基于多目标数学规划的网络多源数据调度仿真

传统的目标网络多源数据调度方法通常以时间或费用为单一调度优化目标,无法实现任务完成时间以及任务执行成本之间的均衡,造成系统资源利用率较低.针对上述问题,提出一种基于多目标数学规划的网络多源数据调度方法.使用DAG构建网络多源数据流,确定多源数据调度任务模型的信任关系,以任务完成时间、任务完成成本、资源利用率为优化目标,...     

Optimizing green production strategies: An integrated approach

Selection of green production strategy is a critical but difficult task due to the fact that it affects not only green benefits, but also production economy. The problem is essentially multi-objective and involves dynamic and uncertain conditions. This study focused on an integrated approach to improve the analysis and facilitate decision-making process. Discrete-event simulation model was developed to capture production flow and decision logic under real world conditions. A multi-objective genetic algorithm (MOGA), combined with improving heuristics, was developed to search the best solutions (Pareto optimums). The two modules are integrated to work in evolutionary cycles to achieve the optimization. Experiments were designed and carried out via a prototype system developed to verify and validate proposed concepts, including sensitivity analysis of related model parameters.     

Fault-tolerant scheduling on parallel systems with non-memoryless failure distributions

As large parallel systems increase in size and complexity, failures are inevitable and exhibit complex space and time dynamics. Most often, in real systems, failure rates are increasing or decreasing over time. Considering non-memoryless failure distributions, we study a bi-objective scheduling problem of optimizing application makespan and reliability. In particular, we determine whether one can optimize both makespan and reliability simultaneously, or whether one metric must be degraded in order to improve the other. We also devise scheduling algorithms for achieving (approximately) optimal makespan or reliability. When failure rates decrease, we prove that makespan and reliability are opposing metrics. In contrast, when failure rates increase, we prove that one can optimize both makespan and reliability simultaneously. Moreover, we show that the largest processing time (LPT) list scheduling algorithm achieves good performance when processors are of uniform speed. The implications of our findings are the accelerated completion and improved reliability of parallel jobs executed across large distributed systems. Finally, we conduct simulations to investigate the impact of failures on the performance, which is done using an actual application of biological sequence comparison.     

A multi-objective optimization for green supply chain network design

In this paper, we study a supply chain network design problem with environmental concerns. We are interested in the environmental investments decisions in the design phase and propose a multi-objective optimization model that captures the trade-off between the total cost and the environment influence. We conduct a comprehensive set of numerical experiments. The results show that our model can be applied as an effective tool in the strategic planning for green supply chain. Meanwhile, the sensitivity analysis provides some interesting managerial insights for firms.     

Use of distributed data sources in facility location

Facility location decisions are usually determined by cost and coverage related factors although empirical studies show that such factors as infrastructure, labor conditions and competition also play an important role in practice. The objective of this paper is to develop a multi-objective facility location model accounting for a wide range of factors affecting decision-making. The proposed model selects potential facilities from a set of pre-defined alternative locations according to the number of customers, the number of competitors and real-estate cost criteria. However, that requires large amount of both spatial and non-spatial input data, which could be acquired from distributed data sources over the Internet. Therefore, a computational approach for processing input data and representation of modeling results is elaborated. It is capable of accessing and processing data from heterogeneous spatial and non-spatial data sources. Application of the elaborated data gathering approach and facility location model is demonstrated using an example of fast food restaurants location problem.     

Intelligent controllers for bi-objective dynamic scheduling on a single machine with sequence-dependent setups

This article addresses the problem of dynamic job scheduling on a single machine with Poisson arrivals, stochastic processing times and due dates, in the presence of sequence-dependent setups. The objectives of minimizing mean earliness and mean tardiness are considered. Two approaches for dynamic scheduling are proposed, a Reinforcement Learning-based and one based on Fuzzy Logic and multi-objective evolutionary optimization. The performance of the two scheduling approaches is tested against the performance of 15 dispatching rules in four simulation scenarios with different workload and due date pressure conditions. The scheduling methods are compared in terms of Pareto optimal-oriented metrics, as well as in terms of minimizing mean earliness and mean tardiness independently. The experimental results demonstrate the merits of the proposed methods.     

Multi-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems

The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy.This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms.The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance.     

