Flexible Vehicle Scheduling Optimization with Uncertainty in Intelligent Logistic Systems

Abstract

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.

An Advanced System to Enhance and Optimize Delivery Operations in a Smart Logistics Environment

“…The smart logistics systems reveal uncertainty factors for instance network congestion. To overcome such issues, Sun [37] proposed an evolutionary mechanism with adaptive parameters, Wang et al [38] proposed an emergency logistics network design problem with the resource sharing under collaborative alliances..…”