[雙語翻譯]物流專業(yè)外文文獻(xiàn)翻譯 ---城市物流的機(jī)遇與挑戰(zhàn)（英文）

1、 Transportation Research Procedia 12 ( 2016 ) 5 – 13 2352-1465 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses

2、/by-nc-nd/4.0/). Peer-review under responsibility of the organising committee of the 9th International Conference on City Logistics doi: 10.1016/j.trpro.2016.02.004 Available online at www.sciencedirect.com ScienceDirec

3、tThe 9th International Conference on City Logistics, Tenerife, Canary Islands (Spain), 17-19 June 2015 New opportunities and challenges for city logistics Eiichi Taniguchia*, Russell G. Thompsonb, and Tadashi Yamadaa aK

4、yoto University, Kyotodaigaku-Katsura, Kyoto 615-8540, Japan bThe University of Melbourne, Parkville 3010, Australia Abstract The information revolution is creating both opportunities and challenges for improving the s

5、ustainability of urban freight systems. A range of vehicle movement data can now be automatically collected from low cost sensors that are able to assist in improving understanding distribution systems and increasing t

6、heir efficiency. Vehicle monitoring technologies that have the potential to charge both passenger and goods vehicles for using the road system, allow a new array of pricing schemes to be introduced. However, E-commerce

7、 (B2C) is creating a surge in home deliveries that is increasing the social and environmental costs of goods distribution systems. This paper describes some applications of big data systems and decision support systems

8、that can be used to enhance the design and evaluation city logistics schemes. The need to develop improved tools for understanding logistics sprawl and reducing its effects are described. Developments in alternative fu

9、el vehicles and advanced manufacturing systems are also presented. © 2016 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the organising committee of the 9th International Conference on

10、 City Logistics. Keywords: big data; e-commerce; decision support systems; road pricing; logistic sprawls; co-modality; alternative fuel vehicles 1. Introduction City Logistics is based on the systems approach that invol

11、ves a number of technical processes including modelling, evaluation and the application of information technologies (Taniguchi and Thompson, 2014). Advances in Information and Communication Technology (ICT) provide opp

12、ortunities for improving the performance of urban freight systems. ICT also creates the potential for developing more advanced urban freight management systems such as joint delivery * Corresponding author. Tel.: +81-75

13、-383-3229; fax: +81-75-950-3800. E-mail address: taniguchi@kiban.kuciv.kyoto-u.ac.jp © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecomm

14、ons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organising committee of the 9th International Conference on City Logistics7Eiichi Taniguchi et al. / Transportation Research Procedia 12 ( 201

15、6 ) 5 – 13 approaches and policy measures or scenarios, (c) duplicating behaviour of stakeholders, and (d) evaluating policy measures. Multi-agent models are often used for representing the behaviour of stakeholders

16、and evaluating policy measures in terms of economic, financial, social, environmental, and energy impacts based on the estimation of effects of policy measures. Davidsson et al. (2005) pointed out that agent-based appr

17、oaches are very suitable for freight logistics. Duin et al. (2007) discussed auctioning of shippers and carriers using agent based modelling. Taniguchi et al. (2007) dealt with dynamic vehicle routing and scheduling pr

18、oblems using multi-agent models. Donnelly (2009) developed a multi-agent model with micro simulation of freight flows that was applied in Portland, Oregon. Tamagawa et al. (2010) analysed the interaction between shippe

19、rs, carriers, administrators, and residents using multi-agent models with reinforcement learning for evaluating city logistics measures, and pointed out that win-win situations for stakeholders are possible by implemen

20、ting truck flow restrictions and joint delivery systems. Roorda et al. (2010) presented a conceptual framework for agent-based modelling of logistics services. Duin et al. (2012) presented multi-agent simulation models

21、 for analysing the dynamic demand of urban distribution centres (UDC). Teo et al. (2012, 2014) used multi-agent models for evaluating city logistics policy measures, including road pricing, load factor controls and buil

22、ding motorways on urban road networks and clarified the effects of pricing and provision of motorways on the efficiency of vehicle operations and CO2, NOx and SPM (Suspended Particle Material) emissions generated by tr

23、ucks. Anand et al. (2014) discussed decision making using ontology based multi-agent models for city logistics. Wangapisit et al. (2014) investigated joint delivery systems with UDC and parking management using multi-a

24、gent models. These models allow an understanding of the response behaviour of stakeholders to actions taken by other actors and effects of policy measures. However, the validation of multi-agent simulation is a challen

25、ging issue and more experience and case studies of practical application of multi-agent models is needed. Recently the Internet of Things (IoT) can provide a platform for decentralized management for city logistics. R

26、eaidy et al. (2015) discussed bottom up approach based on Internet of Things for order fulfilment in a collaborative warehousing environment. They used multi-agent systems and integrated a bottom up approach with decisi

27、on support mechanism such as self-organisation and negotiation protocols between agents based on “com-peration = competition + cooperation” concept. The behaviour of stakeholders highly affects the results of policy me

28、asures. Stathopoulos et al. (2012) studied the reaction of stakeholders to urban freight policies using nested logit model based on surveys in Rome. Gatta and Marcucci (2014) discussed an agent-specific approach to inc

29、rease decision-makers’ awareness and ability to make better decisions in case of Rome’s Limited Traffic Zone. Multi-Criteria Decision Making (MCDM) models have also been studied for choosing city logistics policy measu

30、res. Awasthi (2012) presented a hybrid approach using affinity diagrams, the Analytic Hierarchy Process (AHP) and fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for evaluating city logi

31、stics initiatives. Tadic et al. (2014) introduced hybrid MCDM model using fuzzy method and applied this in the city of Belgrade. Bouhana et al. (2015) highlighted intelligent decision support systems which integrate on

32、tology supported case base reasoning and multi-criteria decision making approaches with the Choquet integral for sustainable urban freight transport. Rao et al. (2015) discussed location selection of city logistics cen

33、tres using fuzzy multi-attribute group decision making (FMAGDM) technique. 4. E-commerce E-commerce has become more popular in business using Internet. The growth of Internet shopping Business to Consumer (B2C) affects

34、 urban delivery systems. Taniguchi and Kakimoto (2004) studied the effects of e-commerce on the urban freight transport using vehicle routing and scheduling problem model. They pointed out that the penetration of B2C e

35、-commerce may increase the truck flows for home delivery with time windows but this can be alleviated by introducing joint delivery systems and pickup points where customers visit to pick up their commodities. Campbell

36、 (2006) investigated incentives to influence the consumer behaviour to reduce home delivery costs. Hong et al. (2013) studied the optimisation of vehicle routing and scheduling for B2C e-commerce logistics distribution

37、 systems. Ehmke (2014) discussed customer acceptance on home deliveries with tight time windows at customers on congested road networks. Using simulation they analysed the effects of travel time information on decision

38、making investigating whether the delivery requests could be accommodated. All authors are required to complete the Procedia exclusive license transfer agreement before the article can be published, which they can do on