Inventory Control Strategy Research Articles

Joint Optimization of Empty Container Repositioning and Inventory Control Applying Dynamic Programming and Simulated Annealing

Based on the division of public and exclusive hinterlands, this paper studies the joint optimization problem of empty container repositioning and inventory control under non-stationary demand within the port cluster. This paper established a stochastic mixed integer programming model using distributed robust optimization methods, combined with quantitative and periodic inventory control strategies. After conducting a deterministic transformation, this paper designed a hybrid algorithm of dynamic programming and simulated annealing to solve the model, and compared the various costs under stationary and non-stationary scenarios. The results show that the joint optimization of empty container inventory control and repositioning can always reduce the total cost of empty container management for shipping companies. Periodic inventory control strategies are more suitable for shipping companies to apply to empty container management in non-stationary situations. Sensitivity analysis shows that there is a positive correlation between uncertain demand parameters and the total cost of shipping companies. The superiority of the empty container repositioning mode proposed in this paper under sea-land coordination has also been demonstrated by changing the accessibility parameters between terminals.

Investigation of control strategies for the hydrogen fueled R-Graz cycle

In the context of large-scale hydrogen power generation, the Graz cycle and its improved version, the R-Graz cycle, exhibits excellent performance and technological feasibility. For such semi-closed, split cycles, apart from the conventional adjustments of fuel consumption and compressor IGV angle, compressor inlet pressure and split ratio are two additional crucial control parameters. In this study, six control strategies are firstly formulated and compared. The results indicate that the control strategy aiming to maintain the turbine inlet temperature outperforms the strategy aiming to maintain the exhaust temperature in terms of efficiency. Among them, the inventory control strategy demonstrates superior efficiency compared to the split ratio and IGV control strategies. Taking into account the advantages of various strategies, a hybrid inventory control strategy suitable for the R-Graz cycle is proposed. Based on this strategy, at 40% load and the ambient temperature of 19 °C, the efficiency reaches 87.06% of the design point, surpassing conventional F-class gas-steam combined cycle units. When the ambient temperature varies, under winter conditions of 10 °C and summer conditions of 30 °C, the full load net efficiency of the cycle reaches 72.06% and 69.60%, respectively. This research holds practical guidance value for the engineering application of the R-Graz cycle and the efficient utilization of hydrogen energy.

Design optimization and off-design performance analysis of one-dimensional supercritical CO2 Brayton cycle

Supercritical carbon dioxide (SCO2) recompression Brayton cycle (RBC) is an encouraging technology for thermal power conversion and heat harvest for a wide range of heat sources due to its high-efficiency, low auxiliary power consumption, and compact size. However, most studies on its design and off-design performance analysis have been limited to the simple zero-dimensional component models without considering aerothermodynamics and ambient conditions. In this study, we developed a set of fully one-dimensional components models for SCO2 compressors, turbines, and heat exchangers, and, then, applied them to the multi-objective design optimization of a SCO2 cycle targeting a 50 MW net power output. The cycle off-design performance analysis methods and the control strategies were developed to countermeasure the performance degradation against the varying cooling water temperatures and part loads. The multi-objective cycle design optimization demonstrates that the cycle thermal efficiency and total product unit cost can be 0.476 and 11.652$/GJ, respectively, for the 50 MW SCO2 RBC cycle. The choke margin of the main compressor is 0.136 less than that of the re-compressor, and condensation can lead to a sharp decline in efficiency under high-flow conditions, causing the earlier onset of choking. The off-design performance analysis shows that the turbine inlet pressure control improves cycle efficiency below the design point of 317 K but does otherwise above the point. In contrast, inventory control strategies can maintain stable cycle efficiency and achieve adjustable net power output between 10 % and 110 %. The findings have the potential to advance further the commercial application of SCO2 cycles in high-temperature thermal energy systems.

Optimization of Waste Power Battery Recycling Process from the Perspective of Supply Chain

With the rapid popularization of electric vehicles, the recycling of used power batteries has become an increasingly prominent issue. Recycling of used power batteries not only involves environmental protection, but also relates to the efficient utilization of resources. The current recycling system faces problems such as low recycling efficiency, high cost and resource waste. From the perspective of supply chain management, this paper analyzes the current situation and problems in the recycling process of used power batteries, and puts forward a series of optimization strategies, including recycling network optimization, logistics management improvement and inventory control strategies. This paper aims to provide theoretical support and practical guidance for the optimization of waste power battery recycling supply chain, and promote the sustainable development of recycling industry.

