This paper presents a solution to a predict then optimise problem which goal is to reduce the electricity cost of a university campus. The proposed methodology combines a multi-dimensional time series forecast and a n...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
This paper presents a solution to a predict then optimise problem which goal is to reduce the electricity cost of a university campus. The proposed methodology combines a multi-dimensional time series forecast and a novel approach to large-scale optimization. Gradient-boosting method is applied to forecast both generation and consumption time-series of the Monash university campus for the month of November 2020. For the consumption forecasts we employ log transformation to model trend and stabilize variance. Additional seasonality and trend features are added to the model inputs when applicable. The forecasts obtained are used as the base load for the schedule optimisation of university activities and battery usage. The goal of the optimisation is to minimize the electricity cost consisting of the price of electricity and the peak electricity tariff both altered by the load from class activities and battery use as well as the penalty of not scheduling some optional activities. The schedule of the class activities is obtained through evolutionary optimisation using the covariance matrix adaptation evolution strategy and the genetic algorithm. This schedule is then improved through local search by testing possible times for each activity oneby-one. The battery schedule is formulated as a mixed-integer programming problem and solved by the Gurobi solver. This method obtains the second lowest cost when evaluated against 6 other methods presented at an IEEE competition that all used mixed-integer programming and the Gurobi solver to schedule both the activities and the battery use. The code and data used for the paper are publicly available1.
Attracted by the advantages of cloud computing, more and more services and applications are migrated to this new paradigm. As promising as it is, cloud computing also brings new challenges to many research issues, suc...
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ISBN:
(纸本)9781509026753
Attracted by the advantages of cloud computing, more and more services and applications are migrated to this new paradigm. As promising as it is, cloud computing also brings new challenges to many research issues, such as service scheduling. Most existing scheduling methods are offline and could not deal with the uncertainties and dynamics during the execution, especially in the dynamic cloud environment. In view of this challenge, in this paper, we propose an uncertainty-aware evolutionary scheduling method for cloud service provisioning. It aims at dealing with uncertainties during execution and updating the scheduling so as to meet the deadline and optimize the execution cost of cloud applications. Our method consists of two phases, baseline scheduling and evolutionary scheduling during execution. In baseline scheduling, we suggest a reverse-auction-based pricing mechanism for service provisioning. In evolutionary scheduling, an uncertain model with three types of uncertainties is considered and four uncertain events are discussed. Accordingly, the evolutionary scheduling policy is presented based on intermediate workflow to get a global optimal schedule, so as to improve the success rate for the execution of the cloud applications. Finally, experiments are designed and performed to demonstrate the effectiveness of our method.
Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems This book provides insight into the use ...
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ISBN:
(数字)9781119574293
ISBN:
(纸本)9781119573845
Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems This book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches. evolutionary Computation in scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book: Provides a representative sampling of real-world problems currently being tackled by practitioners Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence Consists of four main parts: Introduction to scheduling Problems, Computational Issues in scheduling Problems, evolutionary Computation, and evolutionary Computations for scheduling Problems Evoluti
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