Pattern-based evacuation planning for urban areas

The population of an urban area may be in danger due to disasters like floods, hurricanes, chemical or nuclear accidents. This requires decisions to protect the affected population. One decision may be to evacuate the affected area. For the exceptional case of an evacuation an approach to reorganize the traffic routing of the endangered area is developed. In this paper a two-stage heuristic solution approach for a pattern-based mixed integer dynamic network flow model is presented. The model restructures the traffic routing such that the evacuees leave the evacuation area as safe as possible and as early as possible within the considered time horizon.     

自然灾害区域疏散的预警优化模型研究

在可预知的灾害来临前,交通堵塞问题是影响应急疏散效率的主要因素。在灾前防御阶段,有策略的发布预警消息可以使疏散更加有秩序的进行,从而提高疏散效率。本文为区域应急疏散预案构建了应急疏散预警发布研究框架,首先建立了避难点分配模型,将其结果代入到疏散预警模型,来优化特定地区预警发布时间和类型。其中,预警模型加入了时间成分,构建成了多时段模型,并使用贪婪的启发式搜索过程求解非线性的公式。最后,通过算例分析了模型算法的应用范围及其可行性,并用模拟退火算法进行了计算,验证了本算法的有效性。本研究更改以往在同一时间通过全部渠道发布消息的方式,通过疏散预警信息的发布策略的优化,可以有效避免在区域内大规模人群同时出发所导致的交通拥堵现象,为政府制定科学的应急疏散预案提供理论和技术支持。     

Assessing systems for offshore emergency evacuation

Emergency evacuation is a rare event in the offshore oil industry. Nonetheless, emergency procedures must be practiced routinely for the benefit of the work force and the emergency services. These practices typically take place in good weather conditions where there is little threat to those involved. However, in reality an emergency could occur in adverse weather conditions which can affect the capabilities of vessels and helicopters. This paper describes a study in which the data from various sources are synthesised in order to estimate the effectiveness of emergency evacuation and rescue systems in a stochastic environment. The study employed a discrete event simulation incorporating a model of the evacuation and rescue operations interfaced with a file of weather data. This approach provided a measure, the probability of completing the evacuation within N hours, for the comparison of alternative systems.     

Integrating simulation modelling and GIS: spatial decision support systems for evacuation planning

A prototype spatial decision support system (SDSS) has been designed for contingency planning for emergency evacuations which combines simulation techniques with spatial data handling and display capabilities of a geographical information system (GIS). It links together the topographical support and analysis provided by the GIS–ARC/INFO, with a simulation model designed to simulate the dynamics of an evacuation process in detail. Our aim has been to design a SDSS so that it provides an interactive evacuation simulator with dynamic graphics that allows for experimentation with policies by providing rapid feedback from the simulation. The idea is that emergency planners will be able to use the SDSS to experiment with emergency evacuation plans in order to plan for different contingencies. This paper concentrates on the issues involved in designing an effective integration link interface between the GIS and the simulation model when building a SDSS of this type.     

Decision dependent stochastic processes

Managers, typically, are unaware of the significant impact their decisions could have on the random mechanism driving a data generating process. Here, a new parametric Bayesian technique is introduced that would allow managers to obtain an estimate of the impact of their decisions on the stochastic process driving the data; this, in turn, should enhance a company’s overall decision-making capabilities. This general approach to modeling decision-dependency is carried out via an efficient Markov chain Monte Carlo method. A simulated example, and a real-life example, using historical maintenance and failure time data from a system at the South Texas Project Nuclear Operating Company, exemplifies the paper’s theoretical contributions. Conclusive evidence of decision dependence in the failure time distribution is reported, which in turn points to an optimal maintenance policy that results in potentially large financial savings to the Texas-based company.     

Evacuation planning using multiobjective evolutionary optimization approach

In an emergency situation, evacuation is conducted in order to displace people from a dangerous place to a safer place, and it usually needs to be done in a hurry. It is necessary to prepare evacuation plans in order to have a good response in an emergency situation. A central challenge in developing an evacuation plan is in determining the distribution of evacuees into the safe areas, that is, deciding where and from which road each evacuee should go. To achieve this aim, several objective functions should be brought into consideration and need to be satisfied simultaneously, though these objective functions may often conflict with each other.     

