Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

The traditional production scheduling problem considers performance indicators such as processing time, cost, and quality as optimization objectives in manufacturing systems; however, it does not take energy consumption or environmental impacts completely into account. Therefore, this paper proposes an energy-efficient model for flexible flow shop scheduling (FFS). First, a mathematical model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as a NP-hard problem, an improved, genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption to implement a feasible scheduling. Finally, a case study of a production scheduling problem for a metalworking workshop in a plant is simulated. The experimental results show that the relationship between the makespan and the energy consumption may be apparently conflicting. In addition, an energy-saving decision is performed in a feasible scheduling. Using the decision method, there could be significant potential for minimizing energy consumption.     

System capacity for a two-commodity multistate flow network with unreliable nodes and capacity weight

The system capacity of a single-commodity flow network is the maximum flow from the source to the destination. This paper discusses the system capacity problem for a two-commodity multistate flow network composed of multistate components (edges and nodes). In particular, each component has both capacity and cost attributes. Both types of commodity, which are transmitted through the same network simultaneously, consume the capacities of edges and nodes differently. That is, the capacity weight varies with types of commodity, edges and nodes. We first define the system capacity as a 2-tuple vector and then propose a performance index, the probability that the upper bound of the system capacity is a given pattern subject to the budget constraint. Such a performance index can be easily computed in terms of upper boundary vectors. An efficient algorithm based on minimal cuts is thus presented to generate all upper boundary vectors. The manager can apply this performance index to measure the quality level of supply-demand systems such as computer, logistics, power transmission, telecommunication and urban traffic systems.     

基于混合差分进化算法的无等待流水车间总流水时间优化*

以调度的总流水时间为优化目标, 提出一种混合差分进化算法。 首先, 建立无等待流水车间调度的问题模型,并用快速方法评估总流水时间指标。 其次,采用LPV规则,实现离散问题的连续编码; 用差分进化算法对总流水时间指标执行优化;引入插入邻域和基于pairwise的局部搜索算法, 分别对差分进化算法产生的新个体和差分进化算法的最优解执行邻域搜索, 达到优化目标全局和局部的最优。 最后,通过计算标准算例, 并与其他算法比较, 验证该混合差分进化算法的有效性。     

基于UML的混合流水线制造系统仿真软件设计

UML作为当前流行的一种标准建模语言,已越来越广泛地应用于各种应用领域的软件系统设计。该文通过运用UML对混合流水线制造系统仿真软件的设计,阐明UML在系统仿真软件设计中的应用过程,说明其如何支持面向对象的开发技术以及如何实现软件结构设计等问题,同时通过混合流水线制造系统实例开发出相应的仿真软件以说明UML的应用前景。     

求解柔性流水车间调度问题的高效分布估算算法

针对最小化最大完工时间的柔性流水车间调度,利用事件建模思想,线性化0-1混合整数规划模型,使得小规模调度问题通过Cplex可以准确求解,同时设计了高效分布估算算法来求解大规模调度问题.该算法采用的是一种新颖的随机规则解码方式,工件排序按选定的规则安排而机器按概率随机分配.针对分布估算算法中的概率模型不能随种群中个体各位置上工件的更新而自动调整的缺点,提出了自适应调整概率模型,该概率模型能提高分布估算算法的收敛质量和速度.同时为提高算法局部搜索能力和防止算法陷入局部最优,设计了局部搜索和重启机制.最后,采用实验设计方法校验了高效分布估算算法参数的最佳组合.算例和实例测试结果都表明本文提出的高效分布估算算法在求解质量和稳定性上均优于遗传算法、引力搜索算法和经典分布估算算法.     

A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines,setup times,and dual criteria

This paper considers a flexible flow shop scheduling problem, where at least one production stage is made up of unrelated parallel machines. Moreover, sequence- and machine-dependent setup times are given. The objective is to find a schedule that minimizes a convex sum of makespan and the number of tardy jobs in a static flexible flow shop environment. For this problem, a 0–1 mixed integer program is formulated. The problem is, however, a combinatorial optimization problem which is too difficult to be solved optimally for large problem sizes, and hence heuristics are used to obtain good solutions in a reasonable time. The proposed constructive heuristics for sequencing the jobs start with the generation of the representatives of the operating time for each operation. Then some dispatching rules and flow shop makespan heuristics are developed. To improve the solutions obtained by the constructive algorithms, fast polynomial heuristic improvement algorithms based on shift moves and pairwise interchanges of jobs are applied. In addition, metaheuristics are suggested, namely simulated annealing (SA), tabu search (TS) and genetic algorithms. The basic parameters of each metaheuristic are briefly discussed in this paper. The performance of the heuristics is compared relative to each other on a set of test problems with up to 50 jobs and 20 stages and with an optimal solution for small-size problems. We have found that among the constructive algorithms the insertion-based approach is superior to the others, whereas the proposed SA algorithms are better than TS and genetic algorithms among the iterative metaheuristic algorithms.     

