An Innovative Modelling and Decision-Support Approach for Evaluating Urban Transshipment Problems Using Electrical Trucks

An Innovative Modelling and Decision-Support Approach for Evaluating Urban Transshipment Problems Using Electrical Trucks

Yavuz Gunalay (Bahcesehir University, Turkey) and Julian Scott Yeomans (York University, Canada)
Copyright: © 2020 |Pages: 19
DOI: 10.4018/IJSVST.2020070102
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Abstract

As a consequence of urban intensification, logistics planning becomes more important than ever. Electric vehicles have proved to be both environmentally friendly and a lower-cost alternative to internal combustion engine vehicles. However, existing decision methods employed by businesses and municipalities are not universally conducive to the optimization and evaluation of urban transportation systems. An innovative model and planning approach is proposed to enable urban planners to more readily evaluate the contribution of electric vehicles in city logistics and to support the decision-making process. When faced with decision-making situations that involve multiple and inconsistent performance objectives, it is often preferable to consider several quantifiably good alternatives that provide various, very different perspectives. This paper provides a modeling-to-generate-alternatives (MGA) decision-support procedure that uses the firefly algorithm (FA) metaheuristic for generating sets of maximally different alternatives for electric vehicle planning in urban transshipment problems.
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Introduction To Urban Logistics And Electric Vehicles

Solving urban freight transshipment problems plays an important role in the sustainable development infrastructure planning within major metropolitan areas (FREVUE, 2016; Gerst & Gao, 2016; Pelletier et al., 2016; Thiel et al., 2016; Winkelhaus & Grosse, 2020). Effective city transportation planning must concatenate numerous incongruent constraints related to traffic congestion, high fuel usage, lack of human resources, infrastructure deficiencies, and various overriding environmental factors (De Marco et al., 2018; European Commission, 2009; Kiba-Janiak & Witkowski, 2019). A major sub-field within urban logistics planning has emerged to address newly instituted environmental requirements (EEA 2019; Taniguchi, 2014; UNESCAP, 2003). When operating under these sustainability components, freight carriers are expected to simultaneously combine low-cost, just-in-time transportation systems together with high levels of customer satisfaction (Adnan et al., 2018; FREVUE, 2016; Taniguchi et al., 2001).

All logistics activities must take place within the many inherent constraints and limitations of cities. In addition to economic returns, urban planning must simultaneously target social, sustainability, and environmental issues (Kiba-Janiak & Witkowski, 2019; Richardson, 2013). The reason urban logistics has become a separate field within market-economy frameworks is due to the many unique characteristics of city systems (Winkelhaus et al., 2020). The goal of urban logistics and freight transport is to optimize logistics operations within the cities under social, environmental, energy usage, economic, traffic congestion, and financial constraints (De Marco et al., 2018; Kiba-Janiak & Witkowski, 2019; Taniguchi et al., 2001; Winkelhaus & Grosse, 2020). Without an effective logistics plan, city planning will be unable to improve quality of life, thereby rendering urban logistics as one of the key components for successful urban planning (Taniguchi & van der Heijden, 2000; Taniguchi, 2014).

Street-level vehicle emissions and noise pollution have become two of the major problem areas in urban logistics planning (De Marco et al., 2018; Kiba-Janiak & Witkowski, 2019). Although there has been a significant decrease in vehicle emissions due to recent stringent measures imposed by governments (both in the US and EU), total emissions have increased due to higher traffic volumes (EEA, 2004; EEA, 2019; Thiel et al., 2016). While the air quality has actually increased on most rural roadways, it has remained a major problem in urban settings (Hernandez et al., 2017; Tuncay & Ostun, 2012). Hence, additional measures are requisite in order to improve vehicle emission impacts on human health (Kiba-Janiak & Witkowski, 2019). Noise pollution is also a major cost arising from transportation (European Commission 2016). Heavy trucks, buses, diesel-engine cars, and motorcycles all contribute to the elevated urban noise levels. Replacing internal combustion engines (ICE) with silent-running, zero-emission electric motor vehicles provides one primary means to overcome excessive urban noise and emissions issues (Pelletier et al., 2019; Khemakhem et al., 2017; Hernandez et al., 2017; Tuncay & Ostun, 2012; Weiss et al., 2012). Electric vehicles using high efficiency electric motors have advantages such as low fuel and maintenance costs, zero emissions (improves air quality while decreasing emissions), and silent operations issues relative to ICE vehicles (Adnan et al., 2018; Biswas et al., 2020; EEA, 2019; Khemakhem et al., 2017; Richardson, 2013; Weiss et al., 2012).

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