Improving the sales process of profitable perishable goods: an inventory control strategy in a planned economy

PurposeThe purpose is to have appropriate planning for reducing costs during the procurement process and increasing profits during the sales period in a supply cycle at a retail store. To make the proposed model practical, efforts have been made to implement the existing constraints in a business environment and legal factors.Design/methodology/approachTo achieve the objectives of this research, a mixed-methods approach was employed. Initially, sales data was collected, and industry experts were consulted to identify key products. A mathematical model and a particle swarm optimization algorithm were utilized for simulation and optimization. Furthermore, time series forecasting techniques were employed to estimate demand accurately.FindingsThe findings of this research emphasize the importance of considering all factors affecting costs simultaneously when utilizing tools to reduce costs during the planning period. It was observed that certain tools, which individually have a favorable effect on costs, may collectively result in an increase in costs over the examined period. This highlights the need for a comprehensive and integrated approach to planning.Research limitations/implicationsThe business environment and legal factors in each country are among the factors that affect the performance of retailers, which has received less attention in previous research. This research aimed to address this gap by considering various factors. However, it is important to note that the proposed model has been investigated in research in hypermarkets and is applicable, but it cannot be generalized to other retailers.Originality/valueApplicability and consideration of new limitations in simulation along with consideration of the complete process from buying to selling goods.

Research on Inventory Control Method Based on Demand Response of Power Big Data

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.

Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach

This study delves into optimizing sustainable inventory management practices through the integration of advanced data analytics methodologies. In response to the complex dynamics of modern supply chains, where inventory control significantly impacts sustainability goals, this research aims to address the intricate interplay between decentralized decision-making, government policies, and strategic choices within supply chain networks. Employing models such as Game Theory and Gated Recurrent Unit (GRU), alongside statistical analyses, our investigation explores the transformative potential of informed decision-making frameworks. Through a comprehensive evaluation of inventory data, including statistical analyses, visual representations, and model evaluations, we illuminate the nuanced relationships and dependencies prevalent within inventory control strategies. Our findings underscore the significance of data-driven decision-making in optimizing inventory practices, mitigating risks, and fostering sustainability within supply chains. The insights gleaned from this study advocate for the continued application of advanced data analytics to pave the way for resilient, environmentally conscious, and economically viable supply chain practices.

A neural network-based model for cross-border e-commerce supply chain demand forecasting and inventory optimization

Abstract The development of the Internet makes the e-commerce transaction scale in the total global trade share grow year by year, and cross-border e-commerce has become an important growth point of global trade by virtue of its unique advantages. In this paper, the ARIMA model is used to obtain the time series demand change of a cross-border e-commerce supply chain, and the results are input into the LSTM model to realize the construction of a cross-border e-commerce supply chain demand forecasting model. The ABC inventory classification method and economic lot ordering model are used as the basis for the establishment of cross-border e-commerce supply chain inventory control strategies and multi-cycle inventory control models. Taking the sales data of WT enterprise from May 2022 to May 2023 as an example, the effectiveness of the ARIMA-LSTM model in cross-border e-commerce supply chain demand forecasting is analyzed, and the inventory control optimization results of the multi-period inventory control model are verified. The relative error fluctuation range of supply chain demand forecasting of the ARIMA-LSTM model is between [-0.1,0.2], and the cross-border e-commerce supply chain’s monthly demand forecast MAPE value is only 0.0135. After using the inventory control optimization model, the annual average inventory is reduced by 178.42 tons, and the total cost of inventory is reduced by 0.09*108 yuan. Relying on neural networks can achieve accurate prediction of cross-border e-commerce supply chain demand and optimize cross-border e-commerce supply chain inventory.

