4,409 research outputs found
A multi-dimensional rescheduling model in disrupted transport network using rule-based decision making
Apart from daily recurrent traffic congestion, unforeseen events such as flood induced road damages or bridge collapses can degrade the capacity of traffic supply and cause a significant influence on travel demand. An individual realising the unexpected events would take action to reschedule its day plan in order to fit into the new circumstance. This paper analyses the potential reschedule possibilities by augmenting the Within-Day Replanning simulation model implemented in the Multi-Agent Transport Simulation (MATSim) framework. Agents can adjust day plan through multi-dimensional travel decisions including route choice, departure time choice, mode switch, trip cancellation. The enhanced model not only improves the flexibility of MATSim in rescheduling a plan during an execution day, but also lays the foundation of integrating more detailed heterogeneity decision rules into the travel behaviour simulation to cope with unexpected incidents. Furthermore, the proposed rescheduling model is capable of predicting the network performance in the real-world picture and gives a hint on how best react to transport disruptions for transport management agency
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
PUMA - a multi-agent model of urban systems
It is increasingly recognised that land use change processes are the outcome of decisions made by individual actors, such as land owners, authorities, firms and households. As multi-agent models provide a natural framework for modelling urban processes on the level of individual actors, Utrecht University, Eindhoven University of Technology and RIVM are developing PUMA (Predicting Urbanisation with Multi-Agents), a full fledged multi-agent system of urban processes. PUMA consists of various modules, representing the behaviours of specific actors. The land conversion module describes farmers', authorities', investors' and developers' decisions to sell or buy land and develop it into other uses. The households module describes households' housing careers in relation to life cycle events (marriage, child birth, aging, job change etc.). The firms module includes firms' demography and their related demand for production facilities leading to location choice processes. The daily activity pattern module describes the trips made and locations visited by individuals to carry out certain tasks. This module generates aggregated effects of individual behaviours (congestion, pollution, noise), affecting households' or firms' longer term location decisions. The paper describes the model system architecture and the interactions between the modules. Particular attention is devoted to the households module that includes a behaviourally sophisticated model of households' process of awakening (deciding to actively search for another dwelling), search and acceptance of an offered dwelling. This model was calibrated on the Dutch Housing Preferences Survey. Based on the disaggregate housing search and acceptance model, the households module describes housing market dynamics and indicates the demand for new dwellings per region. The paper describes the model specification and calibration in detail. The households module was implemented and tested for the Northwing of the Dutch Randstad, including about 1.5 million households and 1.6 million dwellings. The paper describes the implementation and the first model results.
Multi-day activity scheduling reactions to planned activities and future events in a dynamic agent-based model of activity-travel behavior
Modeling multi-day planning has received scarce attention today in activity-based transport demand modeling. Elaborating and combining previous work on event-driven activity generation, the aim of this paper is to develop and illustrate an extension of a need-based model of activity generation that takes into account possible influences of pre-planned activities and events. This paper describes the theory and shows the results of simulations of the extension. The simulation was conducted for six different activities and parameter values. The results show that the model works well and that the influences of the parameters are consistent, logical and have clear interpretations. These findings offer further evidence of face and construct validity to the suggested modeling approach
Real-time train driver rescheduling by actor-agent techniques
Passenger railway operations are based on an extensive planning process for generating the timetable, the rolling stock circulation, and the crew duties for train drivers and conductors. In particular, crew scheduling is a complex process. After the planning process has been completed, the plans are carried out in the real-time operations. Preferably, the plans are carried out as scheduled. However, in case of delays of trains or large disruptions of the railway system, the timetable, the rolling stock circulation and the crew duties may not be feasible anymore and must be rescheduled. This paper presents a method based on multi-agent techniques to solve the train driver rescheduling problem in case of a large disruption. It assumes that the timetable and the rolling stock have been rescheduled already based on an incident scenario. In the crew rescheduling model, each train driver is represented by a driver-agent. A driver-agent whose duty has become infeasible by the disruption starts a recursive task exchange process with the other driver-agents in order to solve this infeasibility. The task exchange process is supported by a route-analyzer-agent, which determines whether a proposed task exchange is feasible, conditionally feasible, or not feasible. The task exchange process is guided by several cost parameters, and the aim is to find a feasible set of duties at minimal total cost. The train driver rescheduling method was tested on several realistic disruption instances of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. In general the rescheduling method finds an appropriate set of rescheduled duties in a short amount of time. This research was carried out in close cooperation by NS and the D-CIS Lab
Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning
The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques
Intelligent Procedures for Intra-Day Updating of Call Center Agent Schedules
For nearly all call centers, agent schedules are typically created several days or weeks prior to the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra-day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents’ schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade-off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center
Smart digital twin for ZDM-based job-shop scheduling
[EN] The growing digitization of manufacturing processes is revolutionizing the production job-shop by leading it toward the Smart Manufacturing (SM) paradigm. For a process to be smart, it is necessary to combine a given blend of data technologies, information and knowledge that enable it to perceive its environment and to autonomously perform actions that maximize its success possibilities in its assigned tasks. Of all the different ways leading to this transformation, both the generation of virtual replicas of processes and applying artificial intelligence (AI) techniques provide a wide range of possibilities whose exploration is today a far from negligible sources of opportunities to increase industrial companies¿ competitiveness. As a complex manufacturing process, production order scheduling in the job-shop is a necessary scenario to act by implementing these technologies. This research work considers an initial conceptual smart digital twin (SDT) framework for scheduling job-shop orders in a zero-defect manufacturing (ZDM) environment. The SDT virtually replicates the job-shop scheduling issue to simulate it and, based on the deep reinforcement learning (DRL) methodology, trains a prescriber agent and a process monitor. This simulation and training setting will facilitate analyses, optimization, defect and failure avoidance and, in short, decision making, to improve job-shop scheduling.The research that led to these results received funding
from the European Union H2020 Programme with grant
agreement No. 825631 Zero-Defect Manufacturing
Platform (ZDMP) and Grant agreement No. 958205
Industrial Data Services for Quality Control in Smart
Manufacturing (i4Q), and from the Spanish Ministry of
Science, Innovation and Universities with Grant Agreement
RTI2018-101344-B-I00 "Optimisation of zero-defects
production technologies enabling supply chains 4.0
(CADS4.0)"Serrano Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart digital twin for ZDM-based job-shop scheduling. IEEE. 510-515. https://doi.org/10.1109/MetroInd4.0IoT51437.2021.948847351051
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