City Logistics 1

New Opportunities and Challenges

Eiichi Taniguchi, Russell G. Thompson

• Publisher: Wiley-ISTE
• Year: 2018
• ISBN:
  9781119527756
• Edition: 1
• Topic: Architecture
• Subtopic: Urban Planning & Landscaping

This volume of three books presents recent advances in modelling, planning and evaluating city logistics for sustainable and liveable cities based on the application of ICT (Information and Communication Technology) and ITS (Intelligent Transport Systems). It highlights modelling the behaviour of stakeholders who are involved in city logistics as well as planning and managing policy measures of city logistics including cooperative freight transport systems in public-private partnerships. Case studies of implementing and evaluating city logistics measures in terms of economic, social and environmental benefits from major cities around the world are also given.

1
Recent Developments and Prospects for Modeling City Logistics

Recent developments in digital-based technologies such as sensor networks as well as alternative fuel vehicles such as electric vans present many exciting opportunities for enhancing city logistics. New concepts based on the sharing economy including co-modality and the Physical Internet provide good prospects for improving the sustainability of urban freight systems. However, it will be important to create new models as well as adapt existing modeling approaches to effectively plan, design and operate city logistics schemes in the future. This chapter describes how developments in simulation and optimization models are being applied to facilitate the implementation of contemporary concepts and utilize emerging advanced technologies for city logistics.

1.1. Introduction

There are numerous complicated and difficult problems relating to urban freight transport systems, including how we can recognize the behavior of stakeholders, how we can evaluate and implement policy measures of city logistics and how we can promote collaboration among stakeholders. In order to overcome these problems, technological advances have contributed toward collecting data, developing mathematical models and applying them for evaluating policy measures. This paper highlights recent advances in using ITS (Intelligent Transport Systems), ICT (Information and Communication Technology), IoT (Internet of Things), Physical Internet (PI), big data, co-modality and electric vehicles. These innovative technologies and systems will have tremendous impacts on modeling, planning and managing city logistics for establishing efficient and environmentally friendly urban freight transport systems. Also, innovative modeling methods of vehicle routing and scheduling with time windows and multi-agent simulation in city logistics as well as road network strengthening are discussed toward sustainable urban freight transport.

1.2. VRPTW with consideration of environment, energy efficiency and safety

The vehicle routing and scheduling problem (VRP) can be used as a principal tool for evaluating many of such types of city logistics schemes [VAN 07]. The VRP is a well-known NP-hard problem which consists of determining a set of optimum routes covering all demands of a given set of customers without violating the capacity of vehicles. Since its inception in 1959 [DAN 60], the VRP has attracted many researchers, and a number of variants have found their way into the literature based on the inclusion of different practical constraints. The addition of the time window constraints leads to the vehicle routing problem with time windows (VRPTW) [SOL 87]. Whether or not a delayed service with penalties is allowed, the VRPTW can be further extended to include soft time window [QUR 09, BHU 14] and hard time window variants [KOH 99]. A heterogeneous fleet vehicle routing problem (HVRP) [CHO 07] deals with the availability of vehicles of different capacities at a central depot.

A relatively recent trend is to consider environmentally sustainable vehicles such as electric vehicles (i.e. EVRPTW [CON 11]). Electric trucks have the constraint of a limited range; therefore, EVRPTW models consider recharging at charging stations [AFR 14, SCH 14] or even battery swaps [YAN 15]. Earlier, a similar problem was introduced considering alternative fuel vehicles, their limited range (based on the size of the fuel tank) and limited filling station infrastructure [ERD 12]. This problem has been called the green vehicle routing problem (GVRP). Collaboration (sharing of electric vehicles, routes and customers) among different companies by formulating a multi-depot vehicle routing problem (MDVRP) has also been studied [MUÑ 17]. In addition to the routing, some researchers try to optimize the mix of conventional trucks and electric trucks (e.g. [VAN 13, GOE 15, LEB 15]). Instead of electric vehicles, a hybrid electric vehicle travelling salesman problem considering hybrid vehicles, capable of switching between electric and/or conventional fuels, has been proposed [DOP 16]. Later, the hybrid vehicle routing problem (HVRP) was developed [ZHA 17, MAN 17].

There has been considerable research interest in incorporating the environmental impact of urban freight in the vehicle routing model. For example, a pollution routing problem (PRP) considering the CO₂ emissions based on the fuel consumption along the arcs depending on the vehicle’s speed and load has been formulated [BEK 11]. A number of studies can be listed in the same class, although they used somewhat different equations to calculate fuel consumption and corresponding CO₂ emissions [KUO 11, JAB 12, XIA 12, KRA 15]. A third objective of customers’ satisfaction along with distance and emissions (fuel consumption) has been included [AFS 16]. An exhaustive review of the GVRP and other variants has been undertaken [LIN 14].

Although crashes and safety issues have been identified as one of the typical problems posed by the urban freight [TAN 02], crash risks have only been considered, exclusively, in hazardous material transport (HazMat) [PRA 14a, PRA 14b, LOZ 11]. A VRPTW model adding service hour regulations for drivers in order to avoid fatigue-related crashes has been presented [GOE 14]. The difference between the maximum and minimum route lengths as a social objective for fairness has also been considered [MEL 14]. Recently, another objective of the GVRP in the form of a difference between the trip durations of all vehicles to incorporate social and safety issues of equity and fatigue among drivers has been included [SHA 17].

