Decision making viva genetic algorithm for the utilization of leftovers

Ali, Raiz; Muhammad, Shakoor; Takahashi, Ricardo H. C.

doi:10.1002/int.22359

Cited by 8 publications

(15 citation statements)

References 59 publications

(68 reference statements)

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“…as shown in Figure 3. Moreover, the approach suggested in [42] cannot be applied to joining steel and electric wires rather than soldering and welding. For this purpose, we extend the suggested approach [42] for overlapping materials as shown in Figure 2.…”

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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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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“…Finally, a genetic algorithm approach is presented in [2]. A genetic-based decision support system is applied to validate the solution feasibility of the problem and the objective is to minimize the leftovers in pipes cutting.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Methods for Minimizing Cut Bars and Using Leftovers from the One-dimensional Cutting Process

Bressan

Pimenta-Zanon

Sakuray

2022

Preprint

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The cutting problems consist in cutting a set of objects available in stock in order to produce the desired items in specified quantities and sizes. The one-dimensional cutting stock problem involves only one of the relevant dimensions in the cutting process, as in cutting bars, rolls and tubes. The cutting process can generate leftover (which can be reused in a new demand) or losses (which are discarded). This paper presents two heuristic methods for minimizing the number of cut bars in the one-dimensional cutting process, satisfying the items demand in an unlimited bars quantity of just one type. The results of simulations are compared with methods from literature and with the limiting values for this considered type of problem. The results show that proposed heuristics reduce processing time and the number of bars needed in cutting process, while it provides a greater leftover (by grouping losses) for the one-dimensional cutting stock problem. The heuristics contribute to reduction of raw materials or manufacturing costs in industrial processes, such as the automotive industry, construction, bicycle manufacturing and other purposes.

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“…and many other applications. Among all the CI tools, EC and, thus, evolutionary optimization algorithms (EAs) play a fundamental role in many typical engineering applications, such as optimal design, 7 parameter identification, 8 modelling, 9 portfolio management, 10 decision making, 11 and management systems 12 . Many different Evolutionary Algorithms (EAs) have been developed and are available for solving these problems.…”

Section: Introductionmentioning

confidence: 99%

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

Niccolai

Bettini

Zich

2021

Int J of Intelligent Sys

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Due to the growing importance of electric vehicles, charging stations (CS) deployment is becoming an important issue in many cities. The aim of this paper is to introduce a novel evolutionary‐based approach for solving the CS deployment problem. This study investigates many aspects of the formulation of this approach, such as the design variables selection and the definition of a feasibility function, to improve both effectiveness and flexibility. In particular, the latter is a key factor compared to many other state‐of‐the‐art approaches: in fact, it can be used with most of the available Evolutionary Algorithms (EAs) and can manage different quality‐of‐service performance parameters. The proposed approach is successfully compared with a greedy optimization on the case study of the City of Milan (Italy) using four different EAs. Two different performance parameters have been defined and used to prove the flexibility of the proposed approach. The results show its very good convergence rate and the quality of the obtained solutions.

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Decision making viva genetic algorithm for the utilization of leftovers

Abstract: Pipes have been using for different construction purposes, such as tube wells, oil wells, gas wells, and other sanitary purposes. These pipes have different sizes that can be used according to the need. In a particular

Cited by 8 publications

References 59 publications

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

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

Heuristic Methods for Minimizing Cut Bars and Using Leftovers from the One-dimensional Cutting Process

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

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