Graham Kendall

Ruibin Bai, Xinan Chen, Zhi-Long Chen et al.

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem formulations and algorithmic performance across different problem solving scenarios. However, the relevant papers are scattered in several traditional research fields with very different, sometimes confusing, terminologies. This paper presents a first, comprehensive review of hybrid methods that combine analytical techniques with ML tools in addressing VRP problems. Specifically, we review the emerging research streams on ML-assisted VRP modelling and ML-assisted VRP optimisation. We conclude that ML can be beneficial in enhancing VRP modelling, and improving the performance of algorithms for both online and offline VRP optimisations. Finally, challenges and future opportunities of VRP research are discussed.

Kamal Z. Zamli, Fakhrud Din, Graham Kendall et al.

Software testing relates to the process of accessing the functionality of a program against some defined specifications. To ensure conformance, test engineers often generate a set of test cases to validate against the user requirements. Owing to the growing complexity of software and its increasing diffusion into various application domains, it is no longer unusual for a software project to have testing teams in more than one location or even distributed over many continents. Owing to the intertwined dependencies of many software development activities and their geographical and temporal issues, there are potentially many overlapping test cases which can cause unwarranted redundancies across the shared modules (i.e. a test for one requirement may be covered by more than one test). In this paper, we explore the application of our newly developed hyperheuristic, called Fuzzy Inference Selection (FIS), for addressing test redundancy reduction problem. This paper presents the supplementary results for the paper : An Experimental Study of Hyper-Heuristic Selection and Acceptance Mechanism for Combinatorial t way Test Suite Generation published in Information Sciences.