[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)

1、14000 4000 英文單詞， 英文單詞，2.3 2.3 萬英文字符，中文 萬英文字符，中文 6400 6400 字文獻(xiàn)出處： 文獻(xiàn)出處：Taniguchi Taniguchi E, E, Thompson Thompson R G, G, Yamada Yamada T. T. New New opportunities opportunities and and challenges challenges f

2、or for city city logistics[J]. logistics[J]. Transportation Transportation Research Research Procedia, Procedia, 2016, 2016, 12: 12: 5-13. 5-13.New opportunities and challenges for city logisticsEiichi Taniguchi, Russell

3、 G. Thompson, and Tadashi YamadaAbstract: The information revolution is creating both opportunities and challenges for improving the sustainability of urban freight systems. A range of vehicle movement data can now be au

4、tomatically collected from low cost sensors that are able to assist in improving understanding distribution systems and increasing their efficiency. Vehicle monitoring technologies that have the potential to charge both

5、passenger and goods vehicles for using the road system, allow a new array of pricing schemes to be introduced. However, E-commerce (B2C) is creating a surge in home deliveries that is increasing the social and environmen

6、tal costs of goods distribution systems. This paper describes some applications of big data systems and decision support systems that can be used to enhance the design and evaluation city logistics schemes. The need to d

7、evelop improved tools for understanding logistics sprawl and reducing its effects are described. Developments in alternative fuel vehicles and advanced manufacturing systems are also presented.Keywords: big data; e-comme

8、rce; decision support systems; road pricing; logistic sprawls; co-modality; alternative fuel vehicles1. IntroductionCity Logistics is based on the systems approach that involves a number of technical processes including

9、modelling, evaluation and the application of information technologies (Taniguchi and Thompson, 2014). Advances in Information and Communication Technology (ICT) provide opportunities for improving the performance of urba

10、n freight systems. ICT also creates the potential for developing more advanced urban freight management systems such as joint delivery systems and road pricing schemes.2. Big data and analysisThanks to the development an

11、d deployment of ICT (Information and Communication Technology) and ITS (Intelligent Transport Systems), we can easily collect “big data” of pickup-delivery truck movements or goods movements in urban areas at lower costs

12、. Global Positioning Systems (GPS) devices are typically equipped in trucks allowing the location of trucks to be precisely measured every second. Fig. 1 shows a GPS device which is used for recording the routes of urban

13、 trucks.The analysis of big data of truck movements in urban areas allows us to gain insights into the behaviour of drivers. Ehmke and Mattfeld (2010) highlighted data provision of time-dependent travel times for city lo

14、gistics routing demands. Telematics based traffic data collection and conversion from legal empirical traffic data into information models are discussed. Lin et al. (2013) applied data mining technique to find routing pa

15、tterns from the past cases of vehicle routing plans of truck drivers. They designed a real time mobile intelligent routing system, which was installed on drivers’ smart phone. It was demonstrated that the proposed method

16、 was successful in reducing the travel times on congested urban road networks in case studies. Xu et al. (2014) undertook a study where data was used to design a high-efficient flow path using Petri-Nets and offered a ci

17、ty logistics model based on a cloud based platform. Teo et al. (2015) analysed 3and building motorways on urban road networks and clarified the effects of pricing and provision of motorways on the efficiency of vehicle o

18、perations and CO2, NOx and SPM (Suspended Particle Material) emissions generated by trucks. Anand et al. (2014) discussed decision making using ontology based multi-agent models for city logistics. Wangapisit et al. (201

19、4) investigated joint delivery systems with UDC and parking management using multi-agent models. These models allow an understanding of the response behaviour of stakeholders to actions taken by other actors and effects

20、of policy measures. However, the validation of multi-agent simulation is a challenging issue and more experience and case studies of practical application of multi-agent models is needed.Recently the Internet of Things (

21、IoT) can provide a platform for decentralized management for city logistics. Reaidy et al. (2015) discussed bottom up approach based on Internet of Things for order fulfilment in a collaborative warehousing environment.

22、They used multi-agent systems and integrated a bottom up approach with decision support mechanism such as self-organisation and negotiation protocols between agents based on “com-peration = competition+ cooperation” conc

23、ept.The behaviour of stakeholders highly affects the results of policy measures. Stathopoulos et al. (2012) studied the reaction of stakeholders to urban freight policies using nested logit model based on surveys in Rome

24、. Gatta and Marcucci (2014) discussed an agent-specific approach to increase decision-makers’ awareness and ability to make better decisions in case of Rome’s Limited Traffic Zone.Multi-Criteria Decision Making (MCDM) mo

25、dels have also been studied for choosing city logistics policy measures. Awasthi (2012) presented a hybrid approach using affinity diagrams, the Analytic Hierarchy Process (AHP) and fuzzy TOPSIS (Technique for Order of P

26、reference by Similarity to Ideal Solution) for evaluating city logistics initiatives. Tadic et al. (2014) introduced hybrid MCDM model using fuzzy method and applied this in the city of Belgrade. Bouhana et al. (2015) hi

27、ghlighted intelligent decision support systems which integrate ontology supported case base reasoning and multi-criteria decision making approaches with the Choquet integral for sustainable urban freight transport. Rao e

28、t al. (2015) discussed location selection of city logistics centres using fuzzy multi-attribute group decision making (FMAGDM) technique.4. E-commerceE-commerce has become more popular in business using Internet. The gro

29、wth of Internet shopping Business to Consumer (B2C) affects urban delivery systems. Taniguchi and Kakimoto (2004) studied the effects of e-commerce on the urban freight transport using vehicle routing and scheduling prob

30、lem model. They pointed out that the penetration of B2C e-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

31、 customers visit to pick up their commodities. Campbell (2006) investigated incentives to influence the consumer behaviour to reduce home delivery costs. Hong et al. (2013) studied the optimisation of vehicle routing and

32、 scheduling for B2C e-commerce logistics distribution systems. Ehmke (2014) discussed customer acceptance on home deliveries with tight time windows at customers on congested road networks. Using simulation they analysed