Substantiation of Loading Hub Location for Electric Cargo Bikes Servicing City Areas with Restricted Traffic

Abstract

Electric cargo bicycles have become a popular mode of transport for last-mile goods deliveries under conditions of restricted traffic in urban areas. The indispensable elements of the cargo bike delivery systems are loading hubs: they serve as intermediate points between vans and bikes ensuring loading, storage, and e-vehicle charging operations. The choice of the loading hub location is one of the basic problems to be solved when designing city logistics systems that presume the use of electric bicycles. The paper proposes an approach to justifying the location of a loading hub based on computer simulations of the delivery process in the closed urban area under the condition of stochastic demand for transport services. The developed mathematical model considers consignees and loading hubs as vertices in the graph representing the transport network. A single request for transport services is described based on the set of numeric parameters, among which the most significant are the size of the consignment, its dimensions, and the time interval between the current and the previous requests for deliveries. The software implementation of the developed model in Python programming language was used to simulate the process of goods delivery by e-bikes for two cases—the synthetically generated rectangular network and the real-world case of the Old Town district in Krakow, Poland. The loading hub location was substantiated based on the simulation results from a set of alternative locations by using the minimum of the total transport work as the efficiency criterion. The obtained results differ from the loading hub locations chosen with the use of classical rectilinear and center-of-gravity methods to solve a simple facility location problem.

Last-Mile Logistics Network Design under E-Cargo Bikes

“…Table 4 shows the corresponding results. The best resulting route set for ACO consists of five routes in this case ( [21,20,25,19,2,1], [23,10,11,3], [12,13,5,6,4], [24,17,16,15,14,22], [9,8,7,18]) versus four routes for the GA ( [13,20,21,2,12,9,5], [6,8,3,22,7,4,1], [18,14,24,23,10,11], [16,15,17,19,25]). As seen in Table…”

“…Fontaine [19] presented a model and an adaptive large neighborhood algorithm for the VRP under load-dependent speed as well, considering conventional cargo bicycles. Taking into account demand stochasticity, Naumov [20] combined the Clarke-Wright heuristic with simulation for determining hubs for e-bike deliveries and associated routes. Robichet et al [21] solved the micro-hub facility location problem and subsequently the cargo bike routing problem for parcel delivery in Paris, showing that cargo bikes are economically preferable to trucks under high numbers of daily requests.…”

Explaining the growth in light electric vehicles in city logistics

“…Although the Living Lab initiatives provide new knowledge and experiences with LEFVs, only a few successful cases for LEFVs have been found in scientific research (Schliwa et al, 2015;Lenz and Riehle, 2013). Simulation approaches and ex-ante analyses (Melo and Baptista, 2017;Gruber et al, 2014;Tipagornwong and Figliozzi, 2014;Arnold et al, 2018;Zhang et al, 2018;Gruber and Narayanan, 2019;Sheth et al, 2019;Naumov, 2021;Caggiani et al, 2021;Llorca and Moeckel, 2021) are most commonly applied in practical research. Fiori and Marzano (2018) developed an EFVs energy consumption model.…”

Innovations in Transport

“…Radical innovations may fail because they only focus on long-term impacts while underestimating the strains and barriers inherent to the change of roles required from the actors in the short term. This is the case of innovation processes, including those in transport, that are positioned in a context of Living Labs (this can be a factory, a consumer group, or an entire city) (Quak et al, 2016). Here, innovations are not presented as big-bang scenarios but in an incremental fashion, where the lab context provides feedback about what works and what does not and allows incremental design and implementation of change.…”

“…Although the Living Lab initiatives provide new knowledge and experiences with LEFVs, only a few successful cases for LEFVs have been found in scientific research (Schliwa et al, 2015;Lenz and Riehle, 2013). Simulation approaches and ex-ante analyses (Melo and Baptista, 2017;Gruber et al, 2014;Tipagornwong and Figliozzi, 2014;Zhang et al, 2018;Gruber and Narayanan, 2019;Sheth et al, 2019;Naumov, 2021;Caggiani et al, 2021;Llorca and Moeckel, 2021) are most commonly applied in practical research. Fiori and Marzano (2018) developed an EFVs energy consumption model.…”