A high performing metaheuristic for job shop scheduling with sequence-dependent setup times

This paper investigates scheduling job shop problems with sequence-dependent setup times under minimization of makespan. We develop an effective metaheuristic, simulated annealing with novel operators, to potentially solve the problem. Simulated annealing is a well-recognized algorithm and historically classified as a local-search-based metaheuristic. The performance of simulated annealing critically depends on its operators and parameters, in particular, its neighborhood search structure. In this paper, we propose an effective neighborhood search structure based on insertion neighborhoods as well as analyzing the behavior of simulated annealing with different types of operators and parameters by the means of Taguchi method. An experiment based on Taillard benchmark is conducted to evaluate the proposed algorithm against some effective algorithms existing in the literature. The results show that the proposed algorithm outperforms the other algorithms.     

Adaptive temperature control for simulated annealing: a comparative study

In this paper, two variations of simulated annealing method have been proposed and tested on the minimum makespan job shop scheduling problems. In the conventional simulated annealing, the temperature declines constantly, providing the search with a higher transition probability in the beginning of the search and lower probability toward the end of the search. In the first proposed method, an adaptive temperature control scheme is used that changes temperature based on the number of consecutive improving moves. In the second method, a tabu list has been added to the adaptive simulated annealing algorithm in order to avoid revisits. The performance of these two algorithms is evaluated and favorably compared with the conventional simulated annealing.     

A hybrid immune simulated annealing algorithm for the job shop scheduling problem

A hybrid simulated annealing algorithm based on a novel immune mechanism is proposed for the job shop scheduling problem with the objective of minimizing total weighted tardiness. The proposed immune procedure is built on the following fundamental idea: the bottleneck jobs existing in each scheduling instance generally constitute the key factors in the attempt to improve the quality of final schedules, and thus, the sequencing of these jobs needs more intensive optimization. To quantitatively describe the bottleneck job distribution, we design a fuzzy inference system for evaluating the bottleneck level (i.e. the criticality) of each job. By combining the immune procedure with a simulated annealing algorithm, we design a hybrid optimization algorithm which is subsequently tested on a number of job shop instances. Computational results for different-sized instances show that the proposed hybrid algorithm performs effectively and converges fast to satisfactory solutions.     

基于Hopfield神经网络的作业车间生产调度方法

该文提出了基于Hopfield神经网络的作业车间生产调度的新方法.文中给出了作业车间生产调度问题(JSP)的约束条件及其换位矩阵表示,提出了新的包括所有约束条件的计算能量函数表达式,得到相应的作业车间调度问题的Hopfield神经网络结构与权值解析表达式,并提出相应的Hopfield神经网络作业车间调度方法.为了避免Hopfield神经网络容易收敛到局部极小,从而产生非法调度解的缺点,将模拟退火算法应用于Hopfield神经网络求解,使Hopfield神经网络收敛到计算能量函数的最小值0,从而保证神经网络输出是一个可行调度方案.该文改进了已有文献中提出的作业调度问题的Hopfield神经网络方法,与已有算法相比,能够保证神经网络稳态输出为可行的作业车间调度方案.     

TSP问题的禁忌模拟退火求解

提出了一种加入了禁忌表、并且采用了新的温度控制机制的用于求解TSP问题的模拟退火算法。新算法增加了搜索结束阶段进行“爬坡”移动的概率,吸收了禁忌搜索具有较强局部搜索能力的优点和模拟退火算法产生优质解的能力,并且对问题的依赖性低于传统的模拟退火算法。对标准的TSPLib中不同国家的城市数据进行测试的实验结果表明,新的算法比传统的模拟退火算法在求解TSP问题上有更快的收敛速度,在解的质量上也有一定程度的提高。     

求解TSP的改进模拟退火算法

利用模拟退火算法给出了求解旅行商问题的一种新方法.在模拟退火算法的基本原理基础上,针对解变换只交换两个城市而容易落入局部最优解的缺点,提出了在解变换产生新解的过程中,采用逆转操作的改进方法.这使得迭代过程突破局部最优圈,然后跳到另一个搜索空间.这样能够使其更具多样性,改善了模拟退火算法的局部搜索能力.并将其应用于求解旅行商问题,显著改善了它局部寻优的能力.在几个公共测试数据集上的结果表明,算法稳定可行,在求解组合优化问题方面,具有良好的性能.     

