Abstract
The Service Network Design Problem (SNDP) is generally considered as a fundamental problem in transportation logistics and involves the determination of an efficient transportation network and corresponding schedules. The problem is extremely challenging due to the complexity of the constraints and the scale of real-world applications. Therefore, efficient solution methods for this problem are one of the most important research issues in this field. However, current research has mainly focused on various sophisticated high-level search strategies in the form of different local search metaheuristics and their hybrids. Little attention has been paid to novel neighbourhood structures which also play a crucial role in the performance of the algorithm. In this research, we propose a new efficient neighbourhood structure that uses the SNDP constraints to its advantage and more importantly appears to have better reachability than the current ones. The effectiveness of this new neighbourhood is evaluated in a basic Tabu Search (TS) metaheuristic and a basic Guided Local Search (GLS) method. Experimental results based on a set of well-known benchmark instances show that the new neighbourhood performs better than the previous arc-flipping neighbourhood. The performance of the TS metaheuristic based on the proposed neighbourhood is further enhanced through fast neighbourhood search heuristics and hybridisation with other approaches.
Highlights
E-commerce and online shopping have rapidly transformed the formats of businesses in recent years
The goal of this paper is to address this gap by studying a new neighbourhood structures for Service Network Design Problem (SNDP)
It is much easier to focus on nodes rather than arcs, leading to our κ-node neighbourhood structure which we describe in the subsection
Summary
E-commerce and online shopping have rapidly transformed the formats of businesses in recent years. As indicated in Kendall et al (2016), a lot of optimization research studies merely borrow different metaphors without much deep insights on algorithmic or problem properties These approaches do not satisfy real-world requirements either in terms of solution quality delivered or in computational time required. This is because the SNDP contains some difficult constraints and a flow distribution sub-problem, generally referred to as the Capacitated Multicommodity Min-Cost Flow (CMMCF) problem, which can be very expensive to solve if it is called many times within an iterative metaheuristic approach.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
