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Solving inventory management Problems through Deep Reinforcement Learning

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Journal of Systems Science and Systems Engineering 2022年第6期31卷 677-689页

作者： Qinghao Wang Yijie Peng Yaodong Yang Center for Multi-Agent Research Institute for AIPeking UniversityBeijing 100871China Guanghua School of management Peking UniversityBeijing 100871China College of Engineering Peking UniversityBeijing 100871China

inventory management(*** sales)is a central problem in supply chain *** sales inventory systems with lead times and complex cost function are notoriously hard to *** reinforcement learning(DRL)methods can learn optimal decisions based on trails and errors from the environment due to its powerful complex function representation capability and has recently shown remarkable successes in solving challenging sequential decision-making *** paper studies typical lost sales and multi-echelon inventory *** first formulate inventory management problem as a Markov Decision Process by taking into account ordering cost,holding cost,fixed cost and lost-sales cost and then develop a solution framework DDLS based on Double deep Q-networks(DQN).In the lost-sales scenario,numerical experiments demonstrate that increasing fixed ordering cost distorts the ordering behavior,while our DQN solutions with improved state space are flexible in the face of different cost parameter settings,which traditional heuristics find challenging to *** then study the effectiveness of our approach in multi-echelon *** results demonstrate that parameter sharing can significantly improve the performance of *** a form of information sharing,parameter sharing among multi-echelon suppliers promotes the collaboration of agents and improves the decisionmaking *** research further demonstrates the potential of DRL in solving complex inventory management problems.

关键词： inventory management deep reinforcement learning parameter sharing lost sales multiechelonmodels

Research on inventory management and demand forecasting

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International Journal of Technology management 2014年第1期 57-59页

作者： Qian Kun Zhang Shiwei Shen Hongtao State Grid of Electric Power Company Supplies company in Shandong Province Jinan 250001

With the rapid economic development and the increasing speed and scale of grid construction, material procurement and management, cost control is facing new demands and *** paper proposes on innovative management and forecasting methods, from inventory management and demand forecasting perspective supplies,through these two key nodes in-depth research and analysis, this paper provides a theoretical support for the realization of effective materials management.

关键词： inventory management demand forecasting reverse logistics

Research on inventory management for Muti-Species Systems with Returns

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Research on Inventory Management for Muti-Species Systems wi...

2009中国控制与决策会议

作者： BAI Shi-zhen,ZHAO Jin-li School of management,Harbin University of Commerce,Harbin 150076

＜正＞In order to seek an effective strategy for muti-species system with returns,we study the system experiencing certain returns as well as certain customer *** general muti-species inventory systems,we propose two ordering *** is unified replenishment and the other is integrated *** the second policy,we give a simple heuristics solution,and in numerical examples it is shown to perform *** the results of numerical trials, we find that the policy of integrated replenishment is more effective on inventory control than the policy of unified replenishment;and the total average costs of the policy of integrated replenishment are less than the policy of unified replenishment.

关键词： inventory management Muti-species Systems Returns

Stochastic model predictive control under chance constraints for inventory management in nuclear power plants

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Stochastic model predictive control under chance constraints...

第35届中国过程控制会议

作者： Qi Zhang Weihua Xu Lei Xie Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang University

One of the most important issues in the safe operation of nuclear power plants is inventory management. Uncertainty in demand and delays in delivery make the problem challenging and require a certain degree of conservatism to ensure a safe inventory. In this paper, stochastic model predictive control under chance constraints is proposed to solve this problem. The flexibility of stochastic model predictive control allows for explicit consideration of the different objectives and constraints involved in the problem, while the use of chance constraints provides a trade-off between conservatism and efficiency. The proposed forecasting policy aims to reduce the inventory level fluctuations and maintain them at the desired level compared to traditional inventory control methods. Finally, a case study illustrates the effectiveness of the methodology.

关键词： inventory management Model predictive control Nuclear power plant spare parts inventory optimization

Predictive Control of inventory management in Supply Chain Systems with Uncertain Demands and Time Delays

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Predictive Control of Inventory Management in Supply Chain S...

第40届中国控制会议

作者： Yi Yang Chen Peng Qingkui Li School of Mechanical Engineering and Automation Shanghai University School of Automation Beijing Information Science and Technology University

This paper establishes a predictive control strategy for inventory management in a supply chain system with uncertain demands and time delays. A discrete-time dynamic model with some realistic constraints is proposed by characterizing a general supply chain system with a graph representation. Due to the mismatching between the goods quantity obtained from suppliers and the ordered one and it may lead to stockouts and lower degree of customer satisfaction. A predictive control strategy is well presented to obtain recent demands so that suppliers can make decisions according to their own stock levels on time. The effectiveness of the proposed methodology is verified by a a numerical example.