1.3. Multi-agent models

Multi-agent models help us to understand the behavior of stakeholders who are involved in city logistics. Multi-agent simulation models are often used for the purpose of estimating the social, economic, financial, environmental and energy impacts by implementing policy measures in urban areas [TAN 07, TAM 10, VAN 07, VAN 12, ROO 10, TEO 12, TEO 14, TEO 15, ANA 14, ANA 16]. Multi-agent models can address the behavior of key stakeholders including shippers, freight carriers, residents, administrators and, in addition, other agents such as urban consolidation center operators or urban motorway operators. These models allow city logistics policy measures to be evaluated in a dynamic manner with the updated travel times on road networks given by traffic simulation. Reinforcement learning including Q-learning [TEO 12] techniques can be used for modeling the decision-making of agents to take action for adapting to a changing environment. Adaptive dynamic programming (ADP) [FIR 17] can also be used as a reinforcement learning method for the decision-making of agents in varying environments in terms of customers’ demands and travel times in urban distribution systems.

Although multi-agent modeling is promising for evaluating the effects of policy measures, the validation of these models needs to be carried out carefully based on precise data sets on realistic road networks. A validation framework based on a participatory simulation game and a discussion on how the decision-making process represented in multi-agent models can be validated has been presented [ANA 16]. If public–private partnerships (PPP) are set up, multi-agent models are suitable for providing the basic information on the effects of implementing policy measures in advance and rethinking the policy measures after implementing them and monitoring their impacts. This process represents the evaluation and feedback stages in the PDCA (Plan, Do, Check and Act) cycle which is often adopted in PPP.

Multi-agent models are usually used in simulation, and the results of simulation do not produce optimal solutions. The combination of simulation and optimization has become popular in the Operation Research area and can be a good tool for optimizing urban freight transport, considering the uncertainty of interactions between stakeholders. An outline of how simulation with meta-heuristics can be combined in the city logistics area has been presented [GRU 16]. Sim-heuristics takes the uncertainty of the behavior of entities in combinatorial optimization problems into account [JUA 16].

1.4. Big data analysis

Big data on the flow of goods, freight vehicles and information are available by collecting data using GPS (Global Positioning Systems) and RFID (Radio Frequency Identification) devices as well as the IoT (Internet of Things). Big data can have large impacts on analyzing, managing and operating urban freight transport systems, since big data have the capability of changing competition by transforming processes, altering corporate ecosystems, and facilitating innovation [FOS 15]. Big data are also capable of impacting social dynamics, choices and behavior, public response to events, market trends, services and the demand for goods [DEG 16]. Big data can be characterized by five Vs [FOS 15]: (1) Volume, (2) Velocity, (3) Variety, (4) Value and (5) Veracity.

An analysis of the influence of big data on city transport operations using Markovian models has been analyzed [MEH 17]. This work demonstrates how big data could be used to improve transport efficiency and lower externalities in a smart city. A framework of big data operations and a discussion on how improvement could take place by having a car-free city environment, autonomous vehicles and sh...

Table of contents

• Cover
• Table of Contents
• Preface
• 1 Recent Developments and Prospects for Modeling City Logistics
• 2 Light Commercial Vehicles (LCVs) in Urban Areas, Revisited
• 3 Importance and Potential Applications of Freight and Service Activity Models
• 4 Toward Sustainable Urban Distribution Using City Canals: The Case of Amsterdam
• 5 Effects of Land Use Policies on Local Conditions for Truck Deliveries
• 6 Investigating the Benefits of Shipper-driven Collaboration in Urban Freight Transport and the Effects of Various Gain-sharing Methods
• 7 The Future of City Logistics – Trends and Developments Leading toward a Smart and Zero-Emission System
• 8 A 2050 Vision for Energy-efficient and CO2-free Urban Logistics
• 9 Assessing the Impact of a Low Emission Zone on Freight Transport Emission
• 10 Long-Term Effects of Innovative City Logistics Measures
• 11 Classification of Last-Mile Delivery Models for e-Commerce Distribution: A Global Perspective
• 12 City Logistics with Collaborative Centers
• 13 Exploring Criteria for Tendering for Sustainable Urban Construction Logistics
• 14 Observing Interactions Between Urban Freight Transport Actors: Studying the Construction of Public Policies
• 15 Viewpoint of Industries, Retailers and Carriers about Urban Freight Transport: Solutions, Challenges and Practices in Brazil
• 16 Municipal Co-distribution of Goods: Business Models, Stakeholders and Driving Forces for Change
• 17 Optimizing Courier Routes in Central Business Districts
• 18 A Vehicle Routing Model Considering the Environment and Safety in the Vicinity of Sensitive Urban Facilities
• 19 Remote Assessment Sensor Routing: An Application for Waste Management
• 20 Can Routing Systems Surpass the Routing Knowledge of an Experienced Driver in Urban Deliveries?
• List of Authors
• Index
• End User License Agreement