Latest revision as of 21:51, 1 February 2021

Abstract

Part 3: Smart City Interoperability and Cross-Platform Implementation; International audience; In today’s challenging sector of logistics and transportation, companies, seek to adapt software which leads to efficient solutions at an acceptable cost. Conventional routing software is developed to solve vehicle routing problem and help managers and planners in decision making. Simultaneously, specific constraints and different VRP (Vehicle Routing Problem) variants are considered each time, such as the Capacitated, the Multi Depot and the Pickup and Delivery VRP. However, the last few years the need for more reliable deliveries and better customer services arose. In addition, reducing travel distance, travel cost and environmental impact are important factors encountered in urban freight transportation. Therefore, routing software needs to take into account multiple constraints. Such constraints are traffic congestion, speed limits, transportation regulations and restricted zones. These constraints affect mainly Time dependent VRP, VRP with Time Windows, Dynamic VRP and Green VRP. Data collection and processing are essential in routing software for solving these variants and offering the best solution. The methods for solving these problems, along with technological achievements, including cloud computing, can lead to efficient, easily adaptable routing software. Such software solutions can eventually render companies with complex transportation and logistics problems, competitive. The scope of this paper is to describe the concept and methodological approach for the development of such a routing and scheduling system, operating in a cloud environment. The definition of its requirements and the development of the system is the main purpose of an ongoing research project, being in its first stages of system’s analysis and design.

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The different versions of the original document can be found in:

• http://link.springer.com/content/pdf/10.1007/978-3-319-99707-0_24,

http://dx.doi.org/10.1007/978-3-319-99707-0_24 under the license http://creativecommons.org/licenses/by/

• https://hal.inria.fr/hal-02177850,

https://hal.inria.fr/hal-02177850/document,

https://hal.inria.fr/hal-02177850/file/472851_1_En_24_Chapter.pdf under the license http://www.springer.com/tdm

• https://link.springer.com/chapter/10.1007/978-3-319-99707-0_24,

https://dblp.uni-trier.de/db/conf/ifip5-7/apms2018-2.html#GayialisKPKP18,

https://rd.springer.com/chapter/10.1007/978-3-319-99707-0_24,

https://hal.inria.fr/hal-02177850,

https://academic.microsoft.com/#/detail/2888559674 under the license http://creativecommons.org/licenses/by/