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Evaluating multistate flow network reliability and reducing system cost are important tasks when planning and designing systems. Existing methods are based on (d, c)-minimal paths ((d, c)-MP), which are vectors, such that d units of flow transmit between two specified nodes with a total cost that does not exceed c. However, these methods only work for directed networks. This correspondence paper finds all (d, c)-MPs before calculating network reliability under budget constraints using a novel method. The proposed algorithm is easier to understand and implement and is superior to existing algorithms. This correspondence paper analyzes and proves the correctness of the proposed algorithm, using two examples to demonstrate how to generate, verify, and implement all (d, c)-MPs to solve multistate flow network reliabilities under budget constraints using the proposed algorithm. [ABSTRACT FROM AUTHOR]

Time and resource management and verification are two important aspects of workflow management systems. In this paper, we present a modeling and analysis approach for a kind of workflow constrained by resources and nondetermined time based on Petri nets. Different from previous modeling approaches, there are two kinds of places in our model to represent the activities and resources of a workflow, respectively. For each activity place, its input and output transitions represent the start and termination of the activity, respectively, and there are two timing functions in it to define the minimum and maximum duration times of the corresponding activity. Using the constructed Petri net model, the earliest and latest times to start each activity can be calculated. With the reachability graph of the Petri net model, the timing factors influencing the implementation of the workflow can be calculated and verified. In this paper, the sufficient conditions for the existence of the best implementation case of a workflow are proved, and the method for obtaining such an implementation case is presented. The obtained results will benefit the evaluation and verification of the implementation of a workflow constrained by resources and nondetermined time. [ABSTRACT FROM AUTHOR]

In the presence of naturally occurring and man-made public health threats, the feasibility of regional bio-emergency contingency plans plays a crucial role in the mitigation of such emergencies. While the analysis of in-place response scenarios provides a measure of quality for a given plan, it involves human judgment to identify improvements in plans that are otherwise likely to fail. Since resource constraints and government mandates limit the availability of service provided in case of an emergency, computational techniques can determine optimal locations for providing emergency response assuming that the uniform distribution of demand across homogeneous resources will yield an optimal service outcome. This paper presents an algorithm that recursively partitions the geographic space into subregions while equally distributing the population across the partitions. For this method, we have proven the existence of an upper bound on the deviation from the optimal population size for subregions. [ABSTRACT FROM PUBLISHER]

Resource allocation, as an integral part of business process management (BPM), is more widely acknowledged by its importance for process-aware information systems. Despite the industrial need for efficient and effective resource allocation in BPM, few scientifically-grounded approaches exist to support these initiatives. In this paper, a new approach of resource allocation optimization is proposed, built on the concepts that is part of an operation-oriented view on process optimization. Essentially, the proposed approach automatically generates a specific task operation model (TOM) for a particular business process. In addition, in order to support end users in making sensible resource allocations, an ant colony optimization-based algorithm is presented, which makes it possible to search an optimal task operation path on the generated TOM. This allows one to suggest how a business user should efficiently allocate resources to perform the tasks of a particular process case. The feasibility of the presented approach is demonstrated by a simulation experiment. The experimental results show that the proposed approach outperforms reasonable heuristic approaches to satisfy process performance goals, and it is possible to improve the current state of BPM. [ABSTRACT FROM PUBLISHER]

Budget optimization is one of the primary decision-making issues faced by advertisers in search auctions. A quality budget optimization strategy can significantly improve the effectiveness of search advertising campaigns, thus helping advertisers to succeed in the fierce competition of online marketing. This paper investigates budget optimization problems in search advertisements and proposes a novel hierarchical budget optimization framework (BOF), with consideration of the entire life cycle of advertising campaigns. Then, we formulated our BOF framework, made some mathematical analysis on some desirable properties, and presented an effective solution algorithm. Moreover, we established a simple but illustrative instantiation of our BOF framework which can help advertisers to allocate and adjust the budget of search advertising campaigns. Our BOF framework provides an open testbed environment for various strategies of budget allocation and adjustment across search advertising markets. With field reports and logs from real-world search advertising campaigns, we designed some experiments to evaluate the effectiveness of our BOF framework and instantiated strategies. Experimental results are quite promising, where our BOF framework and instantiated strategies perform better than two baseline budget strategies commonly used in practical advertising campaigns. [ABSTRACT FROM PUBLISHER]