The Flexible Rare Event Sampling Harness System (FRESHS)

We present the software package FRESHS (http://www.freshs.org) for parallel simulation of rare events using sampling techniques from the ‘splitting’ family of methods. Initially, Forward Flux Sampling (FFS) and Stochastic Process Rare Event Sampling (SPRES) have been implemented. These two methods together make rare event sampling available for both quasi-static and full non-equilibrium regimes. Our framework provides a plugin system for software implementing the underlying physics of the system of interest. At present, example plugins exist for our framework to steer the popular MD packages GROMACS, LAMMPS and ESPResSo, but due to the simple interface of our plugin system, it is also easy to attach other simulation software or self-written code. Use of our framework does not require recompilation of the simulation program. The modular structure allows the flexible implementation of further sampling methods or physics engines and creates a basis for objective comparison of different sampling algorithms.     

脱离增广路径的最大流算法

提出了一个深度优先搜索最大流问题的分布式新算法,算法脱离了增广路径框架,具有一定的创新性,而且通俗易懂、形式规范,无论用于教学还是解决实际问题,都有较大的实用价值。     

一种求解随机有限缓冲区流水线调度的混合差分进化算法

针对随机有限缓冲区流水线调度问题(Flow shop scheduling problem, FSSP), 提出混合差分进化(Differential evolution, DE)算法OHTDE, 用来最小化提前/拖后指标和最小化总体完成时间指标. OHTDE将DE和最优计算量分配(Optimal computing budget allocation, OCBA)技术以及假设检验(Hypothesis test, HT)有效结合. DE用于执行全局搜索和局部搜索; OCBA用于对有限计算量进行合理分配, 从而保证优质解得到较多仿真计算量, 提高了在噪声环境下获得优质解的置信度; HT用于在统计意义上比较解的性能, 从而一定程度上避免在解空间相近区域进行重复搜索. 进而, 对由OCBA和HT确定的优质解执行一种特殊的交叉操作, 加强DE的局部开发能力. 同时也采用有限马氏链理论对OHTDE的随机收敛性进行了分析. 仿真实验和算法比较验证了算法的有效性和鲁棒性.     

A branch-and-bound algorithm for solving a two-machine flow shop problem with deteriorating jobs

In this paper we consider a two-machine flow shop scheduling problem with deteriorating jobs. By a deteriorating job we mean that the job's processing time is an increasing function of its starting time. We model job deterioration as a function that is proportional to a linear function of time. The objective is to find a sequence that minimizes the total completion time of the jobs. For the general case, we derive several dominance properties, some lower bounds, and an initial upper bound by using a heuristic algorithm, and apply them to speed up the elimination process of a branch-and-bound algorithm developed to solve the problem.     

基于随机Petri网的P2P存储系统可靠性模型和分析

可靠性是可信性研究的基本内涵之一,也是P2P ( Peer-to-Peer)存储系统研究的关键。P2P存储系统结点具有高动态特征,结点频繁地上下线给系统的可靠性分析带来困难。结点的暂时离线会增加副本数量,从而导致系统不必要的损耗。较多的副本数量会增加系统的可靠性,但是同时会导致系统的一致性维护成本增加。而较少的副本数量又会导致系统的可靠性显著降低。针对副本的数量和可靠性之间的关系,采用随机Pctri建立了P2P存储系统的可靠性模型并加以分析,围绕数据可靠性度量要求和数据副本的数量建立关系模型,从理论上指明研究系统可靠性的目的和基本原则,这可以在系统组建之初帮助优化系统的设计。     

LEO卫星网络中安全协议的建模与性能分析

在分析低轨（LEO）卫星网络的通信过程和几何特性的基础上,建立LEO卫星网络在全双工通信模式下的随机Petri网（SPN）模型,采用SPNP6．0软件,针对2种安全协议IKE和ISAKMP的野蛮交换模式,以及空间通信协议规范SCPS—SP,分析卫星网络的平均时延并进行了模型仿真。     

面向柔性作业车间调度问题的改进变邻域搜索算法

针对柔性作业车间调度问题的特点,提出一种求解该问题的改进变邻域搜索算法。结合问题特点设计合理的编码方式,采用遗传算法进行最优解搜索,将搜索的结果作为变邻域搜索算法的初始解,以提高初始解的质量。为提高局部搜索能力,设计3种不同的邻域结构,构建邻域结构集以产生邻域解,保证邻域解的搜索过程中解的可行性以提高求解效率。针对一系列典型的柔性作业车间调度问题的实例,运用所设计的改进变邻域搜索算法进行测试求解,并将计算结果与文献中其他算法的测试结果进行比较,验证了所提出方法求解柔性作业车间调度问题的可行性和有效性。