Single Machine Total Weighted Tardiness Problem with Genetic Algorithms (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.
International Journal of Production Management and Engineering
This article presents two combinatorial genetic algorithms (GA), unequal earliness tardiness-GA (UET-GA) and job-dependent earliness tardiness-GA (JDET-GA) for the single-machine scheduling problem to minimize earliness tardiness (ET) cost. The sequence of jobs produced in basic UET and JDET as a chromosome is added to the random population of GA. The best sequence from each epoch is also injected as a population member in the subsequent epoch. The proposed improvement seeks to achieve convergence in less time to search for an optimal solution. Although the GA has been implemented very successfully on many different types of optimization problems, it has been learnt that the algorithm has a search ability difficulty that makes computations NP-hard for types of optimization problems, such as permutation-based optimization problems. The use of a plain random population initialization results in this flaw. To reinforce the random population initialization, the proposed enhancement is u...
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.
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.
Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing, 1998
This paper presents several local search heuristics for the problem of scheduling a single machine to minimize total weighted tardiness. We introduce a new binary encoding scheme to represent solutions, together with a heuristic to decode the binary representations into actual sequences. This binary encoding scheme is compared to the usual “natural” permutation representation for descent, simulated annealing, threshold accepting, tabu search and genetic algorithms on a large set of test problems. Computational results indicate that all of the heuristics which employ our binary encoding are very robust in that they consistently produce good quality solutions, especially when multistart implementations are used instead of a single long run. The binary encoding is also used in a new genetic algorithm which performs very well and requires comparatively little computation time. A comparison of neighborhood search methods which use the permutation and binary representations shows that the...
A New Algorithm for Solving the Single Machine Total Tardiness Scheduling Problem
We are analyzing a multifunctional machine and the set of tasks to be performed by the machine. Each task has to be finished before a given due date. We are interested in finding a schedule of the tasks in such a way that the machine complies with the due dates. The problem is formulated as a minimum total tardiness scheduling problem. An heuristic algorithm for the problem is proposed. Finally, a comparative computational experience between this algorithm and other heuristic and exact algorithms is reported. Key-Words: scheduling, tardiness problem, single machine, exact and heuristic algorithms CSCC99, pp.2851-2858
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 memetic algorithm for the total tardiness single machine scheduling problem
European Journal of Operational Research, 2001
In this paper, a new memetic algorithm (MA) for the total tardiness single machine scheduling (SMS) problem with due dates and sequence-dependent setup times is proposed. The main contributions with respect to the implementation of the hybrid population approach are a hierarchically structured population conceived as a ternary tree and the evaluation of three recombination operators. Concerning the local improvement procedure, several neighborhood reduction schemes are developed and proved to be eective when compared to the complete neighborhood. Results of computational experiments are reported for a set of randomly generated test problems. The memetic approach and a pure genetic algorithm (GA) version are compared with a multiple start algorithm that employs the all-pairs neighborhood as well as two constructive heuristics. Ó