Penerapan Metode Economic Order Quantity (EOQ) Dan Reorder Point (ROP) Pada Pengendalian Persediaan Bahan Baku Di UMKM Dapur Bunga Berbintang

UMKM Dapur Bunga Berbintang, a business engaged in the culinary sector, serves various types of products such as boxed rice and snack boxes wet cakes which require a more effective and efficient raw material inventory control strategy. The raw materials analyzed are Thai Cassava raw materials, consisting of cassava, cornstarch, coconut milk, and pandan leaves. The problem faced by UMKM Dapur Bunga Berbintang is the inappropriate control of raw material requirements. The purpose of this study is to understand the stages of controlling Thai Cassava raw materials according to UMKM policies and the EOQ model. The method applied in this research is descriptive qualitative. The data needed in this study are the total use of raw materials, ordering costs, storage costs, order frequency, and the time to obtain raw materials. The results of the study state that the EOQ calculation purchases 39.6 kg of cassava raw materials, 403.07 grams of cornstarch, 27.731 pcs of coconut milk, and 23.77 pieces of pandan leaves. The total cost of raw material inventory applying EOQ can save IDR 448,227. The ordering frequency obtained from EOQ is 5 times a year. Safety inventory of 5 kg of cassava raw materials, 54 grams of cornstarch, 4 pcs of coconut milk, and 3 sheets of pandan leaves. And the reorder time is 30 kg of cassava, 300 grams of cornstarch, 21 pcs of coconut milk, and 21 sheets of pandan leaves.

Review Presentation of Different Economic Order Quantity (EOQ) Models and Their Application

In the context of inventory management, this review presentation offers a thorough overview of several Economic Order Quantity (EOQ) models and their real-world uses. It explores the fundamental EOQ model and broadens to incorporate models that account for perishable items, quantity discounts, and scarcity prices. The talk also looks at the many sectors in which these models are used to optimize order amounts, save costs, and improve operational efficiency. Businesses may improve their inventory control strategies, realize considerable cost savings, and increase performance by making educated decisions based on a thorough grasp of the various EOQ models and their practical implementations.

Linear Programming in a Restaurant Setup

The restaurant industry is a dynamic and pretty competitive zone that calls for powerful aid management and efficient choice-making to thrive inside the marketplace. Linear programming, a mathematical technique that optimizes aid allocation, has emerged as a powerful tool for boosting eating place operations. This paper delves into the application of linear programming inside the context of restaurant management and its profound impact on streamlining various facets of the enterprise, from menu making plans and personnel scheduling to inventory control and pricing strategies. This observe makes a speciality of elucidating how linear programming models can be developed and implemented to restaurant control, enabling owners and executives to make facts-driven decisions that improve efficiency, decrease charges, and ultimately raise profitability whilst retaining client pleasure and provider great. By integrating mathematical optimization strategies, restaurants can navigate the complicated balance between useful resource allocation and customer support, ensuring a harmonious and profitable operation. The paper attracts on actual-international examples and case studies to demonstrate the sensible implementation of linear programming inside the eating place enterprise. It underscores the effectiveness of this approach in real situations, showing how it may deal with common challenges confronted by means of eating place companies and lead to tangible upgrades in diverse domain names.

A study on inventory control strategies of fresh food supply chain considering advertising delay effect

PurposeThe existence of the advertising delay effect and its impact on supply chain operations have been demonstrated in the current study. Therefore, this study develops a timely inventory control strategy for the fresh produce supply chain to address the advertising delay effect in the fresh produce supply chain.Design/methodology/approachThis study proposes a game model based on the Nerlove-Arrow time delay differential equation and Pontryagin's maximum principle. Through comparative analyses of the optimal equilibrium strategies, the authors compare the optimal equilibrium strategies, product goodwill and optimal inventory trajectories for suppliers and retailers under secondary replenishment decisions and decentralized decisions.FindingsThe authors find that (1) Only when the sales cycle meets certain conditions can the overall profit of the supply chain under the secondary replenishment decision be greater than that under the decentralized decision. As the price markup coefficient increases, the total profit of the supply chain first increases and then decreases. (2) With the increase in the delay time, the replenishment quantity during the initial period gradually decreases. After the delay time elapses, the inventory depletion rate under secondary replenishment decisions is faster than that under decentralized decision-making. (3) Although there is a continuously increasing maximum value of product goodwill with the increase in delay time, it becomes difficult to achieve this value for longer delays.Practical implicationsThe authors’ findings provide a theoretical basis for supply chain members of fresh agricultural products to select replenishment and inventory control strategies when adopting different levels of delay in advertising marketing.Originality/valueFirstly, this paper explains the impact of advertising delay effect on fresh produce supply chain from a dynamic perspective, and secondly, it provides guidance on advertising formulation and inventory replenishment for fresh produce retailers under the influence of advertising delay effect.

