Search results

1 – 10 of 215
To view the access options for this content please click here
Article
Publication date: 13 September 2018

Orhan Engin and Batuhan Engin

Hybrid flow shop with multiprocessor task (HFSMT) has received considerable attention in recent years. The purpose of this paper is to consider an HFSMT scheduling under…

Abstract

Purpose

Hybrid flow shop with multiprocessor task (HFSMT) has received considerable attention in recent years. The purpose of this paper is to consider an HFSMT scheduling under the environment of a common time window. The window size and location are considered to be given parameters. The research deals with the criterion of total penalty cost minimization incurred by earliness and tardiness of jobs. In this research, a new memetic algorithm in which a global search algorithm is accompanied with the local search mechanism is developed to solve the HFSMT with jobs having a common time window. The operating parameters of memetic algorithm have an important role on the quality of solution. In this paper, a full factorial experimental design is used to determining the best parameters of memetic algorithm for each problem type. Memetic algorithm is tested using HFSMT problems.

Design/methodology/approach

First, hybrid flow shop scheduling system and hybrid flow shop scheduling with multiprocessor task are defined. The applications of the hybrid flow shop system are explained. Also the background of hybrid flow shop with multiprocessor is given in the introduction. The features of the proposed memetic algorithm are described in Section 2. The experiment results are presented in Section 3.

Findings

Computational experiments show that the proposed new memetic algorithm is an effective and efficient approach for solving the HFSMT under the environment of a common time window.

Originality/value

There is only one study about HFSMT scheduling with time window. This is the first study which added the windows to the jobs in HFSMT problems.

Details

Journal of Enterprise Information Management, vol. 31 no. 6
Type: Research Article
ISSN: 1741-0398

Keywords

Content available
Article
Publication date: 15 December 2017

Maxim A. Dulebenets

The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing…

Abstract

Purpose

The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost.

Design/methodology/approach

A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search.

Findings

Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space.

Research limitations/implications

This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured.

Practical implications

The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases.

Originality/value

The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.

Details

Maritime Business Review, vol. 2 no. 4
Type: Research Article
ISSN: 2397-3757

Keywords

To view the access options for this content please click here
Article
Publication date: 14 August 2017

Pedro Godinho, Luiz Moutinho and Margherita Pagani

The purpose of this study is to propose a measure for earned attention and a model and procedure for the maximization of earned attention by a company over a period of time.

Abstract

Purpose

The purpose of this study is to propose a measure for earned attention and a model and procedure for the maximization of earned attention by a company over a period of time.

Design/methodology/approach

Utility functions are used as the base of the earned attention measure. An evolutionary algorithm – a memetic algorithm – is applied to identify strategies that aim to maximize earned attention. Computational analysis is performed resorting to simulated data, and the memetic algorithm is assessed through the comparison with a standard steepest ascent heuristic.

Findings

The shape of the utility functions considered in the model has a huge impact on the characteristics of the best strategies, with actions focused on increasing a single variable being preferred in case of constant marginal utility, and more balanced strategies having a better performance in the case of decreasing marginal utility. The memetic algorithm is shown to have a much better performance that the steepest ascent procedure.

Originality/value

A new mathematical model for earned attention is proposed, and an approach that has few applications in business problems – a memetic algorithm – is tailored to the model and applied to identify solutions.

Details

Journal of Modelling in Management, vol. 12 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

To view the access options for this content please click here
Article
Publication date: 8 June 2015

Yu Lei, Maoguo Gong, Licheng Jiao and Yi Zuo

The examination timetabling problem is an NP-hard problem. A large number of approaches for this problem are developed to find more appropriate search strategies…

Abstract

Purpose

The examination timetabling problem is an NP-hard problem. A large number of approaches for this problem are developed to find more appropriate search strategies. Hyper-heuristic is a kind of representative methods. In hyper-heuristic, the high-level search is executed to construct heuristic lists by traditional methods (such as Tabu search, variable neighborhoods and so on). The purpose of this paper is to apply the evolutionary strategy instead of traditional methods for high-level search to improve the capability of global search.

Design/methodology/approach

This paper combines hyper-heuristic with evolutionary strategy to solve examination timetabling problems. First, four graph coloring heuristics are employed to construct heuristic lists. Within the evolutionary algorithm framework, the iterative initialization is utilized to improve the number of feasible solutions in the population; meanwhile, the crossover and mutation operators are applied to find potential heuristic lists in the heuristic space (high-level search). At last, two local search methods are combined to optimize the feasible solutions in the solution space (low-level search).

Findings

Experimental results demonstrate that the proposed approach obtains competitive results and outperforms the compared approaches on some benchmark instances.

Originality/value

The contribution of this paper is the development of a framework which combines evolutionary algorithm and hyper-heuristic for examination timetabling problems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

To view the access options for this content please click here
Article
Publication date: 29 March 2011

Huangzhong Pu, Ziyang Zhen and Daobo Wang

Attitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of…

Abstract

Purpose

Attitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.

