Optimizing designs and operations of a single network or multiple interdependent infrastructures under stochastic arc disruption

In this paper, we consider an infrastructure as a network with supply, transshipment, and demand nodes. A subset of potential arcs can be constructed between node pairs for conveying service flows. The paper studies two optimization models under stochastic arc disruption. Model 1 focuses on a single network with small-scale failures, and repairs arcs for quick service restoration. Model 2 considers multiple interdependent infrastructures under large-scale disruptions, and mitigates cascading failures by selectively disconnecting failed components. We formulate both models as scenario-based stochastic mixed-integer programs, in which the first-stage problem builds arcs, and the second-stage problem optimizes recourse operations for restoring service or mitigating losses. The goal is to minimize the total cost of infrastructure design and recovery operations. We develop cutting-plane algorithms and several heuristic approaches for solving the two models. Model 1 is tested on an IEEE 118-bus system. Model 2 is tested on systems consisting of the 118-bus system, a 20-node network, and/or a 50-node network, with randomly generated interdependency sets in three different topological forms (i.e., chain, tree, and cycle). The computational results demonstrate that (i) decomposition and cutting-plane algorithms effectively solve Model 1, and (ii) heuristic approaches dramatically decrease the CPU time for Model 2, but yield worse bounds when cardinalities of interdependency sets increase. Future research includes developing special algorithms for optimizing Model 2 for complex multiple infrastructures with special topological forms of system interdependency.     

Decision support for manufacturing management

Historically, information systems have been used to improve efficiency through such means as clerical automation, inventory status reporting and transactional processing systems. Today, however, to reduce costs, increase return on investments, and achieve competitive advantage, businesses need to have information systems that support managerial decision-making and result in improved effectiveness. To meet this requirement, new approaches are needed in order to define the right problem and work the problem right. By using such techniques as critical success factor analysis followed by a top down system development approach, developing systems through prototyping and using end-user oriented software, these needs can be met.This article describes several company experiences of using a management systems planning and development process. This process in one company presented an opportunity to test the feasibility of developing an alignment between business goals and events critical to the success of the business. Management believed that to succeed in the future they must be forward thinking in their identification and use of information systems to improve managerial effectiveness. Their questions were “What should we do?” and “How should we do it?” By applying these techniques they were able to achieve outstanding results in a very short period of time.     

Using the analytic network process (ANP) in a SWOT analysis - A case study for a textile firm

Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis does not provide an analytical means to determine the importance of the identified factors or the ability to assess decision alternatives according to these factors. Although the analysis successfully pinpoints the factors, individual factors are usually described briefly and very generally. For this reason, SWOT analysis possesses deficiencies in the measurement and evaluation steps. Although the analytic hierarchy process (AHP) technique removes these deficiencies, it does not allow for measurement of the possible dependencies among the factors. The AHP method assumes that the factors presented in the hierarchical structure are independent; however, this assumption may be inappropriate in light of certain internal and external environmental effects. Therefore, it is necessary to employ a form of SWOT analysis that measures and takes into account the possible dependency among the factors. This paper demonstrates a process for quantitative SWOT analysis that can be performed even when there is dependence among strategic factors. The proposed algorithm uses the analytic network process (ANP), which allows measurement of the dependency among the strategic factors, as well as AHP, which is based on the independence between the factors. Dependency among the SWOT factors is observed to effect the strategic and sub-factor weights, as well as to change the strategy priorities.     

Integrating analytical network process and fuzzy logic to prioritize the strategies - A case study for tile manufacturing firm

SWOT analysis is the most important tool for decision makers in strategic management process, because it enables the managers to discover and collect the facts that resulted from internal and external analysis. This analysis can be a good base for strategy formulation, but it is not non-defect. It includes no means of analytically determining the importance of factors or assessing the fit between SWOTS factors and decision alternatives and is mainly based on the qualitative analysis. Many researchers develop different techniques to quantify the SWOT analysis, but these methods are not comprehensive. In this study, we develop a fuzzy method model for quantitative weighing of SWOT strategies. The decision maker’s imprecise information is regarded by fuzzy numbers in this model. To consider dependency among factors and sub-factors, we use ANP technique in our research. We demonstrate our model with a case study example and compare results of our research with prior research.     

