

The use of trade credit finance is becoming more widely acknowledged as a crucial approach to improving inventory system profitability. We review an inventory model with depending on permitted payment delays for which, if the retailer place an orders higher than or equal to a predefined quantity S1, then the supplier will provide a fully pay in later facility of ξ periods (i.e., there will be no charge of interest until ξ). On the other hand the retailer need to pay a partial amount of payment to the supplier if the order quantity is less than S1, and the remaining amount may be deferred for up to ξ periods. Main objective of this study is to investigate the inventory model with different situations under delayed payment facility. In addition, determining the product's demand also involves taking into account the item's greenness and selling price. We have also considered the fact that the cost of buying is influenced by the product's degree of greenness. We employ the meta heuristic algorithm Grey Wolf Optimizar (GWO) to assist us in solving the problem, and we compare the outcomes with the aid of a few other algorithms (Whale optimisation algorithm (WOA) and Artificial electric field algorithm (AEFA)). In the end, we resolve several numerical cases to support the model. The concavity of the desired function is graphically displayed using MATLAB software.

Exponential smoothing method against the gradient boosting machine learning algorithm-based model for materials forecasting to minimize inventory

The optimization of resources and reduction of costs through efficient inventory management are paramount to organizational success. This study undertakes a comparative analysis of two distinct forecasting methodologies, Exponential Smoothing (ES) and Gradient Boosting (GB), within the framework of materials forecasting aimed at inventory minimization. Our study introduces innovation by methodically scrutinizing these approaches within a unified framework, shedding light on their merits and shortcomings. This comparative analysis gives practitioners a practical roadmap for the optimal forecasting strategy to streamline inventory management operations. Methodologies are evaluated based on their efficiency in predicting material demand, encompassing metrics such as accuracy, computational efficiency, and suitability across various inventory management scenarios. Response surface methodology entails refining processes to modify factorial variables’ configurations to attain a desired peak or trough in response. The SPSS results show that the ES method has 43.20%, surpassing the accuracy of the inventory optimization model, which stood at 65.08%. The response surface methodology results show that 45.20% profit was achieved for the variable and operational cost process parameters. This research seeks to unveil the traces of each method, facilitating decision-makers in selecting an optimal forecasting strategy tailored to their specific inventory management requirements. The analysis shows that the ES method surpasses the accuracy of the GB machine learning for material forecasting to minimize inventory.