基于目标增量的无等待流水调度快速迭代贪婪算法

最小化总完工时间无等待流水调度是典型的NP-完全问题,广泛存在于实际生产系统.改变传统求解调度序列目标函数的模式,提出目标增量法,通过目标函数变化量判断新解的优劣,大大降低算法所需计算时间;通过证明启发式算法基本操作的目标增量性质,设计两种基本目标增量法以快速评估新产生解的质量.提出快速迭代贪婪算法FIG(Fast Iterative Greedy algorithm)求解该问题,构造初始解生成算法,提出分段式重构局部搜索方法和迭代改进全局搜索策略以进一步提高解的质量.基于110个经典Benchmark实例,将提出的FIG算法与目前求解该问题较好的启发式算法PHlp和元启发式算法SRTS、DPSOvnd进行比较,实验结果表明FIG在性能上优于SRTS和PHlp,略逊于DPSOvnd;在效率上优于SRTS和DPSOvnd,略逊于PHlp.     

P2P流媒体系统数据调度研究综述

P2P流媒体系统主要涉及成员管理和数据调度等两方面研究.主要研究内容为不同的网络拓扑结构下的数据调度问题的研究问题.P2P流媒体系统网络拓扑结构主要分为三类:树状结构、网状结构和混合结构.树状结构又分为单组播树和多组播树结构,主要采取“推”模式的数据调度机制.网状结构主要采取“拉”模式的数据调度机制.混合结构主要采取“推-拉”模式的数据调度机制.通过介绍每一类方法和相应的代表系统,并给出各类数据调度的优缺点.     

一种P2P流媒体数据调度策略的研究

针对现有P2P流媒体调度策略在确定数据块的调度优先权以及节点服务能力时存在的不足,提出了一种以数据块的紧迫度和稀缺度为基础的凋度优先权的计算方法,以及提出了邻居节点的服务能力的计算方法,经过仿真试验证明町知该策略能有效的解决现有算法的不足,使流媒体启动延迟较小、播放流畅,且能使流媒体系统负载均衡。     

P2P流媒体直播系统数据调度算法及仿真

在数据驱动的P2P流媒体直播系统的研究中,数据调度算法的优劣影响流媒体的播放质量.因此主要研究了P2P流媒体直播系统中的数据调度问题.通过定义请求数据块的播放质量优先级,提出了最大化播放质量优先级的分布式调度模型.模型首先预测出本周期内请求节点与邻居节点之间的实际带宽,然后建立最优化数学模型并将其转换为等价的指派问题,根据该指派问题构造等价的赋权完全二部图,最后利用Kuhn-Munkres算法求出本周期的数据块调度策略.利用P2PStrmSim仿真器仿真,结果表明,所提出的分布式调度算法的性能比传统调度策略有显著提高.     

可分级P2P流媒体的自适应传输调度算法

可分级视频编码(SVC)技术实现了从单一码流中得到不同帧率、分辨率和图像质量的视频数据,使其更能应对网络的抖动.P2P技术已广泛应用到流媒体直播系统中,现有的SVC P2P传输调度机制主要基于传统流媒体,较少考虑SVC流媒体多层的特殊结构.本文提出一种基于层间网络编码的SVC P2P传输调度算法,称为可分级P2P流媒体的自适应传输调度算法.该算法通过预测和调整邻居节点各层的发送概率,使得请求节点能够按照预定的各层接收比例接收数据包.理论分析和仿真结果显示该算法具有较目前主流算法更好的性能.     

炼钢连铸生产中炉机优化匹配问题的算法

将炼钢连铸生产计划中炉机优化匹配问题归结为一个不允许等待的混合流水车间排序问题来进行研究,提出了一个启发式算法-最小偏差算法。通过实验设计,用大量随机数据进行了模拟和统计分析。结果表明,最小偏差算法是一种合理的、实用的、有效的算法。     

Scheduling in a continuous galvanizing line

In this paper we address a sequencing problem in a Continuous Galvanizing Line of a Spanish Steel Company. Production scheduling in this context is an extremely complex task which needs to take into account many constraints. We present a conceptually simple model and a Tabu Search (TS) algorithm that efficiently solves it. The TS moves are defined in order to repair non-satisfied constraints, leading to smaller and more efficient neighbourhoods. The TS co-ordinates several intensification and diversification procedures guided by an evaluation function based on a shifting penalty strategy. This function reinforces the anticycling mechanism and makes the algorithm avoid already visited solutions. Our approach has been tested on some real instances from the galvanizing line. Computational results show that the TS improves, in all instances, the company solutions.     

