A Review of the Vehicle Routing Problem and the Current Routing Services in Smart Cities

Abstract

In this survey, the issues of urban routing are analyzed, and critical considerations for smart and cost-effective delivery services are highlighted. Smart cities require intelligent services and solutions to address their routing issues. This article gives a brief description of current services that either apply classical methods or services that employ machine learning approaches. Furthermore, a comparison of the most promising research options in regard to VRP is provided. Finally, an initial design of a holistic scheme that would optimally combine several tools and approaches to serve the needs of different users with regard to the VRP is presented.

Using Routing Heuristics to Improve Cost Interoperability: Strategy, Modelling Annotations, and Dynamism

“…Priorities are then established using actual and ideal traveling distances, the number of depots and vehicles used, and the maximum permissible vehicle capacity per depot. Combination of several distinctive parameter annotations when devising suitable modeling values when attempting to plan a cost-efficient scheduling system evolves the conventional single-objective normalcy routing instance to multi-fold objective instances, therefore any solution strategies required the further formulation of NP-hard multi-objective functions to provide computationally effective solution quality (Boumpa et al, 2022;Gharib, Bozorgi-Amiri, Tavakkoli-Moghaddam, & Najafi, 2018).…”

“…This can be achieved by first examining the contributions made by each algorithm to the development of a more flexible and dependable logistics model, and then by making some suggestions for improvisation. Additional multi-objective mixed integer optimization issues may be resolved using the technique by incorporating it into other multi-objective optimization frameworks (Boumpa et al, 2022;Yu et al, 2022).…”