in this study, an optimization model is proposed to design a global supply chain (gsc) for a medical device manufacturer under disruption in the presence of pre-existing competitors and price inelasticity of demand. therefore, static competition between the distributors’ facilities to more efficiently gain a further share in market of economic cooperation organization trade agreement (ecota) is...

This paper describes several massively parallel implementations for a global search algorithm DIRECT. Two parallel schemes take different approaches to address DIRECT’s design challenges imposed by memory requirements and data dependency. Three design aspects in topology, data structures, and task allocation are compared in detail. The goal is to analytically investigate the strengths and weakn...

Recently, two new parallel algorithms for on-the-fly model checking of LTL properties were presented at the same conference: Automated Technology for Verification and Analysis, 2011. Both approaches extend Swarmed NDFS, which runs several sequential NDFS instances in parallel. While parallel random search already speeds up detection of bugs, the workers must share some global information in ord...

The chapter presents a general view of parallel meta-heuristics for optimization. It recalls the main concepts and strategies in designing parallel meta-heuristics and identifies trends and promising research directions. The focus is on cooperation-based strategies, which display remarkable performances, in particular strategies based on asynchronous exchanges and the creation of new informatio...

Graphical models factorize a global probability distribution/energy function as the product/sum of local functions. A major inference task, known as MAP in Markov Random Fields and MPE in Bayesian Networks, is to find a global assignment of all the variables with maximum a posteriori probability/minimum energy. A usual distinction on MAP solving methods is complete/incomplete, i.e. the ability ...

We investigate multiple random walks traversing independently and concurrently on complex networks and introduce the concept of mean first parallel passage time (MFPPT) to quantify their search efficiency. The mean first parallel passage time represents the expected time required to find a given target by one or some of the multiple walkers. We develop a general theory that allows us to calcula...

Ant Colony Optimization (ACO) exhibits parallelism, contains certain redundancy and historical information of the past solutions with pheromone trail, and is suitable for implementation on massively parallel architecture. But it is not easy to avoid local optima, especially for large-scale schedule problems. Simulated annealing (SA) is a naturally serial algorithm, but its behavior can be contr...