A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

A study on flexible flow shop and job shop scheduling using meta-heuristic approaches

Scheduling aims at allocation of resources to perform a group of tasks over a period of time in such a manner that some performance goals such as flow time, tardiness, lateness, and makespan can be minimized. Today, manufacturers face the challenges in terms of shorter product life cycles, customized products and changing demand pattern of customers. Due to intense competition in the market place, effective scheduling has now become an important issue for the growth and survival of manufacturing firms. To sustain in the current competitive environment, it is essential for the manufacturing firms to improve the schedule based on simultaneous optimization of performance measures such as makespan, flow time and tardiness. Since all the scheduling criteria are important from business operation point of view, it is vital to optimize all the objectives simultaneously instead of a single objective. It is also essentially important for the manufacturing firms to improve the performance of production scheduling systems that can address internal uncertainties such as machine breakdown, tool failure and change in processing times. The schedules must meet the deadline committed to customers because failure to do so may result in a significant loss of goodwill. Often, it is necessary to reschedule an existing plan due to uncertainty event like machine breakdowns. The problem of finding robust schedules (schedule performance does not deteriorate in disruption situation) or flexible schedules (schedules expected to perform well after some degree of modification when uncertain condition is encountered) is of utmost importance for real world applications as they operate in dynamic environments

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms

Scheduling Models with Additional Features: Synchronization, Pliability and Resiliency

In this thesis we study three new extensions of scheduling models with both practical and theoretical relevance, namely synchronization, pliability and resiliency. Synchronization has previously been studied for flow shop scheduling and we now apply the concept to open shop models for the first time. Here, as opposed to the traditional models, operations that are processed together all have to be started at the same time. Operations that are completed are not removed from the machines until the longest operation in their group is finished. Pliability is a new approach to model flexibility in flow shops and open shops. In scheduling with pliability, parts of the processing load of the jobs can be re-distributed between the machines in order to achieve better schedules. This is applicable, for example, if the machines represent cross-trained workers. Resiliency is a new measure for the quality of a given solution if the input data are uncertain. A resilient solution remains better than some given bound, even if the original input data are changed. The more we can perturb the input data without the solution losing too much quality, the more resilient the solution is. We also consider the assignment problem, as it is the traditional combinatorial optimization problem underlying many scheduling problems. Particularly, we study a version of the assignment problem with a special cost structure derived from the synchronous open shop model and obtain new structural and complexity results. Furthermore we study resiliency for the assignment problem. The main focus of this thesis is the study of structural properties, algorithm development and complexity. For synchronous open shop we show that for a fixed number of machines the makespan can be minimized in polynomial time. All other traditional scheduling objectives are at least as hard to optimize as in the traditional open shop model. Starting out research in pliability we focus on the most general case of the model as well as two relevant special cases. We deliver a fairly complete complexity study for all three versions of the model. Finally, for resiliency, we investigate two different questions: `how to compute the resiliency of a given solution?' and `how to find a most resilient solution?'. We focus on the assignment problem and single machine scheduling to minimize the total sum of completion times and present a number of positive results for both questions. The main goal is to make a case that the concept deserves further study

Serial batch processing machine scheduling: a cement industry case study

Dissertação de mestrado em Engenharia de SistemasThis work arises in the Cement Industry in the process of scheduling the clients to the warehouse and assignment to docking bays. The goal is to solve the scheduling and assignment problem, to improve both company’s service levels and the efficiency of its resources. After the real problem analysis, it was possible to conclude that it could be solved as a batching machine scheduling problem, where the jobs are the clients to be schedule, and the machine is the warehouse. The problem can be described as max 1 | rj,s-batch | Cmax . A Mixed Integer Linear Programming (MILP) model was proposed. However, as the number of jobs increased it started having computational difficulties. To overcome the problems of the MILP model two heuristics were proposed. The first one is a Constructive Algorithm (CA) that creates a first solution for the problem. The second heuristic is a metaheuristic algorithm, based on Simulated Annealing procedures, that starts with the initial solution of the CA and through three possible moves starts constructing the neighboring solutions space. After constructing the neighboring solutions space, it returns the best solution found. The computational tests proved that both the MILP model and the heuristics can ensure both feasible and optimum solutions. However, the MILP model consumes more computational resources. For some larger instances and giving a maximum limit of computational time of 8 hours, the MILP model cannot reach the optimality, nor the good results obtained by the heuristics, for those larger instances. The machine scheduling is a good approach for scheduling the trucks to the warehouse. Since it is also an innovative approach for the problem, considering the literature studied, maybe this work will inspire others to work on this idea or, at least, serve as a basis for future researches.Este trabalho tem como cenário a Indústria Cimenteira no processo de agendamento de clientes para atendimento no armazém e atribuição de pontos de carga. O objetivo é resolver o problema de agendamento visando otimizar tanto os níveis de serviço da empresa bem como a eficiência dos seus recursos. Depois da análise detalhada do problema real foi possível concluir que este podia ser resolvido como um problema de processamento em lotes em máquina única, onde as tarefas a agendar seriam os clientes e a máquina o armazém. O problema pode então ser descrito como 1 | rj,s-batch | Cmax . Um modelo de Programação Linear Inteira Mista (PLIM) foi proposto. Contudo, à medida que o número de tarefas aumentava, o modelo começava a ter dificuldades computacionais na obtenção de solução ótima. Para ultrapassar essas dificuldades, foram desenhadas e propostas duas heurísticas. A primeira é um Algoritmo Construtivo (AC) capaz de retornar uma solução inicial. A segunda, uma meta-heurística, baseada na abordagem do Simulated Annealing, que trabalha a solução inicial gerada pelo AC, através de três movimentos possíveis, e gera uma vizinhança de soluções. Depois, procura e retorna a melhor solução possível dessa vizinhança. Os testes computacionais provaram que tanto o modelo de PLIM como as heurísticas são capazes de retornar tanto soluções possíveis como ótimas. Contudo, o modelo de PLIM consome muitos mais recursos computacionais do que as heurísticas. Para instâncias de tamanho superior, dado um tempo de computação máximo de 8 horas, o PLIM, não conseguindo atingir a solução ótima, nem sequer consegue atingir soluções tão boas como as das heurísticas. A abordagem de agendamento em máquinas, utilizada neste trabalho, mostrou-se ser uma boa abordagem para o agendamento de clientes no armazém. Para além disso, esta é uma abordagem inovadora, tendo em conta a literatura estudada, e, talvez possa inspirar outros autores a trabalhar nesta ideia ou então servir de base para pesquisas futuras

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development