Greedy scheduling with custom-made objectives

We present a methodology to automatically generate an online job scheduling method for a custom-made objective and real workloads. The scheduling problem comprises independent parallel jobs and parallel identical machines and occurs in Massively Parallel Processing systems and computational Grids. The system administrator defines the scheduling objective that may consider job properties and priorities of users or user groups. Our scheduling method combines a Greedy scheduling algorithm with the dynamic sorting of the waiting queue. This sorting algorithm uses a criterion that is modifiable by a set of parameters. Finding good parameter settings for the sorting criterion is viewed as a nonlinear optimization problem which is solved with the help of Evolution Strategies. We evaluate our scheduling method with real workload data and compare it to approximated optimal offline solutions and to the online results of the standard EASY backfill algorithm.     

Nested partitions for the large-scale extended job shop scheduling problem

This paper addresses the large-scale extended job shop scheduling problem while considering the bill of material and the working shifts constraints. We propose two approaches for the problem. One is based on dispatching rules (DR), and the other is an application of the Nested Partitions (NP) Framework. A sampling approach for the exact feasible subregion is developed to complete the NP method. Furthermore, to efficiently search each subregion, a weighted sampling approach is also presented. Computational experiments show that the NP method with weighted sampling can find good solutions for most large-scale extended job shop scheduling problems.     

时延网络控制系统的协同设计方法研究

在搭建的虚拟平台上对多任务的网络控制系统调度和嵌入LQG控制算法进行了仿真.探索了网络控制系统中调度与控制协同设计方法,讨论系统采样周期对网络控制系统的影响.以优化控制系统的性能为目标,以网络的可调度性为条件,结合系统控制和调度算法,对网络控制系统进行静态性能指标估计和动态调度仿真相结合.结果表明该方法既满足了控制系统的性能,又优化了网络的调度,提高了网络的资源率.     

并行多机调度问题的一种遗传算法

运用遗传算法对最小化完工时间的并行多机调度问题进行了研究，给出了最小完工时间的一个下界，由此提出了初始种群的一种构造方法，并用计算实例表明该方法适用于大规模并行多机调度问题     

Parallel sparse linear algebra and application to structural mechanics

The framework of this paper is the parallelization of a plasticity algorithm that uses an implicit method and an incremental approach. More precisely, we will focus on some specific parallel sparse linear algebra algorithms which are the most time-consuming steps to solve efficiently such an engineering application. First, we present a general algorithm which computes an efficient static scheduling of block computations for parallel sparse linear factorization. The associated solver, based on a supernodal fan-in approach, is fully driven by this scheduling. Second, we describe a scalable parallel assembly algorithm based on a distribution of elements induced by the previous distribution for the blocks of the sparse matrix. We give an overview of these algorithms and present performance results on an IBM SP2 for a collection of grid and irregular problems. This revised version was published online in June 2006 with corrections to the Cover Date.     

Complexities and algorithms for synchronized scheduling of parallel machine assembly and air transportation in consumer electronics supply chain

In this paper, we study the problem of synchronized scheduling of assembly and air transportation to achieve accurate delivery with minimized cost in consumer electronics supply chain. This problem was motivated by a major PC manufacturer in consumer electronics industry. The overall problem is decomposed into two sub-problems, which consist of an air transportation allocation problem and an assembly scheduling problem. The air transportation allocation problem is formulated as an integer linear programming problem with the objective of minimizing transportation cost and delivery earliness tardiness penalties. The assembly scheduling problem seeks to determine a schedule ensuring that the orders are completed on time and catch the flights such that the waiting penalties between assembly and transportation is minimized. The problem is formulated as a parallel machine scheduling problem with earliness penalties. The computational complexities of the two sub-problems are investigated. The air transportation allocation problem with split delivery is shown to be solvable. The parallel machine assembly scheduling problem is shown to be NP-complete. Simulated annealing based heuristic algorithms are presented to solve the parallel machine problem.     

Parallel machines scheduling to minimize job tardiness and machine deteriorating cost with deteriorating jobs

This paper studies the parallel machines bi-criteria scheduling problem (PMBSP) in a deteriorating system. Sequencing and scheduling problems (SSP) have seldom considered the two phenomena concurrently. This paper discusses the parallel machines scheduling problem with the effects of machine and job deterioration. By the machine deterioration effect, we mean that each machine deteriorates at a different rate. This deterioration is considered in terms of cost which depends on the production rate, the machine’s operating characteristics and the kind of work done by each machine. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizing total tardiness and machine deteriorating cost. The problem of total tardiness on identical parallel machines is NP-hard, thus the problem with machine deteriorating cost as an additional term is also NP-hard. We propose the LP-metric method to show the importance of our proposed multi-objective problem. A metaheuristic algorithm is developed to locate optimal or near optimal solutions based on a Tabu search mechanism. Numerical examples are presented to show the efficiency of this model.     

