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1 – 10 of over 1000
Article
Publication date: 20 September 2018

Parminder Singh Kang and Rajbir Singh Bhatti

Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this…

Abstract

Purpose

Continuous process improvement is a hard problem, especially in high variety/low volume environments due to the complex interrelationships between processes. The purpose of this paper is to address the process improvement issues by simultaneously investigating the job sequencing and buffer size optimization problems.

Design/methodology/approach

This paper proposes a continuous process improvement implementation framework using a modified genetic algorithm (GA) and discrete event simulation to achieve multi-objective optimization. The proposed combinatorial optimization module combines the problem of job sequencing and buffer size optimization under a generic process improvement framework, where lead time and total inventory holding cost are used as two combinatorial optimization objectives. The proposed approach uses the discrete event simulation to mimic the manufacturing environment, the constraints imposed by the real environment and the different levels of variability associated with the resources.

Findings

Compared to existing evolutionary algorithm-based methods, the proposed framework considers the interrelationship between succeeding and preceding processes and the variability induced by both job sequence and buffer size problems on each other. A computational analysis shows significant improvement by applying the proposed framework.

Originality/value

Significant body of work exists in the area of continuous process improvement, discrete event simulation and GAs, a little work has been found where GAs and discrete event simulation are used together to implement continuous process improvement as an iterative approach. Also, a modified GA simultaneously addresses the job sequencing and buffer size optimization problems by considering the interrelationships and the effect of variability due to both on each other.

Details

Business Process Management Journal, vol. 25 no. 5
Type: Research Article
ISSN: 1463-7154

Keywords

Article
Publication date: 18 March 2019

Roland Eichardt, Daniel Strohmeier, Alexander Hunold, René Machts, Jens Haueisen, Gregor Oelsner, Christian B. Schmidt, Volkmar Schultze, Ronny Stolz and Uwe Graichen

The purpose of this paper is to present a simulation study using a model of a new optically pumped magnetometer sensor for application in the field of magnetoencephalography. The…

Abstract

Purpose

The purpose of this paper is to present a simulation study using a model of a new optically pumped magnetometer sensor for application in the field of magnetoencephalography. The effects of sensor distance and orientation on the measurement information and the sensitivity to neuronal sources are investigated. Further, this paper uses a combinatorial optimization approach for sensor placement to measure spontaneous activity in the region of the occipital cortex.

Design/methodology/approach

This paper studies the effects of sensor distance and orientation on sensitivity to cortical sources and measurement information. A three-compartment model of the head, using the boundary element method, is applied. For sensor setup optimization, a combinatorial optimization scheme is developed.

Findings

The sensor distance to sources considerably affects the sensitivity and the retrieved information. A specific arrangement of four sensors for measuring spontaneous activity over the occipital part of the head is optimized by effectively avoiding position conflicts.

Research limitations/implications

Individual head models, as well as more detailed noise and signal models, will increase the significance for specific-use cases in future studies.

Originality/value

Effects of sensor distance and orientation are specifically evaluated for a new optically pumped magnetometer. A discrete optimization scheme for sensor optimization is introduced. The presented methodology is applicable for other sensor characterization and optimization problems. The findings contribute significantly to the development of new sensors.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 38 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 8 February 2019

Pengpeng Zhi, Yonghua Li, Bingzhi Chen, Meng Li and Guannan Liu

In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but…

Abstract

Purpose

In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but also reduces the fitting accuracy of the response surface. In addition, the uncertainty of the optimal variables and their boundary conditions makes the optimal solution difficult to obtain. The purpose of this paper is to propose a method of fuzzy optimization design-based multi-level response surface to deal with the problem.

Design/methodology/approach

The main optimal variables are determined by Monte Carlo simulation, and are classified into four levels according to their sensitivity. The linear membership function and the optimal level cut set method are applied to deal with the uncertainties of optimal variables and their boundary conditions, as well as the non-fuzzy processing is carried out. Based on this, the response surface function of the first-level design variables is established based on the design of experiments. A combinatorial optimization algorithm is developed to compute the optimal solution of the response surface function and bring the optimal solution into the calculation of the next level response surface, and so on. The objective value of the fourth-level response surface is an optimal solution under the optimal design variables combination.

Findings

The results show that the proposed method is superior to the traditional method in computational efficiency and accuracy, and improves 50.7 and 5.3 percent, respectively.

Originality/value

Most of the previous work on optimization was based on single-level response surface and single optimization algorithm, without considering the uncertainty of design variables. There are very few studies which discuss the optimization efficiency and accuracy of multiple design variables. This research illustrates the importance of uncertainty factors and hierarchical surrogate models for multi-variable optimization design.

