Optimizing the Profitability and Quality of Service in Carshare Systems Under Demand Uncertainty

Lu, Mengshi; Chen, Zhihao; Shen, Siqian

doi:10.2139/ssrn.2992951

Cited by 15 publications

(28 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A review of this literature can be found in Freund et al (2019) and He et al (2019b). Recent papers include Lu et al (2017) and Kabra et al (2018).…”

Section: On-demand Rental Networkmentioning

confidence: 99%

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Benjaafar

2020

M&SOM

210

View full text Add to dashboard Cite

Please scroll down for article-it is on subsequent pagesWith 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit Abstract. The sharing economy, a term we use to refer to business models built around ondemand access to products and services mediated by online platforms that match many small suppliers or service providers to many small buyers, has emerged as an important area of study in operations management. We first describe three "canonical" applications that have garnered much attention from the operations management community. We use these applications to highlight distinguishing features of sharing economy business models and to point out research questions that are new. Then we draw connections between classical operations management theory and models and those that have been used to study sharing economy applications. We do so to put in context some of the recent work on the sharing economy and to showcase the underlying modeling toolkit and identify opportunities for future research.

show abstract

“…A review of this literature can be found in Freund et al (2019) and He et al (2019b). Recent papers include Lu et al (2017) and Kabra et al (2018).…”

Section: On-demand Rental Networkmentioning

confidence: 99%

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Benjaafar

2020

M&SOM

210

View full text Add to dashboard Cite

show abstract

“…Lu (2018) discusses barriers to enter into the automotive industry, focussing on China. He suggests that it is unlikely that today's successful OEMs or Internet companies will be successful in the future automotive industry, but rather a “new species” is successful.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Today, prominent providers include ShareNow, a merger of BMW's DriveNow and Daimler's car2go, Lyft and Uber. All are struggling with profitability, especially due to driver costs involved and yet lower rates compared to traditional taxi companies (Lu et al , 2018; Gilibert and Ribas, 2019). Increasing the geographic coverage, number and distance of rides as well as business expansion, like delivery of goods, are levers to changing cost and revenue structures (Bösch et al , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing dynamic capabilities of incumbents in the face of unprecedented industry transformation: the case of the automotive industry

Hoeft

2021

JSMA

View full text Add to dashboard Cite

PurposeThe purpose of this paper is to develop a holistic approach to the assessment of dynamic capabilities (DCs). Holistic refers to incorporating all DCs of an organisation relevant for determining and executing the firm's strategy.Design/methodology/approachA two-phase study was conducted. First, secondary sources, such as media, industry and annual reports, are being used to initially assess CASE (connected, autonomous, shared and electric) and implications for incumbent car manufacturers in a structured way. Second, semi-structured interviews with automotive managers and further automotive stakeholders offer in-depth insights into CASE, as well as incumbents' strategies and the underlying rationale.FindingsThe proposed framework for assessing DCs offers a holistic approach and provides new angles of analysis. First, the time dimension is considered using scenarios since timing is vital in strategy and implementation. Second, capabilities are broken down into technological and non-technological, sharpening strategic decision-making of automakers. Third, the analysis considers external VUCA (volatility, uncertainty, complexity and ambiguity) as they interplay with internal DCs.Research limitations/implicationsFurther testing of the proposed DC assessment approach offers a promising opportunity for future research. This paper focuses on the automotive industry, but it is worth investigating the extent to which the approach can be used in other dynamic industries, such as finance or retail.Originality/valueThe approach proposed highlights the importance and nuances of considering external perspectives in the DC assessment and the relevance of non-technological capabilities in the automotive industry. Thereby, it contributes to the literature on capability assessments and the operationalisability of the DC lens.

show abstract

“…For example, He et al (2017) consider a one-way car-sharing system (car2go), and model the market dynamics by standard queuing networks and formulate the fleet management problem as a mixed integer SOCP. Lu et al (2017) consider car-sharing systems (Zipcar), and develop a cost-minimization optimization formulation and solve it by branch-andcut algorithms with mixed-integer, roundingenhanced Benders cuts. Hao et al (2020) extend the fleet repositioning problem and provide distributionally robust optimization reformulation.…”

Section: Related Workmentioning

confidence: 99%

Beyond Repositioning: Crowd‐Sourcing and Geo‐Fencing for Shared‐Mobility Systems

Nie

Yang

et al. 2021

Production and Operations Management

View full text Add to dashboard Cite

I n this study, we propose an integrated model of two-sided stochastic matching platforms to understand the design and operations of free-float shared-mobility systems. In particular, we address the joint design of incentives (via "crowdsourcing") and spatial capacity allocations (enabled by "geo-fencing"). From the platform's perspective, we formulate stylized models based on strategic double-ended queues. We optimize the design and operations of such systems in a case study using a data set from a leading free-float bicycle-sharing system, and solve it via mixed-integer second-order conic programs (SOCPs). Both stylized results and computational studies generate insights about fundamental trade-offs and triangular relationships among operational costs, capacity utilization rates and service levels. Interestingly, we identify the role of spatial capacity (parking) management to fine-tune the market thickness (transient service availability) in such a two-sided marketplace. We show that a "capacity-dependent approximation" can be very close to optimality, and outperforms policies ignoring capacity management. We also demonstrate that this framework can be operationalized in multiple directions, which generates insights concerning matching efficiency, performance comparison between crowd-sourcing and repositioning, strategic server behaviors and network externalities. Our insights guide the platform and the policymaker to embrace "crowd-sourcing" and "geo-fencing" technologies for shared-mobility systems.

show abstract

Optimizing the Profitability and Quality of Service in Carshare Systems Under Demand Uncertainty

Cited by 15 publications

References 0 publications

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

Assessing dynamic capabilities of incumbents in the face of unprecedented industry transformation: the case of the automotive industry

Beyond Repositioning: Crowd‐Sourcing and Geo‐Fencing for Shared‐Mobility Systems

Contact Info

Product

Resources

About