Generating feasible strategies in nuclear emergencies—a constraint satisfaction problem

RODOS is a Real-time On-line DecisiOn Support system intended for use throughout a nuclear emergency, extending into the longer term. In this paper we concentrate on the early phases in which decisions on sheltering and evacuation have to be taken quickly and under many pressures. RODOS is designed to assist off-site emergency management by formulating and structuring the evaluation of possible combinations of countermeasures. Because there can be very many such combinations to be evaluated, an expert system has been developed to eliminate those that do not satisfy certain constraints depending on factors such as the wind direction and evacuation practicalities. The system uses the ILOG solver constraint satisfaction package and its high-level programming library to reduce the number of strategies to a manageable fraction. This allows a later careful evaluation of the remaining alternatives.     

Applying Bayesian methodology with a uniform prior to the single period inventory model

We consider the application of the Bayesian approach to parameter estimation to the single period inventory model. We assume complete prior ignorance of the values that the (single) unknown parameter of the demand distribution might take and express this by using a uniform prior over the permitted range of parameter values. Direct analytical and numerical comparisons are made for three distributions and the results show that over a wide range of parameter values, including most of those which are likely to be of practical interest, the application of Bayesian methodology produces better decisions (resulting in lower expected total cost) than the approach of using a point estimate for the parameter, with no increase in computation or complexity. This suggests that this methodology could usefully be applied to this and other decision models and also provides a strong justification for the use of the full Bayesian approach when a meaningful prior is available.     

Bayesian decision-making in inventory modelling

The objective of this paper is to advocate the use of Bayesianmethods in tackling decision problems with limited past data.It is assumed that a Bayesian approach is least likely to besuccessful when there is no information on which to base a meaningfulprior. Here we use a limiting, invariant, form of the conjugateprior distribution to represent this ignorance. The resultsof decisions based on Bayesian methods with this ‘non-informative’prior are compared with those which result from deriving a pointestimate for the unknown parameter. The particular context consideredhere is that of a single-period inventory model with compoundPoisson demand made up of a known demand size distribution butan unknown demand rate. The demand rate is assumed to be highenough for a normal approximation to the compound Poisson distributionto be used, in which case it is possible to analyse the behaviourdirectly. An extension to the multi-period model with zero leadtime is considered briefly. The results lend support to theuse of Bayesian methods, with or without a meaningful prior,for which the analysis and computation are no more complex thanthose required by standard methods.     

Sequential variable sampling plan for normal distribution

In this paper, a sequential variable sampling plan is studied. Suppose that the quality of an item in a batch is measured by a normally distributed random variable with a known variance, but the mean is unknown with a normal prior distribution. Then by using Bayesian approach and considering a Markov decision process, the optimality equations for the minimum total expected cost are formulated. We show that an optimal decision rule will have a control limit structure. An algorithm for a sequence of ϵ-optimal decisions is introduced. Then, the statistical procedure for conducting the sequential sampling plan is presented.     

Developing a straightforward approach for group decision making based on determining weights of decision makers

Group decision making is an active area of research within multiple attribute decision making. This paper assumes that all the decision makers (DMs) are not equally qualified to contribute equitably to the decision process. The aim of this paper is to develop an approach to determine weights of DMs, in which the decision information on alternatives with respect to attributes, provided by each DM, is represented in the form of interval data. We define the average of all individual decisions as the positive ideal decision (PID), and the maximum separation from PID as the negative ideal decision, which are characterized by a matrix, respectively. The weight of each DM is determined according to the Euclidean distances between the individual decision and ideal decisions. By using the obtained weights of DMs, all individual decisions are aggregated into a collective decision. Then the alternatives is ranked based on the collective decision. Meanwhile, this paper also gives a humanized decision method by using an optimistic coefficient, which is used in adjusting the relative importance between profit and risk. Finally, we give an example to illustrate the developed approach.     

