## 1 Introduction  
Mobility, i.e., the ability to move from one place to another, has always been a major concern in human history [1, 2, 3, 4]. In the current age of globalization and world-wide trade, mobility has become an even greater concern than in former times. In view of the increasingly dense and inter-linked supply chains, it is of paramount importance to coordinate the traffic of raw materials, goods and human workforces in a way that is consistent with ever-tightening economic and environmental constraints [4]. The complexity of this task is easily demonstrated by reference to the widely studied \"travelling salesperson (TSP) problem\" [5, 6, 7, 8, 9]. As a demonstration case, we consider in Figure 1a a graph featuring four cities (vertices) with interlinking transport paths (edges) and associated cost factors (numbers associated with edges). With these cost factors in place, the famous TSP problem can be solved by devising an optimum path with minimum expenditure in resources that takes a salesperson from a first city A, successively through all other cities, and finally back home to A again. Similarly, by devising time as a cost factor, the shortest possible travel time can be determined. The TSP problem became an item of scientific interest in the early 19 th century and subsequently turned into a subject of basic and applied research in the field of informatics, mathematics and complexity research [5, 6, 7, 8, 9]. The TSP problem is part of a larger class of combinatorial optimization approaches that are currently applied in the logistics field [10, 11, 12, 13, 14, 15]. As exact solutions through exhaustive search often prove to be computationally intractable, specialized algorithms have been developed which quickly rule out many of all possible paths to obtain quick approximate answers. A problem of considerable current interest is that road transport is very energy intensive, and that to date most vehicles are powered by internal combustion engines which emit large quantities of greenhouse gases (GHGs) which contribute to global warming. In the European Union (EU) in 2022, the transport sector accounted for 31.0% of final energy consumption, the highest share of final energy consumption ahead of households (26.9%), industry (25.1%) and services (13.4%) [16]. A similar expenditure on energy is also quoted for the US in which the contribution of the transport sector is estimated to be approximately 27% [17]. In a recent review [18], it is argued that \"if logistics were a country, its contribution of 12% of energy-related CO\({}_{2}\) emissions would range third in the emission league table behind China and the US\". In view of this situation, the European Union has initiated its Green Deal Initiative [19; 20] with the ambition to become the first climate-neutral continent in 2050 and to reduce the net GHG emissions by 55% relative to the year 1990. With this background in mind, the logistics sector will apparently be challenged by continually tightening environmental constraints and an increasing bureaucratic burden of regularly reporting progress along this line. Key interest of the logistics scene are complex added value chains in which raw materials are converted into various intermediate products which, in turn, are assembled into final products, that are finally absorbed in consumer markets. As all intermediate steps take place in different geographical locations, this workshare results in a plethora of transport requirements with a concomitantly large energy consumption and environmental impact. Regarding the optimization of such interlinked transport chains, the approach of the logistic sector is basically a managerial one in which transport distances to be travelled are minimized and in which transport steps with zero or minimal payload are strictly avoided [12; 13; 14; 15; 18]. In contrast to the state of the art in logistics, the present paper is concerned with individual point-to-point transport steps that build up each added value supply chain.