Framework for integrated oil pipeline monitoring and incident mitigation systems

Wireless Sensor Nodes (motes) have witnessed rapid development in the last two decades. Though the design considerations for Wireless Sensor Networks (WSNs) have been widely discussed in the literature, limited investigation has been done for their application in pipeline surveillance. Given the increasing number of pipeline incidents across the globe, there is an urgent need for innovative and effective solutions for deterring the incessant pipeline incidents and attacks. WSN pose as a suitable candidate for such solutions, since they can be used to measure, detect and provide actionable information on pipeline physical characteristics such as temperature, pressure, video, oil and gas motion and environmental parameters. This paper presents specifications of motes for pipeline surveillance based on integrated systems architecture. The proposed architecture utilizes a Multi-Agent System (MAS) for the realization of an Integrated Oil Pipeline Monitoring and Incident Mitigation System (IOPMIMS) that can effectively monitor and provide actionable information for pipelines. The requirements and components of motes, different threats to pipelines and ways of detecting such threats presented in this paper will enable better deployment of pipeline surveillance systems for incident mitigation. It was identified that the shortcomings of the existing wireless sensor nodes as regards their application to pipeline surveillance are not effective for surveillance systems. The resulting specifications provide a framework for designing a cost-effective system, cognizant of the design considerations for wireless sensor motes used in pipeline surveillance.     

战略决策文本的语义分析研究

针对采用从战略决策提取关键字检索虚拟新闻视频准确率不高的问题,分析了战略决策文本的特点、构成模式,给出了语义分析过程中构建字典库的原则、自动分词的方法、成分分析的方法和语义树构建的方法,以及与虚拟新闻视频匹配的方法,并进行了实验。结果表明,借助语义树的方法对战略决策文本进行语义分析后能够提高虚拟新闻视频检索的准确率。     

A two-stage stochastic programming model for transportation network protection

Network protection against natural and human-caused hazards has become a topical research theme in engineering and social sciences. This paper focuses on the problem of allocating limited retrofit resources over multiple highway bridges to improve the resilience and robustness of the entire transportation system in question. The main modeling challenges in network retrofit problems are to capture the interdependencies among individual transportation facilities and to cope with the extremely high uncertainty in the decision environment. In this paper, we model the network retrofit problem as a two-stage stochastic programming problem that optimizes a mean-risk objective of the system loss. This formulation hedges well against uncertainty, but also imposes computational challenges due to involvement of integer decision variables and increased dimension of the problem. An efficient algorithm is developed, via extending the well-known L-shaped method using generalized benders decomposition, to efficiently handle the binary integer variables in the first stage and the nonlinear recourse in the second stage of the model formulation. The proposed modeling and solution methods are general and can be applied to other network design problems as well.     

A branch-and-cut algorithm for two-level survivable network design problems

This paper approaches the problem of designing a two-level network protected against single-edge failures. The problem simultaneously decides on the partition of the set of nodes into terminals and hubs, the connection of the hubs through a backbone network (first network level), and the assignment of terminals to hubs and their connection through access networks (second network level). We consider two survivable structures in both network levels. One structure is a two-edge connected network, and the other structure is a ring. There is a limit on the number of nodes in each access network, and there are fixed costs associated with the hubs and the access and backbone links. The aim of the problem is to minimize the total cost. We give integer programming formulations and valid inequalities for the different versions of the problem, solve them using a branch-and-cut algorithm, and discuss computational results. Some of the new inequalities can be used also to solve other problems in the literature, like the plant cycle location problem and the hub location routing problem.     

Decision support for emergency situations

Emergency situations occur unpredictably and cause individuals and organizations to shift their focus and attention immediately to deal with the situation. When disasters become large scale, all the limitations resulting from a lack of integration and collaboration among all the involved organizations begin to be exposed and further compound the negative consequences of the event. Often in large-scale disasters the people who must work together have no history of doing so; they have not developed a trust or understanding of one another’s abilities, and the totality of resources they each bring to bear have never before been exercised. As a result, the challenges for individual or group decision support systems (DSS) in emergency situations are diverse and immense. In this contribution, we present recent advances in this area and highlight important challenges that remain. This article is part of the “Handbook on Decision Support Systems” edited by Frada Burstein and Clyde W. Holsapple (2008) Springer.     

