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1 – 10 of 923Bruno Dalanezi Mori, Hélio Fiori de Castro and Katia Lucchesi Cavalca
The purpose of this paper is to present an application of the simulated annealing algorithm to the redundant system reliability optimization. Its main aim is to analyze and…
Abstract
Purpose
The purpose of this paper is to present an application of the simulated annealing algorithm to the redundant system reliability optimization. Its main aim is to analyze and compare this optimization method performance with those of similar application.
Design/methodology/approach
The methods that were used to compare results are the genetic algorithm, the Lagrange Multipliers, and the evolution strategy. A hybrid algorithm composed by simulated annealing and genetic algorithm was developed in order to achieve the general applicability of the methods. The hybrid algorithm also tries to exploit the positive aspects of each method.
Findings
The results presented by the simulated annealing and the hybrid algorithm are significant, and validate the methods as a robust tool for parameter optimization in mechanical projects development.
Originality/value
The main objective is to propose a method for redundancy optimization in mechanical systems, which are not as large as electric and electronic systems, but involves high costs associated to redundancy and requires a high level of safety standards like: automotive and aerospace systems.
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Zineb Ibn Majdoub Hassani, Abdellah El Barkany, Abdelouahhab Jabri, Ikram El Abbassi and Abdel Moumen Darcherif
This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their…
Abstract
Purpose
This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs.
Design/methodology/approach
The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes.
Findings
The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing.
Originality/value
This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.
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Hongbo Shan, Shenhua Zhou and Zhihong Sun
The purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly…
Abstract
Purpose
The purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly sequence planning (ASP) which is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.
Design/methodology/approach
Based on the genetic algorithm (GA), simulated annealing, and ACO algorithm, the GSAA are put forward. A case study is presented to validate the proposed method.
Findings
This GSAA has better optimization performance and robustness. The degree of dependence on the initial assembly sequence about GSAA is decreased. The optimization assembly sequence still can be obtained even if the assembly sequences of initial population are infeasible. By combining GA and simulated annealing (SA), the efficiency of searching and the quality of solution of GSAA is improved. As for the presented ACO algorithm, the searching speed is further increased.
Originality/value
Traditionally, GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non‐optimization of final result for global variable. Similarly, SA algorithms may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution‐searching process. In this paper, the proposed GSAA and ACO algorithm for ASP are possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.
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K. Das, R.S. Lashkari and S. Sengupta
The purpose of this paper is to: develop an effective cellular manufacturing system (CMS) design methodology by simultaneously considering system costs and individual machine…
Abstract
Purpose
The purpose of this paper is to: develop an effective cellular manufacturing system (CMS) design methodology by simultaneously considering system costs and individual machine reliabilities; and propose a combinatorial search‐based solution procedure to solve large‐sized problems.
Design/methodology/approach
This paper presents a multi‐objective mixed integer‐programming model for the design of CMS with the objective of minimizing costs and maximizing system reliability. The approach optimizes inter‐cell material handling costs, the variable cost of machining operations, and the machine under‐utilization costs. It also maximizes the system reliability by selecting process routes for the part types with the highest system reliability for the machines along the routes. To solve the multi‐objective, multiple process plan model, a simulated annealing (SA)‐based algorithm is developed. The algorithm follows the basic steps of SA, but also incorporates the genetic algorithm (GA) operations of crossover and mutations to generate better neighboring solutions from the current good solutions.
Findings
The algorithm in the paper solves the multi‐objective CMS design model and generates near optimal solutions for medium to large‐sized problems within reasonable limits of CPU time.
Practical implications
In the paper the CMS design approach can be implemented to improve reliability performance of the CMS.
Originality/value
A new CMS design methodology in this paper, which minimizes system costs and maximizes machine‐related system reliability, is developed. The proposed algorithm, which combines the basic steps of SA and crossover and mutation operations of GA, will enable CMS designers and users to obtain near optimal solutions for practical‐sized problems within reasonable time limits.
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Qiang Du, Xiaomin Qi, Patrick X.W. Zou and Yanmin Zhang
The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated…
Abstract
Purpose
The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.
Design/methodology/approach
The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.
Findings
The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.
Originality/value
The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.
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Prasad A.Y. and Balakrishna Rayanki
In the present networking scenarios, MANETs mainly focus on reducing the consumed power of battery-operated devices. The transmission of huge data in MANETs is responsible for…
Abstract
Purpose
In the present networking scenarios, MANETs mainly focus on reducing the consumed power of battery-operated devices. The transmission of huge data in MANETs is responsible for greater energy usage, thereby affecting the parameter metrics network performance, throughput, packet overhead, energy consumption in addition to end-to-end delay. The effective parameter metric measures are implemented and made to enhance the network lifetime and energy efficiency. The transmission of data for at any node should be more efficient and also the battery of sensor node battery usage should be proficiently applied to increase the network lifetime. The paper aims to discuss these issues.
Design/methodology/approach
In this research work for the MANETs, the improvement of energy-efficient algorithms in MANETs is necessary. The main aim of this research is to develop an efficient and accurate routing protocol for MANET that consumes less energy, with an increased network lifetime.
Findings
In this paper, the author has made an attempt to improve the genetic algorithm with simulated annealing (GASA) for MANET to minimize the energy consumption of 0.851 percent and to enhance the network lifetime of 61.35 percent.
Originality/value
In this paper, the author has made an attempt to improve the GASA for MANET to minimize the energy consumption of 0.851 percent and to enhance the network lifetime of 61.35 percent.
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Jianhua Zhang, Liangchen Li, Fredrick Ahenkora Boamah, Dandan Wen, Jiake Li and Dandan Guo
Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of…
Abstract
Purpose
Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.
Design/methodology/approach
The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.
Findings
The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.
Practical implications
This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.
Social implications
The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.
Originality/value
This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.
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J.K. Sykulski, M. Rotaru, M. Sabene and M. Santilli
The paper presents a comparison of performance of a number of selected optimization procedures when applied to solving electromagnetic field problems. The optimization techniques…
Abstract
The paper presents a comparison of performance of a number of selected optimization procedures when applied to solving electromagnetic field problems. The optimization techniques assessed encompass simulated annealing and genetic algorithms, as well as deterministic methods, including the Levenberg‐Marquardt procedure. The comparison is performed using a number of test functions followed by a study of two simple configurations relevant to problems encountered in electromagnetics.
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Binghai Zhou and Qiong Wu
The balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both…
Abstract
Purpose
The balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.
Design/methodology/approach
This research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.
Findings
Theory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.
Research limitations/implications
The additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.
Originality/value
For the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.
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Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed…
Abstract
Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.
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