We study the effect of heterogeneous growth in demand on resource extraction. Using the Great Fish War framework of Levhari and Mirman (1980), we show that heterogeneity in demand growth has a profound effect on both non-cooperative (Cournot-Nash and Stackelberg) and cooperative solutions.

Considering water resource scarcity and uncertainty in climate and demand futures, decision-makers require techniques for sustainability analysis in resource management. Through unclear definitions of “sustainability”, however, traditional indices for resource evaluation propose options of limited flexibility by adopting static climate and demand scenarios, limiting analysis variables to a part...

This paper studies a resource allocation problem with unknown functions in the constraints. The resource allocation problem is formulated as a nonlinear optimization problem. A sufficient condition is established to guarantee a unique global optimal solution in the optimization problem, and an extremum seeking control (ESC)-based primal-dual algorithm is developed to generate the optimal soluti...

Multi-agent problem solving using market mechanisms is a primary focus area of Intelligent Automation Inc. We have been developing agent-based planning and scheduling techniques, and investigating their feasibility in manufacturing and transportation domains. These domains involve online streams of jobs being executed in dynamic and uncertain environments. Many NASA applications, such as schedu...

E-commerce platforms host markets for perishable resources in various industry sectors ranging from airline to Internet advertisement to online retail. In these markets, the demand realizes sequentially and the firm needs to make online (irrevocable) decisions regarding how (and at what price) to allocate resources to arriving demand without precise knowledge of the future demand. The success o...

This paper reports on an application of artiicial intelligence to achieve demand-based scheduling within the context of a network-computing infrastructure. The described AI system uses tool-speciic, run-time input to predict the resource-usage characteristics of runs. Instance-based learning with locally weighted polynomial regression is employed because of the need to simultaneously learn mult...

The recent emergence of public cloud offerings, surge computing -outsourcing tasks from an enclosed knowledge centre to a cloud supplier in times of serious loadhas become a lot of accessible to a large vary of customers. Deciding that workload to source to what cloud supplier in such a setting, however, is way from trivial The target of this call is to maximise the use of the interior knowledg...

Proactively managing the performance and resource e ciency of running software systems requires techniques to predict system performance and resource consumption. Typically, performance predictions are based on performance models that capture the performance-relevant aspects of the considered software system. Building performance models involves the estimation of resource demands, i.e., estimat...

loud computing is on demand as it offers dynamic flexible resource allocation for reliable and guaranteed services in pay as-you-use manner to public. In Cloud computing multiple cloud users can request number of cloud services simultaneously. So there must be a provision that all resources are made available to requesting user in efficient manner to satisfy their need. Hence there is no need f...

This paper presents a robust bi-level co-optimization model that promotes the active participation of Internet Data Centers (IDCs) in demand response (DR) programs, thereby enhancing flexibility power systems. Our approach involves leveraging virtual lines to migrate workloads among IDCs, optimizing resource allocations, and benefiting both domains. The incorporates Gaussian Process Regression ...