Details

International Journal of Structural Integrity, vol. 10 no. 2
Type: Research Article
ISSN: 1757-9864

Keywords

Article
Publication date: 21 August 2009

Jelmer Marinus van Ast, Robert Babuška and Bart De Schutter

The purpose of this paper is to propose a novel ant colony optimization (ACO) approach to optimal control. The standard ACO algorithms have proven to be very powerful optimization

Abstract

Purpose

The purpose of this paper is to propose a novel ant colony optimization (ACO) approach to optimal control. The standard ACO algorithms have proven to be very powerful optimization metaheuristic for combinatorial optimization problems. They have been demonstrated to work well when applied to various nondeterministic polynomial‐complete problems, such as the travelling salesman problem. In this paper, ACO is reformulated as a model‐free learning algorithm and its properties are discussed.

Design/methodology/approach

First, it is described how quantizing the state space of a dynamic system introduces stochasticity in the state transitions and transforms the optimal control problem into a stochastic combinatorial optimization problem, motivating the ACO approach. The algorithm is presented and is applied to the time‐optimal swing‐up and stabilization of an underactuated pendulum. In particular, the effect of different numbers of ants on the performance of the algorithm is studied.

Findings

The simulations show that the algorithm finds good control policies reasonably fast. An increasing number of ants results in increasingly better policies. The simulations also show that although the policy converges, the ants keep on exploring the state space thereby capable of adapting to variations in the system dynamics.

Research limitations/implications

This paper introduces a novel ACO approach to optimal control and as such marks the starting point for more research of its properties. In particular, quantization issues must be studied in relation to the performance of the algorithm.

Originality/value

The paper presented is original as it presents the first application of ACO to optimal control problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 2 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 7 July 2020

Fang Yan, Kai Chen and Manjing Xu

This paper studied a bid generation problem in combinatorial transportation auctions that considered in-vehicle consolidations. The purpose of this paper seeks to establish mixed…

Abstract

Purpose

This paper studied a bid generation problem in combinatorial transportation auctions that considered in-vehicle consolidations. The purpose of this paper seeks to establish mixed integer programming to the most profitable transportation task packages.

Design/methodology/approach

The authors proposes a mathematical model to identify the most profitable transportation task packages under vehicle capacity, flow balance and in-vehicle consolidation operational constraints, after which a two-phase heuristic algorithm was designed to solve the proposed model. In the first phase, a method was defined to compute bundle synergy, which was then combined with particle swarm optimization (PSO) to determine a satisfactory task package, and in the second phase, the PSO was adopted to program vehicle routings that considered in-vehicle consolidation.

Findings

Three numerical examples were given to analyze the effects of the proposed model and method, with the first two small-scale examples coming from the same data base and the third being a larger scale example. The results showed that: (1) the proposed model was able to find a satisfactory solution for the three numerical examples; (2) the computation time was significantly shorter than the accurate algorithm and (3) considering in-vehicle consolidations operations could increase the carrier profits.

Originality/value

The highlights of this paper are summarized as following: (1) it considers in-vehicle consolidation when generating bids to maximize profits; (2) it simultaneously identifies the most valuable lane packages and reconstructs vehicle routes and (3) proposes a simple but effective synergy-based method to solve the model.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 33 no. 2
Type: Research Article
ISSN: 1355-5855

Keywords

Article
Publication date: 1 October 2006

Manju Agarwal and Rashika Gupta

Conceiving reliable systems is a strategic issue for any industrial society for its economical and technical development. This paper aims to focus on solving highly constrained…

Abstract

Purpose

Conceiving reliable systems is a strategic issue for any industrial society for its economical and technical development. This paper aims to focus on solving highly constrained redundancy optimization problems in complex systems.

Design/methodology/approach

Genetic algorithms (GAs), one of the metaheuristic techniques, have been used and a dynamic adaptive penalty strategy is proposed, which makes use of feedback obtained during the search along with a dynamic distance metric and helps the algorithm to search efficiently for final, optimal or near optimal solution.

Findings

The effectiveness of the adaptive penalty function is studied and shown graphically on the solution quality as well as the speed of evolution convergence for several highly constrained problems. The investigations show that this approach can be powerful and robust for problems with large search space, even of size 1017, and difficult‐to‐satisfy constraints.

Practical implications

The results obtained in this paper would be applicable on designing highly reliable systems meeting the requirement of today's society. Moreover, an important advantage of applying GA is that it generates several good solutions (mostly optimal or near optimal) providing a lot of flexibility to decision makers. As such, the paper would be of interest and importance to the system designers, reliability practitioners, as well as to the researchers in academia, business and industry. The paper would have wide applications in the fields of electronics design, telecommunications, computer systems, power systems etc.

Originality/value

Genetic algorithms have been recently used in combinatorial optimization approaches to reliable design, mainly for series‐parallel systems. This paper presents a GA for parallel redundancy optimization problem in complex systems.

