基于人工免疫算法和蚁群算法求解旅行商问题

人工免疫算法具有快速随机的全局搜索能力,但对于系统中的反馈信息利用不足,往往做大量无为的冗余迭代,求解效率低。蚁群算法具有分布式并行全局搜索能力,通过信息素的积累和更新收敛于最优路径上,但初期信息素匮乏,求解速度慢。该文提出一种基于人工免疫算法和蚁群算法的混合算法,采用人工免疫算法生成信息素分布,利用蚁群算法求优化解。将该算法用于求解旅行商问题进行计算机仿真,结果表明,该算法是一种收敛速度和寻优能力都比较好的优化方法。     

基于PCTSP的热轧单元计划模型与算法

根据钢铁企业热轧产品生产工艺约束条件,将热轧生产轧制单元计划模型归结为奖金收集旅行商问题,设计了蚁群最优化算法对模型进行求解.引用某钢铁企业热轧生产轧制单元计划编制的实际问题对模型和算法进行了验证,并与遗传算法的求解结果进行了对比.实验结果表明模型和算法的优化效果和时间效率都是令人满意的.该模型和算法经过改进后可应用到包含多个轧制单元计划的轧制批量计划优化问题中.     

基于免疫文化算法的特钢加热炉调度优化

特种钢企业的生产具有品种多、批量小的特点,导致轧制线上在线加热炉组的调度成为NP难问题.本文在详细分析该类问题的基础上,提出了基于免疫文化算法的加热炉优化调度方法.利用免疫克隆算法的全局收敛性对最优调度方案进行搜索,利用文化算法形成的公共认知信念指导和加速搜索,仿真实验表明,该方法在轧机利用率、钢坯在炉内滞留时间及加热炉运行时间等多方面均优于传统的人工调度方法.     

一种求解车间作业调度问题的免疫算法

人工免疫系统是基于生物免疫系统特性而发展的新兴智能系统。基于免疫系统的克隆选择机制,提出一种求解车间作业调度问题的免疫算法。利用免疫算法较强的搜索能力可以实现全局寻优。通过使用克隆、高频变异和抗体抑制等免疫操作,提高了算法的收敛速度和种群的多样性,可以有效地克服遗传算法种群早熟化和收敛速度慢的问题。仿真结果表明,与改进后的遗传算法比较,提出的免疫算法在全局最优解和收敛速度上都有较为明显的优势。     

基于遗传算法的MES热轧型钢生产计划调度系统的研究

针对热轧型钢企业生产计划调度的约束复杂、易延误交货期、寻求最优解困难等问题,提出生产计划调度系统的结构功能设计方案,选用改进的批决策批调度策略建立计划调度模型,并利用遗传算法求解生产调度计划。以某热轧型钢企业设计实现的制造执行系统为依托,研究生产计划调度系统的执行情况,通过不同的订单数据以及不同的计划编制方法进行模拟计算和结果比较,验证了该改进型批决策与批调度模型的解可降低设备调度、节省生产时间、减少交货延误,以此来指导热轧型钢的生产可切实提高企业生产效率。     

考虑柔性机器检修的钢管热轧批量调度方法

本文从无缝钢管生产管理中提取并定义了周期性机器柔性检修环境下的钢管热轧批量调度问题,针对无缝钢管热轧阶段的生产特点,将其抽象为一类考虑序列相关设置成本和机器柔性检修的单机调度问题,建立了以最小化机器闲置时间和机器调整时间为优化目标的数学模型。分析闲置时间和检修时点的关系,证明了闲置时间最小化性质,结合问题特征设计了两阶段启发式算法。算法第一阶段采用最小轧机调整时间规则获取具有最小机器调整时间的初始批量轧制序列,第二阶段对初始轧制序列进行全局寻优搜索。基于实际生产数据设计了多种问题规模的对比实验,实验结果表明模型和算法对求解该类问题具有较好效果。     

基于混合遗传算法的热轧生产调度优化方法

热轧生产调度是一个复杂的约束组合优化问题,其生产约束包括连续轧制板坯的宽度、厚度和硬度跳变要求,轧制单元的最大长度,产品库存及交货期等.基于多旅行商模型,建立了热轧生产批量调度问题的优化模型,并提出一种混合遗传算法(遗传算法、局部搜索)求解该问题.通过应用串行边重组和并行边重组的遗传交叉算子,算法在优化过程中可以很好地处理调度约束.针对工业数据的仿真结果证明该调度模型和混合遗传算法的并行求解策略可以有效地解决热轧生产批量调度问题.     

