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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]

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]

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]

A complex workflow is often executed by geographically dispersed partners or different organizations. As a solution for dealing with the decentralized nature of workflow applications, a workflow can be fragmented into small pieces and scheduled to different servers for its execution. An important challenge in distributed workflows is to optimize the fragmentation and distribution to achieve efficiency with respect to time and server resources. To tackle this challenge, we propose the application of process mining to the fragmentation of a workflow for distributed execution. The workflow model discovered through process mining records the actual execution of a workflow and allows in-depth analysis of its temporal behavior. Based on examination of the model resulting from process mining, we demonstrate how to determine the minimum time to finish a workflow and how to partition the workflow in order to achieve efficient server usage. [ABSTRACT FROM AUTHOR]

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 problem of scheduling a set of tasks on a single (critical) processor under given technological precedence constraints is considered. Before a task is released for processing on the critical processor, it must undergo some preprocessing treatment. This treatment is a dynamic process in which the speed of change of the preprocess state depends on the amount of resource. We assume that the speed can be described as a concave, continuous, positive, and strictly increasing function of resource amount. The total consumption of resource at each moment is limited. The time which is needed to pass from the initial state to the terminal one is called a task release date. The objective is to minimize the maximum task completion time. Such a problem appears e.g., in steel-mill systems, where ingots (before hot rolling on the blooming mill) have to achieve the required temperature in the preheating process in soaking pits. The computational complexity of the problem is analyzed. Due to the problem properties, the difficult dynamic problem of the optimal resource allocation (for a fixed task schedule) is reduced to a task of convex programming. A general approximation algorithm of constructing task schedule along with its experimental and worst case analysis is also presented. [ABSTRACT FROM AUTHOR]