Research on a Scheduling Model for Social Emergency Resource Sharing Based on Emergency Contribution Index

Emergency Resources Management System is an important part of emergency management system. There are many classification systems of emergency resources, which lead to inaccurate description of the structure of emergency resources and negative impacts of the emergency resources sharing and configuration scheduling. In this paper, we compared the strengths and weaknesses of existed emergency resources classification systems, constructed a emergency resources classification system suitable for the whole process of emergency rescue, built an upper emergency resource and domain ontology model rested on SUMO ontology, formally described the concepts in the field of emergency resources and the relationships between these concepts, and satisfied the emergency resources demand of quickly crisis dealing.

When there is an ignition, other places may catch fire at any time in the same area, and the efficiency of schedule emergency resources is often affected by external factors to be lower, making the actual dispatching time much longer. We introduce the path's reliability level which is based on emergency rescue time. Under the precondition of minimizing the total schedule time from the fire stations to both the ignitions and potential ignitions, the ignition's resources demands are met, the optimal path among the paths a fire station to ignition is chosen, and the rapid response of emergency resource has the largest supply rate. The efficiency of emergency rescue is improved.

We discuss robust scheduling models under uncertain environments described by scenario approach. Using the insights revealed by the analysis of traditional uncertain scheduling models involving the conflicting and balancing twofold relevance, which are the motivation of pursuing better performance and the conservatism of resisting risk, we establish a kind of new robust scheduling model. The optimization objective combines expected performance and robustness measure with a balance factor. A risk-resisting robustness measure is defined based on the concept of bad-scenario set, in which the number of bad scenarios can be adjusted by a standard performance. Thus, a set of robust scheduling models is established as the balance factor or the standard performance varies. A series of theorems reveal the relationship among the set of new models proposed in this paper and traditional uncertain scheduling models. And the condition of effectiveness of robustness for the set of new models is proposed as a theorem. Furthermore, an extensive experiment was conducted for job-shop scheduling problems with uncertain processing time. The computational results provide evidence that the set of new models is more comprehensive and more integrated in terms of pursuing better statistic performance and resisting the risk of performance deterioration. Thus, the new model can realize better balance between expected performance and risk-resisting robustness, as comparied against existing uncertain scheduling models.

Based on the characteristics and requirements of railway emergency resources dispatch, the paper established two optimal models respectively, the single objective model is aiming at minimizing the time of emergency resource dispatch, the multi objective model is for the sake of minimizing the time of emergency resource dispatch and the number of emergency rescue base, and the paper used voting analytic hierarchy process of operations research to solve the model. The study shows that the multi objective optimization model can reduce the number of emergency rescue base and the cost of rescue while it can meet the demand of the shortest allocation time. By using Matlab software, the decision-making process of emergency resource optimize dispatch was designed, making the emergency decision fast and scientific, meanwhile, the paper made an empirical analysis on one railway bureau. The research results reduce the rescue time and cost of the railway accident, and provide reference for implement the rapidness response to make decision on emergency resources allocation, scientific resource allocation decisions.

Railways of the National Energy Group using their own trains have become an important mode of transportation for coal-to-oil hazardous chemicals. Under the circumstances of the shortage of emergency resources and the coupling of multiple disasters, how to establish an effective and reasonable emergency resource allocation scheme for the railway transportation of dangerous chemicals from a disaster site is of great significance to the national task of ensuring safety for the transportation of energy. This paper focuses on the allocation of emergency rescue resources for railway transportation accidents involving coal-to-oil hazardous chemicals, considering the scenarios of the leakage of coal-to-oil, railway line damage, etc. According to the number of trapped people at the initial moment, the disaster situation and accident type, affected areas, etc., a multi-objective optimization model with the shortest response time of the emergency team and the lowest cost of transporting emergency materials along the railway transportation channel of coal-to-oil hazardous chemicals is constructed, based on the calculation method using the initial weight and the emergency weight assigned by the emergency rescue team. Furthermore, in order to avoid the problem of the weight of the local accident points being too small to participate in a rescue, a bee colony algorithm model based on pre-allocation was designed and compared with two traditional algorithms, allowing the realization of the search and selection of allocation methods. The analysis of the examples shows that the proposed method is efficient and fast, and the research results are practical and feasible, which can provide a scientific basis for the rapid decision of emergency rescue resource allocation in multi-disaster scenarios for large energy groups, and provide a reference for the allocation of public security emergency resources in the national emergency response.

