Mieczysław Wodecki

A very fast Tabu search algorithm for the permutation flow shop problem with makespan criterion

This paper deals with a classic flow-shop scheduling problem with makespan criterion. Some new properties of the problem associated with the blocks have been presented and discussed, These properties allow us to propose a new very fast local search procedure based on a tabu search approach. Computational experiments (up to 500 jobs and 20 machines) are given and compared with the results yielded by the best algorithms discussed in the literature. These results show that the algorithm proposed solves the flow-shop instances with high accuracy in a very short time. The presented properties and ideas can be applied in any local search procedures.

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.

Block approach-tabu search algorithm for single machine total weighted tardiness problem

Problem of scheduling on a single machine to minimize total weighted tardiness of jobs can be described as follows: there are n jobs to be processed, each job has an integer processing time, a weight and a due date. The objective is to minimize the total weighted tardiness of jobs. The problem belongs to the class of NP-hard problems. Some new properties of the problem associated with the blocks have been presented and discussed. These properties allow us to propose a new fast local search procedure based on a tabu search approach with a specific neighborhood which employs blocks of jobs and a compound moves technique. A compound move consists in performing several moves simultaneously in a single iteration of algorithm and allows us to accelerate the convergence to good solutions. In the algorithm, we use an idea which decreases the complexity for the search of neighborhood from O(n^3) to O(n^2). Additionally, the neighborhood is reduced by using some elimination criteria. The method presented in this paper is deterministic one and has not any random element, as distinct from other effective but non-deterministic methods proposed for this problem, such as tabu search of Crauwels, H. A. J., Potts, C. N., & Van Wassenhove, L. N. (1998). Local search heuristics for the single machine total weighted tardiness Scheduling Problem. INFORMS Journal on Computing, 10(3), 341-350, iterated dynasearch of Congram, R. K., Potts C. N., & Van de Velde, S. L. (2002). An iterated dynasearch algorithm for the single-machine total weighted tardiness scheduling problem. INFORMS Journal on Computing, 14(1), 52-67 and enhanced dynasearch of Grosso, A., Della Croce, F., & Tadei, R. (2004). An enhanced dynasearch neighborhood for single-machine total weighted tardiness scheduling problem. Operations Research Letters, 32, 68-72. Computational experiments on the benchmark instances from OR-Library (http://people.brunel.ac.uk/mastjjb/jeb/info.html) are presented and compared with the results yielded by the best algorithms discussed in the literature. These results show that the algorithm proposed allows us to obtain the best known results for the benchmarks in a short time. The presented properties and ideas can be applied in any local search procedures.

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.

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.

On the theoretical properties of swap multimoves

We prove that any permutation can be transformed into any other permutation by the execution of at most two swap multimoves (i.e. the diameter of the neighborhood graph is 2). We also prove that it is NP-hard to search over such a neighborhood.

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 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.

Solving permutational routing problems by population-based metaheuristics

In this paper we consider two routing problems: traveling salesman problem (TSP, fundamental problem of the combinatorial optimization) and the TSP with times of traveling, times of processing and due dates where the objective is to minimize the total weighted tardiness (TSPTWT). Since problems are NP-hard in the strong sense, we propose a metaheuristic algorithm to determine a good sub-optimal solution. We present an algorithm based on the idea of researching and analyzing local optima. TSP with times of traveling, times of processing and due dates, and with the total weighted tardiness cost function is identical with the single machine total weighted tardiness problem with sequence-dependent setup times. It was possible to find the new best known solutions for 81 of 120 benchmark instances of this scheduling problem using the method proposed here.

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.