一种求解延迟工件数最小的混合流水车间调度问题的模拟退火算法

针对延迟工件数最小的混合流水车间调度问题,给出了一种改进的模拟退火求解算法. 该算法首先给出一个启发式算法来获得初始解,然后用模拟退火算法对初始解改进. 通过交换工件在第一阶段的排序来获得一个新的解,采用最先空闲设备分配规则和先到先被加工规则,对工件在剩余各级的工序进行调度. 实验仿真表明算法是可行有效的.     

求解带组换装时间单机调度问题的禁忌搜索算法

以包头某钢铁线材企业生产实际调度问题为背景,研究了一类带组换装时间的单机调度问题.由于该问题是NP难的,本文提出了一类适合该问题的禁忌搜索算法.此外,本文将问题性质引入了禁忌搜索算法以进一步提高算法寻优性能,降低算法运行时间.本文提出的算法在随机问题和实际问题上均进行了测试,实验结果表明,本文提出的算法能在不到10秒的时间内获得实际问题的一个近似最优解.     

DBR理论求解柔性作业车间调度问题

针对柔性作业车间调度完工时间最小问题,提出一种结合DBR(鼓-缓冲器-绳子)理论和改进遗传算法的方法。在问题初始化时,建立瓶颈机器识别机制改善初始化方法,提高初始解的质量;在运算过程中依据关键路径建立瓶颈机器的识别机制和调度策略。为了更好保留每代中的优良解,采用外部精英库对优良解进行解保留。运用提出的算法求解基准测试问题,实验结果验证了算法的可行性和有效性。     

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.     

基于蝙蝠退火算法的无等待流水线调度问题研究

无等待流水线调度问题(no-wait flow shop scheduling problem,NWFSP)是一类比较重要的复杂生产调度问题,并已经被证明是典型的NP问题.蝙蝠算法(Bat algorithm,BA)是一种较新颖的群体智能算法.本文针对蝙蝠算法在求解无等待流水线调度问题上的不足,提出一种蝙蝠退火算法,它通过采用ROV的编码方式以实现离散问题的连续编码,同时为了避免算法早熟现象引入了模拟退火算法.算法采用基于NEH的局部搜索规则,在很大程度上提高了算法的性能.利用标准Car问题和Rec问题算例进行仿真实验,结果表明了改进算法的可行性和有效性.     

基于外包决策的并行调度随机模型

在传统的并行机器调度问题基础上引入了不确定随机变量,同时考虑了以产品外包为能力拓展形式的现代生产模式,建立了基于外包决策的并行调度随机模型.模型以带有拖期惩罚函数的最大化利润为目标,以遗传算法这种进化的启发式计算方法寻找最优解.同时引入虚拟机器的概念,实现了对外包情形下机器调度问题的有效处理和简化.实例证明,该模型更符合现代生产模式,极大地提高了企业的工作效率和经济效益.     

改进的混合算法求解车间作业调度问题

车间作业调度问题是个典型的NP-hard问题,为了更有效的解决车间作业调度问题,提出了一种改进的混合算法(IGASA).算法设计了一种基于当前最优解的免疫算子,算子对当前最优个体中选取运行时间最少的一台机器上的工件顺序当作疫苗,并用车间调度问题的图论模型解释了此算子的合理性.最后通过大量实验证明改进的混合算法的性能的优越性,从而证明设计的免疫算子是有意义的.     

考虑多阶段共用平行机特征的拖轮作业模式优化

在对拖轮调度问题进行分析的基础之上,将拖轮调度问题归结为一类具有多阶段共用平行机器特征的多处理器任务调度问题。建立了考虑靠泊与停泊两阶段、考虑切换时间的拖轮调度优化模型,并设计了启发式规则与模拟退火相结合的混合算法(HSA)求解该模型。通过运用所设计算法求解仿真算例,并将其结果与三种基于现行调度规则的调度方案进行比较,验证了模型与算法的高效性。基于此,对不同拖轮数量状态下的调度研究结果显示,拖轮总作业时间与平均单艘拖轮作业时间二者存在着一定的矛盾性;并指出采用拖轮适时返回停泊基地的作业模式,可有效地降低拖轮作业时间,进而提高有限拖轮资源的利用率,实现节能减排、绿色运输。     

时变条件下带时间窗车辆调度问题的模拟退火算法

带时间窗车辆调度问题(VRPTW)是一类要求满足容积和时间窗约束的车辆路径优化问题,现 有大部分相关文献只考虑了车辆行驶速度恒定的情况,忽略了各种动态因素的影响.本文研究的时变条件下带时间窗车辆调度问题将车辆行驶速度考虑成时变分段函数,并利用模拟退火算法进行求解,最后通过实验结果说明算法的有效性.     