Design/methodology/approach

Among the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their performance balance; moreover, an evolution mechanism of the best frog is introduced to make the algorithm jump out the local optimum. The modified SFLA is applied to the tuning of the proportional coefficients of pitching and rolling channels of UAV flight control system.

Findings

Simulation of a UAV control system in which the nonlinear model is obtained by the wind tunnel experiment show the rapid dynamic response and high control precision by using the modified SFLA optimized attitude controller, which is better than that of the original SFLA and particle swarm optimization method.

Originality/value

A modification scheme is presented to improve the global searching capability of SFLA. The modified SFLA based intelligent determination method of the UAV flight controller parameters is proposed, in order to improve the attitude control performance of UAV.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 4 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

To view the access options for this content please click here
Article
Publication date: 2 May 2019

Nabil Nahas, Mohamed N. Darghouth, Abdul Qadar Kara and Mustapha Nourelfath

The purpose of this paper is to introduce an efficient algorithm based on a non-linear accepting threshold to solve the redundancy allocation problem (RAP) considering…

Abstract

Purpose

The purpose of this paper is to introduce an efficient algorithm based on a non-linear accepting threshold to solve the redundancy allocation problem (RAP) considering multiple redundancy strategies. In addition to the components reliability, multiple redundancy strategies are simultaneously considered to vary the reliability of the system. The goal is to determine the optimal selection of elements, redundancy levels and redundancy strategy, which maximizes the system reliability under various system-level constraints.

Design/methodology/approach

The mixed RAP considering the use of active and standby components at the subsystem level belongs to the class of NP-hard problems involving selection of elements and redundancy levels, to maximize a specific system performance under a given set of physical and budget constraints. Generally, the authors recourse to meta-heuristic algorithms to solve this type of optimization problem in a reasonable computational time, especially for large-size problems. A non-linear threshold accepting algorithm (NTAA) is developed to solve the tackled optimization problem. Numerical results for test problems from previous research are reported and analyzed to assess the efficiency of the proposed algorithm.

Findings

The comparison with the best solutions obtained in previous studies, namely: genetic algorithm, simulated annealing, memetic algorithm and the particle swarm optimization for 33 different instances of the problem, demonstrated the superiority of the proposed algorithm in finding for all considered instances, a high-quality solution in a minimum computational time.

Research limitations/implications

Considering multiple redundancy strategies helps to achieve higher reliability levels but increases the complexity of the obtained solution leading to infeasible systems in term of physical design. Technological constraints must be integrated into the model to provide a more comprehensive and realistic approach.

Practical implications

Designing high performant systems which meet customer requirements, under different economic and functional constraints is the main challenge faced by the manufacturers. The proposed algorithm aims to provide a superior solution of the reliability optimization problem by considering the possibility to adopt multiple redundancy strategies at the subsystem level in a minimum computational time.

Originality/value

A NTAA is expanded to the RAP considering multiple redundancy strategies at the subsystem level subject to weight and cost constraints. A procedure based on a penalized objective function is developed to encourage the algorithm to explore toward the feasible solutions area. By outperforming well-known solving technique, the NTAA provides a powerful tool to reliability designers of complex systems where different varieties of redundancies can be considered to achieve high-reliability systems.

Details

Journal of Quality in Maintenance Engineering, vol. 25 no. 3
Type: Research Article
ISSN: 1355-2511

Keywords

To view the access options for this content please click here
Article
Publication date: 26 June 2020

Hesam Adarang, Ali Bozorgi-Amiri, Kaveh Khalili-Damghani and Reza Tavakkoli-Moghaddam

This paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust…

Abstract

Purpose

This paper addresses a location-routing problem (LRP) under uncertainty for providing emergency medical services (EMS) during disasters, which is formulated using a robust optimization (RO) approach. The objectives consist of minimizing relief time and the total cost including location costs and the cost of route coverage by the vehicles (ambulances and helicopters).

Design/methodology/approach

A shuffled frog leaping algorithm (SFLA) is developed to solve the problem and the performance is assessed using both the ε-constraint method and NSGA-II algorithm. For a more accurate validation of the proposed algorithm, the four indicators of dispersion measure (DM), mean ideal distance (MID), space measure (SM), and the number of Pareto solutions (NPS) are used.

Findings

The results obtained indicate the efficiency of the proposed algorithm within a proper computation time compared to the CPLEX solver as an exact method.

Research limitations/implications

In this study, the planning horizon is not considered in the model which can affect the value of parameters such as demand. Moreover, the uncertain nature of the other parameters such as traveling time is not incorporated into the model.

Practical implications

The outcomes of this research are helpful for decision-makers for the planning and management of casualty transportation under uncertain environment. The proposed algorithm can obtain acceptable solutions for real-world cases.