The Coordination of Scheduling and Batch Deliveries

This paper considers several scheduling problems where deliveries are made in batches with each batch delivered to the customer in a single shipment. Various scheduling costs, which are based on the delivery times of the jobs, are considered. The objective is to minimize the scheduling cost plus the delivery cost, and both single and parallel machine environments are considered. For many combinations of these, we either provide efficient algorithms that minimize total cost or show that the problem is intractable. Our work has implications for the coordination of scheduling with batch delivery decisions to improve customer service.     

On the two-phase method for preemptive scheduling

Preemptive scheduling problems on parallel processors may in some cases be solved by a two-phase method: forst solve a LP problem which will give the minimum total completion time T and the processing times of the jobs on the various processors; second construct a feasible schedule using T time units. Some extensions of this procedure are discussed. A general model for preemptive scheduling is described with a two-phase method for solving problems of this type.     

Tri-directional Scheduling Scheme: Theory and Computation

In this paper we introduce a new scheduling scheme based on so called tri-directional scheduling strategy to solve the well known resource constrained project scheduling problem. In order to demonstrate the effectiveness of tri-directional scheduling scheme, it is incorporated into a priority rule based parallel scheduling scheme. Theoretical and numerical investigations show that the tri-directional scheduling scheme outperforms forward, backward and even bidirectional schemes depending on the problem structure and the priority rule used. Based on empirical evidence, it seems that as the number of activities are increased, the tri-directional scheduling scheme performs better irrespective of the priority rule used. This suggests that tri-directional scheme should also be applied within the category of heuristic methods.     

A branch-and-price algorithm for scheduling parallel machines with sequence dependent setup times

We consider the problem of scheduling n independent jobs on m unrelated parallel machines with sequence-dependent setup times and availability dates for the machines and release dates for the jobs to minimize a regular additive cost function. In this work, we develop a new branch-and-price optimization algorithm for the solution of this general class of parallel machines scheduling problems. A new column generation accelerating method, termed “primal box”, and a specific branching variable selection rule that significantly reduces the number of explored nodes are proposed. The computational results show that the approach solves problems of large size to optimality within reasonable computational time.     

基于标准规范法的四元行偶优化决策问题探究

把2n个平行工序调整为n个顺序工序对是一类典型的资源限制项目排序问题,到目前为止,没有简便有效的解决方法.为了给该类问题的解决提供理论和方法,针对八个平行工序调整为四个顺序工序对的优化决策问题,在重心定理、行偶亏值定理和最佳行偶定理的基础上,提出了标准规范法,并对其进行了理论上的证明.最后,通过算例对该算法的简单可行性进行阐释.     

An alternative framework to Lagrangian relaxation approach for job shop scheduling

A new Lagrangian relaxation (LR) approach is developed for job shop scheduling problems. In the approach, operation precedence constraints rather than machine capacity constraints are relaxed. The relaxed problem is decomposed into single or parallel machine scheduling subproblems. These subproblems, which are NP-complete in general, are approximately solved by using fast heuristic algorithms. The dual problem is solved by using a recently developed “surrogate subgradient method” that allows approximate optimization of the subproblems. Since the algorithms for subproblems do not depend on the time horizon of the scheduling problems and are very fast, our new LR approach is efficient, particularly for large problems with long time horizons. For these problems, the machine decomposition-based LR approach requires much less memory and computation time as compared to a part decomposition-based approach as demonstrated by numerical testing.     

解特殊工艺约束下提前/拖后调度问题的新遗传算法

针对非一致并行机环境下特殊工艺约束提前/拖后调度问题,设计了一个基于向量组编码的新遗传算法,此算法的编码方法简单,能有效地反映实际调度方案,即清楚地反映出每机器加工产品的代号和顺序.引入浓度概念,对种群中浓度高的个体进行抑制,从而增加群体多样性,同时,利用爬山算法对种群中个体进行局部搜索,提高了种群质量,加快了收敛速度.仿真结果表明,此算法是有效的,适用于解实际的此类调度问题.     

