scholarly journals Nurse Scheduling Using Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Komgrit Leksakul ◽  
Sukrit Phetsawat
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Input Data ◽  
Level Of Service ◽  
Nurse Scheduling ◽  
Random Mutation ◽  
Scheduling Model ◽  
Hospital Staffing ◽  
Service Standards ◽  
The Mathematical Model

This study applied engineering techniques to develop a nurse scheduling model that, while maintaining the highest level of service, simultaneously minimized hospital-staffing costs and equitably distributed overtime pay. In the mathematical model, the objective function was the sum of the overtime payment to all nurses and the standard deviation of the total overtime payment that each nurse received. Input data distributions were analyzed in order to formulate a simulation model to determine the optimal demand for nurses that met the hospital’s service standards. To obtain the optimal nurse schedule with the number of nurses acquired from the simulation model, we proposed a genetic algorithm (GA) with two-point crossover and random mutation. After running the algorithm, we compared the expenses and number of nurses between the existing and our proposed nurse schedules. For January 2013, the nurse schedule obtained by GA could save 12% in staffing expenses per month and 13% in number of nurses when compare with the existing schedule, while more equitably distributing overtime pay between all nurses.

scholarly journals Simulation and Optimization of the VTC200C Machining Center Production Plan

TEM Journal ◽  
2020 ◽  
pp. 1295-1306
Author(s):  
Marian Králik ◽  
Vladimír Jerz
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Input Data ◽  
Simulation Software ◽  
Efficient Production ◽  
Production Plan ◽  
Output Data ◽  
Simulation And Optimization ◽  
Machining Center ◽  
Plant Simulation

The paper describes the use of the Plant Simulation software to create a simulation model of the manufacturing process in the VTC200C machining centre. A genetic algorithm was used to optimize the production process. It is an algorithm that learns itself and looks for the best solution based on input data. The optimization is done based on the assessment of the output data from the simulation model. Based on the results of the optimization, a more efficient production plan was designed in the selected company.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

scholarly journals AGV Brake System Simulation

2019 ◽  
Vol 10 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Daniel Varecha ◽  
Robert Kohar ◽  
Frantisek Brumercik
Keyword(s):  
Mathematical Model ◽  
Input Data ◽  
Initial Conditions ◽  
Brake System ◽  
Disc Brake ◽  
Hydraulic Control ◽  
Stopping Distance ◽  
Braking Force ◽  
Braking Process ◽  
The Mathematical Model

Abstract The article is focused on braking simulation of automated guided vehicle (AGV). The brake system is used with a disc brake and with hydraulic control. In the first step, the formula necessary for braking force at the start of braking is derived. The stopping distance is 1.5 meters. Subsequently, a mathematical model of braking is created into which the formula of the necessary braking force is applied. The mathematical model represents a motion equation that is solved in the software Matlab by an approximation method. Next a simulation is created using Matlab software and the data of simulation are displayed in the graph. The transport speed of the vehicle is 1 〖m.s〗^(-1) and the weight of the vehicle is 6000 kg including load. The aim of this article is to determine the braking time of the device depending from the input data entered, which represent the initial conditions of the braking process.

scholarly journals SCHEDULLING MODEL OF REPLENISHMENT AT SEA FOR STRICKING FORCE UNIT IN SEA OPERATION USING GENETIC ALGORITHM

JOURNAL ASRO ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Aris Tri Ika R ◽  
Benny Sukandari ◽  
Okol Sri Suharyo ◽  
Ayip Rivai Prabowo
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Time Windows ◽  
Lubricating Oil ◽  
Optimal Time ◽  
Routing Problem ◽  
Scheduling Model ◽  
Force Unit ◽  
Replenishment At Sea ◽  
Crossover And Mutation

Navy as a marine core in the defense force is responsible for providing security for realizing stability and security of the country.  At any time there was an invasion of other countries past through sea,  TNI AL must be able to break the enemy resistance line through a sea operation to obtain the sea superiority. But this time the endurance of Striking force Unit at only 7-10 days and required replenishment at sea to maximize the presence in the theater of operations to meet a demand of the logistics: HSD, Freshwater, Lubricating Oil, foodstuffs and amonisi. For the optimal replenishment at sea required scheduling model supporting unit to get the minimum time striking force unit was on node rendezvous. Replenishment at sea scheduling model for striking force unit refers to the problems Vehicle routing problem with time windows using Genetic Algorithms. These wheelbase used is roulette for reproduction, crossover, and mutation of genes. Genetic algorithms have obtained optimum results in the shortest route provisioning scenario uses one supporting unit with a total time of 6.89 days. In scenario two supporting unit with minimal time is 4.97 days. In the scenario, the changing of the node replenishment Genetic Algorithm also get optimal time is 4.97 days with two supporting units. Research continued by changing the parameters of the population, the probability of crossover and mutation that can affect the performance of the genetic algorithm to obtain the solution. Keywords: Genetic Algorithm, Model Scheduling, Striking Force unit

Sign in / Sign up

Export Citation Format

Share Document