An ant colony optimization approach for the single machine total tardiness problem

Bauer, Andreas; Bullnheimer, Bernd; Hartl, Richard F.; Strauß, Christine

doi:10.1109/cec.1999.782653

Cited by 94 publications

(63 citation statements)

References 21 publications

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“…Also, ACO solutions have been developed for many other combinatorial optimisation problems. Algorithms have been proposed for the quadratic assignment problem [20,21], scheduling problems [22,23], the vehicle routing problem (VRP) [24], the graph colouring problem [25], the shortest common super-sequence problem [26], the multiple knapsack problem [27], and many others.…”

Section: Extensions and Other Applicationsmentioning

confidence: 99%

Ant colony optimization and local search for bin packing and cutting stock problems

Levine

Ducatelle

2004

Journal of the Operational Research Society

163

105

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The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimisation problems. Exact solution methods can only be used for very small instances, so for real-world problems we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimisation (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can outperform some existing solution methods, whereas the hybrid approach can compete with the best known solution methods. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO.

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Section: Extensions and Other Applicationsmentioning

confidence: 99%

Ant colony optimization and local search for bin packing and cutting stock problems

Levine

Ducatelle

2004

Journal of the Operational Research Society

163

105

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“…The instances were randomly generated as follows: For each job j (j = 1, ..., n), an integer processing time p{j} was generated from the uniform distribution [1,100] and integer processing weight w{j} was generated from the uniform distribution [1,10]. Instance classes of varying hardness were generated by using different uniform distributions for generating the due dates.…”

Section: Experimental Designmentioning

confidence: 99%

“…Evolutionary Algorithms have been successfully applied to solve scheduling problems (See [3], [4], and [5]). Similarly, the Ant Colony Optimization (ACO) metaheuristic has also been applied to solve scheduling problems such as in [1], [2], [8], [10], [13], [14] and [15]. However, there have been found too few works related, in particular, to Parallel Machines Scheduling Problem (P m ).…”

Section: Introductionmentioning

confidence: 99%

An ACO approach for the Parallel Machines Scheduling Problem

Gatica¹,

Esquivel²,

Leguizamón³

2010

Int. Artif.

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The parallel machines scheduling problem (P m ) comprises the allocation of jobs on the system's resources, i.e., a group of machines in parallel. The basic model consists of m identical machines and n jobs. The jobs are assigned according to resource availability following some allocation rule. In this work, we apply the Ant Colony Optimization (ACO) metaheuristic which includes in the construction solution process different specific heuristic to solve P m for the minimization Maximum Tardiness (T max ). We also present a comparison of previous results obtained by a simple genetic algorithm (GAs) and an evidence of an improved performance of the ACO metaheuristic on this particular scheduling problem.

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“…A path predilection an ant makes depends on this substance: the larger the amount of pheromone deposited on a particular path, the greater the probability an ant selects that path. The ACO has been applied effectively to several combinational optimization problems, [5], [10], [6], [12], [14], [16], [17] such as traveling salesman problem (TSP), quadratic assignment problems, intrusion detection system, job-shop scheduling problem, and adaptive routing.…”

Section: Introductionmentioning

confidence: 99%