scholarly journals Pseudo-scheduling: A New Approach to the Broadcast Scheduling Problem

2013 ◽  
pp. 93-104
Author(s):  
Shaun N. Joseph ◽  
Lisa C. DiPippo
Keyword(s):  
Scheduling Problem ◽  
Broadcast Scheduling ◽  
New Approach

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

Modeling and Simulation of Holonic Meta-Algorithm in Scheduling Process Using PERT Technique − Optimization in Job Shop Scheduling Problem

2015 ◽  
Vol 760 ◽  
pp. 199-204
Author(s):  
Mircea Gorgoi ◽  
Corneliu Neagu
Keyword(s):  
Objective Function ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
New Approach ◽  
Shop Scheduling ◽  
Technique Optimization ◽  
Optimum Solution

In generally scheduling can be viewed as optimization, bound by sequence and resource constrain and the minimization of the makespan is often used as the criterion. In this paper minimization of the makespan or complete time will be used such as an objective function and not the criterion of the decision. The new approach use heuristic elementary priority dispatch rules as the criterion of the decision. This research purpose a new methodology which use a specific elements of PERT techniques to find the optimum solution. New approach establish a solution's space where are find the all solution of the problem. Determination of the solution's space is realized by a meta-algorithm which take in account all the variant of the solutions of the process.

scholarly journals New Approach Combining Branch and Price with Metaheuristics to Solve Nurse Scheduling Problem

2017 ◽  
Vol 21 (7) ◽  
pp. 1251-1261 ◽  
Author(s):  
Junya Inafune ◽  
◽  
Shinya Watanabe ◽  
Masayoshi Okudera
Keyword(s):  
Scheduling Problem ◽  
Nurse Rostering ◽  
Branch And Price ◽  
Nurse Scheduling ◽  
High Quality ◽  
New Approach ◽  
Nurse Rostering Problem ◽  
Benchmark Instances

This paper presents a new approach combining Branch and Price (B&P) with metaheuristics to derive various high-quality schedules as solutions to a nurse scheduling problem (nurse rostering problem). There are two main features of our approach. The first is the combination of B&P and metaheuristics, and the second is the implementation of an efficient B&P algorithm. Through applying our approach to widely used benchmark instances, the effectiveness of our approach is determined.

A mixed neural-genetic algorithm for the broadcast scheduling problem

2003 ◽  
Vol 2 (2) ◽  
pp. 277-283 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
C. Bousono-Calzon ◽  
A.R. Figueiras-Vidal
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problem ◽  
Broadcast Scheduling

scholarly journals Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment

2019 ◽  
Vol 9 (9) ◽  
pp. 1730 ◽  
Author(s):  
Binh Minh Nguyen ◽  
Huynh Thi Thanh Binh ◽  
Tran The Anh ◽  
Do Bao Son
Keyword(s):  
Task Scheduling ◽  
Completion Time ◽  
Fog Computing ◽  
Operating Cost ◽  
Operating Costs ◽  
Scheduling Problem ◽  
Computing Environment ◽  
Swarm Optimization ◽  
New Approach ◽  
Modified Particle Swarm Optimization

In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

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