基于粒子群算法的轧制批量计划问题研究

将轧制批量计划编制问题归结为车辆路径问题,采用粒子群算法对模型求解,设计了轧制批量计划问题的编码方案,阐明了算法的具体实现过程。计算结果表明,利用粒子群算法解决热轧批量计划问题是有效和可行的。     

基于混合遗传算法的热轧生产调度优化方法

热轧生产调度是一个复杂的约束组合优化问题，其生产约束包括连续轧制板坯的宽度、厚度和硬度跳变要求，轧制单元的最大长度，产品库存及交货期等。基于多旅行商模型，建立了热轧生产批量调度问题的优化模型，并提出一种混合遗传算法（遗传算法、局部搜索）求解该问题。通过应用串行边重组和并行边重组的遗传交叉算子，算法在优化过程中可以很好地处理调度约束。针对工业数据的仿真结果证明该调度模型和混合遗传算法的并行求解策略可以有效地解决热轧生产批量调度问题。     

基于蚁群算法的车间作业调度问题研究

对基于蚁群算法的车间作业调度问题求解进行了研究,在分析了传统蚁群算法求解车间作业调度问题容易出现早熟、收敛于局部最优解以及搜索速度慢的缺陷,提出了一种改进的混合蚁群算法。该方法在信息素更新规则上利用信息素局部更新策略和全局更新策略来进行信息素的更新,并将领域搜索与蚁群算法相结合,从而求得问题的可行解。最后,基于benchmarks问题进行了实验仿真,实验结果证明该改进混合算法的有效性及可行性。     

冷轧生产调度模型及算法

针对冷轧生产线调度问题的复杂性, 将该问题规划为拼卷优化和轧制批量计划编制两个部分. 将拼卷优化问题归结为一个多容器装箱问题, 采用一种新的智能搜索算法——离散微分进化 (DDE) 对该问题进行求解; 对于轧制批量计划编制建立了一种特殊的双旅行商问题模型, 采用基于进化策略和邻域搜索的混合启发式方法求解模型. 最后通过上海宝钢生产实际数据对所提方法进行了试验, 试验结果显示本文给出的生产调度方法是有效的.     

解决作业车间调度的微粒群退火算法*

针对微粒群优化算法在求解作业车间调度问题时存在的易早熟、搜索准确度差等缺点,在微粒群优化算法的基础上引入了模拟退火算法,从而使得算法同时具有全局搜索和跳出局部最优的能力,并且增加了对不可行解的优化,从而提高了算法的搜索效率;同时,在模拟退火算法中引入自适应温度衰变系数,使得SA算法能根据当前环境自动调整搜索条件,从而避免了微粒群优化算法易早熟的缺点。对经典JSP问题的仿真实验表明,与其他算法相比,该算法是一种切实可行、有效的方法。     

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.     

关于多处理机调度问题的量子粒子群算法研究

对多处理机调度问题建立数学模型,提出了将量子计算和粒子群算法相结合的方法来解决这类调度问题,该方法不仅寻优速度快,而且提高了进化后期算法的收敛精度。通过对比测试,体现了量子粒子群算法的有效性。     

Development of hybrid evolutionary algorithms for production scheduling of hot strip mill

A hot strip mill (HSM) produces hot rolled products from steel slabs, and is one of the most important production lines in a steel plant. The aim of HSM scheduling is to construct a rolling sequence that optimizes a set of given criteria under constraints. Due to the complexity in modeling the production process and optimizing the rolling sequence, the HSM scheduling is a challenging task for hot rolling production schedulers. This paper first introduces the HSM production process and requirements, and then reviews previous research on the modeling and optimization of the HSM scheduling problem. According to the practical requirements of hot rolling production, a mathematical model is formulated to describe two important scheduling sub-tasks: (1) selecting a subset of manufacturing orders and (2) generating an optimal rolling sequence from the selected manufacturing orders. Further, hybrid evolutionary algorithms with integration of genetic algorithm (GA) and extremal optimization (EO) are proposed to solve the HSM scheduling problem. Computational results on industrial data show that the proposed HSM scheduling solution can be applied in practice to provide satisfactory performance.     