In automated manufacturing systems (AMSs), Petri nets are widely adopted to solve supervisory control problems. Among them, how to enforce liveness and fairness for AMS constitutes an important problem. Enforcing liveness avoids the occurrence of deadlock situations to inhibit the emergence of partial or complete blockness for processing jobs in AMS. Fairness requires the determination of a reasonable regulation scheme for orderly resolution such that a desired ratio can be assigned among different processes. This paper proposes an efficient method to design supervisors that enforce both, which is based on the invariance property of Petri nets. A novel approach is proposed to iteratively identify empty siphons as solutions to a set of linear inequalities. Supervisors are then designed to control these siphons. The applicability of the proposed methodology is illustrated through examples. Its comparison with existing works is presented to demonstrate its advantages. [ABSTRACT FROM PUBLISHER]

In this paper, multistage auction-based intelligence, surveillance, and reconnaissance (ISR) sensor coordination mechanisms are investigated in the context of dynamic and uncertain mission environments such as those faced by expeditionary strike groups. Each attribute of the mission task is modeled using a hidden Markov model (HMM) with controllable emission matrices, corresponding to each ISR asset package (subset of sensors). For each HMM-asset package pair, we evaluate a matrix of information gains (uncertainty reduction measures). The elements of this matrix depend on the asset coordination structure and the concomitant delays accrued. We consider three coordination structures (distributed ISR coordination, ISR officer serving as a coordinator, and ISR officer serving as a commander) here. We evaluate these structures on a hypothetical mission scenario that requires the monitoring of ISR activities in multiple geographic regions. The three structures are evaluated by comparing the task state estimation error cost, as well as travel, waiting, and assignment delays. The results of the analysis were used as a guide in the design of a mission scenario and asset composition for a team-in-the-loop experimentation. Our solution has the potential to be a mixed initiative decision support tool to an ISR coordinator/commander, where the human provides possible ISR asset package-task pairings and the tool evaluates the efficacy of the assignment in terms of task accuracy and delays. We also apply our approach to a hypothetical disaster management scenario involving chemical contamination and discuss the computational complexity of our approach. [ABSTRACT FROM PUBLISHER]

This paper revisits several concepts and model frameworks that have been in the literature of human–machine interaction and control engineering for up to 50 years. The purposes of the revisit are as follows: 1) to sharpen distinctions between adaptive automation, level of automation, allocation authority, supervisory control, and adaptive control engineering as the terms are currently used in the literature; 2) to define modes of human supervisory adaptation from the control engineering perspective; and 3) to suggest comparative taxonomies for adaptive automation in direct control and in supervisory control. [ABSTRACT FROM AUTHOR]

In time-critical systems in which a human operator supervises multiple semiautomated tasks, failure of the operator to focus attention on high-priority tasks in a timely manner can lower the effectiveness of the system and potentially result in catastrophic consequences. These systems must integrate computer-based technologies that help the human operator place attention on the right tasks at the right times to be successful. One way to assist the operator in this process is to compute where the operator's attention should be focused and then use this computation to influence the operator's behavior. In this paper, we analyze the ability of a particular modeling method to make such computations for effective attention allocation in human–multiple-robot systems. Our results demonstrate that it is not sufficient to simply compute and dictate how operators should allocate their attention. Rather, in stochastic domains, where small changes in either the endogenous or exogenous environment can dramatically affect model fidelity, model predictions should guide rather than dictate operator attentional resources so that operators can effectively exercise their judgment and experience. [ABSTRACT FROM PUBLISHER]

The need to improve the efficiency of elevator systems is growing as higher buildings are being constructed. Increasing the number of elevators within a building is a commonly used strategy to achieve a greater passenger transportation capacity. However, the area required by elevator shafts limits the number of elevators that can be built. Multicar-elevator (MCE) systems, consisting of two or more elevator cars within a single shaft, have been proposed as a solution to increase the passenger transportation capacity without requiring extra space within the building. Yet, the implementation of MCEs requires the prevention of interference among elevator cars, which traditional group control methods are unable to achieve. Therefore, an MCE group control method that can perform an efficient allocation of service calls while preventing interference among cars is required. In this paper, we propose an MCE group control algorithm implementing an interference risk evaluation, a schedule-completion-time minimization function, and idle-car parking strategies. With our algorithm, we aim to achieve an efficient allocation of calls and prevent interference, but without restricting the operation of elevator cars like the existing MCE group control methods do. The simulation results show that our algorithm has a good performance under low and medium passenger arrival rates. [ABSTRACT FROM AUTHOR]

