Information Retrieval and Optimization in Distribution and Logistics Management Using Deep Reinforcement Learning

Information Retrieval and Optimization in Distribution and Logistics Management Using Deep Reinforcement Learning

Li Yang, Sathishkumar V. E., Adhiyaman Manickam
DOI: 10.4018/IJISSCM.316166
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Abstract

Resource balance is one of the most critical concerns in the existing logistic domain within dynamic transport networks. Modern solutions are used to maximize demand and supply prediction in collaboration with these problems. However, the great difficulty of transportation networks, profound uncertainties of potential demand and availability, and non-convex market limits make conventional resource management main paths. Hence, this paper proposes an integrated deep reinforcement learning-based logistics management model (DELLMM) to increase and optimize the logistic distribution. An optimization approach can be used in inventors and price control applications. This research methodology gives the fundamentals of information retrieval and the scope of blockchain integration. The conceptual framework of use cases for an efficient logistic management system with blockchain has been discussed. This research designs the deep reinforcement learning system that can boost optimization and other business operations due to impressive improvements in generic self-learning algorithms for optimal management. Thus, the experimental results show that DELLMM improves logistics management and optimized distribution compared to other methods with the highest operability of 94.35%, latency reduction of 97.12%, efficiency of 98.01%, trust enhancement of 96.37%, and sustainability of 97.80%.
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1. Introduction

Resource balance management is the primary factor to success, particularly in managing the supply chain (Wieland, 2021). Effective logistics management includes different factors like automation and perfect coordination (Ranjan et al., 2020). However, the process can always be improvised. If the business increases, one needs to facilitate logistics planning to improve output (Gao et al., 2020). The length of time it takes from placement to delivery is one of the most critical aspects of customer experience. Other parameters – time, costs, and transport are apart from these (Jiang et al., 2017). A supply chain operator should design the entire flow chart (Gaoet al., 2020). Many researchers target various approaches to maximize profits and price control applications (MK et al., 2021). The management of logistic products is the backbone of each company because it ensures shipping, supply, and supply chain (Kumar et al., 2021).

Cost, environment, energy, and quality considerations must be considered while planning and implementing a logistic management design. Scheduling logistics management systems must consider sustainability pillars and model uncertainty (Lotfi et al., 2021). When the model is based on a realistic scenario that considers uncertainties, the product's cost and quality are enhanced, and pollution and energy usage are reduced. The first step towards increasing operational efficiency and productivity is to improve the logistics management process for companies that want to compete (Suifan et al., 2020). Planning is the first step towards any task or project. Now, there are different factors for project planning. In logistics, it requires the acquisition of goods, storage, and supply to the correct place (Lotfi et al., 2017). Technology plays a major role in improving the efficiency of an organization in the automation age. An intelligent transportation system is one of the areas of logistics management that companies are concentrating on to enhance profitability. Various levels of information can have on transportation practices and are processed to ensure corporate profitability. Such information is gathered, processed, and disseminated via intelligent transportation systems, assisting the effective logistics of goods and materials (Wiseman, 2021). Automation is an essential part of business process optimization (Rouari et al., 2021). An optimization approach can be used in inventory and price control applications (Srivastava et al., 2021). The time it takes from order placement to delivery is of the most critical aspects of customer experience and the main decisive factor (Xu et al., 2020). In the motive to reduce logistics company expenditure, the transportation department can be analyzed and, at the same time, upgraded for quick delivery of products (Acevedo et al., 2015).

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