Why do some sustainable urban logistics innovations fail? The case of collection and delivery points

Combined with current trends in e-commerce, demand for urban logistic services are putting significant pressure on the environment. While some European examples show that collection-and-delivery points (CDPs) offer a sustainable solution, this is not always the case. This paper explores the mechanisms that support CDPs as sustainable urban logistics innovations while providing viable market offerings. To do so, it analyses a failure case using multiple data sources, such as a consumer survey, interviews and secondary data. Using diffusion of innovations (DoI) theory, the study explains how CDP failed in a developing market setting. Sustainable logistics innovations fail due to both supply chain-related and market-related factors. Significant factors on the supply chain side include network structures, IT integration and diverse value propositions while the market side includes consumer market characteristics, regulations, security issues and convenience of existing alternatives. Important factors for success include looking for horizontal collaboration opportunities, building strong network partnerships with customers and distribution channel actors. CDPs should be positioned as sustainable solutions and complemented with other urban logistics services to diversify the value proposition

Handbook of Systems Analysis: Volume 1. Overview. Chapter 7. Predicting the Consequences: Models and Modeling

The International Institute for Applied Systems Analysis is preparing a Handbook of Systems Analysis, which will appear in three volumes: Volume 1: Overview is aimed at a widely varied audience of producers and users of systems analysis studies. Volume 2: Methods is aimed at systems analysts and other members of systems analysis teams who need basic knowledge of methods in which they are not expert; this volume contains introductory overviews of such methods. Volume 3: Cases contains descriptions of actual systems analyses that illustrate the diversity of the contexts and methods of systems analysis. Drafts of the material for Volume 1 are being widely circulated for comment and suggested improvement. This Working Paper is the current draft of Chapter 7. Correspondence is invited. Volume 1 will consist of the following ten chapters: 1. The context, nature, and use of systems analysis 2. The genesis of applied systems analysis; 3. Examples of applied systems analysis 4. The methods of applied systems analysis: An introduction and overview 5. Formulating problems for systems analysis 6. Objectives, constraints, and alternatives 7. Predicting the consequences: Models and modeling 8. Guidance for decision 9. Implementation 10. The practice of applied systems analysis To these ten chapters will be added a glossary of systems analysis terms and a bibliography of basic works in the field

Application of multi-agents to power distribution systems

The electric power system has become a very complicated network at present because of re-structuring and the penetration of distributed energy resources. In addition, due to increasing demand for power, issues such as transmission congestion have made the power system stressed. A single fault can lead to massive cascading effects, affecting the power supply and power quality. An overall solution for these issues can be obtained by a new artificial intelligent mechanism called the multi-agent system. A multi-agent system is a collection of agents, which senses the environmental changes and acts diligently on the environment in order to achieve its objectives. Due to the increasing speed and decreasing cost in communication and computation of complex matrices, multi-agent system promise to be a viable solution for today\u27s intrinsic network problems.;A multi-agent system model for fault detection and reconfiguration is presented in this thesis. These models are developed based on graph theory and mathematical programming. A mathematical model is developed to specify the objective function and the constraints.;The multi-agent models are simulated in Java Agent Development Framework and MatlabRTM and are applied to the power system model designed in the commercial software, Distributed Engineering Workstation(c) . The circuit that is used to model the power distribution system is the Circuit of the Future, developed by Southern California Edison.;The multi-agent system model can precisely detect the fault location and according to the type of fault, it reconfigures the system to supply as much load as possible by satisfying the power balance and line capacity constraints. The model is also capable of handling the assignment of load priorities.;All possible fault cases were tested and a few critical test scenarios are presented in this thesis. The results obtained were promising and were as expected

