IEEE PDCO 2019, Rio de Janeiro, Brasil, will be the 9th edition of our Workshop. It will be held in conjunction with the 33rd  IEEE International Parallel and Distributed Processing Symposium. The Workshop PDCO comes from the rmerging of Workshop Parallel Computing and Optimization (PCO) and Workshop Nature Inspired Distributed Computing (NIDISC). The previous editions of the Workshop PDCO were held in Anchorage USA 2011, Shanghai China 2012, Boston USA 2013, Phoenix USA 2014, Hyderabad India 2015, Chicago USA 2016, Orlando USA 2017 and Vancouver Canada 2018. This series of Workshops has been very successful in the past years with many attendees and prestigious Keynote speakers like Laurence T. Yang, Dimitri Bertsekas, Alex Pothen, Keqin Li and Frédéric Vivien. 

Scope:
The IEEE Workshop on Parallel / Distributed Combinatorics and Optimization aims at providing a forum for scientific researchers and engineers on recent advances in the field of parallel or distributed computing for difficult combinatorial optimization problems, like 0-1 multidimensional knapsack problems, cutting stock problems, scheduling problems, large scale linear programming problems, nonlinear optimization problems and global optimization problems. Emphasis is placed on new techniques for the solution of these difficult problems like cooperative methods for integer programming problems. Techniques based on metaheuristics and nature-inspired paradigms are considered. Aspects related to Combinatorial Scientific Computing (CSC) are considered. In particular, we solicit submissions of original manuscripts on sparse matrix computations, graph algorithm and original parallel or distributed algorithms. The use of new approaches in parallel and distributed computing like GPU, MIC, FPGA, volunteer computing are considered. Application to cloud computing, planning, logistics, manufacturing, finance, telecommunications and computational biology are considered.

Topics:
Integer programming, linear programming, nonlinear programming.
Exact methods, heuristics.
Parallel algorithms for combinatorial optimization.
Parallel metaheuristics.
Parallel and distributed computational intelligence methods (e.g. evolutionary algorithms, swarm intelligence, ant colonies, cellular automata, DNA and molecular computing) for problem solving environments.
Parallel and distributed metaheuristics for optimization (algorithms, technologies and tools).
Applications combining traditional parallel and distributed computing and optimization techniques as well as theoretical issues (convergence, complexity).
Distributed optimization algorithms.
Parallel sparse matrix computations, graph algorithms, load balancing.
Hybrid computing and the solution of optimization problems.
Peer-to peer computing and optimization problems.
Applications: cloud computing, planning, logistics, manufacturing, finance, telecommunications,

PDCO 2018
PDCO 2017
PCO 2016
PCO 2015
PCO 2014
PCO 2013
PCO 2012
PCO 2011