scholarly journals A First Fit Type Algorithm for the Coupled Task Scheduling Problem with Unit Execution Time and Two Exact Delays

2021 ◽  
Author(s):  
József Békési ◽  
György Dósa ◽  
Gábor Galambos
Keyword(s):  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Problem ◽  
Type Algorithm

scholarly journals Optimal Cooking Procedure Presentation System for Multiple Recipes and Investigating Its Effect

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 67
Author(s):  
Jin Nakabe ◽  
Teruhiro Mizumoto ◽  
Hirohiko Suwa ◽  
Keiichi Yasumoto
Keyword(s):  
Task Scheduling ◽  
Execution Time ◽  
Cooking Time ◽  
Sequential Optimization ◽  
Task Graph ◽  
Scheduling Problem ◽  
Cooking Skill ◽  
The Difference ◽  
Increasing Demand ◽  
Reduce Execution Time

As the number of users who cook their own food increases, there is increasing demand for an optimal cooking procedure for multiple dishes, but the optimal cooking procedure varies from user to user due to the difference of each user’s cooking skill and environment. In this paper, we propose a system of presenting optimal cooking procedures that enables parallel cooking of multiple recipes. We formulate the problem of deciding optimal cooking procedures as a task scheduling problem by creating a task graph for each recipe. To reduce execution time, we propose two extensions to the preprocessing and bounding operation of PDF/IHS, a sequential optimization algorithm for the task scheduling problem, each taking into account the cooking characteristics. We confirmed that the proposed algorithm can reduce execution time by up to 44% compared to the base PDF/IHS, and increase execution time by about 900 times even when the number of required searches increases by 10,000 times. In addition, through the experiment with three recipes for 10 participants each, it was confirmed that by following the optimal cooking procedure for a certain menu, the actual cooking time was reduced by up to 13 min (14.8% of the time when users cooked freely) compared to the time when users cooked freely.

scholarly journals AN EXACT SOLUTION FOR THE RESOURCE-CONSTRAINED CONSTRUCTION SCHEDULING PROBLEM

2016 ◽  
Vol 8 (2) ◽  
pp. 71-78
Author(s):  
Bartłomiej Sroka ◽  
Elżbieta Radziszewska-Zielina
Keyword(s):  
Execution Time ◽  
Critical Path ◽  
Scheduling Problem ◽  
Resource Constrained ◽  
Building Projects ◽  
Construction Scheduling ◽  
Program Sample ◽  
Resource Constrained Scheduling Problem ◽  
The Cost ◽  
Exponential Complexity

Reduced time and, by the same token, the cost of the project is a crucial factor in contemporary construction. This article presents a method for the exact optimisation of a resource-constrained scheduling problem. Based on the Critical Path Method, graph theory and linear programming, an algorithm was developed and the FROPT program was written in Matlab to minimise the execution time of the task. By using the newly-created program, sample networks were calculated and the results were compared with results obtained by using the MS Project scheduling program (using approximation algorithm). The execution time obtained by using FROPT were on average 10% shorter than those obtained using MS Project. In selected cases the improvement in execution time reached 25%. A deterministic approach to the problem may shorten planned project times and bring financial benefits. Due to the exponential complexity of the algorithm, it is most useful in solving small or highly coherent networks. The algorithm and program may result in benefits not offered by commercial software for planners of building projects.

scholarly journals Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4508
Author(s):  
Xin Li ◽  
Liangyuan Wang ◽  
Jemal H. Abawajy ◽  
Xiaolin Qin ◽  
Giovanni Pau ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Task Scheduling ◽  
Execution Time ◽  
Data Centers ◽  
Big Data Analysis ◽  
Data Locality ◽  
Data Migration ◽  
Task Execution ◽  
Task Execution Time

Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.

scholarly journals InterRC: An Inter-Resources Collaboration Heuristic for Scheduling Independent Tasks on Heterogeneous Distributed Environments

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 179-188
Author(s):  
Abdelhamid Khiat ◽  
Abdelkamel Tari
Keyword(s):  
Task Scheduling ◽  
Initial Solution ◽  
Distributed Environments ◽  
Scheduling Problem ◽  
Reasonable Time ◽  
Independent Task ◽  
Np Hard Problem ◽  
Computing Environments ◽  
Scheduling Heuristic ◽  
Independent Tasks

The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms.

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