Details

Journal of Quality in Maintenance Engineering, vol. 12 no. 4
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 1 August 2000

D.A. Manolas, I. Borchers and D.T. Tsahalis

Active noise control (ANC) became in the last decade a very popular technique for controlling low‐frequency noise. The increase in its popularity was a consequence of the rapid…

Abstract

Active noise control (ANC) became in the last decade a very popular technique for controlling low‐frequency noise. The increase in its popularity was a consequence of the rapid development in the fields of computers in general, and more specifically in digital signal processing boards. ANC systems are application specific and therefore they should be optimally designed for each application. Even though the physical background of the ANC systems is well‐known and understood, tools for the optimization of the sensor and actuator configurations of the ANC system based on classical optimization methods do not perform as required. This is due to the nature of the problem that allows the calculation of the effect of the ANC system only when the sensor and actuator configurations are specified. An additional difficulty in this problem is that the sensor and the actuator configurations cannot be optimized independently, since the effect of the ANC system is directly involved in the combined sensor and actuator configuration. For the solution of this problem several intelligent techniques were applied. In this paper the successful application of a genetic algorithm, an optimization technique that belongs to the broad class of evolutionary algorithms, is presented.

Details

Engineering Computations, vol. 17 no. 5
Type: Research Article
ISSN: 0264-4401

Keywords

Book part
Publication date: 27 February 2009

Manuel Tarrazo

In this study, we analyze the power of the individual return-to-volatility security performance heuristic (ri/stdi) to simplify the identification of securities to buy and…

Abstract

In this study, we analyze the power of the individual return-to-volatility security performance heuristic (ri/stdi) to simplify the identification of securities to buy and, consequently, to form the optimal no short sales mean–variance portfolios. The heuristic ri/stdi is powerful enough to identify the long and shorts sets. This is due to the positive definiteness of the variance–covariance matrix – the key is to use the heuristic sequentially. At the investor level, the heuristic helps investors to decide what securities to consider first. At the portfolio level, the heuristic may help us find out whether it is a good idea to invest in equity to begin with. Our research may also help to integrate individual security analysis into portfolio optimization through improved security rankings.

Details

Research in Finance
Type: Book
ISBN: 978-1-84855-447-4

Article
Publication date: 13 June 2016

Qingzheng Xu, Na Wang and Lei Wang

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum…

Abstract

Purpose

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum (COOBBO) algorithm.

Design/methodology/approach

The improvement measures tested in this paper include different initialization approaches, crossover approaches, local optimization approaches, and greedy approaches. Eight well-known traveling salesman problems (TSP) are employed for performance verification. Four comparison criteria are recoded and compared to analyze the contribution of each modified method.

Findings

Experiment results illustrate that the combination model of “25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy” may be the best choice of all when considering both the overall algorithm performance and computation overhead.

Originality/value

When solving TSP with varying scales, these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees. And an appropriate combination model may make the fullest possible contribution.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 9 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 24 June 2020

Sergey Lupuleac, Tatiana Pogarskaia, Maria Churilova, Michael Kokkolaras and Elodie Bonhomme

The authors consider the problem of optimizing temporary fastener patterns in aircraft assembly. Minimizing the number of fasteners while maintaining final product quality is one…

Abstract

Purpose

The authors consider the problem of optimizing temporary fastener patterns in aircraft assembly. Minimizing the number of fasteners while maintaining final product quality is one of the key enablers for intensifying production in the aerospace industry. The purpose of this study is to formulate the fastener pattern optimization problem and compare different solving approaches on both test benchmarks and rear wing-to-fuselage assembly of an Airbus A350-900.

Design/methodology/approach

The first considered algorithm is based on a local exhaustive search. It is proved to be efficient and reliable but requires much computational effort. Secondly, the Mesh Adaptive Direct Search (MADS) implemented in NOMAD software (Nonlinear Optimization by Mesh Adaptive Direct Search) is used to apply the powerful mathematical machinery of surrogate modeling and associated optimization strategy. In addition, another popular optimization algorithm called simulated annealing (SA) was implemented. Since a single fastener pattern must be used for the entire aircraft series, cross-validation of obtained results was applied. The available measured initial gaps from 340 different aircraft of the A350-900 series were used.

Findings

The results indicated that SA cannot be applicable as its random character does not provide repeatable results and requires tens of runs for any optimization analysis. Both local variations (LV) method and MADS have proved to be appropriate as they improved the existing fastener pattern for all available gaps. The modification of the MADS' search step was performed to exploit all the information the authors have about the problem.

Originality/value

The paper presents deterministic and probabilistic optimization problem formulations and considers three different approaches for their solution. The existing fastener pattern was improved.

Details

Assembly Automation, vol. 40 no. 5
Type: Research Article
ISSN: 0144-5154

Keywords

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