Decision support for risk analysis

Risk analysis requires the analyst to estimate at least the value of assets and annual likelihood of threats materializing. In some cases he has firm benchmarks to which to anchor these estimates. They include information from company books of account, historical records, and the opinions of experts in various fields.Firm benchmarks are not always available, however. Nevertheless, we still can make useful estimates. This is called decision making under uncertainty.     

信息系统生存性分析研究综述

对信息系统生存性分析领域进行了总结。着重回顾了信息系统生存性定义，对生存性分析模型分为基于系统结构和基于系统服务两类、生存性分析手段分为真实测试和模拟测试两类、定量分析从分析参数分为基于物理参数和基于逻辑参数两类分别进行了阐述和探讨，分析了这4个方面存在的问题并提出了未来研究方向。     

A comparison of multi-objective spatial dispersion models for managing critical assets in urban areas

A diverse array of spatial optimization models dealing with protection, service, coverage, equity, and risk can potentially aid with the effective placement of critical assets. Protection of assets can be enhanced using the p-dispersion model, which locates facilities to maximize the minimum distance between any two. Dispersion, however, is rarely the only objective for a system of facilities, and the p-dispersion model is known to be difficult to solve. Therefore, this paper analyzes the trade-offs and computational times of four multi-objective models that combine the p-dispersion model with other facility location objectives relevant to siting critical assets, such as the p-median, max-cover, p-center, and p-maxian models. The multi-objective models are tested on a case study of Orlando, Florida. The dispersion/center model produced the most gradual trade-off curve, while the dispersion/maxian trade-off curve had the most pronounced “elbow.” The center and median multi-objective models were far more computationally demanding than the models using max cover and p-maxian. These findings may inform decision-makers and researchers in deciding what type of multi-objective models to use for planning dispersed networks of critical assets.     

Developing an agent-based hierarchical modeling approach to assess human performance of infrastructure systems

During the last decade, research works related to modeling and simulation of infrastructure systems have primarily focused on the performance of their technical factors, almost ignoring the importance of non-technical factors of these systems, e.g., human operators, consumers. In contrast, the human operator of infrastructure systems has become an essential part in daily operation and in ensuring the security and reliability of the system. In some of the most significant technological incidents of the past century, human error has played a major role. Therefore, developing a modeling approach that is capable of assessing the human performance in a comprehensive way has become crucial. In this paper, an agent-based hierarchical modeling approach is proposed, which aims at the explicit modeling of the impacts of human performance on the operation of infrastructure systems. Within this approach, the cognition component plays a major role. For this purpose, an analytical method based on the Cognitive Reliability Error Analysis Method (CREAM) is developed using a knowledge-based approach. The proposed modeling approach is a pilot work exploring possibilities of simulating performance of human factors in infrastructure systems. The applicability of this modeling approach is demonstrated by a validation experiment using the electric power supply system as an exemplary system.     

网络信息系统生存性分析及实现

随着网络信息系统在各个领域的广泛应用,考虑传统网络安全的同时还需对其生存性进行分析和评估,以保证异常情况下系统基本服务的正常运行。为整个生存性分析过程进行了定义,该文首先提出一种针对网络信息系统的生存性分析框架,随后根据该框架提出了一个管理平台的设计方案,以整合整个生存性分析过程,该平台实现了生存性的定量分析、分析过程的半自动化和重用性。     

Towards critical region reliability support for Grid workflows

Many Directed Acyclic Graph (DAG)-based workflow applications often have timing constraints such that each processing of a workflow needs to be finished within its deadline. There have been some studies to improve the performance of time-constrained workflow processing. Few of them, however, have taken into account the fact that successful execution of a workflow within its deadline is also affected by the ‘normal state’ and ‘abnormal state’ of Grid resources occurring in successive turns and by the relative difference in execution time between tasks on the critical path and tasks on the non-critical path. To solve the problem, we first put forward new some conceptions, such as the critical region and the reliability of the critical region, and then present a scheduling algorithm. In terms of the finite-state continuous-time Markov process, the algorithm selects a resource combination scheme which has the lowest expenditure under a certain credit level of the resource reliability on the critical path in the DAG-based workflow. The simulation shows the validity of theory analysis.     