A very fast TS/SA algorithm for the job shop scheduling problem

The job shop scheduling problem (JSP) is one of the most notoriously intractable NP-complete optimization problems. Over the last 10–15 years, tabu search (TS) has emerged as an effective algorithmic approach for the JSP. However, the quality of solutions found by tabu search approach depends on the initial solution. To overcome this problem and provide a robust and efficient methodology for the JSP, the heuristics search approach combining simulated annealing (SA) and TS strategy is developed. The main principle of this approach is that SA is used to find the elite solutions inside big valley (BV) so that TS can re-intensify search from the promising solutions. This hybrid algorithm is tested on the standard benchmark sets and compared with the other approaches. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times. For example, 17 new upper bounds among the unsolved problems are found in a short time.     

A HYBRID METAHEURISTIC FOR THE SINGLE-MACHINE TOTAL WEIGHTED TARDINESS PROBLEM

This article presents a new hybrid algorithm for combinatorial optimization that combines differential evolution (DE) with variable neighborhood search (VNS). DE (a population heuristic for optimization over continuous search spaces) is used as global optimizer for solution evolution guiding the search toward the optimal regions of the search space; VNS (a random local search heuristic based on the systematic change of neighborhood) is used as a local optimizer performing a sequence of local changes on individual DE solutions until a local optimum is found. The effectiveness of a DE-VNS approach is demonstrated on the solution of the single-machine total weighted tardiness scheduling problem. The concepts of Lamarckian and Baldwinian learning are also investigated and discussed. Experiments on known benchmark data sets show that DE-VNS with Lamarckian learning can produce high-quality schedules in a rather short computation time. DE-VNS uses a self-adapted mechanism for tuning the required control parameters, a critical feature rendering it applicable to real-life scheduling problems.     

Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling

The expanded job-shop scheduling problem (EJSSP) is a practical production scheduling problem with processing constraints that are more restrictive and a scheduling objective that is more general than those of the standard job-shop scheduling problem (JSSP). A hybrid approach involving neural networks and genetic algorithm (GA) is presented to solve the problem in this paper. The GA is used for optimization of sequence and a neural network (NN) is used for optimization of operation start times with a fixed sequence.

After detailed analysis of an expanded job shop, new types of neurons are defined to construct a constraint neural network (CNN). The neurons can represent processing restrictions and resolve constraint conflicts. CNN with a gradient search algorithm, gradient CNN in short, is applied to the optimization of operation start times with a fixed processing sequence. It is shown that CNN is a general framework representing scheduling problems and gradient CNN can work in parallel for optimization of operation start times of the expanded job shop.

Combining gradient CNN with a GA for sequence optimization, a hybrid approach is put forward. The approach has been tested by a large number of simulation cases and practical applications. It has been shown that the hybrid approach is powerful for complex EJSSP.     

基于约束规划的一类排序问题通用求解方法

多约束排序问题是生产调度中常遇到的问题,传统的优化模型及方法在适应约束改变等方面存在诸多不足。鉴于此,将多约束排序问题定义为约束满足问题,系统设计时将模型定义与求解算法分离,利用约束规划平台的基本约束构建特定领域的抽象约束库,形成可重构的多约束排序问题通用求解框架。应用时,根据问题需求不同可利用抽象约束库快速重构优化模型,针对重构的优化模型配置相应的求解算法即可实现问题求解。应用结果表明,提出的方法通用性强,可满足实际应用的要求。     

冷轧机组批量作业计划模型与算法

针对编制冷轧机组作业计划受到钢卷宽度跳跃、入口厚度跳跃和出口厚度跳跃等多个工艺约束的问题, 把排产过程归纳为非对称双旅行商问题, 建立了冷轧机组生产作业计划的Pareto多目标模型. 提出了基于Pareto非支配集的自适应多目标蚁群算法, 利用自适应蚁群算法和Pareto非支配集思想, 综合考虑多个目标, 自适应地提供蚂蚁路径搜索参数, 并对得到的非支配解集对应路径更新信息素, 引导蚂蚁向最优解集方向搜索, 最终提供多个可行的批量作业计划, 根据生产要求从中选择合适的最优排产结果. 利用某冷轧薄板厂实际的生产数据进行仿真实验, 表明模型与算法在冷轧机组批量作业计划编制过程中具有可行性.