scholarly journals A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Atefeh Amindoust ◽  
Milad Asadpour ◽  
Samineh Shirmohammadi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hybrid Genetic Algorithm ◽  
Real Case ◽  
Scheduling Problem ◽  
Nurse Scheduling ◽  
Proposed Model ◽  
The World ◽  
New Situation

Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it.

A Game Theory Approach for Multiple Design Tasks Schedule

2010 ◽  
Vol 26-28 ◽  
pp. 163-166
Author(s):  
Guo Hai Zhang ◽  
Guang Hui Zhou ◽  
Xue Qun Su
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Game Theory ◽  
Solution Algorithm ◽  
Optimal Scheduling ◽  
Theory Approach ◽  
Scheduling Problem ◽  
Tasks Scheduling ◽  
Numerical Case Study

This paper presents a new kind of scheduling solution for multiple design tasks in networked developing environments. The main contributions of this study can be focused on three points: The first is to distinguish the concepts and contents of the task scheduling in the networked developing environments. The second is to construct a game-theory mathematical model to deal with this new multiple design tasks scheduling problem. In the presented mathematical model, the players, strategies and payoff are given separately. Therefore, obtaining the optimal scheduling results is determined by the Nash equilibrium (NE) point of this game. In order to find the NE point, a genetic algorithm (GA)-based solution algorithm to solve this mathematical model is proposed. Finally, a numerical case study is presented to demonstrate the feasibility of the methods.

scholarly journals A Hybrid Genetic Algorithm for Satellite Image Downlink Scheduling Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bingyu Song ◽  
Feng Yao ◽  
Yuning Chen ◽  
Yingguo Chen ◽  
Yingwu Chen
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Satellite Image ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Satellite Mission ◽  
Downlink Scheduling ◽  
The Given

The satellite image downlink scheduling problem (SIDSP) is included in satellite mission planning as an important part. A customer demand is finished only if the corresponding images are eventually downloaded. Due to the growing customer demands and the limited ground resources, SIDSP is an oversubscribed scheduling problem. In this paper, we investigate SIDSP with the case study of China’s commercial remote sensing satellite constellation (SuperView-1) and exploit the serial scheduling scheme for solving it. The idea is first determining a permutation of the downlink requests and then producing a schedule from the given ordered requests. A schedule generation algorithm (SGA) is proposed to assign the downlink time window for each scheduled request according to a given request permutation. A hybrid genetic algorithm (HGA) combined with neighborhood search is proposed to optimize the downlink request permutation with the purpose of maximizing the utility function. Experimental results on six groups of instances with different density demonstrate the effectiveness of the proposed approach.

A Hybrid Genetic Algorithm for Production Capacity Allocation Problem with Multiple Regional Demands in Supply Chain

2014 ◽  
Vol 945-949 ◽  
pp. 3107-3111
Author(s):  
Zhen Wang ◽  
Lei Huang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Supply Chain ◽  
Hybrid Genetic Algorithm ◽  
Production Capacity ◽  
Experimental Tests ◽  
Capacity Allocation ◽  
Allocation Problem ◽  
Proposed Model

Concentrating on the supplier with limited production capacity in supply chain, this paper established a mathematical model for production capacity allocation problem with consideration of multiple regional demands. The genetic algorithm is employed as solution mainframe in which a heuristics rule is developed to initiate the population and an elite pool is adopted to store those solutions with outstanding fitness values. The experimental tests show that the proposed model and algorithm are feasible and effective.

A Hybrid Genetic Algorithm for Solving a Type of Scheduling Problem

2013 ◽  
Vol 380-384 ◽  
pp. 1710-1715
Author(s):  
Meng Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Scheduling Problem ◽  
Tasks Scheduling ◽  
General Mathematical Model ◽  
Simulation Results

Genetic algorithm is the most widely used and successful bionic optimization algorithm. In this paper we will discuss the tasks scheduling problem on equipments, establish a general mathematical model and put forward a hybrid genetic algorithm to solve this problem. The simulation results show the effectiveness of the hybrid genetic algorithm.

scholarly journals A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Wenxiang Xu ◽  
Shunsheng Guo ◽  
Xixing Li ◽  
Chen Guo ◽  
Rui Wu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Dynamic Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Intelligent Manufacturing ◽  
Automated Guided Vehicles ◽  
Scheduling Problem ◽  
Scheduling Model ◽  
Intelligent Logistics ◽  
Simultaneous Scheduling

Aiming at the logistics dynamic scheduling problem in an intelligent manufacturing workshop (IMW), an intelligent logistics scheduling model and response method with Automated Guided Vehicles (AGVs) based on the mode of “request-scheduling-response” were proposed, and they were integrated with Internet of Things (IoT) to meet the demands of dynamic and real time. Correspondingly, a mathematical model was developed and integrated with a double-level hybrid genetic algorithm and ant colony optimization (DLH-GA-ACO) to minimize the finish time with the minimum AGVs and limited time. The mathematical model optimized the logistics scheduling process on two dimensions which include the sequence of tasks assigned to an AGV and the matching relation between transfer tasks and AGVs (AGV-task). The effectiveness of the model was verified by a set of experiments, and comparison among DLH-GA-ACO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO), and tabu search algorithm (TSA) was performed. In the experiments, the DLH-GA-ACO ran in a distributed environment for a faster computing speed. According to the comparisons, the superiority and effectiveness of DLH-GA-ACO on dynamic simultaneous scheduling problem were proved and the intelligent logistics scheduling model was also proved to be an effective model.

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