Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem (original) (raw)
Related papers
Hybrid heuristic algorithms for single machine total weighted tardiness scheduling problems
International Journal of Intelligent Systems Technologies and Applications, 2008
This paper addresses on solving a well known Non Polynomial (NP) hard type problem, namely the single machine total weighted-tardiness problem. The performances of three hybrid heuristic algorithms to solve the single machine scheduling problems with the objective of minimising the total weighted tardiness are presented and compared. In the first hybrid algorithm, a dynamic dispatching rule, namely Modified Due Date (MDD), is hybridised with local search mechanism. In the second hybrid algorithm, a greedy heuristic, namely backward phase, is proposed and hybridised with local search mechanisms. The third hybrid algorithm hybridises the backward phase heuristics with an iterated local search (ILS) having an evolutionary perturbation tool. The algorithms are tested by solving all the 125 benchmark problem instances available in the OR-Library for different sizes and compared with the best known values. It is observed that the hybrid algorithm with evolutionary perturbation tool is performing better than the others.
This paper addresses Single-machine total weighted tardiness scheduling problem with dependent setup time. The problem, even in absence of setup time, is strongly NP-hard and cannot be solved using common optimization methods in reasonable time. In this paper, first a mathematical model for solving the problem is presented and then, two meta-heuristics; Genetic Algorithm (GA) and Simulated Annealing (SA), as well as a hill climbing heuristic are suggested. Each algorithm is examined individually and then two hybrid models are considered. The computational result shows that SA performs more efficient among the non-hybrid models while the hybrid of SA and Hill climbing is the best solution in general.
Heuristic Algorithm for the Parallel Machine Total Weighted Tardiness Scheduling Problem
This paper presents a heuristic algorithm for the parallel machine weighted tardiness scheduling problem (P || w j T j ). The main innovative feature of the algorithm is its representation of a multi-machine schedule by a single sequence, greatly simplifying the treatment of that problem. The single sequence is optimized using an iterated local search over generalized pairwise interchange moves, improved with a suitable tie breaking criterion. Extensive tests on instances, with 2 and 4 machines, and with up to 50 jobs, obtained very good results, finding optimal solutions in almost all cases.
The International Journal of Advanced Manufacturing Technology, 2007
In this paper intelligent search technique of variable structure learning automata (VSLA) has been used to solve single machine total weighted tardiness job scheduling problem. The goal is investigating reduction in delays result in late execution of the jobs after specified deadline as well as reducing the time required to find the best execution order of the jobs. For this reason, fixed structure learning automata and genetic algorithm approaches has been studied and then a new scheduling approach called VSLA-Scheduler has been proposed by employing variable structure learning automata technique. In order to identify strengths and weaknesses of the proposed method, its performance is compared with other intelligent techniques. In this regard, for performance evaluation of the proposed method and comparing it with other methods, computer simulations have been used. Finally, the results produced by the proposed and previous algorithms have been compared with the best solutions in OR library. Experimental results show that the proposed algorithm's performance (VSLA-Scheduler) is more acceptable than other methods.
Single Machine Total Weighted Tardiness Problem with Genetic Algorithms
2007 IEEE/ACS International Conference on Computer Systems and Applications, 2007
Scheduling problems are important NP-hard problems. Genetic algorithms can provide good solutions for such optimization problems. In this paper, we present a genetic algorithm to solve the single machine total weighted tardiness scheduling problem, which is a strong NP-hard problem. The algorithm uses the natural permutation representation of a chromosome, heuristic dispatching rules combined with random method to create the initial population, position-based crossover and order-based mutation operators, and the best members of the population during generations. The computational results of problem examples with 10 and 25 jobs and general problems with 50, 100, 200 and 500 jobs show the good performance and the efficiency of the developed algorithm.
The International Journal of Advanced Manufacturing Technology, 2004
In this paper, an intensive search evolutionary algorithm is proposed to solve single machine total weighted tardiness scheduling problems. A specialised locally improved random swap mutation operator and an ordered crossover operator are used for evolution. The proposed algorithm starts with a pair of sequences: one generated by a greedy heuristic, namely, a backward phase heuristic acts as one parent, and a randomly generated sequence acts as the other. A computational experiment is conducted by applying the mutation operator on the backward phase sequence and the proposed algorithm with the same number of generations as the termination criteria. A total of 125 benchmark instances for sizes 40, 50 and 100 available in the OR library are solved and the results are compared with the available best-known results. It is observed that the proposed evolutionary algorithm provides better results than others.
Algorithms for single machine total tardiness scheduling with sequence dependent setups
European Journal of Operational Research, 2006
We consider the problem of scheduling a single machine to minimize total tardiness with sequence dependent setup times. We present two algorithms, a problem space-based local search heuristic and a Greedy Randomized Adaptive Search Procedure (GRASP) for this problem. With respect to GRASP, our main contributions area new cost function in the construction phase, a new variation of Variable Neighborhood Search in the improvement phase, and Path Relinking using three different search neighborhoods. The problem space-based local search heuristic incorporates local search with respect to both the problem space and the solution space. We compare our algorithms with Simulated Annealing, Genetic Search, Pairwise Interchange, Branch and Bound and Ant Colony Search on a set of test problems from literature, showing that the algorithms perform very competitively.
Metaheuristics for the single machine weighted quadratic tardiness scheduling problem
Computers & Operations Research
This paper considers the single machine scheduling problem with weighted quadratic tardiness costs. Three metaheuristics are presented, namely iterated local search, variable greedy and steady-state genetic algorithm procedures. These address a gap in the existing literature, which includes branch-andbound algorithms (which can provide optimal solutions for small problems only) and dispatching rules (which are efficient and capable of providing adequate solutions for even quite large instances). A simple local search procedure which incorporates problem specific information is also proposed. The computational results show that the proposed metaheuristics clearly outperform the best of the existing procedures. Also, they provide an optimal solution for all (or nearly all, in the case of the variable greedy heuristic) the smaller size problems. The metaheuristics are quite close in what regards solution quality. Nevertheless, the iterated local search method provides the best solution, though at the expense of additional computational time. The exact opposite is true for the variable greedy procedure, while the genetic algorithm is a good all-around performer.
A problem space algorithm for single machine weighted tardiness problems
IIE Transactions, 2003
We propose a problem space genetic algorithm to solve single machine total weighted tardiness scheduling problems. The proposed algorithm utilizes global and time-dependent local dominance rules to improve the neighborhood structure of the search space. They are also a powerful exploitation (intensifying) tool since the global optimum is one of the local optimum solutions. Furthermore, the problem space search method significantly enhances the exploration (diversification) capability of the genetic algorithm. In summary, we can improve both solution quality and robustness over the other local search algorithms reported in the literature.
A tabu search algorithm for the single machine total weighted tardiness problem
European Journal of Operational Research, 2007
In this study, a tabu search (TS) approach to the single machine total weighted tardiness problem (SMTWT) is presented. The problem consists of a set of independent jobs with distinct processing times, weights and due dates to be scheduled on a single machine to minimize total weighted tardiness. The theoretical foundation of single machine scheduling with due date related objectives reveal that the problem is NP-hard, rendering it a challenging area for meta-heuristic approaches. This paper presents a totally deterministic TS algorithm with a hybrid neighborhood and dynamic tenure structure, and investigates the strength of several candidate list strategies based on problem specific characteristics in increasing the efficiency of the search. The proposed TS approach yields very high quality results for a set of benchmark problems obtained from the literature.