Originality/value

A novel robust mixed-integer linear programming (MILP) model is proposed to formulate the problem as a LRP. To solve the problem, two efficient metaheuristic algorithms were developed to determine the optimal values of objectives and decision variables.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. 10 no. 3
Type: Research Article
ISSN: 2042-6747

Keywords

To view the access options for this content please click here
Article
Publication date: 27 November 2020

Bahman Arasteh, Razieh Sadegi and Keyvan Arasteh

Software module clustering is one of the reverse engineering techniques, which is considered to be an effective technique for presenting software architecture and…

Abstract

Purpose

Software module clustering is one of the reverse engineering techniques, which is considered to be an effective technique for presenting software architecture and structural information. The objective of clustering software modules is to achieve minimum coupling among different clusters and create maximum cohesion among the modules of each cluster. Finding the best clustering is considered to be a multi-objective N-P hard optimization-problem, and for solving this problem, different meta-heuristic algorithms have been previously proposed. Achieving higher module lustering quality (MQ), obtaining higher success rate for achieving the best clustering quality and improving convergence speed are the main objectives of this study.

Design/methodology/approach

In this study, a method (Bölen) is proposed for clustering software modules which combines the two algorithms of shuffled frog leaping and genetic algorithm.

Findings

The results of conducted experiments using traditional data sets confirm that the proposed method outperforms the previous methods in terms of convergence speed, module clustering quality and stability of the results.

Originality/value

The study proposes SFLA_GA algorithm for optimizing software module clustering, implementing SFLA algorithm in a discrete form by two operators of the genetic algorithm and achieving the above-mentioned purposes in this study. The aim is to achieve higher performance of the proposed algorithm in comparison with other algorithms.

Details

Data Technologies and Applications, vol. 55 no. 2
Type: Research Article
ISSN: 2514-9288

Keywords

To view the access options for this content please click here
Article
Publication date: 7 April 2015

Ashraf Elazouni, Anas Alghazi and Shokri Z. Selim

The purpose of this paper is to compare the performance of the genetic algorithm (GA), simulate annealing (SA) and shuffled frog-leaping algorithm (SFLA) in solving…

Abstract

Purpose

The purpose of this paper is to compare the performance of the genetic algorithm (GA), simulate annealing (SA) and shuffled frog-leaping algorithm (SFLA) in solving discrete versus continuous-variable optimization problems of the finance-based scheduling. This involves the minimization of the project duration and consequently the time-related cost components of construction contractors including overheads, finance costs and delay penalties.

Design/methodology/approach

The meta-heuristics of the GA, SA and SFLA have been implemented to solve non-deterministic polynomial-time hard (NP-hard) finance-based scheduling problem employing the objective of minimizing the project duration. The traditional problem of generating unfeasible solutions in scheduling problems is adequately tackled in the implementations of the meta-heuristics in this paper.

Findings

The obtained results indicated that the SA outperformed the SFLA and GA in terms of the quality of solutions as well as the computational cost based on the small-size networks of 30 activities, whereas it exhibited the least total duration based on the large-size networks of 120 and 210 activities after prolonged processing time.

Research limitations/implications

From researchers’ perspective, finance-based scheduling is one of the few domain problems which can be formulated as discrete and continuous-variable optimization problems and, thus, can be used by researchers as a test bed to give more insight into the performance of new developments of meta-heuristics in solving discrete and continuous-variable optimization problems.

Practical implications

Finance-based scheduling discrete-variable optimization problem is of high relevance to the practitioners, as it allows schedulers to devise finance-feasible schedules of minimum duration. The minimization of project duration is focal for the minimization of time-related cost components of construction contractors including overheads, finance costs and delay penalties. Moreover, planning for the expedient project completion is a major time-management aspect of construction contractors towards the achievement of the objective of client satisfaction through the expedient delivery of the completed project for clients to start reaping the anticipated benefits.

Social implications

Planning for the expedient project completion is a major time-management aspect of construction contractors towards the achievement of the objective of client satisfaction.

Originality/value

SFLA represents a relatively recent meta-heuristic that proved to be promising, based on its limited number of applications in the literature. This paper is to implement SFLA to solve the discrete-variable optimization problem of the finance-based scheduling and assess its performance by comparing its results against those of the GA and SA.

Details

Journal of Financial Management of Property and Construction, vol. 20 no. 1
Type: Research Article
ISSN: 1366-4387

Keywords

To view the access options for this content please click here
Article
Publication date: 13 June 2019

Samia Ben Amarat and Peng Zong

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning…

Abstract

Purpose

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.

Design/methodology/approach

The major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.

Findings

The research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.

Originality/value

The proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.

Details

Aircraft Engineering and Aerospace Technology, vol. 91 no. 9
Type: Research Article
ISSN: 1748-8842

Keywords

1 – 10 of 215