A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs

As cloud computing evolves, it is becoming more and more apparent that the future of this industry lies in interconnected cloud systems where resources will be provided by multiple “Cloud” providers instead of just one. In this way, the hosts of services that are cloud-based will have access to even larger resource pools while at the same time increasing their scalability and availability by diversifying both their computing resources and the geographical locations where those resources operate from. Furthermore the increased competition between the cloud providers in conjunction with the commoditization of hardware has already led to large decreases in the cost of cloud computing and this trend is bound to continue in the future. Scientific focus in cloud computing is also headed this way with more studies on the efficient allocation of resources and effective distribution of computing tasks between those resources. This study evaluates the use of meta-heuristic optimization algorithms in the scheduling of bag-of-tasks applications in a heterogeneous cloud of clouds. The study of both local and globally arriving jobs has been considered along with the introduction of sporadically arriving critical jobs. Simulation results show that the use of these meta-heuristics can provide significant benefits in costs and performance.     

An integrated three-stage maintenance scheduling model for unrelated parallel machines with aging effect and multi-maintenance activities

We propose an integrated three-stage model for maintenance scheduling of unrelated parallel machines (UPMs) with aging effect and multi-maintenance activities (AEMMAs) using a variety of MODM techniques such as the fuzzy analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), and goal programming (GP). We use fuzzy AHP in the first stage of the proposed model to account for the inherent ambiguity and vagueness in real-life maintenance scheduling problems. In the second stage, we use TOPSIS to reduce the multi-objective problem into an efficient bi-objective problem. Finally, we use GP to solve the resulting bi-objective problem and develop an optimal maintenance schedule in the third stage of the model. We use a numerical example to demonstrate the applicability of the proposed approach and exhibit the efficacy of the procedures and algorithms.     

Optimization models for shale gas water management

There are four key aspects for water use in hydraulic fracturing, including source water acquisition, wastewater production, reuse and recycle, and subsequent transportation, storage, and disposal. Water use life cycle is optimized for wellpads through a discrete‐time two‐stage stochastic mixed‐integer linear programming model under uncertain availability of water. The objective is to minimize expected transportation, treatment, storage, and disposal cost while accounting for the revenue from gas production. Assuming freshwater sources, river withdrawal data, location of wellpads, and treatment facilities are given, the goal is to determine an optimal fracturing schedule in coordination with water transportation, and its treatment and reuse. The proposed models consider a long‐time horizon and multiple scenarios from historical data. Two examples representative of the Marcellus Shale play are presented to illustrate the effectiveness of the formulation, and to identify optimization opportunities that can improve both the environmental impact and economical use of water. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3490–3501, 2014     

Refinery scheduling with varying crude: A deep belief network classification and multimodel approach

In model‐based refinery scheduling, the varying composition of the crude being refined is a major challenge, especially for those reaction processes. A classification based, multimodel approach is proposed to handle the frequently varying crude. The idea is to build a scheduling model for each type of feed crude, and the type can be determined using an online classifier. The recently emerged deep belief network is introduced to develop the classifier, which provides more accurate classification than the traditional neural network. The proposed method is demonstrated through modeling a fluidized catalytic cracking unit (the mostly affected by varying crude), and then the scheduling of a refinery that was carefully simulated to mimic the actual operation of a refinery in northern China. The results reveal that the multimodel approach is effective in handling varying crude. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2525–2532, 2014     

Inventory pinch based,multiscale models for integrated planning and scheduling‐part II: Gasoline blend scheduling

Integration of planning and scheduling optimizes simultaneous decisions at both levels, thereby leading to more efficient operation. A three‐level discrete‐time algorithm which uses nonlinear models and integrates planning and detailed scheduling is introduced: first level optimizes nonlinear blend models via multiperiod nonlinear programming (NLP), where period boundaries are initially determined by the inventory pinch points; second level uses fixed recipes (from the first level) in a multiperiod mixed‐integer linear program to determine first an optimal production plan and then to optimize an approximate schedule which minimizes the total number of switches in blenders and swing tanks; third level computes detailed schedules that adhere to inventory constraints computed in the approximate schedule. If inventory infeasibilities appear at the second or the third level, the first‐level periods are subdivided and blend recipes are reoptimized. Algorithm finds the same or better solutions and is substantially faster than previously published full‐space continuous‐time model. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2475–2497, 2014     

Integration of scheduling and control for batch processes using multi‐parametric model predictive control

Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550–8565) To address the online computation burden, we incorporare multi‐parametric Model Predictive Control (mp‐MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous‐time event‐point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3169–3183, 2014     

煤炭公司级安全生产综合信息平台的设计与实现

基于现有数字化矿山应用现状及煤炭公司级安全生产调度与集约化管理需求,提出了基于煤炭集团公司安全生产综合信息系统平台,作为矿井数字化矿山信息化部分业务内容在公司层面的系统集成与向上延伸扩展与综合应用。平台的建设为集团公司综合调度提供了信息化支撑,使得各级调度管理人员能够分级分责快速获取作业现场安全生产运营数据信息,实现协同调度指挥与现代化集团集约化管控模式,减少重复性投资,提高企业的核心竞争力。     

基于GIS的管网事故状态调度决策系统

结合我国城市供水运行管理现状，将计算机技术、信息技术和决策支持系统理论引入城市供水运行优化调度中，建立了基于GIS的供水管网事故状态决策支持系统，详述了其结构与功能。     

引用视频指挥调度手段实现企业生产安全防护

本文主要根据目前企业安全管理,提出一种新的概念"视频指挥调度"。"视频指挥调度"把通常使用的监控技术与智能监控和可视化现场指挥调度相结合,可以把事故隐患控制在初发阶段,并可实施对事故现场的"面对面"指挥调度和应急处理。     

A nested online scheduling and nonlinear model predictive control framework for multi-product continuous systems

In the pursuit of integrated scheduling and control frameworks for chemical processes, it is important to develop accurate integrated models and computational strategies such that optimal decisions can be made in a dynamic environment. In this study, a recently developed switched system formulation that integrates scheduling and control decisions is extended to closed-loop operation embedded with nonlinear model predictive control (NMPC). The resulting framework is a nested online scheduling and control loop that allows to obtain fast and accurate solutions as no model reduction is needed and no integer variables are involved in the formulations. In the outer loop, the integrated model is solved to calculate an optimal product switching sequence such that the process economics is optimized, whereas in the inner loop, an NMPC implements the scheduling decisions. The proposed scheme was tested on two multi-product continuous systems. Unexpected large disturbances and rush orders were handled effectively.