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GRAPH TRAVERSAL TECHNIQUES AND THE maximum flow problem IN DISTRIBUTED COMPUTATION

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1983年第4期9卷 504-512页

作者： CHEUNG, TY Department of Computer Science University of Ottawa

Fundamental differences are shown to exist in graph traversal techniques between serial and distributed computations in their behaviors, computational complexities, and effects on the design of graph algorithms. Two distributed algorithms are presented together with their computational complexity for graph traversal based on the depth-first search and breadth-first search techniques. Several distributed versions are given of the Ford and Fulkerson algorithm for the depth-first, largest augmentation, and breadth-first search methods. A worst case upper bound on the number of messages transmitted also is acquired for each of these versions. As in the case of serial computation, these complexity results do not clearly indicate that one of these methods is superior to the others. However, combining these bounds with empirical results indicates that, in distributed computation, largest augmentation appears to be a better method than the other 2.

关键词： Model Distributed Computation Distributed Graph Algorithms Graph Traversal Techniques maximum network flow problem

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Modeling social, economic, and health perspectives for optimal pandemic policy decision-making

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年 86卷 101472页

作者： Soltanisehat, Leili Gonzalez, Andres Barker, Kash Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic. The three objectives being pursued include: (i) the epidemiological impact, (ii) the economic impact on the local businesses, and (iii) the economic impact on the trades between industries. The proposed model is implemented on a dataset that includes 11 states, the District of Columbia, and 19 industries in the US. The solved by augmented e-constraint approach is used to solve the multi-objective model, and a final strategy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. The Paretooptimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction, and it is more effective to close most states while keeping the majority of industries open during the planning horizon.

关键词： Pandemic SIRD model maximum network flow problem Mixed-integer programming

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