Solving an one-dimensional cutting stock problem by simulated annealing and tabu search

Jahromi, M.H.M.A.; Tavakkoli–Moghaddam, Reza; Makui, Ahmad; Shamsi, Abbas

doi:10.1186/2251-712x-8-24

Cited by 40 publications

(28 citation statements)

References 18 publications

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“…Bu noktada oluşturulan kesim paterlerinin fire miktarlarının olabildiğince az olması şirketler için oldukça önemlidir. Klasik bir boyutlu stok kesme probleminin matematiksel modeli aşağıdaki gibi verilebilir [20]: ,…”

Section: Problemin Tanımıunclassified

Birleşik Stok Kesme ve Patern Sıralama Problemi için Bir Sezgisel Algoritma

Tanir¹,

Uğurlu²,

Nurıyev³

et al. 2017

SDÜ Fen Bil Enst Der

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Özet: Stok kesme problemleri üretim ve planlamada en çok karşılaşılan problemlerden biridir. Bu çalışmada, çelik endüstrisindeki şirketlerde karşılaşılan Bir-Boyutlu Stok Kesme Problemi ele alınmıştır. Şirketler, müşterilerinin siparişleri hazırlarken hem kesim paternlerindeki fire miktarının hem de kesim sürecindeki kesicinin değişiminin en küçüklenmesini istemektedir. Bu çalışmada, karşılaşılan bu birleşik problem için yeni bir sezgisel algoritma geliştirilmiştir. Son olarak, önerilen algoritmanın etkinliği gerçek hayat problemleri üzerinde gösterilmiştir.

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Section: Problemin Tanımıunclassified

Birleşik Stok Kesme ve Patern Sıralama Problemi için Bir Sezgisel Algoritma

Tanir¹,

Uğurlu²,

Nurıyev³

et al. 2017

SDÜ Fen Bil Enst Der

View full text Add to dashboard Cite

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“…There have been several studies on this issue. For example, Jahromi et al 9 proposed a method that used two metaheuristic algorithms, Simulated Annealing (SA) and Tabu Search (TS), to evaluate the onedimensional problem of cutting stock efficiently. Dikili et al 10 developed a heuristic approach to solve the problem of reducing the stock of raw materials in ship production.…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System Based on a Genetic Algorithm for the Utilization of Leftovers

Chang

2016

IJCIS

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There are usually some leftovers (usable pieces of raw material) and scraps (unusable pieces of raw material).generated after the completion of a manufacturing process. These leftovers consist of many different types, materials, styles and sizes so the use of such materials is difficult to manage, resulting in a significant material management problem for manufacturers. This study examines ways to use these leftovers and proposes the application of a genetic algorithm to handle the problem of matching usable leftovers to incoming orders. In order to verify the feasibility of the solution, a decision support system was implemented, based on a genetic algorithm. With lots of experimental results, we conclude this method is thus of considerable value to the managers of manufacturing factories.

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“…Thus, many individuals including researchers, investment professionals, and average investors are continually looking for this superior system which will yield them high returns. In predicting stock value fluctuations, two hypotheses have emerged (Asadi et al 2012;Aryanezhad et al 2012;Jahromi et al 2012):…”

Section: Introductionmentioning

confidence: 99%

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

Mehmanpazir

Asadi

2016

J Ind Eng Int

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The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a ''data mining-based evolutionary fuzzy expert system'' (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into subpopulations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems.

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Solving an one-dimensional cutting stock problem by simulated annealing and tabu search

Cited by 40 publications

References 18 publications

Birleşik Stok Kesme ve Patern Sıralama Problemi için Bir Sezgisel Algoritma

Birleşik Stok Kesme ve Patern Sıralama Problemi için Bir Sezgisel Algoritma

A Decision Support System Based on a Genetic Algorithm for the Utilization of Leftovers

Development of an evolutionary fuzzy expert system for estimating future behavior of stock price

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