Inventory control strategy: based on demand forecast error

PurposeThis study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.Design/methodology/approachThis study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.FindingsThe study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.Practical implicationsThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.Originality/valueThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Control strategies and transient characteristics of a 5MWth small modular supercritical CO2 Brayton-cycle reactor system

The combination of supercritical CO2 (SCO2) Brayton cycle and small modular reactors (SMR) is considered as one of the most promising energy conversion system. However, the nuclear safety and thermo-hydraulic constraints will impose more stringent control strategy requirements, and it is unclear whether the existing control strategies can ensure the safe operation of the reactor. Therefore, this paper establishes a dynamic model of the small modular SCO2 Brayton reactor system, which includes bypass, inventory, and combined control strategy. The transient characteristics of the reactor, heat exchangers and rotating machine are analyzed, and the control performance of different control strategies is evaluated and compared. The results show that the maximum load following range of the bypass control strategy (30 %–100 %) is larger than that of the inventory control strategy (50.6 %–100 %). When the external load decreases, bypass control strategy will increase the temperature of reactor fuel elements and the system's maximum pressure, and bring a certain degree of thermal shock to the heat exchangers. Although the inventory control strategy has slower control speed, it has better reactor safety and higher system efficiency. The combination control strategy has the advantages of both bypass control and inventory control, which can meet the system loads from 0 % to 100 %. This work can provide guidance for the design and operation of the SCO2 Brayton reactor system.

Discontinuous Economic Growing Quantity Inventory Model

The classical economic growing quantity (EGQ) model is a key concept in the inventory control problems research literature. The EGQ model is commonly employed for the purpose of inventory control in the management of growing items, such as fish and farm animals, within industries such as livestock, seafood, and aviculture. The economic order quantity (EOQ) model assumes that customer demand is satisfied without interruption in each cycle; however, this assumption is not always true for some companies as they do not have continuous operations, except for item storage, during non-working times such as weekends, natural idle periods, or spare time. In this study, we extend the traditional EGQ model by incorporating the concept of working and non-working periods, resulting in the development of a new model called discontinuous economic growing quantity (DEGQ). Unlike the conventional EGQ model, the DEGQ model considers the presence of intermittent operational periods, in which the firm is actively engaged in its activities, and non-working periods, during which only storage-related operations occur. By incorporating this discontinuity, the DEGQ model provides a more accurate representation of real-world scenarios where businesses operate in a non-continuous manner, thus enhancing the effectiveness of inventory control and management strategies. The study aims to obtain the optimal number of periods in each cycle and the optimal slaughter age for the breeding items, and, subsequently, to find the optimal order size to minimize the total cost. Finally, we propose an optimal analytical procedure to determine the optimal solutions. This procedure entails finding the optimal number of periods using a closed-form equation and determining the optimal slaughter age by exhaustively searching the entire range of possible growth times.

Off-design performance and control strategies of sCO2 recompression power systems considering compressor operating safety

The supercritical carbon dioxide (sCO2) power cycle is a promising power cycle in the field of thermal power generation. The system is often operated under off-design conditions, which would lead to the unsafe or unstable operation of the sCO2 power system. The performance of the sCO2 recompression cycle system under off-design working conditions by different control strategies is investigated in this paper. A one-dimensional design prediction model for centrifugal compressors is introduced to predict off-design system performance and the operating conditions and safety of centrifugal compressors under the 5 control strategies are simulated and compared. To show the difference between the control strategies, the thermal efficiency was compared, the highest thermal efficiency of the system is 11.2 % by the inventory control method, and the lowest thermal efficiency is 5.45 % by the turbine bypass control method. The compressor backflow control method makes the main compressor has a choking risk and the recompressor has a surging risk when the output power of the system is small. The speed variations of centrifugal compressors are 39.55 % (MC) and 34.71 % (RC) using the inventory control method. A hybrid control strategy (the compressor backflow and inventory control strategies) is proposed based on the performance maps of centrifugal compressors. The turbine efficiency is improved by 4.5 % and the system thermal efficiency is improved by 7.1 % by using the hybrid control strategy. The hybrid control strategy can solve the problem of dangerous working conditions of centrifugal compressors. The results of this study can guide the system control and operational safety of sCO2 recompression cycles.

