An Agent Based Approach to Patient Scheduling Using Experience Based Learning

2010 ◽  
Vol 2 (4) ◽  
pp. 1-15 ◽  
Author(s):  
E. Grace Mary Kanaga ◽  
M. L. Valarmathi ◽  
Juliet A Murali
Keyword(s):  
Processing Time ◽  
Programming Model ◽  
Optimal Solution ◽  
Patient Scheduling ◽  
Agent Based ◽  
Scheduling System ◽  
Experience Based Learning ◽  
Learning Techniques ◽  
Time Required ◽  
Resource Agent

This paper describes an agent based approach to patient scheduling using experience based learning and an integer programming model. The evaluation on different learning techniques shows that the experience based learning (EBL) provides a better solution. The time required to process a particular job is reduced as the experience processed by it increases. The processing time can be calculated with the help of EBL. The main objective of this patient scheduling system is to reduce the waiting time of patient in hospitals and to complete their treatment in minimum required time. The proposed framework is implemented in JADE. In this approach the patients are represented as patient agent (PA) and resources as resource agent (RA). This mathematical model provides an optimal solution. The comparisons of the proposed framework with other scheduling rules shows that an agent based approach to patient scheduling using EBL gives better results.

MULTI-AGENT-BASED COOPERATIVE COEVOLUTIONARY MODEL FOR DYNAMIC SHOP FLOOR RECONFIGURATION CONSIDERING PROCESS PLANNING

2011 ◽  
Vol 10 (01) ◽  
pp. 53-60
Author(s):  
SHUXIA LI ◽  
HONGBO SHAN ◽  
LIPING LIU
Keyword(s):  
Mathematical Programming ◽  
Process Planning ◽  
Programming Model ◽  
Optimal Solution ◽  
Shop Floor ◽  
Prototype System ◽  
Market Demand ◽  
Resource Assignment ◽  
Agent Based ◽  
Multi Agent

In order to react to volatile market demand and uncertain production objectives, distributed manufacturing resources have to be dynamically configured. However, the dynamics of process planning and shop floor information makes shop floor reconfiguration a challenging problem. The shortcomings of the existing research on shop floor reconfiguration are first discussed. Then a multi-agent-based framework for dynamic shop floor reconfiguration is presented, and a mathematical programming model taking process planning into consideration is constructed as well. To coordinate the resource assignment among agents, a cooperative coevolutionary algorithm is also put forward to find the optimal solution of the reconfiguration model. The advantages of the proposed model are using combined multi-agent and mathematical programming method to decompose and optimize the problem of reconfiguration and considering alternative process plans. Furthermore, a prototype system for dynamic shop reconfiguration is developed. The results of this research will help solve the problem of shop floor reconfiguration with complex and dynamic interactive structure.

scholarly journals A High-speed Train Timetable Rescheduling Approach in case of Disturbances

2020 ◽  
Vol 308 ◽  
pp. 02001
Author(s):  
Xinyu Gao
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Optimal Solution ◽  
Computation Time ◽  
Mixed Integer ◽  
Railway Line ◽  
Train Timetable ◽  
Train Operation ◽  
Time Required ◽  
Short Time

This paper from a macroscopic viewpoint develops a train timetable rescheduling approach on a single high-speed railway line under disturbances, i.e. inevitable train delays in the duration of the train operation. A mixed-integer linear programming model is formulated to minimize the arrival delay and the departure delay altogether. The commercial optimization software CPLEX is adopted in an effort to seek the optimal solution in an acceptably short time required in the real-time rescheduling process. The proposed approach is further tested on a real-world case study and the numerical results show that compared with the results obtain by the traditional genetic algorithm, using CPLEX to solve the model can yield better solutions and consume the desired computation time, thereby demonstrating its effectiveness and efficiency.

scholarly journals A Probabilistic Patient Scheduling Model with Time Variable Slots

