Program-Budget Marginal-Analysis for University Strategies concept of Planning and Execution

Research addressing budgeting allocation planning on budget allocation and execution planning on Priotization of strategies are scanty in literature. This study presents program-budget marginal-analysis for university budget planning and execution aimed at priotizing budget allocation on strategies used for improving university rating. The research will illustrate the program-budget marginal-analysis with little adjustment to suit the university strategic budget allocations. This paper proposes a conceptional frame work for budget planning execution on university strategies. The framework for implementing PBMA will identify the total amount of available resources or funding allocated to priorities, examination of the current allocation activity, evaluation of benefit of cost of expansion with regards to both existing and new introduced strategies, in any of the existing services in use, which is effective with fewer resources allocation. Alternatives to be allocated fewer resources with greater effectiveness included in the priotized list. The budget allocation has the potential to maximize efficiency of each strategic allocation for improving the university rating

Finite Simulation Budget Allocation for Ranking and Selection

We consider a simulation-based ranking and selection (R&S) problem under a fixed budget setting. Existing budget allocation procedures focus either on asymptotic optimality or on one-step-ahead allocation efficiency. Neither of them depends on the fixed simulation budget, the ignorance of which could lead to an inefficient allocation, especially when the simulation budget is finite. In light of this, we develop a finite-budget allocation rule that is adaptive to the simulation budget. Theoretical results show that the budget allocation strategies are distinctively different between a finite budget and a sufficiently large budget. Our proposed allocation rule can dynamically determine the ratio of budget allocated to designs according to different simulation budget and is optimal when the simulation budget goes to infinity, indicating it not only possesses desirable finite-budget properties but also achieves asymptotic optimality. Based on the proposed allocation rule, two efficient finite simulation budget allocation algorithms are developed. In the numerical experiments, we use both synthetic examples and a case study to show the superior efficiency of our proposed allocation rule

Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data

We consider stochastic optimization under distributional uncertainty, where the unknown distributional parameter is estimated from streaming data that arrive sequentially over time. Moreover, data may depend on the decision of the time when they are generated. For both decision-independent and decision-dependent uncertainties, we propose an approach to jointly estimate the distributional parameter via Bayesian posterior distribution and update the decision by applying stochastic gradient descent on the Bayesian average of the objective function. Our approach converges asymptotically over time and achieves the convergence rates of classical SGD in the decision-independent case. We demonstrate the empirical performance of our approach on both synthetic test problems and a classical newsvendor problem

Supply chain et crises systémiques : l’apport des méthodes de modélisation et de simulation pour améliorer la résilience -cas de la pandémie de covid-19-

Systemic crises, whether natural or man-made, are starting to become recurrent, including epidemics / pandemics like the one we are currently experiencing, COVID-19. The occurrence of an epidemic, always in an unpredictable and brutal manner, poses serious challenges to decision-makers, like the managers of Supply Chain (S.C), especially, of global dimension. In normal times, the management of S.C is designed with a logic of optimization, but, in times of uncertainty, born of a turnaround, it is necessary to acquire new know-how in the management of epidemic crises. This is why, the use of modeling and simulation approaches, it is presented as decision support tools, which are able to mitigate risks and predict the future of a CS with more than lucidity and efficiency. The academic literature on the subject and the good practices identified, show that thanks to simulation methods, S.Cs impacted by systemic (pandemic) crises can quickly recover and come back in force on the market, in short, strengthen their resilience. Therefore, simulation algorithms constitute robust decision-making artefacts, both for public and private decision-makers.Les crises systémiques, qu’elles soient d’origine naturelle ou humaine, commencent à devenir récurrentes, notamment, les épidémies/pandémies comme celle que nous vivons actuellement, la Covid-19. La survenance d’une épidémie, toujours d’une manière imprévisible et brutale, pose de sérieux défis aux décideurs, à l’image des gestionnaires des Supply Chain (S.C), spécialement, de dimension mondiale. En temps normaux, le management des S.C est conçu dans une logique d’optimisation, mais, en temps d’incertitudes, nées d’un retournement de situation, il faut acquérir de nouveaux savoir-faire en matière de gestion de crises épidémiques. C’est pourquoi, l’usage des approches de modélisation et de simulation, il se présente comme des outils d’aide à la décision, qui sont capables d’atténuer les risques et de prédire l’avenir d’une S.C avec plus de lucidité et d’efficacité. La littérature académique sur le sujet et les bonnes pratiques recensées, montrent que grâce aux méthodes de simulation, les S.C impactées par les crises systémiques (pandémiques) peuvent récupérer rapidement et revenir en force sur le marché, en somme, renforcer leur résilience. Donc, les algorithmes de simulations constituent des artefacts robustes d’aide à la décision, aussi bien pour les décideurs publics que privés

SIMULATION OPTIMIZATION USING OPTIMAL COMPUTING BUDGET ALLOCATION

Ph.DDOCTOR OF PHILOSOPH

Optimal computing budget allocation for complete ranking

10.1109/TASE.2013.2239289IEEE Transactions on Automation Science and Engineering112516-52

Optimal Computing Budget Allocation for Complete Ranking with Input Uncertainty

IISE Transactions525489-49