Integration design and control strategy of sCO 2 Brayton cycle for concentrated solar power system

The concentrated solar power (CSP) system integrated with supercritical carbon dioxide (sCO 2) Brayton cycle is considered as the major development trend of clean energy technology in mitigating climate change and promoting sustainable energy owing to its more compact structure, higher efficiency and lower cost. While the system integration and control are more complex because the thermal energy storage and heat exchange units are obliged to be provided with capacity and property matching with sCO 2 compression and expansion units. Therefore, there is an urgent need for the whole system modeling and dynamic simulation of sCO 2-CSP plants. In the paper, running logic of the integrated system is determined and novel system dynamic simulation platform is built. Moreover, for the sake of optimizing the system design, different control strategies are further studied. When the extremum-seeking control strategy is used, the average daily generating capacity of the system is 2.06% higher than that of the traditional fixed inventory control strategy; and when iterative learning control strategy is used, the average daily generating capacity of the system is 2.15% higher than that of the traditional fixed inventory control strategy. The progress of the technology will promote the development of new power systems and early realization of carbon neutrality.

The Pros and Cons of Inventory Control Strategies, and How Does it Affect the Company’s Performance

Inventory management plays a critical role in every organization’s performance. It is an essential part of any business. Financially, it is the most valid current asset, easily converted to cash. Therefore, every organization must be able to manage its inventory according to its needs and customer’s demand. Furthermore, inventory requires space inside the warehouse and keeping more than the required stock can increase the cost of carrying inventory. Hence it is essential to keep enough stock but not more than needed. Nevertheless, some organizations find it challenging to balance the supply of inventory with their customers or demand. Thus, this research aims at understanding the concept of inventory management and inventory control and identifying the issues faced by the companies in their inventory management by finding the flaws within the strategies or systems used to manage the inventory. This research employs a qualitative data collection method through journal articles, books, and trusted websites to ascertain the objectives.

An Omnichannel Retailing Operation for Solving Joint Inventory Replenishment Control and Dynamic Pricing Problems From the Perspective of Customer Experience

Customer experience, a key element in the domain of omnichannel retailing, plays a vital role in attracting consumers. Implementing a new BOPS (buy online, pick up in store) strategy will not only affect the inventory structure and pricing strategy of retail enterprises, but will also impact the original sales channel. In accordance with the consumer experience, a dynamic optimization model of a retailer inventory system was established in this research. Based on the optimal control theory, analyze the optimal control strategies under four sales modes: online, offline, BOPS, and BOPS-PLUS (BOPS and buy offline online-logistics distribution). Through numerical experiments, the inventory control strategies and optimal discounted profits under the four modes are compared and analyzed. The results show that the optimal experience investment and dynamic pricing levels gradually decrease with time, and the inventory state has a vital influence on the discounted profit of the system. The traditional dual-channel retail model is optimal when the channel inventory is not out of stock. In situations where channel inventory is in short supply (both online and offline), a deeply integrated omnichannel retail mode BOPS-PLUS is best. The numerical analysis also reveals that price sensitivity coefficient, experience service coefficient and online return rate also have significant influence on the selection of optimal channel strategy.

The Construction of Decision Model of Multi-channel Closed-loop Supply Chain Based on Big Data

In today's era, science and technology have undergone tremendous development, and the research on big data technology has become more and more mature. Many areas of human life have begun to apply big data technology, and big data technology has indeed brought more convenience to human life. With innovation, human life has also become better because of the development of big data. With the increase of people's awareness of environmental protection and sustainable development, more and more scholars have begun to pay attention to the research of multi-channel closed-loop supply chain. The return products caused by reverse logistics greatly affect the supply chain inventory system and the butterfly effect of the supply chain. This paper uses the Agent technology in big data to construct a multi-channel closed-loop supply chain decision model, and applies the Agent technology to the research of multi-channel closed-loop supply chain inventory and butterfly effect. This paper analyzes the functional agents included in each structural agent in the closed-loop supply chain, designs the simulation process, and designs the final data collection and analysis. Through the analysis of the simulation results, find out the influence of the reverse logistics recycling and return links and the changes in inventory control parameters on the butterfly effect of the members of the closed-loop supply chain at all levels, and provide guidance for the members of the closed-loop supply chain to formulate reasonable and effective inventory control strategies Sexual opinions.