Meetings/Workshops on Operational research in the United States (USA)

Co-hosted with the 50th International Conference on Parallel Processing (ICPP 2021)

Welcome to the First International Workshop on Parallel and Distributed Algorithms for Decision Sciences (PDADS), 2021. The goal of this workshop is to create a forum for scientists, engineers and practitioners to share new results and collaborate on ideas at the intersection of parallel and distributed algorithms, decision sciences and combinatorial optimization. There is a growing recognition that a variety of science and engineering use-cases can benefit from parallel and distributed optimization frameworks that can deliver real-time decision-making capabilities. Especially in this age of ever-increasing powerful computing technologies, optimization-driven decision science problems that were otherwise considered computationally prohibitive for integration within scientific and engineering applications, especially those needing real-time decision-making capabilities, are now becoming possible. This workshop will focus on the theory, design, development and deployment of parallel and distributed algorithms for faster and more accurate solutions of different types of optimization problems that arise within the broader purview of the decision sciences.

Event Organizers;     Email: pdads-chairs@email.ornl.gov

Parallel algorithms for integer/mixed-integer programming, linear/nonlinear programming, stochastic programming, robust optimization, combinatorial optimization, feasibility problems (SAT, CP, etc.), Parallel heuristic and meta-heuristic algorithms, Parallel evolutionary algorithms, swarm intelligence, ant colonies, other, Parallel local and complete search methods, Learning approaches for optimization in parallel and distributed environments, Parallel and distributed approaches for parameter tuning, simulation-based optimization, and black box optimization, Parallel algorithm portfolios, Quantum optimization algorithms, Use of randomization techniques for scalable decision support systems, Application of decision support systems on novel computing platforms (shared/distributed memory, edge devices, cloud platforms, field programable gate arrays, quantum computers, etc.), Use of parallel computing for timely and/or higher quality decision support * Theoretical analysis of convergence and/or complexity of parallel optimization algorithms and decision support systems.

High Performance Computing, Supercomputing, Parallel Computing,    Distributed, Edge and Cloud Computing

The organizers of the XVI International Conference on Stochastic Programming (ICSP2022) are honored to host the tri-annual Conference at the University of California, Davis Graduate School of Management. The ICSP is the premier event of the Stochastic Programming Society (SPS) , a technical session of the Mathematical Optimization Society that brings together researchers who work on decisions under uncertainty, practitioners in the industrial and institutional sectors share recent theoretical and applied results. The conference aims to present the state-of-the-art in this field as well as neighboring scientific areas.