Immune Based Evolutionary Algorithm for Determining the Optimal Sequence of Multiple Disinfection Operations

Hsieh, Yi-Chih; Lee, Pei-Ju; You, Peng-Sheng

doi:10.24200/sci.2018.20324

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…A multi‐objective problem, in the form of a bi‐objective grid patrol routing problem, is introduced by Hsieh et al. ( 2015 ). Their model includes both the objectives of cost minimization and maximum route coverage.…”

Section: Patrol Routingmentioning

confidence: 99%

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Safe and secure vehicle routing: a survey on minimization of risk exposure

Fröhlich

Gansterer

Doerner

2022

Int Trans Operational Res

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Safe and secure vehicle routing problems refer to the transportation of dangerous (e.g., flammable liquids) or valuable goods (e.g., cash), the surveillance of streets (e.g., police patrols) or other areas (e.g., those within a factory or building), and the response to sudden incidents (e.g., robberies or street disruptions). It thus covers a multitude of models and methods with each having its own objective and constraints, such as unpredictability or risk. We review and classify literature in this field and thereby identify a starting point for researchers in this evolving and practically relevant field. Our study reveals that there are 82 articles that cover aspects related to safe and secure routing, a majority of which were published in the last five years. We classify the articles into five main categories: (i) transportation of hazardous materials, (ii) patrol routing, (iii) cash‐in‐transit, (iv) dissimilar routing problems, and (v) modeling of multi‐graphs. Categories (i)–(iv) elaborate on the problem studied, while (v) provides a general concept based on road network characteristics most commonly found in safe and secure routing problems. Relevant methods and instances, along with their similarities and dissimilarities, have also been discussed in the paper. Furthermore, specific problem characteristics and future research directions are identified.

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Section: Patrol Routingmentioning

confidence: 99%

“… Hsieh et al. ( 2015 ): A 8x8‐grid for patrol routing with 16 nodes to be patrolled between 1 and 3 times is examined. Keskin et al.…”

Section: Instancesmentioning

confidence: 99%

Safe and secure vehicle routing: a survey on minimization of risk exposure

Fröhlich

Gansterer

Doerner

2022

Int Trans Operational Res

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“…In a Genetic Algorithm, every individual in the population represents a candidate solution to the designated problem. The Genetic Algorithm changes a population of individuals by using several genetic functions such as selection, crossover, and mutation [33] [34]. Genetic Algorithm is a wrapper method which evaluates every composition of parameter features using machine learning performance as the criteria of evaluation.The genetic algorithm approach is acceptable for various types of solving solutions such as optjmization and calls for scheduling [35].…”

Section: Feature Selection Dan Modellingmentioning

confidence: 99%

Development of Path Loss Prediction Model using Feature Selection-Machine Learning Approach

Alfaresi¹,

Nawawi²,

Suprapto³

2022

IJACSA

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Wireless network planning requires accurate coverage predictions to get good quality. The path loss accurate model requires a flexible model for each area including land and water. The purpose of this research is to develop a Cost-Hatta model that can be applied to the mixed land-water area. The approach used of this research is the three methods of feature selection of machine learning. The first stage of the research was the collection of field data. The measurement data included system, weather, and geographical parameters. The next stage was feature selection to obtain the best composition of features for the development of the model. The feature selection methods used were Univariate FS, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). After obtaining the best features from each method, the next stage was to form a model using four machine learning algorithms, namely Random Forest Regression (RF), Deep Neural Network (DNN), K-Nearest Neighbor Regression (KNN), and Support Vector Regression (SVR). The results of the improvements to the path loss prediction model were tested using the evaluation parameters of Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). The results of the testing showed that the improved Cost-Hatta model using the proposed Univariate-RF combination produced a very small RMSE value of 1.52. This indicates that the proposed model framework is highly suitable to be used in a mixed land-water area.

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“…Specifically, IA uses the memory mechanism to delete excessively similar solutions, although they are excellent in the objective value; hence, the variety of solutions can be kept to improve the objective. Consequently, IA could have a higher probability to outperform GA in solving various optimization problems (Hsieh et al 31 and Jiang et al 30 ). In this study, we adopt IA to solve the PDP-LT. We also provide, discuss, and compare the numerical results of IA with those of GA and PSO.…”

Section: Approachesmentioning

confidence: 99%

Scheduling the periodic delivery of liquefied petroleum gas tank with time window by using artificial intelligence approaches: An example in Taiwan

2021

Self Cite

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Introduction In Taiwan, liquefied petroleum gas tank users have to call a gas company to deliver a full liquefied petroleum gas tank when their tank is out of gas. The calls usually congest in the cooking time and the customers have to wait for a long time for a full liquefied petroleum gas tank. Additionally, allocating manpower is difficult for the gas company. Objectives A strategy of periodic delivery for gas companies was presented to deliver liquefied petroleum gas tanks in advance and charge the gas fee based on the weight of returned tanks. Additionally, a new encoding scheme was proposed and embedded into three evolutionary algorithms to solve the nondeterministic polynomial-hard problem. The objective of the problem is to minimize the total traveling distance of the vehicle such that the delivery efficiency of tanks increases and the waiting time of customer decreases. Methods A new encoding scheme was presented to convert any random sequence of integers into a solution of the problem and embedded into three evolutionary algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, to solve the delivery problem. Additionally, the encoding scheme can be used to different frequency types of demand based on customers’ requests. Results Numerical results, including a practical example in Yunlin, Taiwan, were provided to show that the adopted approaches can significantly improve the efficiency of delivery. Conclusions The periodic delivery strategy and the new encoding scheme can effectively solve the practical problem of liquefied petroleum gas tank in Taiwan.

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Immune Based Evolutionary Algorithm for Determining the Optimal Sequence of Multiple Disinfection Operations

Cited by 6 publications

References 27 publications

Safe and secure vehicle routing: a survey on minimization of risk exposure

Safe and secure vehicle routing: a survey on minimization of risk exposure

Development of Path Loss Prediction Model using Feature Selection-Machine Learning Approach

Scheduling the periodic delivery of liquefied petroleum gas tank with time window by using artificial intelligence approaches: An example in Taiwan

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