Optimizing pig marketing decisions under price fluctuations

In the manufacturing of fattening pigs, pig marketing refers to a sequence of culling decisions until the production unit is empty. The profit of a production unit is highly dependent on the price of pork, the cost of feeding and the cost of buying piglets. Price fluctuations in the market consequently influence the profit, and the optimal marketing decisions may change under different price conditions. Most studies have considered pig marketing under constant price conditions. However, because price fluctuations have an influence on profit and optimal marketing decisions it is relevant to consider pig marketing under price fluctuations. In this paper we formulate a hierarchical Markov decision process with two levels which model sequential marketing decisions under price fluctuations in a pig pen. The state of the system is based on information about pork, piglet and feed prices. Moreover, the information is updated using a Bayesian approach and embedded into the hierarchical Markov decision process. The optimal policy is analyzed under different patterns of price fluctuations. We also assess the value of including price information into the model.

     

A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution

Remaining useful life (RUL) estimation is regarded as one of the most central components in prognostics and health management (PHM). Accurate RUL estimation can enable failure prevention in a more controllable manner in that effective maintenance can be executed in appropriate time to correct impending faults. In this paper we consider the problem of estimating the RUL from observed degradation data for a general system. A degradation path-dependent approach for RUL estimation is presented through the combination of Bayesian updating and expectation maximization (EM) algorithm. The use of both Bayesian updating and EM algorithm to update the model parameters and RUL distribution at the time obtaining a newly observed data is a novel contribution of this paper, which makes the estimated RUL depend on the observed degradation data history. As two specific cases, a linear degradation model and an exponential-based degradation model are considered to illustrate the implementation of our presented approach. A major contribution under these two special cases is that our approach can obtain an exact and closed-form RUL distribution respectively, and the moment of the obtained RUL distribution from our presented approach exists. This contrasts sharply with the approximated results obtained in the literature for the same cases. To our knowledge, the RUL estimation approach presented in this paper for the two special cases is the only one that can provide an exact and closed-form RUL distribution utilizing the monitoring history. Finally, numerical examples for RUL estimation and a practical case study for condition-based replacement decision making with comparison to a previously reported approach are provided to substantiate the superiority of the proposed model.     

A framework for optimization under limited information

In many real world problems, optimisation decisions have to be made with limited information. The decision maker may have no a priori or posteriori data about the often nonconvex objective function except from on a limited number of data points. The scarcity of data may be due to high cost of observation or fast-changing nature of the underlying system. This paper presents a “black-box” optimisation framework that takes into account the information collection, estimation, and optimisation aspects in a holistic and structured manner. Explicitly quantifying the observations at each optimisation step using the entropy measure from information theory, the often nonconvex-objective function to be optimised is modelled and estimated by adopting a Bayesian approach and using Gaussian processes as a state-of-the-art regression method. The resulting iterative scheme allows the decision maker to address the problem by expressing preferences for each aspect quantitatively and concurrently.     

An insurance risk management framework for disaster relief and supply chain disruption inventory planning

Government agencies, not-for-profit organizations, and private corporations often assume leading roles in the delivery of supplies, equipment, and manpower to support initial response operations after a disaster strikes. These organizations are faced with challenging logistics decisions to ensure that the right supplies (including equipment and personnel) are in the right places, at the right times, and in the right quantities. Such logistics planning decisions are further complicated by the uncertainties associated with predicting whether or not a potential threat will materialize into an emergency situation. This paper introduces newsvendor variants that account for demand uncertainty as well as the uncertainty surrounding the occurrence of an extreme event. The optimal inventory level is determined and compared to the classic newsvendor solution and the difference is interpreted as the insurance premium associated with proactive disaster-relief planning. The insurance policy framework represents a practical approach for decision makers to quantify the risks and benefits associated with stocking decisions related to preparing for disaster relief efforts or supply chain disruptions.     

