A tabu search tutorial based on a real-world scheduling problem

Schneider, Ulrike

doi:10.1007/s10100-010-0137-8

Cited by 9 publications

(3 citation statements)

References 13 publications

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“…Tabu tenure is a number of previous solutions to be stored. In static memory, the value of tabu tenure is fixed and remains fixed during the search, while in dynamic memory, the value of tabu tenure is not constant during the search [21]. In this research, the static memory is selected in the search process.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Application of Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem at a Packaging Manufacturing Company to Minimize Total Weighted Tardiness

2023

IRJMETS

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Flexible job shop scheduling problem is one of the most important, interest and complex topics in production, in which the job shop comprises parallel machines at some workstations. It is a challenge to find out the optimum solution. In this research, a mixed-integer linear programming model was built up. The problem is known as NP-hard one. Therefore, heuristic techniques are more appropriate to find near-optimal solutions. Tabu Search (TS) algorithm utilizing for a large-size instance would be used. The obtained results would be compared with experience-based production alternative of the company. It shows that the proposed Tabu search procedure generally outperforms in comparison with the existing method for large-sized problems.

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Section: The Proposed Algorithmmentioning

confidence: 99%

Application of Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem at a Packaging Manufacturing Company to Minimize Total Weighted Tardiness

2023

IRJMETS

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“…James et al (2009) showed the advantages of applying strategic diversification within Tabu search (TS) model for the quadratic assignment problem by including many diversification and multi start TS variants [61]. Ulrike (2011) applied TS to Scheduling problem then with the help of intensification and diversification strategies demonstrates that the result achieved is quite better [115].…”

Section: Tabu Searchmentioning

confidence: 99%

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Baghel¹,

Agrawal²,

Silakari³

2012

IJCA

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This paper is intended to give a review of metaheuristic and their application to combinatorial optimization problems. This paper comprises a snapshot of the rapid evolution of metaheuristic concepts, their convergence towards a unified framework and the richness of potential application in combinatorial optimization problems. Over the years, combinatorial optimization problems are gaining awareness of the researchers both in scientific as well as industrial world. This paper aims to present a brief survey of different metaheuristic algorithms for solving the combinatorial optimization problems. Basically we have divided the metaheuristic into three broad categories namely trajectory methods, population based methods and hybrid methods. Trajectory methods are those that deal with a single solution. These include simulated annealing, tabu search, variable neighborhood search and greedy randomized adaptive search procedure. Population based methods deal with a set of solutions. These include genetic algorithm, ant colony optimization and particle swarm optimization. Hybrid methods deal with the hybridization of single point search methods and population based methods. These are further categorized into five different types. Finally we conclude the paper by giving some issues which are needed to develop a well performed metaheuristic algorithm.

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“…Metaheuristic search methods include local search or neighborhood search algorithms, which are iterative procedures and start with an initial solution and browse through the search space, picking up better solutions on the way, which could give good, or eventually optimal, solutions (Schneider 2011). Therefore, this study considered the application of a genetic algorithm (GA), a simulated annealing (SA) and a heuristic algorithm to solve the considered problem with minimization of the makespan.…”

Section: The Proposed Solving Approach and Proceduresmentioning

confidence: 99%

Some heuristics for the hybrid flow shop scheduling problem with setup and assembly operations

Hosseini¹,

Jolai²

2013

10.5267/j.ijiec

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This paper presents a two-stage hybrid flow shop scheduling problem with setup and assembly operations. The proposed study of this paper considers one kind of product with a quantity of demand where each product is made by assembling a set of different parts. At first, the parts are manufactured in a two-stage hybrid flow-shop and then the parts are assembled into products on assembly stage. Setup operations are needed when a machine starts processing the parts or it changes items. The considered objective is minimizing the completion time of all products. Since the problem is classified as NP-hard class, a combinatorial algorithm is proposed. The proposed algorithm is a three-step procedure where we use heuristic, genetic algorithm (GA), simulated annealing (SA), NEH and Johnson's algorithm. Three lower bounds are presented and improved to evaluate the proposed algorithms. An extensive computational experiment is conducted to compare the performances of the proposed algorithms.

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A tabu search tutorial based on a real-world scheduling problem

Cited by 9 publications

References 13 publications

Application of Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem at a Packaging Manufacturing Company to Minimize Total Weighted Tardiness

Application of Tabu Search Algorithm for Flexible Job-Shop Scheduling Problem at a Packaging Manufacturing Company to Minimize Total Weighted Tardiness

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Some heuristics for the hybrid flow shop scheduling problem with setup and assembly operations

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