We present a simulation/optimization model combining optimization with BIOPLUME II simulation for optimizing in-situ bioremediation system design. In-situ bioremediation of contaminated groundwater has become widely accepted because of its cost-effective ability to achieve satisfactory cleanup. We use parallel recombinative simulated annealing to search for an optimal design and apply the BIOPL...

We describe an approach to data analytics on large systems using a suite of robust parallel algorithms running on both clouds and HPC systems. We apply this to cases where the data is defined in a vector space and when only pairwise distances between points are defined. We introduce improvements to known algorithms for functionality, features and performance but review state of the art as this ...

delivering perishable products to customers as soon as possible and with the minimum cost has been always a challenge for producers and has been emphasized over recent years due to the global market becoming more competitive. in this paper a multi-objective mix integer non-linear programming model is proposed to maximize both profits of a distributer and the total freshness of the several produ...

assigning facilities to locations is one of the important problems, which significantly is influence in transportation cost reduction. in this study, we solve quadratic assignment problem (qap), using a meta-heuristic algorithm with deterministic tasks and equality in facilities and location number. it should be noted that any facility must be assign to only one location. in this paper, first o...

in this paper,  a heuristic algorithm is proposed in order to solve a nonlinear lexicography goal programming (nlgp) by using an efficient initial point. some numerical experiments showed that the search quality by the proposed heuristic in a multiple objectives problem depends on the initial point features, so in the proposed approach the initial point is retrieved by data envelopment analysis...

Solving optimization problems in highdimensional stochastic search spaces is a difficult task. In this work, we present a hybrid parallel simulated annealing algorithm to find optimal solutions in such domains. The algorithm uses a population of several starting points initially to explore the search space in a parallel fashion and after each evaluation better members reproduce asexually while ...

The paper describes an application of Parallel Simulated Annealing (PSA) for solving one of the most studied NP-hard optimization problems: Graph Coloring Problem (GCP). Synchronous master-slave model with periodic solution update is being used. The paper contains description of the method, recommendations for optimal parameters settings and summary of results obtained during algorithm’s evalua...

In this paper, the flow-shop scheduling problem with unrelated parallel machines at each stage as well as sequence-dependent setup times under minimization of the sum of earliness and tardiness are studied. The processing times, setup times and due-dates are known in advance. To solve the problem, we introduce a hybrid memetic algorithm as well as a particle swarm optimization algorithm combine...