Comparative analysis of genetic crossover operators in knapsack problem

The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem.Keywords: Genetic Algorithm, Crossover, Heuristic, Arithmetic, Intermediate, Evolutionary Algorith

Comparing efficiencies of genetic crossover operators for one machine total weighted tardiness problem

WOS: 000255320900019In this study, the well-known one machine problem with the performance criterion of minimizing total weighted tardiness is considered. This problem is known to be NP-hard, and consists of one machine and n independent jobs. Each of these jobs has a distinct integer processing time, a distinct integer weighting factor, and a distinct integer due date. The purpose of this problem is to find a sequence of these jobs minimizing the sum of the weighted tardiness. Using benchmarking problems, this study compares performances of eleven genetic crossover operators which have been widely used to solve other types of hard scheduling problems. (c) 2007 Elsevier Inc. All rights reserved

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area

A real-time simulation-based optimisation environment for industrial scheduling

n order to cope with the challenges in industry today, such as changes in product diversity and production volume, manufacturing companies are forced to react more flexibly and swiftly. Furthermore, in order for them to survive in an ever-changing market, they also need to be highly competitive by achieving near optimal efficiency in their operations. Production scheduling is vital to the success of manufacturing systems in industry today, because the near optimal allocation of resources is essential in remaining highly competitive. The overall aim of this study is the advancement of research in manufacturing scheduling through the exploration of more effective approaches to address complex, real-world manufacturing flow shop problems. The methodology used in the thesis is in essence a combination of systems engineering, algorithmic design and empirical experiments using real-world scenarios and data. Particularly, it proposes a new, web services-based, industrial scheduling system framework, called OPTIMISE Scheduling System (OSS), for solving real-world complex scheduling problems. OSS, as implemented on top of a generic web services-based simulation-based optimisation (SBO) platform called OPTIMISE, can support near optimal and real-time production scheduling in a distributed and parallel computing environment. Discrete-event simulation (DES) is used to represent and flexibly cope with complex scheduling problems without making unrealistic assumptions which are the major limitations of existing scheduling methods proposed in the literature. At the same time, the research has gone beyond existing studies of simulation-based scheduling applications, because the OSS has been implemented in a real-world industrial environment at an automotive manufacturer, so that qualitative evaluations and quantitative comparisons of scheduling methods and algorithms can be made with the same framework. Furthermore, in order to be able to adapt to and handle many different types of real-world scheduling problems, a new hybrid meta-heuristic scheduling algorithm that combines priority dispatching rules and genetic encoding is proposed. This combination is demonstrated to be able to handle a wider range of problems or a current scheduling problem that may change over time, due to the flexibility requirements in the real-world. The novel hybrid genetic representation has been demonstrated effective through the evaluation in the real-world scheduling problem using real-world data

Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem

Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address some of the issues, utilizing the existing nurses through an effective work schedule is the best alternative. However, there exists a problem of creating undesirable and non-stable nurse schedules for nurses' shift work. Thus, this research attempts to overcome these challenges by integrating components of a nurse scheduling and rescheduling problem which have normally been addressed separately in previous studies. However, when impromptu schedule changes are required and certain numbers of constraints need to be satisfied, there is a lack of flexibility element in most of scheduling and rescheduling approaches. By embedding the element, this gives a potential platform for enhancing the Evolutionary Algorithm (EA) which has been identified as the solution approach. Therefore, to minimize the constraint violations and make little but attentive changes to a postulated schedule during a disruption, an integrated model of EA with Cuckoo Search (CS) is proposed. A concept of restriction enzyme is adapted in the CS. A total of 11 EA model variants were constructed with three new parent selections, two new crossovers, and a crossover-based retrieval operator, that specifically are theoretical contributions. The proposed EA with Discovery Rate Tournament and Cuckoo Search Restriction Enzyme Point Crossover (DᵣT_CSREP) model emerges as the most effective in producing 100% feasible schedules with the minimum penalty value. Moreover, all tested disruptions were solved successfully through preretrieval and Cuckoo Search Restriction Enzyme Point Retrieval (CSREPᵣ) operators. Consequently, the EA model is able to fulfill nurses' preferences, offer fair on-call delegation, better quality of shift changes for retrieval, and comprehension on the two-way dependency between scheduling and rescheduling by examining the seriousness of disruptions