变邻域模拟退火算法求解速度时变的VRPTW问题

本文在经典的带时间窗的车辆路径问题(VRPTW)的基础上,考虑不同时间段车辆行驶速度不同的情况,研究速度时变的带时间窗车辆路径问题(TDVRPTW),使问题更具实际意义。本文用分段函数表示不同时间段下的车辆行驶速度,并解决了速度时变条件下行驶时间计算的问题。针对模拟退火算法(SA)在求解VRPTW问题时易陷入局部最优解,变邻域搜索算法(VNS)在求解VRPTW问题时收敛速度慢的问题,本文将模拟退火算法以一定概率接受非最优解的思想和变邻域搜索算法系统地改变当前解的邻域结构以拓展搜索范围的思想结合起来,提出了一种改进的算法——变邻域模拟退火算法(SAVN),使算法在退火过程中一陷入局部最优解就改变邻域结构,更换搜索范围,以此提升算法跳出局部最优解的能力,加快收敛速度。通过在仿真实验中将SAVN算法的求解结果与VNS算法、SA算法进行对比,验证了SAVN算法确实能显著提升算法跳出局部最优解的能力。     

Minimizing the total completion time on a single machine with the learning effect and multiple availability constraints

This paper considers a single machine scheduling problem with the learning effect and multiple availability constraints that minimizes the total completion time. To solve this problem, a new binary integer programming model is presented, and a branch-and-bound algorithm is also developed for solving the given problem optimally. Since the problem is strongly NP-hard, to find the near-optimal solution for large-sized problems within a reasonable time, two meta-heuristics; namely, genetic algorithm and simulated annealing are developed. Finally, the computational results are provided to compare the result of the binary integer programming, branch-and-bound algorithm, genetic algorithm and simulated annealing. Then, the efficiency of the proposed algorithms is discussed.     

基于模拟退火算法的矩形优化排样问题的研究

考虑了一种矩形优化排样系统中遗传算法和模拟退火算法的结合算法.首先建立了该系统的通用数学模型.然后给出了求解该问题的遗传模拟退火算法.最后用VC++6.0模拟算例的结果表明该算法是一种行之有效的方法.     

Resource constraint scheduling with a fractional shared resource

We consider a scheduling problem motivated by mining in remote off-grid areas. In this model, mines have pre-assigned mineral processing jobs and their own machine for executing these jobs. Each job also needs a certain amount of electricity in order to get completed. The electricity, on the other hand, is of limited supply and must be shared between the mines. We present a mathematical formulation of the problem and a Lagrangian relaxation based heuristic. Computational results which compares our heuristic with genetic algorithm and simulated annealing are also presented.     

A simulated annealing technique for multi-objective simulation optimization

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.     

Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times

We compare several heuristics for solving a single machine scheduling problem. In the operating situation modelled, setup times are sequence-dependent and the objective is to minimize total tardiness. We describe an Ant Colony Optimization (ACO) algorithm having a new feature using look-ahead information in the transition rule. This feature shows an improvement in performance. A comparison with a genetic algorithm, a simulated annealing approach, a local search method and a branch-and-bound algorithm indicates that the ACO that we describe is competitive and has a certain advantage for larger problems.     

Simulated N-Body: New Particle Physics-Based Heuristics for a Euclidean Location-Allocation Problem

The general facility location problem and its variants, including most location-allocation and P-median problems, are known to be NP-hard combinatorial optimization problems. Consequently, there is now a substantial body of literature on heuristic algorithms for a variety of location problems, among which can be found several versions of the well-known simulated annealing algorithm. This paper presents an optimization paradigm that, like simulated annealing, is based on a particle physics analogy but is markedly different from simulated annealing. Two heuristics based on this paradigm are presented and compared to simulated annealing for a capacitated facility location problem on Euclidean graphs. Experimental results based on randomly generated graphs suggest that one of the heuristics outperforms simulated annealing both in cost minimization as well as execution time. The particular version of location problem considered here, a location-allocation problem, involves determining locations and associated regions for a fixed number of facilities when the region sizes are given. Intended applications of this work include location problems with congestion costs as well as graph and network partitioning problems.     

Quantum annealing of the graph coloring problem

Quantum annealing extends simulated annealing by introducing artificial quantum fluctuations. The path-integral Monte Carlo version chosen is population-based and designed to be implemented on a classical computer. Its first application to the graph coloring problem is presented in this paper. It is shown by experiments that quantum annealing can outperform classical thermal simulated annealing for this particular problem. Moreover, quantum annealing proved competitive when compared with the best algorithms on most of the difficult instances from the DIMACS benchmarks. The quantum annealing algorithm has even found that the well-known benchmark graph dsjc1000.9 has a chromatic number of at most 222. This is an improvement on its best upper-bound from a large body of literature.     

A Hybrid Genetic Algorithm with Boltzmann Convergence Properties

Stochastic global search algorithms such as genetic algorithms are used to attack difficult combinatorial optimization problems. However, genetic algorithms suffer from the lack of a convergence proof. This means that it is difficult to establish reliable algorithm braking criteria without extensive a priori knowledge of the solution space. The hybrid genetic algorithm presented here combines a genetic algorithm with simulated annealing in order to overcome the algorithm convergence problem. The genetic algorithm runs inside the simulated annealing algorithm and provides convergence via a Boltzmann cooling process. The hybrid algorithm was used successfully to solve a classical 30-city traveling salesman problem; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm. This work was supported by the University of Colorado at Colorado Springs.     

A simulated annealing algorithm with the random compound move for the sequential partitioning problem of directed acyclic graphs

In this paper, we present an approach for finding a minimum cost partition of the nodes of a directed acyclic graph into subsets of a given size, subject to the constraint that the precedence relationships among the elements are satisfied, based on the concept of simulated annealing. Simulated annealing is generally applicable, and can be used to obtain solutions arbitrarily close to an optimum. However, the standard simulated annealing approach with a conventional neighbourhood structure does not yield good solutions for this problem, since this is a multiple partitioning problem and the number of subsets is not fixed. For this problem, we develop an effective neighbourhood structure and a new acceptance criterion. We also assess the effectiveness of the developed algorithm. The results show that this proposed algorithm outperforms, in terms of solution quality, any other algorithm using tabu search. The computational time of the procedure is proportional to the number of nodes in the graph.