A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization

Merkle, Daniel; Middendorf, Martin

doi:10.1007/3-540-45365-2_50

Cited by 20 publications

(6 citation statements)

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“…To address this issue, some authors attacked the problem by focusing on specific goals (Cheng and Smith 1997;Crawford and Baker 1994;Pinedo 1995;Sadeh and Fox 1995;Frutos and Olivera 2008) and applying a variety of alternative algorithms (Zalzala and Flemming 1997;Merkle and Middendorf 2001;Cortés Rivera et al 2004;Storer et al 1992;Heinonen and Pettersson 2007). This way of proceeding, by using ad-hoc algorithms is, of course, usual in combinatorial optimization (Armentano and Scrich 2000;Larrazábal 2003; Mollaghasemi et al 1998).…”

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confidence: 96%

A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem

2010

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The Flexible Job-Shop Scheduling Problem is concerned with the determination of a sequence of jobs, consisting of many operations, on different machines, satisfying several parallel goals. We introduce a Memetic Algorithm, based on the NSGAII (NonDominated Sorting Genetic Algorithm II) acting on two chromosomes, to solve this problem. The algorithm adds, to the genetic stage, a local search procedure (Simulated Annealing). We have assessed its efficiency by running the algorithm on multiple objective instances of the problem. We draw statistics from those runs, which indicate that this Memetic Algorithm yields good and low-cost solutions.

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confidence: 96%

A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem

2010

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“…The Ant System (AS) was first introduced by Dorigo and his colleagues [18,19,20,23]. Since then, ACO algorithms have been applied to different problems such as the traveling salesman problem (TSP) [4,6,9,10,11,21,22,23,25,30,46], the quadratic assignment problem (QAP) [40,52], the generalized assignment problem [48], the vehicle routing problem [5,7,16,17,27], telecommunication networks [15], graph coloring [13,34,35], scheduling [1,12,14,32,42,43], the shortest supersequence problem [44,45], the Hamiltonian graph problem [55], the multiple knapsack problem [37], the sequential ordering problem [26], the redundancy allocation problem [39], water distribution network design [41], the constraint satisfaction problem [51], and continuous function problems [2,3,57]. Local search plays an important role in improving the solution quality of ACO algorithms.…”

Section: Background Of the Ant Colony Methodsmentioning

confidence: 99%

An Ant Colony Approach to the Orienteering Problem

Liang

Smith

2006

Journal of the Chinese Institute of Industrial Engineers

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This paper develops an ant colony optimization approach to the orienteering problem, a general version of the well-known traveling salesman problem with many relevant applications in industry. Based on mainstream ant colony ideas, an unusual sequenced local search and a distance based penalty function are added to result in a method that is convincingly shown to be the best heuristic published for this problem class. Results on 67 test problems from the literature show that the ant colony method performs as well or better in all cases and does so at very modest computational cost. Furthermore, the ant colony method is insensitive to seed, problem instance, problem size and degree of constraint.

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“…Besides, its usefulness made it a problem worth to scrutinize. Due to its complexity, several alternative presentations of the problem have been tried (Cheng & Smith, 1997), (Sadeh & Fox, 1995), (Crawford & Baker, 1994), (De Giovanni & Pezzella, 2010), in order to apply particular algorithms like Clonal Selection (Cortés Rivera et al, 2003), Taboo Search (Armentano & Scrich, 2000), Ant Colony Optimization (Merkle & Middendorf, 2001), Genetic Algorithms (Zalzala & Flemming, 1997), Priority Rules (Panwalker & Iskander, 1977), Shifting Bottlenecks (Adams et al, 1998), etc. The performance of these meta-heuristic procedures varies, and some seem fitter than others (Chinyao & Yuling, 2009).…”

Section: Jssp Treatments: State Of the Artmentioning

confidence: 99%

Real-World Applications of Genetic Algorithms

Roeva¹

2012

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Dr. Olympia Roeva received the M.Sc. degree (1998) and Ph.D. degree (2007) from the Technical University -Sofia, Bulgaria. At present she is an associate professor in Department of Bioinformatics and Mathematical Modeling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences. Dr. Roeva is one of the scientists playing a significant role in the renovation of International Journal Bioautomation. She has received various scientific awards, including a Diploma from Union of Scientists in Bulgaria for High Scientific Achievements 2008. Her research interests are in the fields of modeling, optimization and control of cultivation processes, functional state modeling approach, metaheuristic algorithms and generalized nets.

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A New Approach to Solve Permutation Scheduling Problems with Ant Colony Optimization

Cited by 20 publications

References 5 publications

A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem

A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem

An Ant Colony Approach to the Orienteering Problem

Real-World Applications of Genetic Algorithms

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