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Danae Carreras-García ◽  
David Delgado-Gómez ◽  
Enrique Baca-García ◽  
Antonio Artés-Rodriguez
Keyword(s):  
Programming Model ◽  
Waiting Times ◽  
Real Data ◽  
Appointment Scheduling ◽  
Mixed Integer ◽  
Access Time ◽  
Patient Scheduling ◽  
Scheduling System ◽  
Variable Time ◽  
Number Of Patients

One of the current challenges faced by health centers is to reduce the number of patients who do not attend their appointments. The existence of these patients causes the underutilization of the center’s services, which reduces their income and extends patient’s access time. In order to reduce these negative effects, several appointment scheduling systems have been developed. With the recent availability of electronic health records, patient scheduling systems that incorporate the patient’s no-show prediction are being developed. However, the benefits of including a personalized individual variable time slot for each patient in those probabilistic systems have not been yet analyzed. In this article, we propose a scheduling system based on patients’ no-show probabilities with variable time slots and a dynamic priority allocation scheme. The system is based on the solution of a mixed-integer programming model that aims at maximizing the expected profits of the clinic, accounting for first and follow-up visits. We validate our findings by performing an extensive simulation study based on real data and specific scheduling requirements provided by a Spanish hospital. The results suggest potential benefits with the implementation of the proposed allocation system with variable slot times. In particular, the proposed model increases the annual cumulated profit in more than 50% while decreasing the waiting list and waiting times by 30% and 50%, respectively, with respect to the actual appointment scheduling system.

Agent based patient scheduling system

2012 ◽  
Author(s):  
Daniela Bordencea ◽  
Honoriu Valean ◽  
Silviu Folea ◽  
Ancuta Dobircau ◽  
Rares Banut
Keyword(s):  
Patient Scheduling ◽  
Agent Based ◽  
Scheduling System

scholarly journals Wind Farm Cable Connection Layout Optimization with Several Substations

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3615
Author(s):  
Adelaide Cerveira ◽  
Eduardo J. Solteiro Pires ◽  
José Baptista
Keyword(s):  
Energy Production ◽  
Wind Farm ◽  
Programming Model ◽  
Optimal Solution ◽  
Wind Farms ◽  
Green Energy ◽  
Fossil Energy ◽  
Proposed Model ◽  
Short Time ◽  
Cable Connection

Green energy has become a media issue due to climate changes, and consequently, the population has become more aware of pollution. Wind farms are an essential energy production alternative to fossil energy. The incentive to produce wind energy was a government policy some decades ago to decrease carbon emissions. In recent decades, wind farms were formed by a substation and a couple of turbines. Nowadays, wind farms are designed with hundreds of turbines requiring more than one substation. This paper formulates an integer linear programming model to design wind farms’ cable layout with several turbines. The proposed model obtains the optimal solution considering different cable types, infrastructure costs, and energy losses. An additional constraint was considered to limit the number of cables that cross a walkway, i.e., the number of connections between a set of wind turbines and the remaining wind farm. Furthermore, considering a discrete set of possible turbine locations, the model allows identifying those that should be present in the optimal solution, thereby addressing the optimal location of the substation(s) in the wind farm. The paper illustrates solutions and the associated costs of two wind farms, with up to 102 turbines and three substations in the optimal solution, selected among sixteen possible places. The optimal solutions are obtained in a short time.

scholarly journals Allocating Students to Industry Placements Using Integer Programming and Ant Colony Optimisation

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 219
Author(s):  
Dhananjay Thiruvady ◽  
Kerri Morgan ◽  
Susan Bedingfield ◽  
Asef Nazari
Keyword(s):  
Integer Programming ◽  
Gender Equity ◽  
Programming Model ◽  
Original Data ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
Integer Programming Model ◽  
Problem Instances ◽  
Work Integrated Learning ◽  
Time Required