Ordering Networks: Motorways and the Work of Managing Disruption

This thesis contributes to a new understanding of the motorway network and its traffic movements as a problem of practical accomplishment. It is based on a detailed ethnomethodological study of incident management in the Highways Agency’s motorway control room, which observes the methods operators use to detect, diagnose and clear incidents to accomplish safe and reliable traffic. Its main concern is how millions of vehicles can depend on the motorway network to fulfil obligations for travel when it is constantly compromised by disruption from congestion, road accidents and vehicle breakdowns. It argues that transport geography and new mobilities research have overlooked questions of practical accomplishment; they tend to treat movement as an inevitable demand, producing fixed technical solutions to optimise it, or a self-evident phenomenon, made meaningful only through the intensely human experience of mobility. In response, the frame of practical accomplishment is developed to analyse the ways in which traffic is ongoingly organised through the situated and contingent practices that take place in the control room. The point is that traffic does not move by magic; it has to be planned for, produced and persistently worked at. This is coupled with an understanding of network topology that reconsiders the motorway network as always in process by virtue of the materially heterogeneous relations it keeps, drawing attention to the intensely collaborative nature of work between operators and technology that permits the management of disruption at-a-distance and in real time. This work is by no means straightforward – the actions of monitoring, detecting, diagnosing and classifying incidents and managing traffic are revealed to be complexly situated and prone to uncertainty, requiring constant ordering work to accomplish them. In conclusion, this thesis argues for the frame of practical accomplishment to be taken seriously, rendering the work of transport networks available for sustained analysis

LimSim: A Long-term Interactive Multi-scenario Traffic Simulator

With the growing popularity of digital twin and autonomous driving in transportation, the demand for simulation systems capable of generating high-fidelity and reliable scenarios is increasing. Existing simulation systems suffer from a lack of support for different types of scenarios, and the vehicle models used in these systems are too simplistic. Thus, such systems fail to represent driving styles and multi-vehicle interactions, and struggle to handle corner cases in the dataset. In this paper, we propose LimSim, the Long-term Interactive Multi-scenario traffic Simulator, which aims to provide a long-term continuous simulation capability under the urban road network. LimSim can simulate fine-grained dynamic scenarios and focus on the diverse interactions between multiple vehicles in the traffic flow. This paper provides a detailed introduction to the framework and features of the LimSim, and demonstrates its performance through case studies and experiments. LimSim is now open source on GitHub: https://www.github.com/PJLab-ADG/LimSim .Comment: Accepted by 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

Learning behavior in an asynchronous Web-based executive program

Web-based learning (WBL) of the asynchronous type provides great potential for today’s managers and professionals to upgrade their knowledge and skills. Managers and professional staff, unlike full-time students, have to balance work, family and learning commitments. However, most research focuses on full-time students, with less concern for managers and professional staff. In this study, we adopt the ethnographic method to conduct a case study of the learning behavior and experience of managers and professional staff in an asynchronous Web-based Strategic Management course. Taking an interpretive stance, we reach several important findings: contingencies exist and influence learning behavior; deadlines play a significant but different role for different learners; learners spend more time and effort on an asynchronous Web-based course; learners adopt different strategies and build for themselves different combinations from the same set of teaching materials; and learners struggle to create for themselves a “classroom” where none in fact exists. These findings are substantial and contribute greatly to our understanding of how managers and professional staff learn in the asynchronous WBL environment

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

On the Benefits of Inoculation, an Example in Train Scheduling

The local reconstruction of a railway schedule following a small perturbation of the traffic, seeking minimization of the total accumulated delay, is a very difficult and tightly constrained combinatorial problem. Notoriously enough, the railway company's public image degrades proportionally to the amount of daily delays, and the same goes for its profit! This paper describes an inoculation procedure which greatly enhances an evolutionary algorithm for train re-scheduling. The procedure consists in building the initial population around a pre-computed solution based on problem-related information available beforehand. The optimization is performed by adapting times of departure and arrival, as well as allocation of tracks, for each train at each station. This is achieved by a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic scheduler to gradually reconstruct the schedule by inserting trains one after another. Experimental results are presented on various instances of a large real-world case involving around 500 trains and more than 1 million constraints. In terms of competition with commercial math ematical programming tool ILOG CPLEX, it appears that within a large class of instances, excluding trivial instances as well as too difficult ones, and with very few exceptions, a clever initialization turns an encouraging failure into a clear-cut success auguring of substantial financial savings

Rethinking Closed-loop Training for Autonomous Driving

Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment and allowing training to be scaled both safely and cheaply. However, there is a lack of understanding of how to build effective training benchmarks for closed-loop training. In this work, we present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents, such as how to design traffic scenarios and scale training environments. Furthermore, we show that many popular RL algorithms cannot achieve satisfactory performance in the context of autonomous driving, as they lack long-term planning and take an extremely long time to train. To address these issues, we propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead and exploits cheaply generated imagined data for efficient learning. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines. For more information, visit the project website: https://waabi.ai/research/travlComment: ECCV 202