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Article
Publication date: 26 May 2021

Mostafa Moghimi and Mohammad Ali Beheshtinia

The purpose of this study is to investigate the optimization of the scheduling of production and transportation systems while considering delay time (DT) and environmental…

Abstract

Purpose

The purpose of this study is to investigate the optimization of the scheduling of production and transportation systems while considering delay time (DT) and environmental pollution (EP) concurrently. To this, an integrated multi-site manufacturing process using a cumulative transportation system is investigated. Additionally, a novel multi-society genetic algorithm is developed to reach the best answers.

Design/methodology/approach

A bi-objective model is proposed to optimize the production and transportation process with the objectives of minimizing DT and EP. This is solved by a social dynamic genetic algorithm (SDGA), which is a novel multi-society genetic algorithm, in scenarios of equal and unequal impacts of each objective. The impacts of each objective are calculated by the analytical hierarchical process (AHP) using experts’ opinions. Results are compared by dynamic genetic algorithm and optimum solution results.

Findings

Results clearly depict the efficiency of the proposed algorithm and model in the scheduling of production and transportation systems with the objectives of minimizing DT and EP concurrently. Although SDGA’s performance is acceptable in all cases, in comparison to other genetic algorithms, it needs more process time which is the cost of reaching better answers. Additionally, SDGA had better performance in variable weights of objectives in comparison to itself and other genetic algorithms.

Research limitations/implications

This research is an improvement which allows both society and industry to elevate the levels of their satisfaction while their social responsibilities have been glorified through assuaging the concerns of customers on distribution networks’ emission, competing more efficient and effective in the global market and having the ability to make deliberate decisions far from bias. Additionally, implications of the developed genetic algorithm help directly to the organizations engaged with intelligent production and/or transportation planning which society will be merited indirectly from their outcomes. It also could be utilitarian for organizations that are engaged with small, medium and big data analysis in their processes and want to use more effective and more efficient tools.

Originality/value

Optimization of EP and DT are considered simultaneously in both model and algorithm in this study. Besides, a novel genetic algorithm, SDGA, is proposed. In this multi-society algorithm, each society is focused on a particular objective; however, in one society all the feasible answers will have been integrated and optimization will have been continued.

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Article
Publication date: 10 August 2021

Wan Liu, Zeyu Li, Li Chen, Dexin Zhang and Xiaowei Shao

This paper aims to innovatively propose to improve the efficiency of satellite observation and avoid the waste of satellite resources, a genetic algorithm with entropy…

Abstract

Purpose

This paper aims to innovatively propose to improve the efficiency of satellite observation and avoid the waste of satellite resources, a genetic algorithm with entropy operator (GAE) of synthetic aperture radar (SAR) satellites’ task planning algorithm.

Design/methodology/approach

The GAE abbreviated as GAE introduces the entropy value of each orbit task into the fitness calculation of the genetic algorithm, which makes the orbit with higher entropy value more likely to be selected and participate in the remaining process of the genetic algorithm.

Findings

The simulation result shows that in a condition of the same calculate ability, 85% of the orbital revisit time is unchanged or decreased and 30% is significantly reduced by using the GAE compared with traditional task planning genetic algorithm, which indicates that the GAE can improve the efficiency of satellites’ task planning.

Originality/value

The GAE is an optimization of the traditional genetic algorithm. It combines entropy in thermodynamics with task planning problems. The algorithm considers the whole lifecycle of task planning and gets the desired results. It can greatly improve the efficiency of task planning in observation satellites and shorten the entire task execution time. Then, using the GAE to complete SAR satellites’ task planning is of great significance in reducing satellite operating costs and emergency rescue, which brings certain economic and social benefits.

Details

Aircraft Engineering and Aerospace Technology, vol. 93 no. 7
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 1 February 1999

HASHEM AL‐TABTABAI and ALEX P. ALEX

Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the…

Abstract

Genetic algorithm (GA) is a model of machine learning. The algorithm can be used to find sub‐optimum, if not optimum, solution(s) to a particular problem. It explores the solution space in an intelligent manner to evolve better solutions. The algorithm does not need any specific programming efforts but requires encoding the solution as strings of parameters. The field of application of genetic algorithms has increased dramatically in the last few years. A large variety of possible GA application tools now exist for non‐computer specialists. Complicated problems in a specific optimization domain can be tackled effectively with a very modest knowledge of the theory behind genetic algorithms. This paper reviews the technique briefly and applies it to solve some of the optimization problems addressed in construction management literature. The lessons learned from the application of GA to these problems are discussed. The result of this review is an indication of how the GA can contribute in solving construction‐related optimization problems. A summary of general guidelines to develop solutions using this optimization technique concludes the paper.

Details

Engineering, Construction and Architectural Management, vol. 6 no. 2
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 1 June 2007

Orestes Chouchoulas and Alan Day

Although the idea of linking a shape grammar to a genetic algorithm is not new, this paper proposes a novel way of combining these two elements in order to provide a tool…

Abstract

Although the idea of linking a shape grammar to a genetic algorithm is not new, this paper proposes a novel way of combining these two elements in order to provide a tool that can be used for design exploration. Using a shape grammar for design generation provides a way of creating a range of potential solutions to a design problem which fit with the designer's stylistic agenda. A genetic algorithm can then be used to take these designs and develop them into a much richer set of solutions which can still be recognised as part of the same family. By setting quantifiable targets for design performance, the genetic algorithm can evolve new designs which exhibit the best features of previous generations. The designer is then presented with a wide range of high scoring solutions and can choose which of these to take forward and develop in the conventional manner. The novelty of the proposed approach is in the use of a shape code, which describes the steps that the shape grammar has taken to create each design. The genetic algorithm works on this shape code by applying crossover and mutation in order to create a range of designs that can be tested. The fittest are then selected in order to provide the genetic material for the next generation. A prototype version of such a program, called Shape Evolution, has been developed. In order to test Shape Evolution it has been used to design a range of apartment buildings which are required to meet certain performance criteria.