Implementation of Backward Induction for Sequentially Adaptive Clinical Trials

In many clinical trials, patients are enrolled and data are collected sequentially, with interim decisions, including what treatment the next patient should receive and whether or not the trial should be terminated or continued, being based on the accruing data. This naturally leads to application of Bayesian sequential procedures for trial monitoring. This article discusses the implementation and computational tasks involved in the use of backward induction for making decisions during a clinical trial. An efficient method is presented for storing and retrieving decision tables that represent the decision trees characterizing all possible decisions made when implementing a clinical trial using backward induction. We address the general computational needs, and illustrate the ideas with a specific example of a two-arm trial with a binary outcome and a maximum sample size of 200 patients.     

Bayesian decision making and military command and control

We discuss firstly the problem of military decision, in the context of the more general development of ideas in the representation of decision making. Within this context, we have considered a mathematical model—Bayesian Decision—of decision making and military command. Previous work has been extended, and applied to this problem. A distribution of belief in outcome, given that a decision is made, and a Loss function—a measure of the effect of this outcome relative to a goal—are formed. The Bayes' Decision is the decision which globally minimises the resultant bimodal (or worse) Expected Loss function. The set of all minimising decisions corresponds to the surface of an elementary Catastrophe. This allows smooth parameter changes to lead to a discontinuous change in the Bayes' decision. In future work this approach will be used to help develop a number of hypotheses concerning command processes and military headquarters structure. It will also be used to help capture such command and control processes in simulation modelling of future defence capability and force structure.     

Supporting triage of children with abdominal pain in the emergency room

The paper describes a methodology used for selecting the most relevant clinical features and for generating decision rules based on selected attributes from a medical data set with missing values. These rules will help emergency room (ER) medical personnel in triage (initial assessment) of children with abdominal pain. Presented approach is based on rough set theory extended with the ability of handling missing values and with the fuzzy measures allowing estimation of a value of information brought by particular attributes. The proposed methodology was applied for analyzing the data set containing records of patients with abdominal pain, collected in the emergency room of the cooperating hospital. Generated rules will be embedded into a computer decision support system that will be used in the emergency room. The system based on results of presented approach should allow improving of triage accuracy by the emergency room staff, and reducing management costs.     

Optimal adaptive replacement in a renewal process

In this paper we present an exact method for computing the Weibull renewal function and its derivative for application in maintenance optimization. The computational method provides a solid extension to previous work by which an approximation to the renewal function was used in a Bayesian approach to determine optimal replacement times. In the maintenance scenario, under the assumption an item is replaced by a new one upon failure, the underlying process between planned replacement times is a renewal process. The Bayesian approach takes into account failure and survival information at each planned replacement stage to update the optimal time until the next planned replacement. To provide a simple approach to carry out in practice, we limit the decision process to a one‐step optimization problem in the sequential decision problem. We make the Weibull assumption for the lifetime distribution of an item and calculate accurately the renewal function and its derivative. A method for finding zeros of a function is adapted to the maintenance optimization problem, making use of the availability of the derivative of the renewal function. Furthermore, we develop the maximum likelihood estimate version of the Bayesian approach and illustrate it with simulated examples. The maintenance algorithm retains the adaptive concept of the Bayesian methodology but reduces the computational need. Copyright © 2014 John Wiley & Sons, Ltd.     

Sequence of decisions on discrete event systems modeled by Petri nets with structural alternative configurations

The management of certain systems, such as manufacturing facilities, supply chains, or communication networks implies assessing the consequences of decisions, aimed for the most efficient operation. This kind of systems usually shows complex behaviors where subsystems present parallel evolutions and synchronizations. Furthermore, the existence of global objectives for the operation of the systems and the changes that experience the systems or their environment during their evolution imply a more or less strong dependence between decisions made at different time points of the life cycle. This paper addresses a complex problem that is scarcely present in the scientific literature: the sequences of decisions aimed for achieving several objectives simultaneously and with strong influence from one decision to the rest of them. In this case, the formal statement of the decision problem should take into account the whole decision sequence, making impractical the solving paradigm of “divide and conquer”. Only an integrated methodology may afford a realistic solution of such a type of decision problem. In this paper, an approach based on the formalism of the Petri nets is described, several considerations related to this problem are presented, a solving methodology based on the previous work of the authors, as well as a case-study to illustrate the main concepts.