求解SAT问题的拟人退火算法

该文利用一个简单的变换，将可满足性（SAT）问题转换为一个求相应目标函数最小值的优化问题，提出了一种用于跳出局部陷阱的拟人策略，基于模拟退火算法和拟人策略，为SAT问题的高效近注解得出了拟人退火算法（PA），该方法不仅具有模拟退火算法的全局收敛性质，而且具有一定的并行性，继承性。数值实验表明，对于本文随机产生的测试问题例，采用拟人策略的模拟退火算法的结果优于局部搜索算法，模拟退火算法以及近来国际上流行的WALKSAT算法，因此拟人退火算法是可行的和有效的。     

Improved orthogonal array based simulated annealing for design optimization

Recent research shows that simulated annealing with orthogonal array based neighbourhood functions can help in the search for a solution to a parametrical problem which is closer to an optimum when compared with conventional simulated annealing. Previous studies of simulated annealing analyzed only the main effects of variables of parametrical problems. In fact, both main effects of variables and interactions between variables should be considered, since interactions between variables exist in many parametrical problems. In this paper, an improved orthogonal array based neighbourhood function (IONF) for simulated annealing with the consideration of interaction effects between variables is described. After solving a set of parametrical benchmark function problems where interaction effects between variables exist, results of the benchmark tests show that the proposed simulated annealing algorithm with the IONF outperforms significantly both the simulated annealing algorithms with the existing orthogonal array based neighbourhood functions and the standard neighbourhood functions. Finally, the improved orthogonal array based simulated annealing was applied on the optimization of emulsified dynamite packing-machine design by which the applicability of the algorithm in real world problems can be evaluated and its effectiveness can be further validated.     

基于融合算法的测试优化选择问题研究

测试优化选择是个集覆盖问题,而启发式算法是求解集覆盖问题的有效方法。文中将遗传算法、BP神经网络和模拟退火算法进行融合,提出了一种融合算法,该算法充分利用遗传算法全局搜索能力强、BP神经网络训练能力强和模拟退火算法搜索速度快的优点,既避免陷入局部最优的现象,又提高了搜索的效率和精度。该算法已应用于求解测试优化问题。实例证明,该算法能够快速有效地求得测试优化问题的最优解。     

Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.     

一种基于模拟退火的多目标Memetic算法

为了改善多目标进化算法的搜索效率，提出了基于模拟退火的多目标Memetic算法．此算法根据Pareto占优关系评价个体适应值，采用模拟退火进行局部搜索，并结合交叉算子和基于网格密度的选择机制改善算法的收敛速度和解的均衡分布．flowshop调度问题算例的仿真结果表明，基于模拟退火的多目标Memetic算法能够产生更接近Pareto前沿的近似集．     

考虑学习效应的单人作业车间调度算法

单人负责多台机器的单一工序作业车间场景中,工人由于重复操作机器而产生学习效应.针对考虑依赖工件位置学习效应的单人单工序作业车间最小化最大完工时间的调度问题,建立一种混合整数规划模型.为解决该问题,设计一个考虑学习效应的贪婪算子,利用该算子构造两种贪婪算法,并提出一种基于贪婪的模拟退火算法.为衡量混合整数规划模型、贪婪算法和基于贪婪的模拟退火算法的性能,设计两种规模问题的数据实验.通过实验得出:现代混合整数规划模型求解器可以解决机器数量和工件总数量乘积小于75的小规模问题;基于贪婪的模拟退火算法求解此问题具有有效性,适用于各种规模的问题;间隔插入贪婪算法解决此问题速度较快,效果良好,可以应用于需要快速求解的场景.     

A grasp algorithm for m-machine flowshop scheduling problem with bicriteria of makespan and maximum tardiness

In this paper we address the problem of minimizing the weighted sum of makespan and maximum tardiness in an m-machine flow shop environment. This is a NP-hard problem in the strong sense. An attempt has been made to solve this problem using a metaheuristic called Greedy Randomized Adaptive Search Procedure (GRASP). GRASP is a competitive algorithm and is a meta-heuristic for solving combinatorial optimization problems. We have customized the basic concepts of GRASP algorithm to solve a bicriteria flow shop problem and a new algorithm named B-GRASP (Bicriteria GRASP algorithm) is proposed. The new proposed algorithm is evaluated using benchmark problems taken from Taillard and compared with the existing simulated annealing based heuristic developed by Chakravarthy and Rajendran. Computational experiments indicate that the proposed algorithm is much better than the existing one in all cases.     

