Real-Time Fleet Management and Rerouting in City Logistics

Real-Time Fleet Management and Rerouting in City Logistics

Vasileios Zeimpekis (Department of Financial & Management Engineering, University of the Aegean, Chios, Greece), Ioannis Minis (Department of Financial & Management Engineering, University of the Aegean, Chios, Greece), George M. Giaglis (Department of Management Science & Technology, Athens University of Economics and Business, Athens, Greece) and Kostis Mamassis (Department of Financial & Management Engineering, University of the Aegean, Chios, Greece)
DOI: 10.4018/ijoris.2013100101
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Abstract

The urban freight distribution is highly susceptible to unexpected events that often occur during delivery, such as delays at customer locations or due to traffic conditions. Such events may lead to inferior customer service, or higher costs, areas in which intelligent real-time fleet management may prove beneficial. In this paper, the authors present such a system that incorporates methods to estimate the expected travel time of a delivery vehicle, combining AVL-based real-time and historical data, with algorithms for efficient vehicle re-routings. The system continuously monitors the delivery process, detects possible delays in real-time, and adjusts the delivery schedule accordingly by suggesting effective re-routing strategies. The authors report results from testing the system via simulation and in a case study, and illustrate the extent of delivery performance improvements that may be achieved through such an approach.
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Introduction

Distribution is a major activity of supply chain operations and contributes significantly to total logistics costs (Ballou, 2004). Consequently, over the last five decades, both professionals and academics have devoted considerable efforts to improve and streamline key distribution processes. Considerable attention has been focused on city logistics environments, and. in particular, on dynamic incident handling through real-time fleet management (Crainic, Gendreau, & Potvin, 2009; Awasthi, Chauhan, Parent, & Proth, 2011). In an urban environment, the use of an initial distribution plan, although necessary, is by no means sufficient to address unexpected events that may have adverse effects on the performance of delivery execution (Zeimpekis, Tatarakis, Giaglis, & Minis, 2007). Table 1 presents a typical classification of incidents and their effects on goods delivery.

Table 1.
Dynamic incidents in urban freight distributions
Source of incident    IncidentEffect in delivery
Road Infrastructure & EnvironmentTraffic congestion, adverse weather conditions, road construction, flea markets, protestsIncreased vehicle travel time
CustomersNo available unloading area, problems with the delivered products (e.g. wrong order)Longer customer service times
New customer requests (delivery or pickup),Increased demand
Delivery VehicleCar accident, mechanical failureCustomer Service interruption

Various systems have been developed for fleet monitoring and incident detection in urban environments (see Powell, 1990; Savelsbergh & Sol, 1997; Gendreau, Laporte, & Semet, 2001; Slater, 2002; Ichoua, 2003; Kim, Lewis, & White, 2005; Cheung, Choy, Li, Shi, & Tang, 2008). However, most of these systems typically focus on handling customer orders that arrive during the execution of the delivery plan and need to be assigned to vehicles en route (Gendreau & Potvin, 1998; Yan, Jaillet, & Mahmassani, 1999; Fleischmann, Gietz, & Grutzmann, 2004; Cheung et al., 2008).

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