In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads. Firstly, a prioritized experience replay automated reinforcement learning (PER-AutoRL) is designed to simplify the deployment of deep reinforcement learning (DRL)-based forecasting model in a customized manner, the single-step multi-period forecasting method based on PER-AutoRL is proposed for the first time to address the error accumulation issue suffered by existing multi-step forecasting methods, then the prediction values obtained by the proposed forecasting method are revised via the error distribution to improve the prediction accuracy; secondly, a scheduling model considering demand response is constructed to minimize the total microgrid operating costs, where the revised forecasting values are used as the dispatch basis, and a spinning reserve chance constraint is set according to the error distribution; finally, by transforming the original scheduling model into a readily solvable mixed integer linear programming via the sequence operation theory (SOT), the transformed model is solved by using CPLEX solver. The simulation results show that compared with the traditional scheduling model without forecasting, this approach manages to significantly reduce the system operating costs by improving the prediction accuracy.

Objective To study the influence of implementing APN continuous scheduling model on the high quality nursing services.Methods APN scheduling mode was implemented in the quality care demonstration project activities in October 2010,and the monthly nursing quality,patients' satisfaction,mental and physical health of nurses were compared with those in same period of last year that traditional scheduling model was implemented.Results During the APN continuous scheduling mode was implemented,patients' satisfaction was 95.0％ significantly higher than 75.0％ of before ( P ＜ 0.05 ),and monthly nursing quality,mental and physical health of nurses were significantly higher than that of traditional scheduling model (P＜0.05).Conclusions APN scheduling model can significantly improve the quality of nursing,patients'satisfaction and nurses' mental and physical health,which promote “quality care demonstration project” to launch. Key words: Nursing administration research; APN scheduling model; High quality nursing services; Satisfaction

This paper describes a new framework for dynamically simulating activity planning and scheduling within an activity-based microsimulation model. The dynamic activity planning framework is a process model which attempts to replicate time-dependent activity scheduling. By modeling the actual underlying activity and travel planning and scheduling processes rather than revealed activity-travel patterns, the model can represent a much wider range of travel demand management policies, especially policies which are expected to impact the planning process of individuals. In contrast with previous activity scheduling models, the proposed model considers activity scheduling steps as discrete events within the overall activity-travel simulation, and furthermore considers each attribute decision as a separate event. The paper develops a framework for modeling each activity and its attributes, and allows for a non-fixed attribute planning order, so that there is no pre-determined planning order assumed in the model. Various stages of the model that are implemented in an overall simulation framework are discussed. In addition, some initial data results from a pilot test of a new GPS-based prompted recall survey used to capture the underlying activity attribute planning process are presented and discussed in the context of the overall model framework. The initial data tend to support the hypothesis that significant variation exists in the manner in which activities are actually planned.

Emergency decision-making, especially emergency resource scheduling decision-making, is one of the key problems in emergency management. This article begins with the dynamism of emergency resources, provides a way of decision thinking about multi-stage scheduling of emergency resources based on the two-stage emergency rescue process and dynamic structure with continuous consumption, on the basis of the identification and integration of emergency resources.

This paper first analyzes the characteristics and principles of the layout planning of emergency resources, explores the problems of emergency resource layout and distribution planning, and mentions the multi-constraint conditions of layout planning. Then, by describing the layout planning problem of emergency resources under multi-constraints and the related variable symbols, we constructed a two-layer layout planning model of emergency resources under multi-constraints, and after analyzing the particle swarm algorithm, we designed the layout planning model solving process based on particle swarm optimization. Finally, by constructing an emergency resource layout case, the centrality of the emergency resource layout network is explored, and the shortest distance and the best site selection of each emergency resource point corresponding to the demand point are divided. The results show that the structural degree centrality is between [0,0.78], the mileage degree centrality is between [0,1], the flow degree centrality is between [0.1,1], the structural median is between [0,0.32], the mileage median is between [0,2], and the structural proximity centrality and the mileage proximity centrality scores are both within the range of [0,1]. The shortest distribution distance of A, B, C, D, and E is selected to be only 393886m, and the error with the actual is around 0.009, which is able to carry out the layout planning effectively.

