50th Anniversary Invited Article—City Logistics: Challenges and Opportunities

Savelsbergh, Mwp Martin; Woensel, Tom Van

doi:10.1287/trsc.2016.0675

Cited by 520 publications

(295 citation statements)

References 54 publications

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“…First, by involving a crowd of randomly united citizens, awareness is created of the challenges that last mile transportation and home delivery impose on the city. E-commerce and omnichannel retail, which integrates online and offline sales channels, stimulate a rising amount of individual parcels and delivery vans [17]. At the same time, altered mobility, demographic change and increasing employment reduce the possibility to successfully receive deliveries in person [28].…”

Section: Literaturementioning

confidence: 99%

Crowd logistics: an opportunity for more sustainable urban freight transport?

Buldeo

Verlinde

Merckx³

et al. 2017

Eur. Transp. Res. Rev.

200

157

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Purpose Passenger car occupancy has been falling for years. Partly empty vehicles on our road networks decrease passenger transport sustainability but also contain an opportunity for freight transport. Within Crowd logistics (CL), delivery operations are carried out by using passengers' excess capacity on journeys that are already taking place, resulting in economic, social and environmental benefits. Existing CL initiatives show, however, that there are important differences between concepts in terms of sustainability. The research aims to develop a suitable and comprehensive definition for CL and identify which factors determine the sustainability potential of CL. Methods We systematically analysed a set of 42 papers and interviewed 11 logistics practitioners in order to capture the state of practice. Results Following the literature and interviews, we firstly define CL as 'an information connectivity enabled marketplace concept that matches supply and demand for logistics services with an undefined and external crowd that has free capacity with regards to time and/or space, participates on a voluntary basis and is compensated accordingly'. Secondly, we identify a set of 18 characteristics that can describe the variety of CL concepts. Thirdly, we indicate whether the identified characteristics affect the economy, society and/or environment. The research shows that all characteristics influence economic sustainability while 11 characteristics also affect social and/ or environmental sustainability. Conclusions Our research helps local policy-makers to adapt laws and regulations to the sharing economy developments and provides insight for businesses which CL concept fits their company's corporate social responsibility strategy.

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Section: Literaturementioning

confidence: 99%

Crowd logistics: an opportunity for more sustainable urban freight transport?

Buldeo

Verlinde

Merckx³

et al. 2017

Eur. Transp. Res. Rev.

200

157

View full text Add to dashboard Cite

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“…This study also demonstrates other benefits of a pull-centric system, including lower capital investments and possible carry-over (or halo) effects into in-store sales ( Johnson and Whang, 2002). Savelsbergh and Van Woensel (2016) Notes: GRP, goods reception point; RB, reception box (independent/with docking mechanism/shared); ULB, urban logistics boxes …”

Section: Review Of Lml Distribution Structuresmentioning

confidence: 68%

“…Airbnb, Uber, Amazon Prime Now) in several sectors (e.g. housing, transportation, and logistics, respectively) and exploits collaborative consumption (Hamari et al, 2016(Hamari et al, , p. 2047 and logistics (Savelsbergh and Van Woensel, 2016), there is an immense research scope at the intersection between LML and sharing economy models. First, we propose empirical studies to examine how retailers can effectively employ crowdsourcing models for the last-mile and to show how they can effectively integrate these models into their existing last-mile operations, such as combining in-store fulfilment through delivery using "Uber-type" solutions.…”

Section: Operational Challenges In Executing Last-mile Operationsmentioning

confidence: 99%

“…Hitherto, existing studies provide limited or no guidance on how contingency variables influence the selection of LML configurations (Agatz et al, 2008;Fernie et al, 2010;Mangiaracina et al, 2015;Lagorio et al, 2016;Savelsbergh and Van Woensel, 2016). Our paper addresses this knowledge deficiency by reviewing the disparate academic literature to capture key contingency and structural variables characterizing the different forms of last-mile distribution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Consumer-driven e-commerce

