Disruption Management of Resource Schedule in Transportation Sector: Understanding the Concept and Strategy

Transportation or transport sector is a legal source to take or carry things from one place to another. With the passage of time, transportation faces many issues like high accidents rate, traffic congestion, traffic & carbon emissions air pollution, etc. In some cases, transportation sector faced alleviating the brutality of crash related injuries in accident. Due to such complexity, researchers integrate virtual technologies with transportation which known as Intelligent Transport System. Intelligent Transport Systems (ITS) provide transport solutions by utilizing state-of-the-art information and telecommunications technologies. It is an integrated system of people, roads and vehicles, designed to significantly contribute to improve road safety, efficiency and comfort, as well as environmental conservation through realization of smoother traffic by relieving traffic congestion. This paper aims to elucidate various aspects of ITS - it's need, the various user applications, technologies utilized and concludes by emphasizing the case study of IBM ITS.

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The Regulatory Challenges of the Brazilian Transportation Sector

SSRN Electronic Journal ◽

10.2139/ssrn.259291 ◽

2001 ◽

Author(s):

Newton de Castro

Keyword(s):

Transportation Sector ◽

Regulatory Challenges

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Biofuel Substitution in the U.S. Transportation Sector

SSRN Electronic Journal ◽

10.2139/ssrn.3577371 ◽

2020 ◽

Author(s):

A K M Nurul Hossain ◽

Apostolos Serletis

Keyword(s):

Transportation Sector ◽

The U.S

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Working Time under Alternative Pay Contracts in the Ride-Sharing Industry

The B E Journal of Theoretical Economics ◽

10.1515/bejte-2020-0026 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Filippo Belloc

Keyword(s):

Network Effects ◽

Network Externalities ◽

Profit Sharing ◽

Static Model ◽

Peer To Peer ◽

Working Time ◽

Intensive Margin ◽

Hours Worked ◽

Ride Sharing ◽

Transportation Sector

AbstractWe study hours worked by drivers in the peer-to-peer transportation sector with cross-side network effects. Medallion lease (regulated market), commission-based (Uber-like pay) and profit-sharing (“pure” taxi coop) compensation schemes are compared. Our static model shows that network externalities matter, depending on the number of active drivers. When the number of drivers is limited, in the presence of positive network effects, a regulated system always induces more hours worked, while the commission fee influences the comparative incentives towards working time of Uber-like pay versus profit-sharing. When the number of drivers is infinite (or close to it), the influence of network externalities on optimal working time vanishes. Our model helps identifying which is the pay scheme that best remunerates longer working times and offers insights to regulators seeking to improve the intensive margin of coverage by taxi services.

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A next step in disruption management: combining operations research and complexity science

Public Transport ◽

10.1007/s12469-021-00261-5 ◽

2021 ◽

Author(s):

Mark M. Dekker ◽

Rolf N. van Lieshout ◽

Robin C. Ball ◽

Paul C. Bouman ◽

Stefan C. Dekker ◽

...

Keyword(s):

Operations Research ◽

Complexity Science ◽

Rolling Stock ◽

Accurate Information ◽

Disruption Management ◽

Railway Systems ◽

Decentralized Decision Making ◽

Control Situations ◽

Management Techniques ◽

The Impact

AbstractRailway systems occasionally get into a state of being out-of-control, meaning that barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. Because of the large number of affected resources and the absence of detailed, timely and accurate information, currently existing disruption management techniques cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework combining techniques from complexity science and operations research, aiming at reducing the impact of these situations and—if possible—avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations, (ii) isolating a specific region such that delay stops propagating, and (iii) the application of decentralized decision making, more suited for information-sparse out-of-control situations.

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Research on disruption management of urgent arrival in job shop with deteriorating effect

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210166 ◽

2021 ◽

pp. 1-13

Author(s):

Ning Tao ◽

Duan Xiaodong ◽

An Lu ◽

Gou Tao

Keyword(s):

Prospect Theory ◽

Job Shop ◽

Job Shop Scheduling ◽

Quantum Particle ◽

Particle Swarm ◽

Cumulative Prospect Theory ◽

Disruption Management ◽

Particle Swarm Algorithm ◽

Shop Scheduling ◽

Management Method

A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified.

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Changing Technology or Behavior? The Impacts of a Behavioral Disruption

Sustainability ◽

10.3390/su13115861 ◽

2021 ◽

Vol 13 (11) ◽

pp. 5861

Author(s):

Marianne Pedinotti-Castelle ◽

Pierre-Olivier Pineau ◽

Kathleen Vaillancourt ◽

Ben Amor

Keyword(s):

Energy Transition ◽

Energy System ◽

Behavior Changes ◽

Ghg Emission ◽

The North ◽

Behavioral Disruption ◽

Transportation Sector ◽

Key Factor ◽

Additional Costs

Transportation is a key factor in the fight against climate change. Consumer behavior changes in transportation are underrepresented in energy policies, even if they could be essential to achieve the fixed GHG emission reduction targets. To help quantify the role of behaviors in energy transition and their implications on the dynamics of an energy system, this study is conducted using the North American TIMES Energy Model, adapted to Quebec (Canada). A behavioral disruption scenario (an increase in carpooling) is introduced in the model’s transportation sector and is compared to a massive electrification scenario. Our results highlight the fact that a behavioral disruption can lead to the same GHG emission reductions (65%) by 2050 as an electrification policy, while alleviating different efforts (such as additional electrical capacity and additional costs) associated with massive electrification. Moreover, the results are sensitive to behavior-related parameters, such as social discount rates and car lifetimes.

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