Wojciech Bożejko

Solving the Flow Shop Problem by Parallel Simulated Annealing

In this paper we present two simulated annealing algorithms (sequential and parallel) for the permutation flow shop sequencing problem with the objective of minimizing the flowtime. We propose a neighbourhood using the so-called blocks of jobs on a critical path and specific accepting function. We also use the lower bound of cost function. By computer simulations on Taillard [17] and other random problems, it is shown that the performance of the proposed algorithms is comparable with the random heuristic technique discussed in literature. The proposed properties can be applied in any local search procedures.

Parallel path relinking method for the single machine total weighted tardiness problem with sequence-dependent setups

We propose a new method of sequential and parallel speeding up of the process of solving a single machine scheduling problem with total tardiness cost function. This improvement is done on two levels: generating a neighborhood inside path relinking method (basing on new approach—blocks in solutions) and parallelization of generating paths. The obtained results are compared to the benchmark ones taken from the literature. It was possible to find new the best solutions for many benchmark instances by using the proposed method.

The new golf neighborhood for the exible job shop problem

Abstract job shop problem. A main idea of the proposed neighborhood is to execute a ‘long shot’ of an operation from the current operation’s machine to another machine of the same type, and then to the make a small move by using a local optimization algorithm without changing operationsto-machines assignment. We call this method ‘the golf neighborhood’. Computational experiments executed on the benchmark instances from the literature show the efficiency of this solution.

Applying metaheuristic strategies in construction projects management

This work deals with the application of artificial intelligence instruments in a building schedule. In this article there was presented an original optimization scatter search algorithm taking into consideration both technological and organizational restrictions. This algorithm was applied to the real analysis of the industrial building project realization.

Parallel Simulated Annealing for the Job Shop Scheduling Problem

This paper describes two parallel simulated annealing algorithms for the job shop scheduling problem with the sum of job completion times criterion. Some properties of the problem associated with the block theory have been presented and discussed. These properties allow us to introduce the effective neighborhood based on the adjacent swap type moves. In this paper, an original method for parallel calculation of optimization criterion value for set of solutions, recommended for the use in metaheuristics with single- and multiple- search trajectories is proposed. Additionally, the vector calculation method, that uses multiple mathematical instructions MMX supported by suitable data organization, is presented. Properties of parallel calculations are empirically verified on the PC with Intel Core 2 Duo processor on Taillards benchmarks.

Parallel Genetic Algorithm for Minimizing Total Weighted Completion Time

We have considered the problem of job scheduling on a single machine with deadlines. The objective is to find a feasible job sequence (satisfying the deadlines) to minimize the sum of weighted completion times. Since the problem is NP-hard, heuristics have to be used. Methods of artificial intelligence: simulated annealing, neural networks and genetic algorithms, are some of the recent approaches. We propose a very effective parallel genetic algorithm PGA and methods of determining lower and upper bounds of the objective function. Since there are difficulties with determining the initial population of PGA for this scheduling problem, therefore the algorithm also adds random generated unfeasible solutions to the population. We announce a method of elimination of these kind of solutions. The examined algorithms are implemented in Ada95 and MPI. Results of computational experiments are reported for a set of randomly generated test problems.

Parallel Genetic Algorithm for the Flow Shop Scheduling Problem

The permutation flow shop sequencing problem with the objective of minimizing the sum of the job’s completion times, in literature known as the F||C sum , has been considered. The parallel genetic algorithm based on the island model of migration has been presented. By computer simulations on Taillard benchmarks [10] and the best known results from literature [9] we have obtained not only acceleration of the computation’s time but also better quality and stability of the results.

Parallel scatter search algorithm for the flow shop sequencing problem

In the paper we consider strongly NP-hard flow shop problem with the criterion of minimization of the sum of jobs finishing times. We present the parallel algorithm based on the scatter search method. Obtained results are compared to the best known from the literature. Superlinear speedup has been observed in the parallel calculations.

Multi-GPU parallel memetic algorithm for capacitated vehicle routing problem

The goal of this paper is to propose and test a new memetic algorithm for the capacitated vehicle routing problem in parallel computing environment. In this paper we consider a simple variation of the vehicle routing problem in which the only parameter is the capacity of the vehicle and each client only needs one package. We analyze the efficiency of the algorithm using the hierarchical Parallel Random Access Machine (PRAM) model and run experiments with code written in CUDA.

Parallel Calculating of the Goal Function in Metaheuristics Using GPU

We consider a metaheuristic optimization algorithm which uses single process (thread) to guide the search through the solution space. Thread performs in the cyclic way (iteratively) two main tasks: the goal function evaluation for a single solution or a set of solutions and management (solution filtering and selection, collection of history, updating). The latter task takes statistically 1-3% total iteration time, therefore we skip its acceleration as useless. The former task can be accelerated in parallel environments in various manners. We propose certain parallel small-grain calculation model providing the cost optimal method. Then, we carry out an experiment using Graphics Processing Unit (GPU) to confirm our theoretical results.