In the environment of scientific and technological innovation, the human and material resources that can reach the project threshold are often limited. For enterprise projects that require high R&D and production level to produce rapid emergency resources, they often face the problem of difficult effective allocation of emergency resources, resulting in project delay. Therefore, in the process of high-tech projects, it is necessary to deal with the lack of emergency scientific and technological resources. Therefore, based on the particle swarm optimization algorithm which is easy to be realized by scientific and technological innovation enterprises, this study improves its two shortcomings of easy local optimization and low performance, constructs the CD-PSO project scheduling model, verifies the performance of the improvement points, and tests the performance of the model in the actual project case of enterprise rapid emergency resource scheduling. The results show that the optimal solution of CD-PSO model in the three sample sets is higher than that of D-PSO model in the same group, and the average scheduling duration is the shortest, which is 350 days, and the proportion of reaching the optimal duration is 100%. The operation effect is obviously better than other scheduling models. The CD-PSO project scheduling model designed in this study is easier to implement and can calculate the optimal solution of the first project more efficiently. It not only has excellent performance, but also has a strong popularization foundation.

In this paper, the emergency resource scheduling problem as a starting point, the first analysis of the emergency response to the problem of resource demand, in order to analyze the types of emergency resources and emergency resources. The emergency resource scheduling model considering the emergency rescue time is constructed, and the analytic algorithm is given. The calculation process and scheduling scheme of emergency resource scheduling are given by examples.

Obhective To investigate emergency resources and ability to deal with sudden chemical poisoning in the west of Gnangdong province.Methods Related information of 11 Poison Control Centers(PCCs)and Cemteof Diseases Control(CDCO were collected and investigated by questionnaire survey,and then used statistical methods to analyze the databases.Results In 11 PCCs or CDCs,there were only 7 institutions found to have established work place monitoring system;while only 11 institutions allocated 15 equipments on average,including 3 kinds of personal protective equipment,10 kinds of sampling and preserving equipment and 2 kinds of rapid identification equipments.Conclution The PCCs and CDCs in the west of Guangdong province still have a long way to adapt sudden chemical poisoning in many aspects such as teanl instruction,personal protective equipmem allocation,laboratory equipment,material reserve,information network,contingency plan system,etc. Key words: Sudden; Chemical poisoning; Emergency resources; Emergency ability; Investigation

The outbreak of emergencies usually triggers the phenomenon of a lack of emergency resources, and the lack of resources may lead to untimely rescue affecting the social life order. How to guarantee a smooth and efficient emergency resource allocation process and improve the efficiency of emergency relief disposal is the key to ensuring the successful completion of disaster relief tasks. To this end, this paper focuses on the emergency resource allocation process and considers the recycling of emergency resources after disaster relief with the background of emergencies. An emergency resource allocation process model is constructed based on the generalized stochastic Petri net, and the activity, reachability, boundedness, and security of the model are analyzed. The homogeneous Markov chain is utilized for simulation, and the performance analysis is carried out by calculating the steady-state probability and transition utilization rate of each state using examples. Simulation results show that the database and the recovery process should be the key steps to optimizing the emergency resource allocation process. Therefore, improving the effectiveness of these critical links’ emergency disposal will further improve the effectiveness of emergency resource allocation.

The travel activity scheduler for household agents (TASHA) is an operational rule-based model that generates activity schedules and travel patterns for a 24-h typical weekday for all persons in a household. This paper reports the application and validation of the model in Changzhou, China. The data cleaning procedure of the traditional household survey and the verification results of the rules used in the TASHA are presented. After the results of the activity generation and scheduling models were analyzed, an iterative approach was applied to inflating the observed activity rates to calibrate the results. The model was shown to replicate observed activity frequency, tour frequency, and trip start time fairly accurately at the regional level. The activity duration of the model was underestimated by 13.1%, but this underestimate did not add much bias to the shape of the duration distribution by start time. The final model results show that the TASHA is an attractive alternative to conventional modeling systems currently used in Changzhou.