The band collocation problem

Kutucu, Hakan; Gürsoy, Arif; Kurt, Mehmet; Nurıyev, Urfat

doi:10.1007/s10878-020-00576-2

Cited by 3 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the BCP have been recently proposed, there are not many research works on this problem. In particular, the best method found in the literature is the Simulated Annealing (SA) algorithm proposed in [2], which is able to outperform the results obtained with the Genetic Algorithm, and the Artificial Bee Colony algorithm also presented in that work. Therefore, we will compare our best proposal with the SA procedure.…”

Section: Computational Resultsmentioning

confidence: 95%

“…In order to reduce the computational effort, the author of [21] proposes a Dynamic Programming (DP) algorithm that memorizes the already explored solutions to reduce the computing time for finding the minimum cost. In particular, they consider each column as a subproblem, solving it with the DP method, memorizing during the search the solutions found.…”

Section: Evaluation Of the Objective Functionmentioning

confidence: 99%

“…Most of these problems can be classified into two different types: finding the optimal route for the signal and reducing the costs of the equipment required to deploy and maintain the network [1]. This paper is focused on solving one of the most extended problems of the second type, the Band Collocation Problem [2].…”

Section: Introductionmentioning

confidence: 99%

“…Notice that each data fragment in every target station must be covered by exactly one band. We refer the reader to [2] for a formal definition of the problem and a mathematical formulation.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, the first heuristic approach for the BCP is a classical Genetic Algorithm [18]. Then, the same authors developed several bioinspired algorithms for further improving the results obtained [2], but the results obtained were not satisfactory. In particular, a new Genetic Algorithm (GA), a Simulated Annealing (SA), an Artificial Bee Colony (ABC) algorithm are proposed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimizing Computer Networks Communication with the Band Collocation Problem: A Variable Neighborhood Search Approach

et al. 2020

View full text Add to dashboard Cite

The Band Collocation Problem appears in the context of problems for optimizing telecommunication networks with the aim of solving some concerns related to the original Bandpass Problem and to present a more realistic approximation to be solved. This problem is interesting to optimize the cost of networks with several devices connected, such as networks with several embedded systems transmitting information among them. Despite the real-world applications of this problem, it has been mostly ignored from a heuristic point of view, with the Simulated Annealing algorithm being the best method found in the literature. In this work, three Variable Neighborhood Search (VNS) variants are presented, as well as three neighborhood structures and a novel optimization based on Least Recently Used cache, which allows the algorithm to perform an efficient evaluation of the objective function. The extensive experimental results section shows the superiority of the proposal with respect to the best previous method found in the state-of-the-art, emerging VNS as the most competitive method to deal with the Band Collocation Problem.

show abstract

Section: Computational Resultsmentioning

confidence: 95%

Section: Evaluation Of the Objective Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimizing Computer Networks Communication with the Band Collocation Problem: A Variable Neighborhood Search Approach

et al. 2020

View full text Add to dashboard Cite

show abstract

Exploiting the Crow Search Algorithm to Overcome the Bandpass Problem

Al-Sabaawi,

Hussein,

Al Gburi

et al. 2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

A new technique for determination of the optimum cut height according to the fabric type in garment production

Küçük

2024

RJTA

View full text Add to dashboard Cite

Purpose Fabrics, which are one of the raw materials of the clothing industry, constitute approximately 40–45% of the total cost of an apparel product. Due to the labor-intensive nature of this industry and failure to apply scientific methods along with the manufacturing processes, the wastes in the raw materials, including fabrics, become higher. Besides, quality deficiencies are encountered due to the same reasons. This study aims to determine the optimum total fabric layer height based on the fabric type during the cutting process with a straight knife cutting machine, which provided a decrease in the cutting errors. Design/methodology/approach Frequently used fabric types in an enterprise operating in organic cotton knitwear were listed. During the cutting tests, the straight knife cutting machine was used as the cutting device. The weight and thickness values of the fabrics were obtained to provide a comparison basis. Two different algorithms were created to evaluate the defective pieces according to fabric type, cutting height and error placement. Cutting resistances of these fabrics were also determined to evaluate the defect reasons. In the end, optimum total fabric layer count and total cutting height suggestions were proposed for each fabric type for a minimum cutting error. Findings At the end of this study, the error-free layers were identified per fabric type. At the same time, the optimum cutting height was suggested for each fabric basis. For 40/1 single jersey fabrics, the cutting height should be between 2.10 cm and 10.40 cm; for 30/1 single jersey fabrics, between 1.65 cm and 5.70 cm; for 20/1 single jersey fabrics, between 1.83 cm and 6.70 cm; for two-thread fleece fabrics, between 2.13 cm and 4.70 cm; and for three-thread fleece fabrics, between 0 cm and 4.90 cm. Research limitations/implications Within the scope of the study, since the products made of knitted fabric were produced more frequently and in large quantities, the study was carried out with 15 different types of knitted fabrics at 10 different layers. The same methods should be applied for woven, denim and nonwoven fabric types, which would shed light on the following studies. Originality/value Due to scarce research carried out on the cutting procedure of the clothing industry in regards to sustainability, this study aims to contribute to this area. The main difference between this study and the studies that mostly make mathematical predictions about the cutting procedure is that it is practice-oriented.

show abstract

The band collocation problem

Cited by 3 publications

References 16 publications

Optimizing Computer Networks Communication with the Band Collocation Problem: A Variable Neighborhood Search Approach

Optimizing Computer Networks Communication with the Band Collocation Problem: A Variable Neighborhood Search Approach

Exploiting the Crow Search Algorithm to Overcome the Bandpass Problem

A new technique for determination of the optimum cut height according to the fabric type in garment production

Contact Info

Product

Resources

About