The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more engaged when they are allocated students that meet their industry requirements. In this paper, both an integer programming model and an ant colony optimisation heuristic are proposed, with the aim of automating the allocation of students to industry placements. The emphasis is on maximising student engagement and industry partner satisfaction. As part of the objectives, these methods incorporate diversity in industry sectors for students undertaking multiple placements, gender equity across placement providers, and the provision for partners to rank student selections. The experimental analysis is in two parts: (a) we investigate how the integer programming model performs against manual allocations and (b) the scalability of the IP model is examined. The results show that the IP model easily outperforms the previous manual allocations. Additionally, an artificial dataset is generated which has similar properties to the original data but also includes greater numbers of students and placements to test the scalability of the algorithms. The results show that integer programming is the best option for problem instances consisting of less than 3000 students. When the problem becomes larger, significantly increasing the time required for an IP solution, ant colony optimisation provides a useful alternative as it is always able to find good feasible solutions within short time-frames.

scholarly journals Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Juan-Ignacio Latorre-Biel ◽  
Emilio Jiménez-Macías ◽  
Mercedes Pérez de la Parte ◽  
Julio Blanco-Fernández ◽  
Eduardo Martínez-Cámara
Keyword(s):  
Discrete Event ◽  
Optimal Solution ◽  
Solution Space ◽  
Discrete Systems ◽  
Discrete Control ◽  
Classic Approach ◽  
Event Dynamic ◽  
Time Required ◽  
Computational Resources ◽  
Control Methodology

Artificial intelligence methodologies, as the core of discrete control and decision support systems, have been extensively applied in the industrial production sector. The resulting tools produce excellent results in certain cases; however, the NP-hard nature of many discrete control or decision making problems in the manufacturing area may require unaffordable computational resources, constrained by the limited available time required to obtain a solution. With the purpose of improving the efficiency of a control methodology for discrete systems, based on a simulation-based optimization and the Petri net (PN) model of the real discrete event dynamic system (DEDS), this paper presents a strategy, where a transformation applied to the model allows removing the redundant information to obtain a smaller model containing the same useful information. As a result, faster discrete optimizations can be implemented. This methodology is based on the use of a formalism belonging to the paradigm of the PN for describing DEDS, the disjunctive colored PN. Furthermore, the metaheuristic of genetic algorithms is applied to the search of the best solutions in the solution space. As an illustration of the methodology proposal, its performance is compared with the classic approach on a case study, obtaining faster the optimal solution.

A fuzzy multi-objective programming optimization model for emergency resource dispatching under equitable distribution principle

2021 ◽  
pp. 1-10
Author(s):  
Zhaoping Tang ◽  
Wenda Li ◽  
Shijun Yu ◽  
Jianping Sun
Keyword(s):  
Optimization Model ◽  
Programming Model ◽  
Ideal Point ◽  
Optimal Solution ◽  
Multi Objective ◽  
Emergency Rescue ◽  
Satisfaction Degree ◽  
Multi Objective Programming ◽  
Parameter Interval ◽  
Objective Programming

In the initial stage of emergency rescue for major railway emergencies, there may be insufficient emergency resources. In order to ensure that all the emergency demand points can be effectively and fairly rescued, considering the fuzzy properties of the parameters, such as the resource demand quantity, the dispatching time and the satisfaction degree, the railway emergency resources dispatching optimization model is studied, with multi- demand point, multi-depot, and multi-resource. Based on railway rescue features, it was proposed that the couple number of relief point - emergency point is the key to affect railway rescue cost and efficiency. Under the premise of the maximum satisfaction degree of quantity demanded at all emergency points, a multi-objective programming model is established by maximizing the satisfaction degree of dispatching time and the satisfaction degree of the couple number of relief point - emergency point. Combined with the ideal point method, a restrictive parameter interval method for optimal solution was designed, which can realize the quick seek of Pareto optimal solution. Furthermore, an example is given to verify the feasibility and effectiveness of the method.

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