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.     

超启发式遗传算法求解模糊柔性作业车间调度

本文提出一种混合超启发式遗传算法(HHGA),用于求解一类采用三角模糊数表示工件加工时间的模糊柔性作业车间调度问题(FFJSP),优化目标为最小化最大模糊完工时间(即makespan).首先,详细分析现有三角模糊数排序准则性质,并充分考虑取大操作的近似误差和模糊度,设计一种更为准确的三角模糊数排序准则,可合理计算FFJSP和其他各类调度问题解的目标函数值.其次,为实现对FFJSP解空间不同区域的有效搜索, HHGA将求解过程分为两层,高层利用带自适应变异算子的遗传算法对6种特定操作(即6种有效邻域操作)的排列进行优化;低层将高层所得的每种排列作为一种启发式算法,用于对低层相应个体进行操作来执行紧凑的变邻域局部搜索并生成新个体,同时加入模拟退火机制来避免搜索陷入局部极小.最后,仿真实验和算法比较验证了所提排序准则和HHGA的有效性.     

A simulated annealing algorithm for single machine scheduling problems with family setups

Motivated by the real-life scheduling problem in a steel-wire factory in China, this paper studies the problem of minimizing the maximum lateness on a single machine with family setups. In view of the NP-hard nature of the problem, neighborhood properties of the problem are investigated. It is found that the traditional move-based neighborhood is inefficient to search. Then a new neighborhood, which is based on batch destruction and construction, is developed. A simulated annealing algorithm with the new neighborhood is proposed. Experiments are carried out on the randomly generated problems and the real-life instances from a factory in China. Computational results show that the proposed algorithm can obtain better near optimal solutions than the existing algorithm.     

A simple simulated annealing algorithm for the maximum clique problem

Simulated annealing technique has mostly been used to solve various optimization and learning problems, and it is well known that the maximum clique problem is one of the most studied NP-hard optimization problems owing to its numerous applications. In this note, a simple simulated annealing algorithm for the maximum clique problem is proposed and tested on all 80 DIMACS maximum clique instances. Although it is simple, the proposed simulated annealing algorithm is efficient on most of the DIMACS maximum clique instances. The simulation results show that the proposed simulated annealing algorithm outperforms a recent efficient simulated annealing algorithm proposed by Xu and Ma, and the solutions obtained by the proposed simulated annealing algorithm have the equal quality with those obtained by a recent trust region heuristic algorithm of Stanislav Busygin.     

Two-dimensional equilibrium constraint layout using simulated annealing

This paper studies the layout optimization problem with equilibrium constraint. It is a two-dimensional packing problem with the industrial background of simplified satellite module layout design, and is known as NP-hard problem. By incorporating the heuristic neighborhood search mechanism and the adaptive gradient method into the simulated annealing procedure, a heuristic simulated annealing algorithm is put forward for this problem. The special neighborhood search mechanism can avoid the disadvantage of blind search in the simulated annealing algorithm, and the adaptive gradient method is used to execute local search and speed up finding the global optimal solution. Numerical examples are illustrated to verify the effectiveness of the proposed algorithm.     

Two-dimensional equilibrium constraint layout using simulated annealing

This paper studies the layout optimization problem with equilibrium constraint. It is a two-dimensional packing problem with the industrial background of simplified satellite module layout design, and is known as NP-hard problem. By incorporating the heuristic neighborhood search mechanism and the adaptive gradient method into the simulated annealing procedure, a heuristic simulated annealing algorithm is put forward for this problem. The special neighborhood search mechanism can avoid the disadvantage of blind search in the simulated annealing algorithm, and the adaptive gradient method is used to execute local search and speed up finding the global optimal solution. Numerical examples are illustrated to verify the effectiveness of the proposed algorithm.     

不同尺寸工件批调度问题的自适应蚁群退火算法*

研究不同尺寸工件单机批调度问题,将蚁群算法与模拟退火算法相结合,引入自适应状态转移概率,提出了一种自适应蚁群退火算法AACSA(adaptive ant colony simulated annealing)。该算法利用模拟退火算法实现了一种新的混合信息素更新策略,此外根据停滞次数,动态改变状态转移概率,有效地避免算法陷入停滞以及局部最优,提高算法的性能。仿真实验结果表明,AACSA与蚁群优化算法BACO、模拟退火算法SA、启发式规则BFLPT相比,算法求解的性能更好。