The challenge of integrating non-continuous processes-milk powder plant case study

The integration of non-continuous processes such as a milk powder plant present a challenge for existing process integration techniques. Current techniques are generally based on steady and continuous operation which for some industries is not the case. Milk production varies considerably during the year as dairy cows in New Zealand are grazed on pasture, which affects the scheduling and operation of plants on site. The frequency and duration of cleaning cycles and non-productive operating states can have a major affect on energy demand and the availability of heat sources and heat sinks. In this paper the potential for indirect heat transfer between the several plants using a heat recovery loop and stratified tank at a typical New Zealand dairy factory is investigated. The maximum amount of heat recovery is calculated for a range of recirculation loop temperatures. The maximum amount of heat recovery can be increased considerably if the temperature of the hot fluid in the recirculation loop is varied depending on which condition the site is operating under

ERA: A Framework for Economic Resource Allocation for the Cloud

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lower-level cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -- Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations

Variable retort temperature optimization benefit in scheduling for retorts of different capacities in food canneries

In the majority of small- to medium-sized canneries, retorting is carried out in a battery of retorts as a batch process. For such canneries, the unloading and reloading operations for each retort are labor-intensive; therefore, a well-designed and well-managed plant should be utilized in order to optimize the whole sterilization process. In other words, it is necessary to develop a suitable mathematical model for the operation of the whole plant and to determine the optimal values of its decision variables. The result of such a model involves the quantities of each product to be loaded onto the retorts for each of the batches, and the optimal solution provides an optimum scheduling. On the other hand, it is well-known that variable retort temperature processing can be used for reducing the sterilization processing time required for sterilization using the traditional constant retort temperature processing. Therefore, the objective of this research consisted of utilizing a variable retort temperature processing in developing a mathematical model for scheduling at food canneries for the case of retorts of different capacities. The developed model was based on mixed-integer linear programming and simultaneous sterilization based on variable retort temperature processing. The adaptive random search algorithm coupled with penalty functions approach, and the finite difference method with cubic spline approximation are utilized in this study to obtain the simultaneous sterilization vectors to be processed under time-variable retort temperature. The proposed in this study methodology can be useful for small- and medium-sized food canneries, which work with many different products simultaneously

Multi-core job submission and grid resource scheduling for ATLAS AthenaMP

AthenaMP is the multi-core implementation of the ATLAS software framework and allows the efficient sharing of memory pages between multiple threads of execution. This has now been validated for production and delivers a significant reduction on the overall application memory footprint with negligible CPU overhead. Before AthenaMP can be routinely run on the LHC Computing Grid it must be determined how the computing resources available to ATLAS can best exploit the notable improvements delivered by switching to this multi-process model. A study into the effectiveness and scalability of AthenaMP in a production environment will be presented. Best practices for configuring the main LRMS implementations currently used by grid sites will be identified in the context of multi-core scheduling optimisation

Tasks, cognitive agents, and KB-DSS in workflow and process management

The purpose of this paper is to propose a nonparametric interest rate term structure model and investigate its implications on term structure dynamics and prices of interest rate derivative securities. The nonparametric spot interest rate process is estimated from the observed short-term interest rates following a robust estimation procedure and the market price of interest rate risk is estimated as implied from the historical term structure data. That is, instead of imposing a priori restrictions on the model, data are allowed to speak for themselves, and at the same time the model retains a parsimonious structure and the computational tractability. The model is implemented using historical Canadian interest rate term structure data. The parametric models with closed form solutions for bond and bond option prices, namely the Vasicek (1977) and CIR (1985) models, are also estimated for comparison purpose. The empirical results not only provide strong evidence that the traditional spot interest rate models and market prices of interest rate risk are severely misspecified but also suggest that different model specifications have significant impact on term structure dynamics and prices of interest rate derivative securities.

Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systems

In many Cyber-Physical Systems, we encounter the problem of remote state estimation of geographically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors have to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenarios

A framework of justification criteria for advanced manufacturing technology implementation in small and medium enterprises

Today in order to stay in businesses and prosper, Small and Medium Enterprises (SMEs) are seeking higher electiveness and competitiveness across the entire cycle of marketing, product design, manufacture, test and sales. SMEs play an increasingly important role in all aspects of competitiveness: both products and production techniques, but also management methods, the organization of the firm and human resources training. One of the ways by which SMEs can achieve a competitive advantage in manufacturing is through the implementation of Advanced Manufacturing Technology (AMT). An increasing number of them have chosen and are choosing various levels of AMT as the solution. Realizing the importance of SMEs, an attempt has been made in this paper to review the application of AMT in SMEs. Also, a framework has been offered for the implementation of AMT in SMEs. Finally, a summary of findings and conclusions are presented