考虑加工速率相互干扰的平行机调度最优策略研究

随着智能互联网的应用深入、个性化消费时代的来临,制造服务企业开始注重利用网络平台为客户提供个性化的定制服务,在此过程中派生出了产品设计师可与多名客户在线同步交互的一种新型服务模式。本文根据设计师服务效率受并行服务客户数量影响的特征,将问题刻画为机器处理速度相互影响的一类平行机调度模型,以最小化总完工时间为优化目标,研究设计最优调度方案。首先,对于只有两名设计师且各自同时处理最多两个任务的情形,提出了改进的SPT调度规则,运用归纳法证明了该规则可以生成最优加工方案。其次,对改进的SPT规则进行任务分配方式的适当松驰以便更加易于操作,并证明松驰后的新分配方案保持了解的最优性。最后,将相关结论推广至多名设计师的一般情形。上述研究为个性化在线定制服务模式下的有效调度策略制定提供了良好的理论支撑。     

Idle regulation in non-clairvoyant scheduling of parallel jobs

The optimization of parallel applications is difficult to achieve by classical optimization techniques because of their diversity and the variety of actual parallel and distributed platforms and/or environments. Adaptive algorithmic schemes, capable of dynamically changing the allocation of jobs during the execution to optimize global system behavior, are the best alternatives for solving this problem. In this paper, we focus on non-clairvoyant scheduling of parallel jobs with known resource requirements but unknown running times, with emphasis on the regulation of idle periods in the context of general list policies. We consider a new family of scheduling strategies based on two phases which successively combine sequential and parallel execution of jobs. We generalize known worst-case performance bounds by considering two extra parameters, in addition to the number of processors and maximum processor requirements considered in the literature, namely, job parallelization penalty and idle regulation factor. Furthermore, we prove that under certain conditions of idle regulation, the performance guarantee of parallel job scheduling in space-sharing mode can be improved.     

A dynamic edge covering and scheduling problem: complexity results and approximation algorithms

We consider a new dynamic edge covering and scheduling problem that focuses on assigning resources to nodes in a network to minimize the amount of time required to process all edges in it. Resources need to be co-located at the endpoints of an edge for it to be processed and, therefore, this problem contains both edge covering and scheduling decisions. These new problems have motivating applications in traffic systems and military intelligence operations. We provide complexity results for the dynamic edge covering and scheduling problem over different types of networks. We then show that existing approximation algorithms for parallel machine scheduling problems can be leveraged to provide approximation algorithms for this new class of problems over certain types of networks.     

基于遗传禁忌算法的双资源约束下并行生产线调度研究

并行生产线调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题。在考虑遗传算法早熟收敛特性和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传算法和禁忌搜索算法的双资源并行作业车间的调度优化问题算法,即考虑到了产品的调度受到机器、工人等资源制约的影响,对算法中种群的构造,适应度计算,遗传操作等方面进行了研究,最后给出了实例仿真和结论。     

The resource-constrained modulo scheduling problem: an experimental study

In this paper, we focus on the resource-constrained modulo scheduling problem (RCMSP), a general periodic scheduling problem, abstracted from the problem solved by compilers when optimizing inner loops at instruction level for VLIW parallel processors. Heuristic solving scheme have been proposed since many years to solve this problem, among which the decomposed software pipeling method. In this method, a cyclic scheduling problem ignoring resource constraints is first considered and a so-called legal retiming of the operations is issued. Second, a standard acyclic problem, taking this retiming as input, is solved through list scheduling techniques. In this paper, we propose a novel hybrid approach, which uses the decomposed software pipeling method to obtain a good retiming. Then the obtained retiming is used to build an integer linear programming formulation of reduced size, which allows to solve it exactly. Experimental results show that a lot more problems are solved with this new approach. The gap to the optimal solution is less than 1 % on most of the tested problem instances and the method appears to be competitive with a recently proposed constraint programming algorithm (Bonfietti et al., Lect. Notes Comput. Sci. 6876:130–144, 2011).     

A united search particle swarm optimization algorithm for multiobjective scheduling problem

The performance of a scheduling system, in practice, is not evaluated to satisfy a single objective, but to obtain a trade-off schedule regarding multiple objectives. Therefore, in this research, I make use of multiple objective decision-making method, a global criterion approach, to develop a multi-objective scheduling problem model with different due-dates on parallel machines processes, in which consider three performance measures, namely minimum run time of every machine, earlierness time (no tardiness) and process time of every job, simultaneously. According to this special multi-objective scheduling problem, the method of reverse order drawing GATT will be proposed, at the same time, bring forward a united search particle swarm optimization algorithm (USPSOA) solves this multi-objective scheduling problem. The validity and adaptability of the USPSOA is investigated through experimental results.