Lim

Jin

Srai

2018

IJPDLM

242

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The purpose of this paper is to re-examine the extant research on last-mile logistics (LML) models and consider LML's diverse roots in city logistics, home delivery and business-to-consumer distribution, and more recent developments within the e-commerce digital supply chain context. The review offers a structured approach to what is currently a disparate and fractured field in logistics. Design/methodology/approach -The systematic literature review examines the interface between e-commerce and LML. Following a protocol-driven methodology, combined with a "snowballing" technique, a total of 47 articles form the basis of the review.Findings -The literature analysis conceptualises the relationship between a broad set of contingency variables and operational characteristics of LML configuration (push-centric, pull-centric, and hybrid system) via a set of structural variables, which are captured in the form of a design framework. The authors propose four future research areas reflecting likely digital supply chain evolutions.Research limitations/implications -To circumvent subjective selection of articles for inclusion, all papers were assessed independently by two researchers and counterchecked with two independent logistics experts. Resulting classifications inform the development of future LML models. Practical implications -The design framework of this study provides practitioners insights on key contingency and structural variables and their interrelationships, as well as viable configuration options within given boundary conditions. The reformulated knowledge allows these prescriptive models to inform practitioners in their design of last-mile distribution. Social implications -Improved LML performance would have positive societal impacts in terms of service and resource efficiency. Originality/value -This paper provides the first comprehensive review on LML models in the modern e-commerce context. It synthesises knowledge of LML models and provides insights on current trends and future research directions.

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“…If logistics is not managed well, it will cause congestion and enormous greenhouse gas (GHG) emissions (Savelsbergh and Van Woensel 2016;Hazen et al 2016). Recently, logistics and distribution systems have been identified as one of the most expensive aspects for an organization to deal with the ecological impact which is one of the biggest challenges (Savelsbergh and Van Woensel 2016;Sinuany-Stern and Sherman 2014). Freight GHG emissions increased by 46% from 1990 to 2003 according to United States Environmental Protection Agency (EPA 2006) and therefore this is an emergent need to reduce the amount of emission from this sector.…”

Section: Introductionmentioning

confidence: 99%

Data driven safe vehicle routing analytics: a differential evolution algorithm to reduce CO $$_{2}$$ 2 emissions and hazardous risks

2016

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Abstract:Contemporary vehicle routing requires ubiquitous computing and massive data in order to deal with the three aspects of transportation such as operations, planning and safety. Out of the three aspects, safety is the most vital and this study refers safety as the reduction of CO2 emissions and hazardous risks. Hence, this paper presents a data driven multi-objective differential evolution (MODE) algorithm to solve the safe capacitated vehicle routing problems (CVRP) by minimizing the greenhouse gas emissions and hazardous risk. The proposed data driven MODE is tested using benchmark instances associated with real time data which have predefined load for each of the vehicle travelling on a specific route and the total capacity summed up from the customers cannot exceed the stated load. Pareto fronts are generated as the solution to this multi-objective problem. Computational results proved the viability of the data driven MODE algorithm to solve the multi-objective safe CVRP with a certain trade-off to achieve an efficient solution. Overall the study suggests 5% increment in cost function is essential to reduce the risk factors. The major contributions of this paper are to develop a multi-objective model for a safe vehicle routing and propose a multi-objective differential evolution (MODE) algorithm that can handle structured and unstructured data to solve the safe capacitated vehicle routing problem.

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50th Anniversary Invited Article—City Logistics: Challenges and Opportunities

Cited by 520 publications

References 54 publications

Crowd logistics: an opportunity for more sustainable urban freight transport?

Crowd logistics: an opportunity for more sustainable urban freight transport?

Consumer-driven e-commerce

Data driven safe vehicle routing analytics: a differential evolution algorithm to reduce CO $$_{2}$$ 2 emissions and hazardous risks

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