A hybrid route planning approach for logistics with pickup and delivery. (15th March 2019)

• Record Type: Journal Article
• Authors: Lu, Eric Hsueh-Chan; Yang, Ya-Wen
• Is Part Of: Expert systems with applications. Volume 118(2019)
• Page Start: 482
• Page End: 492
• Publication Date: 2019-03-15
• Subjects: Hybrid approach -- Logistics planning -- Smart city -- Expert system; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.10.031
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Real logistics problem including constraints and requirements is addressed. Hybrid approach ILSP is proposed for planning the high-quality logistic solution. Quickly finding initial solutions by partition, routing and insertion strategies. Iteratively improving the solution quality by ACO -based strategies. The performance of ILSP is evaluated based on real logistics data. Abstract: With the busy life of modern people, more and more consumers are preferring to shop online. This change on shopping behavior results in large volumes of packages must be transported, and thus research on logistics planning considering real constraints has increased. To solve this problem, several heuristics or evolutionary methods with expert knowledge were proposed previously, but they are usually inefficient or need a large amount of memory. In this paper, we propose a hybrid approach called Iterative Logistics Solution Planner ( ILSP ) for not only quickly finding a nice logistics solution but also iteratively improving the solution quality while meeting the real logistics constraints. ILSP contains two main phases including initial logistics solution generation and iterative logistics solution improvement based on the intelligence and knowledge from domain experts. Several algorithms and strategies are designed in ILSP for package partitioning, route planning and quality improvement. From the view of expert systems, the significance and impact of ILSP are simultaneously taking both computational efficiency and iterative quality improvement based on the expert knowledge into account on logistics planning problem with pickup and delivery. Through the rigorous experimental evaluations of real logistics data, the results demonstrated the excellent performance of ILSP .