The resource-transition circuit ( RTC) and siphon are two different structural objects of Petri nets and used to develop deadlock control policies for automated manufacturing systems. They are related to the liveness property of Petri net models and thus used to characterize and avoid deadlocks. Based on them, there are two kinds of methods for developing deadlock controllers. Such methods rely on the computation of all maximal perfect RTCs and strict minimal siphons (SMSs), respectively. This paper concentrates on a class of Petri nets called a system of simple sequential processes with resources, establishes the relation between two kinds of control methods, and identifies maximal perfect RTCs and SMSs. A graph-based technique is used to find all elementary RTC structures. They are then used to derive all RTCs. Next, an iterative method is developed to recursively construct all maximal perfect RTCs from elementary ones. Finally, a one-to-one correspondence between SMSs and maximal perfect RTCs and, hence, an equivalence between two deadlock control methods are established. [ABSTRACT FROM PUBLISHER]

A non-polynomial (NP)-hard combinatorial optimization problem that is associated with expanding service capacities and increasing service reliability in grid-based utility computing is investigated in this paper. The considered problem is decomposed into master and slave subproblems, with theoretical justification, and a computationally efficient two-level iterative method that is used in solving it is proposed. To solve the slave subproblem, an ordinal optimization-based n-stage method, associated with an approximate model for objective value evaluation, is employed. To solve the master subproblem, a bisection method is used. Under some conditions, the solution obtained using the proposed iterative two-level method is optimal. The validity of the proposed method is tested by ten cases on a 12-node 17-link computing grid. Five of the ten cases are randomly selected, and the solutions that are obtained in these cases are optimal, rather than “good enough.” The average CPU time required by the proposed method in obtaining the optimal solution of the considered NP-hard combinatorial optimization problem is 2.278 h, when executed using a Pentium IV PC with a 2-GB RAM. Additionally, the computational efficiency of the proposed method greatly exceeds a genetic algorithm with an exact model. [ABSTRACT FROM PUBLISHER]

This paper investigates the impact of framing and time pressure on human judgment performance in a complex multiattribute judgment task. We focus on the decision process of human participants who must choose between pairwise alternatives in a resource-allocation task. We used the Analytic Hierarchy Process (AHP) to calculate the relative weights of the four alternatives (i.e., C1, C2, C3, and C4) and the judgment consistency. Using the AHP, we examined two sets of hypotheses that address the impact of task conditions on the weight prioritization of choice alternatives and the internal consistency of the judgment behavior under varying task conditions. The experiment simulated the allocation of robotic assets across the battlefield to collect data about an enemy. Participants had to make a judgment about which asset to allocate to a new area by taking into account three criteria related to the likelihood of success. We manipulated the information frame and the nature of the task. We found that, in general, participants gave significantly different weights to the same alternatives under different frames and task conditions. Specifically, in terms of ln-transformed priority weights, participants gave significantly lower weights to C2 and C4 and higher weight to C3 under gain frame than under loss frame, and also, under different task conditions (i.e., Tasks #1, #2, and #3), participants gave significantly higher weight to C4 in Task #1, lower weights to C1 and C4, higher weight to C3 in Task #2, and lower weight to C3 in Task #3. Furthermore, we found that the internal consistency of the decision behavior was worse, first, in the loss frame than the gain frame and, second, under time pressure. Our methodology complements utility-theoretic frameworks by assessing judgment consistency without requiring the use of task-performance outcomes. This work is a step toward establishing a coherence criterion to investigate judgment under naturalistic conditions. The results will be useful for the design of multiattribute interfaces and decision aiding tools for real-time judgments in time-pressured task environments. [ABSTRACT FROM AUTHOR]

Grid computing is a recently developed technique for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Although the development tools and techniques for the grid have been extensively investigated, the availability of the grid Resource Management System (RMS) has not been comprehensively studied. In order to contribute to this lacking but important field, this paper first models the grid RMS availability by considering both the failures of Resource Management (RM) Servers and the length limitationpf request queues. A hierarchical Markov reward model is implemented to evaluate the grid RMS availability. Based on the availability model, an optimization problem for designing the grid RMS is studied in order to minimize the cost by determining the best number of RM servers. Then, the sensitivity analysis is conducted, and a dynamic switching scheduling method is further presented based on the sensitivity analysis. [ABSTRACT FROM AUTHOR]