关键词： Predictive control inventory management Uncertain demands Time delays

Research on improving the inventory management of coal in thermal power plants——Take H Corporation as an example

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Research on improving the inventory management of coal in th...

2011 International Conference on Business management and Electronic Information(BMEI 2011)

作者： Wang Zhanwu School of Economics and management China University of Geosciences Wuhan ChinaYan Xiao Feng Zhonglei Xie Xiongbiao School of Economics and management China University of Geosciences Wuhan China

The inventory management of coal plays a significant role in the whole operation of thermal power *** paper analysis the features of coal inventory management of thermal power *** to H Corporation’s own situation,especially the changing prices of coal and demand of electricity during a year,a practical inventory model with the target of minimizing the loss of profit and the permission of generating excess electricity was *** the basis of the solving results of the 18 concrete models of the combination of 3 coal prices,3 excess generating rates and 2 safety stock levels,the optimal safety stock was *** the quantity of excess electricity to generate and coal to purchase during a year were also suggested.

关键词： thermal power plant coal inventory management

Intelligent Decision-Making in Warehouse management: How AI Automation Improves inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

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Intelligent Control and Automation 2024年第1期15卷 1-8页

作者： Somil Nishar Department of Engineering Colorado State University Pueblo Pueblo USA

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

关键词： Warehouse management Artificial Intelligence Automation inventory management Order Fulfillment

STUDY ON THE CENTRALIZATION STRATEGY OF THE BLOOD ALLOCATION AMONG DIFFERENT DEPARTMENTS WITHIN A HOSPITAL

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Journal of Systems Science and Systems Engineering 2018年第4期27卷 417-434页

作者： Jingnan Duan Qiang Su Yanhong Zhu Yuanshan Lu School of Economies and management Tongii University Shanghai 200092 China Shanghai General Hospital Shanghai 200080 China

By far, the researches on how to distribute blood products among different departments in hospital have not been further studied, though the problem of blood shortage and wastage that caused by improper blood allocation is severe, which may endanger patient＇s lives and impose considerable costs on hospitals. In order to solve this problem, this paper mainly studies on how to distribute the blood items among different departments within a hospital and investigates the allocation approach with the novel management method by centralizing the inventory of several different departments. By integrating the blood inventory requirements of some departments, the hospital could reduce the rate of blood shortage and wastage effectively, release the pressure of the occupancy of resources and reduce the bullwhip effect of blood products. This paper illustrates the centralization principle in hospital and formulates the mixed integer programming model to work out the optimal allocation network scheme and the optimal inventory setting for every department. And the results of the numerical example demonstrate that this centralization method could considerably reduce blood shortage and wastage in hospital by about 72% and 90% respectively. Furthermore, it could decrease the total cost by about 108,540 RMB a month on blood supply chain management in the hospital and improve the effect of some certain surgeries by transfusing the fresh blood to patients.

关键词： Blood allocation inventory management centralization strategy bullwhip effect

Combined heuristics for determining order quantity under time-varying demands

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Journal of Systems Engineering and Electronics 2008年第1期19卷 99-111页

作者： Tang Jiafu Pan Zhendong Gong Jun Liu Shixin Dept. of Systems Engineering Key Lab of Integrated Automation of Process Industry of MOE Northeastern Univ. Shenyang 110004 P. R. China

The time-varying demands for a certain period are often assumed to be less than the basic economic order quantity （EOQ） so that total replenishment quantity rather than economic order quantity is normally considered by most of the heuristics. This acticle focuses on a combined heuristics method for determining order quantity under generalized time-varying demands. The independent policy （IP）, abnormal independent policy （AIP） and dependent policies are studied and compared. Using the concepts of normal/abnormal periods and the properties of dependent policies, a dependent policy-based heuristics （DPH） is proposed for solving the order quantity problems with a kind of time-varying demands pattern under which the first period is normal. By merging the Silver-Meal （S-M） heuristics and the dependent policy-based heuristics （DPH）, a combined heuristics （DPH/S-M） is developed for solving order quantity problems with generalized time-varying demands. The experimentation shows that （1） for the problem with one normal period, no matter which position the normal period stands, the DPH/S-M could not guarantee better than the S-M heuristics, however it is superior to the S-M heuristics in the case that the demands in the abnormal periods are in descending order, and （2） The DPH/S-M is superior to the S-M heuristics for problems with more than one normal period, and the more the number of normal periods, the greater the improvements.

关键词： heuristics EOQ time-varying demands inventory management