Mariusz Uchroński

Parallel hybrid metaheuristics for the flexible job shop problem

A parallel approach to flexible job shop scheduling problem is presented in this paper. We propose two double-level parallel metaheuristic algorithms based on the new method of the neighborhood determination. Algorithms proposed here include two major modules: the machine selection module refer to executed sequentially, and the operation scheduling module executed in parallel. On each level a metaheuristic algorithm is used, therefore we call this method meta^2heuristics. We carry out a computational experiment using Graphics Processing Units (GPU). It was possible to obtain the new best known solutions for the benchmark instances from the literature.

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.

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.

Parallel meta 2 heuristics for the flexible job shop problem

In this paper we consider a double-level metaheuristic optimization algorithm. The algorithm proposed here includes two major modules: the machine selection module which is executed sequentially, and the operation scheduling module executed in parallel. On each level a metaheuristic algorithm is used, so we call this method meta2heuristics. We carry out computational experiment using Graphics Processing Units (GPU). It was possible to obtain new the best known solutions for the benchmark instances from the literature.

Solving resource-constrained construction scheduling problems with overlaps by metaheuristic

AbstractThe paper concerns the problem of roadworks scheduling executed in the flow-shop system. Works may be performed parallelly with the acceleration (overlaps) of construction project, i.e. the following work on the assembly line can begin before the completion of the predecessor work. Taking into account the acceleration enables accurate modeling of complex real construction processes. The above fact can greatly shorten the time of realization of construction process which has a direct impact on reducing costs. The considered issue belongs to the class of NP-hard problems. We introduce the new: mathematical model, specific properties as an acceleration tools, as well as two new optimization algorithms for the problem considered: construction and tabu search. The execution of algorithms was illustrated on the example of a case study concerning the construction of roads. They were also verified on the examples taken from the literature and on already completed construction processes. The obtained resul...

Solving the Flexible Job Shop Problem on Multi-GPU

Abstract We propose the new framework of the distributed tabu search metaheuristic designed to be executed using a multi-GPU cluster, i.e. cluster of nodes equipped with GPU computing units. We propose a hybrid single-walk parallelization of the tabu search, where hybridization consists in examining a number of solutions from a neighborhood concurrently by several GPUs (multi-GPU). The methodology is designed to solve the flexible job shop scheduling problem, diffcult problem of discrete optimization.

Multi-GPU Tabu Search Metaheuristic for the Flexible Job Shop Scheduling Problem

We propose a new framework of the distributed tabu search metaheuristic designed to be executed using a multi-GPU cluster, i.e. cluster of nodes equipped with GPU computing units. The methodology is designed to solve difficult discrete optimization problems, such as a job shop scheduling problem, which we introduce to solve as a case study for the framework designed.

Parallel Neuro-Tabu Search Algorithm for the Job Shop Scheduling Problem

We propose two parallel algorithms based on neuro-tabu search method, designed to solve the jobs shop problem of scheduling. The fist algorithm is based on independent runs of the neuro-tabu with different starting points. The second one uses sophisticated diversification method based on path-relinking methodology applied to the set of elite solutions. Proposed approaches are especially effective for the instances of large size.

A neuro-tabu search algorithm for the job shop problem

This paper deals with tabu search with neural network instead of classic tabu list applied for solving the classic job shop scheduling problem with makespan criterion. Computational experiments are given and compared with the results yielded by the best algorithms discussed in the literature. These results show that the proposed algorithm solves the job shop instances with high accuracy in a very short time. Presented ideas can be applied for many scheduling problems.

Parallel Tabu Search Algorithm with Uncertain Data for the Flexible Job Shop Problem

In many real production systems the parameters of individual operations are not deterministic. Typically, they can be modeled by fuzzy numbers or distributions of random variables. In this paper we consider the flexible job shop problem with machine setups and uncertain times of operation execution. Not only we present parallel algorithm on GPU with fuzzy parameters but also we investigate its resistance to random disturbance of the input data.