Hierarchical Integrated Intelligent Logistics System Platform

Adamski, A.

doi:10.1016/j.sbspro.2011.08.109

Cited by 16 publications

(3 citation statements)

References 2 publications

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“…Some researchers consider intelligent logistics as a system with management and coordination, adaptation, optimization and scheduling, monitoring and intelligent supervision and direct control layers dedicated to dynamic, stochastic, multicriteria intelligent logistics decision problems (Adamski, 2011). Kirch et al (2017) thought that intelligent logistics will achieve the efficient organization of logistics and information flows within the transportation network across companies or across industries, and it is an inevitable trend in the development of modern logistics.…”

Section: Intelligent Logisticsmentioning

confidence: 99%

Effect of intelligent logistics policy on shareholder value: Evidence from Chinese logistics companies

Liu

Wang

Lin

et al. 2020

Transportation Research Part E: Logistics and Transportation Re

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This paper empirically investigates the effects of intelligent logistics policies (ILPs) on shareholder value of the 149 listed logistics companies from 2013-2018 in China. The research shows that ILPs will result in a positive stock market reaction, and this effect is increasing along with the increase of policy contents related to intelligent logistics or the decrease of company service breadth. The market reaction will be affected by the way that policies are issued and type of company. The logistics service providers are more positive when their financial performances are better, while the opposite result is observed for logistics equipment manufacturers.

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Section: Intelligent Logisticsmentioning

confidence: 99%

Effect of intelligent logistics policy on shareholder value: Evidence from Chinese logistics companies

Liu

Wang

Lin

et al. 2020

Transportation Research Part E: Logistics and Transportation Re

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“…The flexible vehicle scheduling problem (FVSP) in an intelligent logistic system (ILS) has been an area of research in recent years applicable to many real-world application systems. (1) FVSP can be treated as a typical discrete combinatorial optimization problem (COP). The objective function is minimizing the transportation cost through the optimization of the schedule of different cargoes with a set of precedence constraints.…”

Section: Introductionmentioning

confidence: 99%

Flexible Vehicle Scheduling Optimization with Uncertainty in Intelligent Logistic Systems

Sun¹,

Lin²,

Li³

et al. 2019

Sensors and Materials

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The flexible vehicle scheduling problem (FVSP) plays an important role in intelligent logistic systems (ILSs) as it improves transportation efficiency and reduces logistic costs through the optimization of the schedule of cargoes. FVSP is difficult to solve because it is a typical combinatorial optimization problem (COP). It has also been proved to be an NP-hard problem. In idealized models, the transportation time of cargoes in a logistic system is determined and given in advance. However, the uncertain factors in real-world logistic systems, such as traffic jams and emergencies, always lead to an uncertain transportation time. Fuzzy numbers can represent more information in real-world applications than constant or random values. Thus, in this paper, we focus on FVSP with an uncertain transportation time (uFVSP), in which the transportation time is modeled as a fuzzy number. A cooperative hybrid evolutionary algorithm (hEA) with a self-adaptive parameter mechanism is proposed and five uFVSP instances with different scales are adopted in numerical experiments to verify the effectiveness of the proposed algorithm. The results show that our proposed algorithm has better performance than other algorithms for solving uFVSP in ILSs.

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“…Nowadays, the traditional point-to-point distribution model can no longer satisfy the demand in an increasingly competitive market [3]. To solve the problem, it is an imperative to set up an intelligent logistics system that highlights the importance of vehicle scheduling, considers the links between the upstream, midstream and downstream of the logistics chain and supports intelligent decision-making in logistics scheduling [4,5].…”

Section: Introductionmentioning

confidence: 99%

Automatic Task Matching and Negotiated Vehicle Scheduling for Intelligent Logistics System

2019

IJEM

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The decision-making of logistics vehicle scheduling is difficult under varying constraints, multiple disturbances and strong time-variation. The multi-agent system (MAS) is a new approach to investigate the real-time decision-making of logistics vehicle scheduling. It satisfies the various requirements of the logistics system, such as the geographical distribution of vehicles, the dynamic changes of information, and the constant changes in consumer orders. In view of the theoretical and practical significance of the MAS, this paper explores the decision-making of logistics vehicle scheduling based on the MAS, and relies on two-level planning modelling method to construct the mathematical model of outsourcing-based container port vehicle scheduling problem. Then, an effectively exchange neighbourhood tabu search algorithm was designed to solve the model. Through the research, it is concluded that the proposed hierarchical decomposition method of logistics distribution task can reduce the overall scheduling difficulty and reduce the actual planning error effectively; the established MAS-based intelligent logistics scheduling model can minimize the total distribution cost through continuous adjustment of resources according to the distribution task. Finally, the feasibility of the proposed algorithm was verified by the results of a calculation example.

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Hierarchical Integrated Intelligent Logistics System Platform

Cited by 16 publications

References 2 publications

Effect of intelligent logistics policy on shareholder value: Evidence from Chinese logistics companies

Effect of intelligent logistics policy on shareholder value: Evidence from Chinese logistics companies

Flexible Vehicle Scheduling Optimization with Uncertainty in Intelligent Logistic Systems

Automatic Task Matching and Negotiated Vehicle Scheduling for Intelligent Logistics System

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