Lower tolerance-based Branch and Bound algorithms for the ATSP

In this paper, we develop a new tolerance-based Branch and Bound algorithm for solving NP-hard problems. In particular, we consider the asymmetric traveling salesman problem (ATSP), an NP-hard problem with large practical relevance. The main algorithmic contribution is our lower bounding strategy that uses the expected costs of including arcs in the solution to the assignment problem relaxation of the ATSP, the so-called lower tolerance values. The computation of the lower bound requires the calculation of a large set of lower tolerances. We apply and adapt a finding from [23] that makes it possible to compute all lower tolerance values efficiently. Computational results show that our Branch and Bound algorithm exhibits very good performance in comparison with state-of-the-art algorithms, in particular for difficult clustered ATSP instances.     

A travelling salesman problem with allocation, time window and precedence constraints — an application to ship scheduling

A Travelling Salesman Problem with Allocation, Time Window and Precedence Constraints (TSP-ATWPC) is considered. The TSP-ATWPC occurs as a subproblem of optimally sequencing a given set of port visits in a real bulk ship scheduling problem, which is a combined multi-ship pickup and delivery problem with time windows and multi-allocation problem. Each ship in the fleet is equipped with a flexible cargo hold that can be partitioned into several smaller holds in a given number of ways, thus allowing multiple products to be carried simultaneously by the same ship. The allocation constraints of the TSP-ATWPC ensure that the partition of the ship's flexible cargo hold and the allocation of cargoes to the smaller holds are feasible throughout the visiting sequence. The TSP-ATWPC is solved as a shortest path problem on a graph whose nodes are the states representing the set of nodes in the path, the last visited node and the accumulated cargo allocation. The arcs of the graph represent transitions from one state to another. The algorithm is a forward dynamic programming algorithm. A number of domination and elimination tests are introduced to reduce the state space. The computational results show that the proposed algorithm for the TSP-ATWPC works, and optimal solutions are obtained to the real ship scheduling problem.     

Online and Offline Algorithms for the Time-Dependent TSP with Time Zones

The time-dependent traveling salesman problem (TDTSP) is a variant of TSP with time-dependent edge costs. We study some restrictions of TDTSP where the number of edge cost changes are limited. We find competitive ratios for online versions of TDTSP. From these we derive polynomial time approximation algorithms for graphs with edge costs one and two. In addition, we present an approximation algorithm for the orienteering problem with edge costs one and two.     

A Puzzle-Based Genetic Algorithm with Block Mining and Recombination Heuristic for the Traveling Salesman Problem

In this research, we introduce a new heuristic approach using the concept of ant colony optimization (ACO) to extract patterns from the chromosomes generated by previous generations for solving the generalized traveling salesman problem. The proposed heuristic is composed of two phases. In the first phase the ACO technique is adopted to establish an archive consisting of a set of non-overlapping blocks and of a set of remaining cities (nodes) to be visited. The second phase is a block recombination phase where the set of blocks and the rest of cities are combined to form an artificial chromosome. The generated artificial chromosomes (ACs) will then be injected into a standard genetic algorithm (SGA) to speed up the convergence. The proposed method is called "Puzzle-Based Genetic Algorithm" or "p-ACGA". We demonstrate that p-ACGA performs very well on all TSPLIB problems, which have been solved to optimality by other researchers. The proposed approach can prevent the early convergence of the genetic algorithm (GA) and lead the algorithm to explore and exploit the search space by taking advantage of the artificial chromosomes.     

An Explicit Lower Bound for TSP with Distances One and Two

Abstract. We show that, for any ɛ>0 , it is NP-hard to approximate the asymmetric traveling salesman problem with distances one and two within 2805/2804-ɛ . For the special case where the distance function is constrained to be symmetric, we show a lower bound of 5381/5380-ɛ , for any ɛ>0 . While it was previously known that there exists some constant, strictly greater than one, such that it is NP-hard to approximate the traveling salesman problem with distances one and two within that constant, this result is a first step towards the establishment of a good bound. In our proof we develop a new gadget construction to reduce from systems of linear equations mod 2 with two unknowns in each equation and at most three occurrences of each variable. Compared with earlier reductions to the traveling salesman problem with distances one and two, ours reduces the number of cities to less than a tenth of what was previously necessary.     

