CHN and Min-Conflict Heuristic to Solve Scheduling Meeting Problems

Bouhouch, Adil; Loqman, Chakir; Qadi, Abderrahim El

doi:10.1007/978-3-319-95104-1_11

Cited by 3 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…availability or preferences of participants. The goal is to map these meetings into empty slots while minimizing the violation of constraints [1]. This approach is not suitable for dynamic environment, in which the list of meetings in a period is not predetermined.…”

Section: Aswin Wibisurya Ikhtiar Faahakhododomentioning

confidence: 99%

“…Several researches have been done in meeting scheduling problem. They either propose a theoretical model to calculate the optimal meeting schedule [1]- [4], or the architecture of a system to acquire the existing schedule and negotiation for a new schedule [5]- [7]. Very few deal with them both, which are both required to create an applied solution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Meeting Scheduling Application using Availability and Priority Attendance Heuristics

Wibisurya,

Faahakhododo

2019

IJEAT

View full text Add to dashboard Cite

Meeting scheduling is a repetitive and time consuming task for many organizations. Emails and electronic calendars has been used to help a meeting host in this process. However, it does not automate the process of searching the optimal time slot. Manual scheduling may result in suboptimal schedule. Therefore, automation is needed for meeting scheduling problem. The purpose of this research is to propose an applied model consisting of both acquiring participants’ existing schedule, and searching for an optimal time slot. Previous studies groups the solution of meeting scheduling into either constraint satisfaction or heuristics approach. Heuristics is more appropriate for a dynamic environment. The heuristics-based model is designed to consider participant availability and participant prioritization. The more participants are available, the better the time is as a candidate for optimum schedule. In the proposed model, the availability of certain key person, experts, or host may carry more weight than normal participant. An Android based application is developed as a prove of concept of the proposed model. Google Calendar API is used in this model to acquire the existing schedule, then each time slot is assigned a score based on availability weighting. The time slot with the highest score is considered the optimal solution. Evaluation is done by simulating the scheduling part for various numbers of meetings and time slots. The result shows that the model is capable of searching the optimal meeting schedule in less than one second for each of the experiment.

show abstract

Section: Aswin Wibisurya Ikhtiar Faahakhododomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Meeting Scheduling Application using Availability and Priority Attendance Heuristics

Wibisurya,

Faahakhododo

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…In this neural network approach, the constraints are encoded in the network topology, biases strengths connection, and problem is formulated as quadratic cost function which is a Lyapunov function. Particularly, a very different approach has been taken investigating Hopfield network with continuous times for solving CSPs, as we can see in [5]- [8] authors propose mapping CSP to a quadratic model and giving appropriate parameters setting to reach an equilibrium point of CHN. In the practice, there are two important problem with approaches based on conventional neural network architectures, The first is that network is partially mitigate the problem of getting stuck in local optimum, the second is due to dynamic Hopfield network which continuously explore the search space and will not stabilize at border 0 or 1, if the same case appear, we get low solution quality or an incomplete assignment of variables.…”

Section: Introductionmentioning

confidence: 99%

“…In the practice, there are two important problem with approaches based on conventional neural network architectures, The first is that network is partially mitigate the problem of getting stuck in local optimum, the second is due to dynamic Hopfield network which continuously explore the search space and will not stabilize at border 0 or 1, if the same case appear, we get low solution quality or an incomplete assignment of variables. In order to improve solution or to complete invalid solution, we propose to use Min-Conflict heuristic [8] after that CHN reached stabilisation. In this paper we extend our previous study by comparing CHN-MNC with Genetic algorithm [9] and Swarm optimisation [10].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of CHN-MNC, GA and PSO for solving constraints satisfaction problems

Bouhouch¹,

Loqman²,

Bennis³

et al. 2019

Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 20

Self Cite

View full text Add to dashboard Cite

Our approach CHN-MNC, based Continuous Hopfield neural network and Min-Conflict heuristic), have proved that is more efficient than using CHN alone to solve Constraints Satisfaction Problem (CSP). In This paper we study the performance of CHN-MNC by comparing it robustness with two evolutionary algorithms. We choose a Genetic Algorithm and Swarm optimisation to performers this study. Some numerical experiments are done over a variety of problems to verify the efficiency and fast convergence of our approach. abstract needs to summarize the content of the paper.

show abstract

A Min-conflict Algorithm for Power Scheduling Problem in a Smart Home Using Battery

Makhadmeh

Khader

Al-Betar

et al. 2020

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

CHN and Min-Conflict Heuristic to Solve Scheduling Meeting Problems

Cited by 3 publications

References 20 publications

Meeting Scheduling Application using Availability and Priority Attendance Heuristics

Meeting Scheduling Application using Availability and Priority Attendance Heuristics

A comparative study of CHN-MNC, GA and PSO for solving constraints satisfaction problems

A Min-conflict Algorithm for Power Scheduling Problem in a Smart Home Using Battery

Contact Info

Product

Resources

About