Study of one-dimensional cutting stock problem with multi-objective optimization

Ge, Feifei; Liu, Lin; Pei, Jun; Zhang, Xiazi

doi:10.1109/csip.2012.6308918

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…This shows the efficiency and dominancy of the presented approach in the sense that fulfills the given demand from the nonstandard leftovers. The presented algorithm in this article is a novel approach to this era, because the previous approaches minimized leftovers in the stock by using the cutting concept, [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] whereas the proposed one used the combinatorial concept. The maximum utilization of the stock in Reference [56] is up to 99.15%, whereas the presented approach used the stock up to 100%.…”

Section: F I G U R E 4 Pseudo Code Of Genetic Algorithmmentioning

confidence: 99%

“…9 Furthermore, an optimal model whose aim is the minimization of waste, cutting patterns, and usable leftover in the whole cutting process, and a 1D cutting stock is minimized. 10 On similar lines, a pattern-set generation algorithm (PSG) for minimization of one-dimensional multiple stock size cutting-stock problem (1DMSSCSP) is presented that has considered those leftovers that are usually left after demand cutting. [11][12][13][14] Gradisar et al 15 have presented a method that handles 1D cutting-stock problem with usable leftovers (CSPUL) in a situation where the ratio between the average stock and average order length is less than 3.…”

Section: Introductionmentioning

confidence: 99%

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Decision making viva genetic algorithm for the utilization of leftovers

2021

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Section: F I G U R E 4 Pseudo Code Of Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision making viva genetic algorithm for the utilization of leftovers

2021

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“…Kantorovich provides two examples in his paper to make the CSP easier to understand [26]. Since then, many scholars have conducted research to obtain a solution to the CSP using linear programming [29][30][31][32][33][34][35][36][37][38][39][40][41][42], genetic, or heuristic approaches [43][44][45][46][47][48].…”

Section: Literature Review Of Cwm Problemsmentioning

confidence: 99%

Special-Length-Priority Algorithm to Minimize Reinforcing Bar-Cutting Waste for Sustainable Construction

et al. 2020

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Reinforcing bars (rebar), which have the most embodied carbon dioxide (CO2) per unit weight in built environments, generate a significant amount of cutting waste during the construction phase. Excessive cutting waste not only increases the construction cost but also contributes to a significant amount of CO2 emissions. The objective of this paper is to propose a special-length-priority cutting waste minimization (CWM) algorithm for rebar, for sustainable construction. In the proposed algorithms, the minimization method by special and stock lengths was applied. The minimization by special length was performed first, and then the combination by stock length was performed for the remaining rebar. As a result of verifying the proposed algorithms through a case application, it was confirmed that the quantity of rebar was reduced by 6.04% compared with the actual quantity used. In the case building, a CO2 emissions reduction of 406.6 ton-CO2 and a cost savings of USD 119,306 were confirmed. When the results of this paper are applied in practice, they will be used as a tool for sustainable construction management as well as for construction cost reduction.

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“…Two approaches, simulated annealing (SA) and tabu search (TS) [8], have been used for the evaluation of the one-dimensional cutting stock problem (1DCSP). A one-dimensional cutting stock design [9] is carried out for the minimization of waste as an optimal model. A technique is presented by Gradisar et al [10] that handles 1D-CSP with usable leftovers in a situation where the average order and stock length are less than three.…”

Section: Introductionmentioning

confidence: 99%

A decision support system to utilize leftovers by using meta-heuristic technique

Muhammad,

Al-Sawalha,

Alqudah

et al. 2024

Preprint

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There are a lot of leftovers produced by industrial and building activities worldwide. These leftovers include wood planks, electric wires, circular pipes, steel, aluminium window frames, etc., of which some are considered to be standard while others are considered to be non-standard. Since these leftovers vary in size and kind, it can be difficult for manufacturers, electricians, and builders to handle and reuse them, particularly when using non-standard materials. In this paper, we have taken into account different approaches to reuse such leftovers. However, some material requires overlapping sections when connecting. In this regard, the previous techniques fail to handle the problem under consideration. We have modified a meta-heuristic technique (genetic algorithm) to evaluate overlapping sections and handle those leftovers that are considered scraps that were left unused after the implementation of cutting stock and bin packing concepts in order to maximize a given company’s profit and minimize its stock load as much as possible. In this scenario, we extended the leftover minimization approaches for leftovers that have overlapping materials. Moreover, the proposed algorithm is applicable for overlapping pipes, electric wires, steels, and anything else connected by overlapping sections. We have observed from the simulation results that the proposed method handles left-overs in the stock from 76.47% to 100%, which shows the maximum utilization of leftovers at proper places. Similarly, the results and comparisons show the validity and improvements of the modified algorithm. In future work, we intend to modify the proposed technique by including more dimensions like width, weight, and diameter. For such scenarios, we will use different meta-heuristic techniques like PSO, ant colony, and particle swarm (PS) in addition to fuzzy decision-making techniques.

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Study of one-dimensional cutting stock problem with multi-objective optimization

Cited by 8 publications

References 2 publications

Decision making viva genetic algorithm for the utilization of leftovers

Decision making viva genetic algorithm for the utilization of leftovers

Special-Length-Priority Algorithm to Minimize Reinforcing Bar-Cutting Waste for Sustainable Construction

A decision support system to utilize leftovers by using meta-heuristic technique

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