A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems

This paper presents a new, carefully designed algorithm for five bi-objective permutation flow shop scheduling problems that arise from the pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) both, the weighted and non-weighted total tardiness of all jobs. The proposed algorithm combines two search methods, two-phase local search and Pareto local search, which are representative of two different, but complementary, paradigms for multi-objective optimization in terms of Pareto-optimality. The design of the hybrid algorithm is based on a careful experimental analysis of crucial algorithmic components of these two search methods. We compared our algorithm to the two best algorithms identified, among a set of 23 candidate algorithms, in a recent review of the bi-objective permutation flow-shop scheduling problem. We have reimplemented carefully these two algorithms in order to assess the quality of our algorithm. The experimental comparison in this paper shows that the proposed algorithm obtains results that often dominate the output of the two best algorithms from the literature. Therefore, our analysis shows without ambiguity that the proposed algorithm is a new state-of-the-art algorithm for the bi-objective permutation flow-shop problems studied in this paper.     

求解高维多目标调度的新型人工蜂群算法

高维多目标连续优化问题已得到广泛研究,而高维多目标组合优化问题的进展相对较小,虽然人工蜂群(Artificial Bee Colony,ABC)算法已成功应用于多种生产调度问题,但很少被用来求解高维多目标调度问题,而且高维多目标调度自身的研究进展也非常小。针对高维多目标柔性作业车间调度问题,文中提出了一种新型ABC算法以同时优化最大完成时间、总延迟时间、总能耗和机器总负荷。与常规柔性作业车间调度问题不同,上述问题考虑了总能耗,使其成为绿色调度问题。新型ABC具有明显不同于现有ABC算法的新特点,其跟随蜂(onlooker bee)的数量小于引领蜂(employed bee),引领蜂侧重于全局搜索,而跟随蜂只进行局部搜索,通过两类蜜蜂彼此各异的搜索方式来避免算法陷入局部最优。同时,该算法将跟随对象限定为质量较好的部分引领蜂和外部档案成员,其他引领蜂无法成为跟随对象,以避免计算资源浪费在较差解的搜索上,并给出了侦查蜂(scout)新的处理策略。测试实例的仿真实验表明,高维多目标调度问题中非劣解数量占种群规模的比例明显低于高维连续优化问题。将新型ABC与多目标遗传算法和变邻域搜索进行比较,实...     

MobiWay应用中基于Hadoop的多目标多任务调度算法

针对Hadoop应用环境复杂、网络带宽等因素多变而影响调度算法性能的问题,提出适用于多任务多目标的Hadoop调度算法(MOSMT)。在分析已有调度算法工作原理的基础上,构建MOSMT算法的数学模型和调度策略;在负载模拟器中进行算法评估,并将MOSMT算法应用于MobiWay;对MobiWay应用中的MOSMT算法性能进行测试分析,以最少的资源和最低的时间成本完成任务的调度。通过与FIFO和Fair调度算法对比表明,该算法能够实现类似的功能,并且在处理多任务多目标时耗时更少,更为经济。     

An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting

This paper proposes several novel hybrid ant colony optimization (ACO)-based algorithms to resolve multi-objective job-shop scheduling problem with equal-size lot splitting. The main issue discussed in this paper is lot-splitting of jobs and tradeoff between lot-splitting costs and makespan. One of the disadvantages of ACO is its uncertainty on time of convergence. In order to enrich search patterns of ACO and improve its performance, five enhancements are made in the proposed algorithms including: A new type of pheromone and greedy heuristic function; Three new functions of state transition rules; A nimble local search algorithm for the improvements of solution quality; Mutation mechanism for divisive searching; A particle swarm optimization (PSO)-based algorithm for adaptive tuning of parameters. The objectives that are used to measure the quality of the generated schedules are weighted-sum of makespan, tardiness of jobs and lot-splitting cost. The developed algorithms are analyzed extensively on real-world data obtained from a printing company and simulated data. A mathematical programming model is developed and paired-samples t-tests are performed between obtained solutions of mathematical programming model and proposed algorithms in order to verify effectiveness of proposed algorithms.     

Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems

Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective problems have been proposed. Production scheduling, as with other operations management fields, is no different. The flowshop problem is among the most widely studied scheduling settings. Recently, the Iterated Greedy methodology for solving the single-objective version of the flowshop problem has produced state-of-the-art results. This paper proposes a new algorithm based on Iterated Greedy technique for solving the multi-objective permutation flowshop problem. This algorithm is characterised by an effective initialisation of the population, management of the Pareto front, and a specially tailored local search, among other things. The proposed multi-objective Iterated Greedy method is shown to outperform other recent approaches in comprehensive computational and statistical tests that comprise a large number of instances with objectives involving makespan, tardiness and flowtime. Lastly, we use a novel graphical tool to compare the performances of stochastic Pareto fronts based on Empirical Attainment Functions.     

Optimizing thermal design of data center cabinets with a new multi-objective genetic algorithm

There is an ever increasing need to use optimization methods for thermal design of data centers and the hardware populating them. Airflow simulations of cabinets and data centers are computationally intensive and this problem is exacerbated when the simulation model is integrated with a design optimization method. Generally speaking, thermal design of data center hardware can be posed as a constrained multi-objective optimization problem. A popular approach for solving this kind of problem is to use Multi-Objective Genetic Algorithms (MOGAs). However, the large number of simulation evaluations needed for MOGAs has been preventing their applications to realistic engineering design problems. In this paper, details of a substantially more efficient MOGA are formulated and demonstrated through a thermal analysis simulation model of a data center cabinet. First, a reduced-order model of the cabinet problem is constructed using the Proper Orthogonal Decomposition (POD). The POD model is then used to form the objective and constraint functions of an optimization model. Next, this optimization model is integrated with the new MOGA. The new MOGA uses a “kriging” guided operation in addition to conventional genetic algorithm operations to search the design space for global optimal design solutions. This approach for optimal design is essential to handle complex multi-objective situations, where the optimal solutions may be non-obvious from simple analyses or intuition. It is shown that in optimizing the data center cabinet problem, the new MOGA outperforms a conventional MOGA by estimating the Pareto front using 50% fewer simulation calls, which makes its use very promising for complex thermal design problems. Recommended by: Monem Beitelmal     

Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters

Developing energy-efficient clusters not only can reduce power electricity cost but also can improve system reliability. Existing scheduling strategies developed for energy-efficient clusters conserve energy at the cost of performance. The performance problem becomes especially apparent when cluster computing systems are heavily loaded. To address this issue, we propose in this paper a novel scheduling strategy–adaptive energy-efficient scheduling or AEES–for aperiodic and independent real-time tasks on heterogeneous clusters with dynamic voltage scaling. The AEES scheme aims to adaptively adjust voltages according to the workload conditions of a cluster, thereby making the best trade-offs between energy conservation and schedulability. When the cluster is heavily loaded, AEES considers voltage levels of both new tasks and running tasks to meet tasks’ deadlines. Under light load, AEES aggressively reduces the voltage levels to conserve energy while maintaining higher guarantee ratios. We conducted extensive experiments to compare AEES with an existing algorithm–MEG, as well as two baseline algorithms–MELV, MEHV. Experimental results show that AEES significantly improves the scheduling quality of MELV, MEHV and MEG.     

动态报表的打印对象的控制设计

每个信息系统的功能大多不相同,但一般都要把表或视图的联接集作为数据集进行报表打印。由于数据库不同,表结构不同,因此每个报表数据集的字段类型、宽度都不可能完全相同,这导致报表的多样性与复杂性,给报表设计带来诸多不便,通过控制报表的打印对象实现动态报表是一个有效解决该问题的方法。在水晶报表中先创建若干个打印对象,程序运行时对SQL语句获取的动态打印数据集的元素进行枚举,使每个元素与水晶报表上已存在的打印对象进行逐一匹配绑定,进而对绑定的打印对象进行控制,从而实现动态报表的目的。     

多目标微粒群优化算法及其应用研究进展*

多目标微粒群优化(MOPSO)算法是一类基于群体智能的新型全局多目标优化方法,已受到广泛关注,并在许多领域得到应用。针对近几年来MOPSO算法及其应用的进展进行了综述和评论。首先描述了MOPSO算法的基本框架;接着对MOPSO算法进行了分类和分析,并给出了MOPSO算法的一些改进策略;然后介绍了MOPSO算法的应用进展;最后,展望了MOPSO算法值得进一步研究的方向。