Reducing network operation costs by optimal resource planning

2009 ◽  
Author(s):  
A. Berg ◽  
H.-J. Haubrich
Keyword(s):  
Resource Planning ◽  
Operation Costs ◽  
Network Operation ◽  
Optimal Resource

scholarly journals Evaluation of Simulation-Based Optimization in Grafting Labor Allocation

2018 ◽  
Vol 34 (3) ◽  
pp. 479-489 ◽  
Author(s):  
Sara Masoud ◽  
Young-Jun Son ◽  
Chieri Kubota ◽  
Russell Tronstad
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Simulation Models ◽  
Resource Planning ◽  
Labor Management ◽  
Labor Allocation ◽  
Event Simulation ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Optimal Resource

Abstract.Vegetable grafting is a labor-intensive operation with many management decisions. Labor management and resource planning are critical allocations in grafting nurseries, yet optimization is challenging due to the dynamic nature of workers’ performance in vegetable seedling propagation. To this end, we developed a simulation-based optimization framework for labor management to optimize labor allocation. This approach was evaluated by comparing its result with those suggested by selected domain experts (a plant scientist and a nursery manager). Furthermore, the simulation models were validated with a dataset from a developing tomato grafting company. Simulation-based optimization is demonstrated as an effective approach to find the optimal/near optimal labor allocation for horticultural nurseries, where discrete event simulation is used to represent the dynamics of the grafting work environment and meta-heuristics are used to devise optimal/ near optimal resource allocation strategies. Results reveal that a potential annual savings between $2,510 (0.6%) and $97,388 (20%) can be achieved for a grafting facility of 6 million plants per year. Keywords: Simulation-based optimization, Grafting, Labor allocation, Discrete event simulation.

scholarly journals Temporal prediction of in-hospital falls using tensor factorisation

2018 ◽  
Vol 4 (2) ◽  
pp. 75-83 ◽  
Author(s):  
Haolin Wang ◽  
Qingpeng Zhang ◽  
Hing-Yu So ◽  
Angela Kwok ◽  
Zoie Shui-Yee Wong
Keyword(s):  
Predictive Models ◽  
Healthcare Providers ◽  
Area Under The Curve ◽  
Resource Planning ◽  
Time Series Models ◽  
Real World Data ◽  
Temporal Prediction ◽  
The Past ◽  
Latent Features ◽  
Optimal Resource

In-hospital fall incidence is a critical indicator of healthcare outcome. Predictive models for fall incidents could facilitate optimal resource planning and allocation for healthcare providers. In this paper, we proposed a tensor factorisation-based framework to capture the latent features for fall incidents prediction over time. Experiments with real-world data from local hospitals in Hong Kong demonstrated that the proposed method could predict the fall incidents reasonably well (with an area under the curve score around 0.9). As compared with the baseline time series models, the proposed tensor based models were able to successfully identify high-risk locations without records of fall incidents during the past few months.

ERP Systems' Life Cycle

2011 ◽  
pp. 1426-1431
Author(s):  
Cesar Alexandre de Souza ◽  
Ronaldo Zwicker
Keyword(s):  
Life Cycle ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Erp Systems ◽  
Control Operation ◽  
Erp System ◽  
Operation Costs ◽  
It Architecture ◽  
Last Stage ◽  
Business Needs

The 1990’s witnessed an impressive growth of Enterprise Resource Planning (ERP) systems in the market of corporate IT solutions, and now they are an important component of IT architecture in many companies. The ERP systems are introduced in companies following well-defined stages, namely the stages of decision, selection, implementation, stabilization and utilization. This last stage (utilization) is also characterized by the development of an organized effort to continuously ensure that the ERP system meets business needs regarding functionality, performance, availability, and to control operation costs, at the ERP management stage. This chapter presents aspects involved in each stage of this life cycle, based on the referenced bibliography.

scholarly journals Using efficiency frontier to inform operational decision-making

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Matthew Benchener ◽  
Lingfeng Li
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Resource Planning ◽  
Base Case ◽  
List Type ◽  
Efficiency Frontier ◽  
Marginal Return ◽  
Optimal Resource ◽  
Operational Decision Making ◽  
Planning Strategies

Abstract In this study, we introduce Efficiency Frontier, an economic assessment framework for business operations. The proposed framework emphasizes efficiency and sustainability. It aims to help organizations translate a complex metric system into a value index, conduct performance evaluation, and determine efficient resource planning strategies. Unlike the traditional methods that majorly focus on the achievement of performance targets, the proposed framework also accounts for the amount of resource that is invested in the achievement, which encourages managers to implement sustainable strategies that aim for long-term success. To validate the new framework, we systematically study the case of the City of Chicago’s 311 contact center with a base case analysis and a sensitivity analysis. In the base case, an Efficiency Frontier architected by a set of efficient solutions from 2013 to 2016 is used to identify outperforming scenarios given resource constraints. In the sensitivity analysis, we demonstrate the application of Efficiency Frontier in determining optimal resource planning strategies in conservative, moderate, and aggressive business investment scenarios. The base case Efficiency Frontier reveals that the marginal return on investment in improving the performance may diminish as investment increases. The sensitivity study suggests that optimal resource planning strategies should be adjusted according to value propositions. This work builds a framework for business leaders to economically assess their operational decision-making. Article highlights Efficient strategies can be identified by exploring the maximum value increase per unit of resource given cost scenarios The fund of a strategy should be reduced when the marginal return of the strategy begins to diminish. The framework’s ability to suggest cost-saving strategies is especially informative in resource-limited settings.

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