Real vehicle usage rarely matches the predictions made during early phases of vehicle development and sales processes at commercial road vehicle manufacturers. The automotive industry needs multidisciplinary vehicle design methods to predict real-world vehicle operations by considering the vehicle level and the transport system level simultaneously, in a more holistic approach. The aim of this study was to analyse how realistic vehicle usage of driverless multipurpose vehicles can be modelled in Vehicle Routing Problems (VRPs) by conducting a systematic literature review. We found that real vehicle usage modelling of driverless multipurpose vehicles in VRPs mainly depended on the following elements: VRP variant, energy consumption model, energy consumption rate class, number of vehicle-specific design variables and transport system-level factors. Furthermore, we identified in the literature five classes of energy consumption rate edge behaviour in VRPs. These findings can support decision-making in the modelling process to select the most suitable combination of elements, and their level of detail for the overall modelling aim and purpose.

Planning paths that are length or time optimized or both is an age-long problem for which numerous approaches have been proposed with varied degree of success depending on the imposed constraints. Among classical instances in the literature, the Traveling Salesman Problem and the Vehicle Routing Problem have been widely studied and frequently considered in the realm of mobile robotics. Understandably, the classical formulation for such problems do not take into account many different issues that arise in real-world scenarios, such as motion constraints and dynamic environments, commonly found in actual robotic systems, and consequently the solutions have been generalized in several ways. In this work, we present a broad and comprehensive review of the classical works and recent breakthroughs regarding the routing techniques ordinarily used in robotic systems and provide references to the most fundamental works in the literature.

This article deals with the vehicle routing problem with timewindows (VRPTW). This problem consists in determining a least-costset of trips to serve customers during specific time windows. Theproposed solution method is a memetic algorithm (MA), a geneticalgorithm hybridised with a local search. Contrary to most paperson the VRPTW, which minimize first the number of vehicles, ourmethod is also able to minimize the total distance travelled. Theresults on 56 classical instances are compared to those of thebest metaheuristics. The efficiency of the MA is similar for theclassical criterion, but it becomes the best algorithm availablefor the total distance, being much faster and improving 20best-known solutions.

The purpose of this study is to investigate and optimize the dynamics of state transitions during a process of multistage fuzzy control systems. A variation of the methodology for solving the Vehicle Routing Problem, known as the Savings Method, is applied to finding a sequence of controls to maximize the overall performance of multistage control processes. The Savings Method has been modified into a heuristic to traverse state precedence when transitioning from one known system state to another. In addition, the procedure allows for imposing of two different types of precedences (mandatory and optional) among transition states, as well as a pair of fuzzy constraints and goals throughout each stage of the process. As the control of the system progresses, fuzzy goals may be preset or introduced interactively.