改进的求解TSP混合分支裁剪法

分支裁减法是一种有效的求解小规模TSP的整数规划方法.随着TSP规模的逐步扩大,问题求解的复杂性也随之增加.在TSP的可计算数学研究领域中,局部搜索算法能快速求解TSP的局部最优解.通过将局部搜索算法与分支裁减法结合,利用局部搜索算法对分支裁减法获得上界所对应环路进行优化,使分支限界算法的上界更快地向全局最优解靠近,提高算法的求解效率,扩大了分支裁减法求解TSP的规模.     

流程企业计划调度体系结构研究

以石化企业为背景，在深入分析流程企业计划调度系统结构特征的基础上，结合流程企业计划调度的关键目标，提出了一种基于模型的中心化流程企业计划调度通用结构，突出了计划调度的中心地位．以此为指导，进一步建立并实现了基于知识的智能计划调度总体结构，为流程企业生产系统的运行优化提供了方法论的指导。     

一种基于案例和约束的排课系统

排课问题其本质就是时间表问题,属于典型的组合优化和不确定性调度问题,已经被证明为NP—Complete类问题。针对已有排课案例中大量知识和排课过程大量存在的教师和学生的特殊需求,提出了面向规则的形式化描述：TPQE描述体系,对排课问题中的强规则和弱规则进行了描述和形式化表示,同时提出了规则约束力的表示方法,设计了用于求解最大化WTPQE权值的算法——weighted SAT,开发相应的原型系统。     

蚁群算法的收敛性分析及其在TSP上的求解

根据基本蚁群算法的特点对其收敛性进行分析,给出寻找最短路径的蚁群算法收敛的充分条件.并把算法运用到旅行商问题上,试验结果表明该算法在求解TSP问题上解的精度优于组合优化算法以及遗传算法且收敛速度比较快.     

多亲交叉遗传算法及其在旅行商问题中的应用

针对传统遗传算法早熟收敛的缺点,提出多亲交叉遗传算法。与贪婪交叉算子相比,该交叉算子能够有效保留父代优质个体特征,维持群体多样性,同时能够很好地平衡深度搜索与广度搜索。基于旅行商问题的仿真测试表明了多亲交叉遗传算法的有效性。     

多工艺路线车间作业调度遗传算法实现

针对简单遗传算法在解决作业车间调度问题时只适用于简单问题的局限,研究了多工艺路线的批量调度遗传算法实现,论述了3种提高生产效率的调度策略,即采用最小批量原则对零件进行分批调度生产;将批量准备时间和零件加工时间相分离,在工件到达加工机床前做好批量加工准备;在生产加工过程中,将同批加工零件进行多次机床间转移,缩短后续机床的等待时间.同时将工序优先级调度算法加入到简单遗传算法,提出了一种全局优化的多工艺路线批量生产调度混合遗传算法.仿真结果表明,该调度算法能取得较好的效果.     

A branch-and-cut algorithm for a production scheduling problem with sequence-dependent and time-dependent setup times

This paper introduces and compares three different formulations of a production scheduling problem with sequence-dependent and time-dependent setup times on a single machine. The setup is divided into two parts: one that can be performed at any time and another one that is restricted to be performed outside of a given time interval. As a result, the setup time between two jobs is a function of the completion time of the first job. The problem can be formulated as a time-dependent traveling salesman problem, where the travel time between two nodes is a function of the departure time from the first node. We show that the resulting formulation can be strengthened to provide better linear programming relaxation lower bounds. We also introduce several families of valid inequalities which are used within a branch-and-cut algorithm. Computational experiments show that this algorithm can solve some instances with up to 50 jobs within reasonable computing times.     

New Single Machine and Job-Shop Scheduling Problems with Availability Constraints

In this paper we deal with variants of traditional cases of unavailability constraints in scheduling problems. In the literature, two main approaches are usually found. In the first one, operations can be interrupted by unavailability periods and in the second one, operations cannot be interrupted. The context we consider is more general; some operations can be interrupted, the others cannot. Moreover, we assume that information can be related to operations as well as to unavailability periods. Consequently an unavailability period can make possible or not the interruption of an operation. As an application to this new problem, the single machine problem with heads and tails and the job-shop scheduling problem are tackled. All combinations of possible cases are studied and after a review of the state-of-the-art, branch-and-bound algorithms are proposed to solve these problems. Finally, computational experiments are conducted and discussed.