Decision support for Cybersecurity risk planning

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.     

A fast genetic algorithm for a critical protection problem in biomedical supply chain networks

In this paper, we present a new bilevel model for a biomedical supply chain network with capacity and budget constraint due to the protection and interdiction operations. The components considered in this model are biomedical devices, distribution centers (DCs), medical suppliers (MSs), and hospitals and patients as the demand points. On the other hand, two levels of decisions in the network planning is suggested: (1) the defender’s decision about protection operations of MSs and DCs, the assignment of clients to the DCs, and quantity of products shipped to DCs from MSs to minimize the demand-weighted traveling costs and transport costs and (2) the attacker’s decision about interdiction operations of MSs and DCs to maximize the capacity or service reduction and losses. Because of nondeterministic polynomial time (NP)-hardness of the problem under consideration, an efficient and fast approach based on a genetic algorithm and a fast branch and cut method (GA–FBC) was developed to solve the proposed model. Also, the efficiency via the comparison of results with the genetic algorithm based on CPLEX (GA-CPLEX) and decomposition method (DM) is investigated. In order to assess the performance of the presented GA–FBC, a set of 27 instances of the problem is used. Comprehensive analysis indicates that the proposed approach significantly solves the problem. In addition, the benefits and advantages of preference with running times and its accuracy is shown numerically. Simulation results clearly demonstrate that the defender’s objective effectively reduced and CPU time also within the large-sized instances of the problem in comparison with the GA-CPLEX and DM.     

Decision support for integrated cash management

Cash management has attracted the increasing attention of both academicians and practitioners in recent time. The expanding role and responsibilities of cash managers and corporate treasurers is likely to increase the focus on cash management as a vital organizational function. Academic research, however, has primarily focused on providing analytical tools to solve well structured problems that are relatively isolated in nature. Conspicuously, no attempt has been made to integrate the various sub-problems of cash management explicitly recognizing interrelationships among the sub-problems as well as between the sub-problems and other financial decisions. More importantly, there is an even greater need to provide a framework that in addition to recognizing the above interrelationships will enable the cash manager to recognize a more inclusive set of dependencies that exist in practice.Decision support systems have the potential to overcome the above deficiencies to a significant extent. This paper is aimed at providing a conceptual framework for designing an effective model-based decision support system (DSS) for integrated cash management. The framework should form a useful basis for any attempts at designing computer-based support systems for cash management.     

Decision support methodology using rule-based reasoning coupled to non-parametric measurement for industrial wastewater network management

EU water framework directive [Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 Establishing a Framework for Community Action in the Field of Water Policy, Official Journal L 327, 22/12/2000 p. 0001–0073] encourages industrialists to improve the management of wastewater networks and thus reduce both pollution and treatment costs. Due to the complexity of wastewater treatment plant, the use of decision support system (DSS) becomes a complementary tool to assist decision-markers.A decision support methodology (DSM) was created which can optimise both the placement and number of physical and chemical sensors in industrial wastewater networks at petroleum refineries. Ongoing research concerns a promising method of analysing this dataflow to check whether the wastewater network is malfunctioning in any observable way.This method consists in five steps. First is to model the network to know each unit, node, etc. Second, provisional sampling points are selected in order to measure wastewater quality. Third, data is collected. Fourth, non-parametric measurement is studied: variability calculation on UV spectra. Fifth, the final selection of sampling points according to the variability study is made.With all these data, algorithm for the monitoring was developed to spot a dysfunction in the network (study of the monitoring section).These five steps of the DSM were developed and applied on a refinery wastewater treatment plant.     

A decision support system for strategic asset allocation

Strategic asset allocation is a crucial activity for any institutional or individual investor. Given a set of asset classes, the problem concerns the definition and management over time of the best asset mix to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. Although a considerable attention has been placed by the scientific community to address this problem by proposing sophisticated optimization models, limited effort has been devoted to the design of integrated framework that can be systematically used by financial operators. The paper presents a decision support system which integrates simulation techniques for forecasting future uncertain market conditions and sophisticated optimization models based on the stochastic programming paradigm. The system has been designed to be accessed via web and takes advantages of the increased computational power offered by high performance computing platforms. Real-world instances have been used to assess the performance of the decision support system also in comparison with more traditional portfolio optimization strategies.