scholarly journals Collaborative Opportunistic Scheduling in Heterogeneous Networks: A Distributed Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Donglai Sun ◽  
Yang Liu ◽  
Jianhua Li ◽  
Yue Wu
Keyword(s):  
Iterative Algorithm ◽  
Network Model ◽  
Heterogeneous Networks ◽  
Opportunistic Scheduling ◽  
Scheduling Problem ◽  
Decentralized Systems ◽  
Scheduling Scheme ◽  
Distributed Approach ◽  
Total Performance ◽  
Decentralized Mechanism

We consider a collaborative opportunistic scheduling problem in a decentralized network with heterogeneous users. While most related researches focus on solutions for optimizing decentralized systems’ total performance, we proceed in another direction. Two problems are specifically investigated. (1) With heterogenous users having personal demands, is it possible to have it met by designing distributed opportunistic policies? (2) With a decentralized mechanism, how can we prevent selfish behaviors and enforce collaboration? In our research, we first introduce a multiuser network model along with a scheduling problem constrained by individual throughput requirement at each user’s side. An iterative algorithm is then proposed to characterize a solution for the scheduling problem, based on which collaborative opportunistic scheduling scheme is enabled. Properties of the algorithm, including convergence, will be discussed. Furthermore in order to keep the users staying with the collaboration state, an additional punishment strategy is designed. Therefore selfish deviation can be detected and disciplined so that collaboration is enforced. We demonstrate our main findings with both analysis and simulations.

A multi-shop integrated scheduling algorithm with fixed output constraint

2021 ◽  
pp. 1-9
Author(s):  
Yingchun Xia ◽  
Zhiqiang Xie ◽  
Yu Xin ◽  
Xiaowei Zhang
Keyword(s):  
Constraint Programming ◽  
Programming Model ◽  
Scheduling Algorithm ◽  
Processing Parameters ◽  
Scheduling Problem ◽  
Integrated Scheduling ◽  
Scheduling Scheme ◽  
Wide Range ◽  
Constraint Programming Model ◽  
Output Constraint

The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible.

A network model for the rotating workforce scheduling problem

Networks ◽  
1990 ◽  
Vol 20 (1) ◽  
pp. 25-42 ◽  
Author(s):  
Nagraj Balakrishnan ◽  
Richard T. Wong
Keyword(s):  
Network Model ◽  
Scheduling Problem ◽  
Workforce Scheduling

scholarly journals A Distributed Approach for Estimating Battery State-Of-Charge in Solar Farms

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4998 ◽  
Author(s):  
MCarmen Romero-Ternero ◽  
David Oviedo-Olmedo ◽  
Alejandro Carrasco ◽  
Joaquín Luque
Keyword(s):  
Response Time ◽  
Expert Systems ◽  
Bayesian Network ◽  
Network Model ◽  
Multiagent System ◽  
State Of Charge ◽  
Bayesian Network Model ◽  
Distributed Sensors ◽  
Distributed Approach ◽  
Battery State Of Charge

A common problem in solar farms is to predict when accumulators stop working optimally and start losing efficiency. This paper proposes and describes how to use Bayesian networks together with expert systems to predict this moment by using a telecontrol multiagent system for monitoring solar farms with distributed sensors, which was developed in a previous work. To this end, a Bayesian network model and its implementation are proposed. The resulting system meets the requirements of telecontrol systems (reliability, flexibility, and response time), yields a solution for the prediction of lifespan batteries, and provides the multiagent system with autonomous intelligent capabilities and integrated learning.

Research on Multitask Collaborative Scheduling Problem with Heuristic Strategies

2011 ◽  
Vol 66-68 ◽  
pp. 758-763
Author(s):  
Fan Zhang ◽  
Gui Fa Teng ◽  
Jian Bin Ma ◽  
Jie Yao
Keyword(s):  
Scheduling Algorithm ◽  
Scheduling Problem ◽  
Agricultural Machinery ◽  
Start Time ◽  
Farm Machinery ◽  
Scheduling Scheme ◽  
Scheduling Model ◽  
Heuristic Strategies ◽  
Collaborative Scheduling ◽  
Minimal Resource

According to problems existed in the current farm machinery scheduling process, a new farm machinery scheduling scheme is adopted in this dissertation. The collaborative scheduling model of farm machinery is established and multitask collaborative scheduling algorithm is designed through analyzing the differences between Vehicle Scheduling Problem and agricultural machinery scheduling in the dissertation. Earliest Start Time First and minimal resource allocated capacity first strategies are used in the farm machinery scheduling. The algorithm is useful for the case of machinery owner with sufficient farm machinery. The experiment proves that the collaborative scheduling algorithm is more effective than the serial scheduling algorithm.

scholarly journals Real-Time Scheduling of Mixed Model Assembly Line with Large Variety and Low Volume Based on Event-Triggered Simulated Annealing (ETSA)

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chunzhi Cai ◽  
Shulin Kan
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Mixed Model ◽  
Scheduling Problem ◽  
Real Time Scheduling ◽  
Scheduling Scheme ◽  
Time Scheduling ◽  
Low Volume ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

In the contemporary industrial production, multiple resource constraints and uncertainty factors exist widely in the actual job shop. It is particularly important to make a reasonable scheduling scheme in workshop manufacturing. Traditional scheduling research focused on the one-time global optimization of production scheduling before the actual production. The dynamic scheduling problem of the workshop is getting more and more attention. This paper proposed a simulated annealing algorithm to solve the real-time scheduling problem of large variety and low-volume mixed model assembly line. This algorithm obtains three groups of optimal solutions and the optimal scheduling scheme of multiple products, with the shortest product completion time and the lowest cost. Finally, the feasibility and efficiency of the model are proved by the Matlab simulation.

scholarly journals Research on Berth-Quay Crane Joint Scheduling Considering Carbon Emission

2021 ◽  
pp. 1-6
Author(s):  
Rui Yang ◽  
◽  
Junqing Sun
Keyword(s):  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Carbon Emission ◽  
Programming Model ◽  
Container Terminal ◽  
Optimal Scheduling ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Scheduling Scheme ◽  
Elite Strategy

With the increasing awareness of environmental protection, all walks of life a are paying more and more attention to the carbon dioxide emissions brought by their own industries. For the container terminal, a large proportion of carbon emissions come from the fuel consumption of vessels. In this paper, the consideration of carbon emissions is added to the original berth quay crane joint scheduling problem, and the constraints such as vessel preference for berths and quay crane interference are added. A dual-objective nonlinear mixed integer programming model is established to minimize carbon emissions and minimize costs. The model is solved by the Non-Dominated Sorting Genetic Algorithm with Elite Strategy, and the optimal scheduling scheme is obtained. Finally, the calculation examples are verified to prove the effectiveness and practicability of the model and algorithm.

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