Details

Open House International, vol. 32 no. 2
Type: Research Article
ISSN: 0168-2601

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Article
Publication date: 21 May 2021

Mohammad Khalilzadeh

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective…

Abstract

Purpose

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources.

Design/methodology/approach

The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time.

Findings

The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon.

Originality/value

There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1726-0531

Keywords

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Article
Publication date: 1 January 2000

SHAMIL NAOUM and ALI HAIDAR

This paper describes the development of a hybrid knowledge base system and genetic algorithms to select the optimum excavating and haulage equipment in opencast mining…

Abstract

This paper describes the development of a hybrid knowledge base system and genetic algorithms to select the optimum excavating and haulage equipment in opencast mining. The knowledge base system selects the equipment in broad categories based on the geological, technical and environmental characteristics of the mine. To further identify the make, size and number of each piece of equipment that minimizes the total cost of the operation, the problem is solved using the genetic algorithms mechanism. Results of four case studies are presented to show the validation of the developed system.

Details

Engineering, Construction and Architectural Management, vol. 7 no. 1
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 1 February 2002

Amar Khoukhi

In the context of systems and cybernetics theory, we present a new general stochastic method of search and optimization of solutions of problems that we have named…

Abstract

In the context of systems and cybernetics theory, we present a new general stochastic method of search and optimization of solutions of problems that we have named Prototyped Genetic Search. Our new method is based mainly on prototype and learning concepts, although it uses concepts of population and evolution just as Evolutionary Algorithms. Moreover, and in order to show the interest of this method and to demonstrate its real potential, we have chosen to apply it on the Job‐Shop Scheduling Problem in the context of the flexible production. This paper is also the opportunity for us to present an other new kind of genetic algorithms, resulting from the integration of the recursivity in the basis functioning of genetic algorithms, and that we have named Recursive Genetic Algorithm.

Details

Kybernetes, vol. 31 no. 1
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 16 October 2007

Bruno 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…

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.

Details

International Journal of Quality & Reliability Management, vol. 24 no. 9
Type: Research Article
ISSN: 0265-671X

Keywords

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Article
Publication date: 26 September 2008

C.Y. Lam, S.L. Chan, W.H. Ip and C.W. Lau

The aim of this paper is to propose a genetic algorithms approach to develop a collaborative supply chain network, i.e. a supply chain network with genetic algorithms

Abstract

Purpose

The aim of this paper is to propose a genetic algorithms approach to develop a collaborative supply chain network, i.e. a supply chain network with genetic algorithms embedded (GA‐SCN), so as to increase the efficiency and effectiveness of a supply chain network.

Design/methodology/approach

The methodologies of the GA‐SCN are illustrated through a case study of a supply chain network of a Hong Kong lamp manufacturing company involving 10 entities, whose roles range from suppliers, purchasers, designers and manufacturers, to sales and distributors. A GA‐SCN is developed according to the information provided by the company, the performance results in the case study are discussed, and the concepts of network analysis are then introduced to analyze the equivalence structure of the developed GA‐SCN.

Findings

The genetic algorithms approach is a suitable approach for developing an efficient and effective supply chain network in terms of shortening the processing time and reducing operating time in the network: the processing time and operating cost are reduced by around 45 percent and 35 percent per order, respectively, in the case study.

Originality/value

This paper is the first known study to apply genetic algorithms for the development of a collaborative supply chain network to increase the competitiveness of a supply chain.

Details

Industrial Management & Data Systems, vol. 108 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

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Article
Publication date: 29 March 2013

Peter Paul Beran, Elisabeth Vinek and Erich Schikuta

The optimization of quality‐of‐service (QoS) aware service selection problems is a crucial issue in both grids and distributed service‐oriented systems. When several…

Abstract

Purpose

The optimization of quality‐of‐service (QoS) aware service selection problems is a crucial issue in both grids and distributed service‐oriented systems. When several implementations per service exist, one has to be selected for each workflow step. This paper aims to address these issues.

Design/methodology/approach

The authors proposed several heuristics with specific focus on blackboard and genetic algorithms. Their applicability and performance has already been assessed for static systems. In order to cover real‐world scenarios, the approaches are required to deal with dynamics of distributed systems.

Findings

The proposed algorithms prove their feasibility in terms of scalability and runtime performance, taking into account their adaptability to system changes.

Research limitations/implications

In this paper, the authors propose a representation of the dynamic aspects of distributed systems and enhance their algorithms to efficiently capture them.

Practical implications

By combining both algorithms, the authors envision a global approach to QoS‐aware service selection applicable to static and dynamic systems.

Originality/value

The authors prove the feasibility of their hybrid approach by deploying the algorithms in a cloud environment (Google App Engine), that allows simulating and evaluating different system configurations.

Details

International Journal of Web Information Systems, vol. 9 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

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