An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem

This paper proposes a Tabu-mechanism improved iterated greedy (TMIIG) algorithm to solve the no-wait flowshop scheduling problem with a makespan criterion. The idea of seeking further improvement in the iterated greedy (IG) algorithm framework is based on the observation that the construction phase of the original IG algorithm may not achieve good performance in escaping from local minima when incorporating the insertion neighborhood search. To overcome this limitation, we have modified the IG algorithm by utilizing a Tabu-based reconstruction strategy to enhance its exploration ability. A powerful neighborhood search method that involves insert, swap, and double-insert moves is then applied to obtain better solutions from the reconstructed solution in the previous step. Empirical results on several benchmark problem instances and those generated randomly confirm the advantages of utilizing the new reconstruction scheme. In addition, our results also show that the proposed TMIIG algorithm is relatively more effective in minimizing the makespan than other existing well-performing heuristic algorithms.     

一种求解无等待流水车间调度优化的启发式算法

提出了一种新的启发式算法,用于求解无等待流水车间调度问题的总流水时间指标。该算法命名为标准差启发,基于著名的NEH启发算法。首先阐述了总流水时间指标;其次描述了标准差启发算法的过程;最后用标准差启发算法求解标准实验案例,通过实验并与其他启发式算法比较,验证了标准差启发算法在求解无等待流水车间调度问题总流水时间指标的有效性。     

An evolutionary clustering search for the no-wait flow shop problem with sequence dependent setup times

This paper examines the m machine no-wait flow shop problem with setup times of a job separated from its processing time. The performance measure considered is the makespan. The hybrid metaheuristic Evolutionary Cluster Search (ECS_NSL) proposed in Nagano et al. (2012) is extended to solve the scheduling problem. The ECS_NSL performance is evaluated and the results are compared with the best method reported in the literature. Experimental tests show superiority of the ECS_NSL regarding the solution quality.     

Hybrid monkey search algorithm for flow shop scheduling problem under makespan and total flow time

This paper addresses a sub-population based hybrid monkey search algorithm to solve the flow shop scheduling problem which has been proved to be non-deterministic polynomial time hard (NP-hard) type combinatorial optimization problems. Minimization of makespan and total flow time are the objective functions considered. In the proposed algorithm, two different sub-populations for the two objectives are generated and different dispatching rules are used to improve the solution quality. To the best of our knowledge, this is the first application of monkey search algorithm to solve the flow shop scheduling problems. The performance of the proposed algorithm has been tested with the benchmark problems addressed in the literature. Computational results reveal that the proposed algorithm outperforms many other heuristics and meta-heuristics addressed in the literature.     

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。     

离散和声求解带启动时间批量流水线调度问题

分别在有等待和无等待的情况下,深入分析了带有启动时间的批量调度问题,以最小化最大完成时间为目标,提出了两种离散和声搜索算法。针对算法本质连续而问题离散的矛盾,对和声搜索算法进行改进。首先提出了基于工序的编码方式,采用inver-over和重组两种离散算子产生候选解的进化机制;并利用改进的NEH(Nawaz-Enscore-Ham)方法进行初始化,产生的高质量和多样化的初始种群有效地指导了算法的进化方向,提高收敛速度;最后将一种简单而有效的局部邻域搜索方法嵌入到和声搜索算法中以增强其局部搜索能力。仿真实验和比较结果表明了所提算法的有效性。     

A hybrid variable neighborhood search algorithm for solving the limited-buffer permutation flow shop scheduling problem with the makespan criterion

This paper investigates the limited-buffer permutation flow shop scheduling problem (LBPFSP) with the makespan criterion. A hybrid variable neighborhood search (HVNS) algorithm hybridized with the simulated annealing algorithm is used to solve the problem. A method is also developed to decrease the computational effort needed to implement different types of local search approaches used in the HVNS algorithm. Computational results show the higher efficiency of the HVNS algorithm as compared with the state-of-the-art algorithms. In addition, the HVNS algorithm is competitive with the algorithms proposed in the literature for solving the blocking flow shop scheduling problem (i.e., LBPFSP with zero-capacity buffers), and finds 54 new upper bounds for the Taillard's benchmark instances.     

动态子和声算法求解无等待批量流水线问题

针对无等待批量流水线调度问题,根据和声算法的机理,提出了一种改进的和声算法对其进行求解。利用NEH和混沌序列相结合的方法产生初始解,并实现了和声向量与工序之间的转换;充分利用最优解,设计新的更新算子,为了避免陷入局部最优,引入了变异策略;结合蛙跳算法分组的特点,将和声库随机动态的分成了几个子和声;为平衡算法的全局开发和局部搜索的能力,对子和声中的最优解执行了局部搜索。通过仿真实验与其他几种算法进行比较,证明了算法的有效性。     

解决具有混合约束柔性流水车间调度问题的粒子群优化算法

针对既存在阻塞限制工件又存在无等待约束工件的柔性流水车间调度问题, 提出了一种离散粒子群优化的求解方法。该方法采用基于排列的编码形式, 设计了推进—迭代算法进行解码并计算问题目标值, 利用离散粒子群优化算法进行全局优化, 利用迭代贪婪(iterated greedy, IG)算法提高种群个体的局部搜索能力。此外, 根据问题特点, 提出最早释放优先(first release first, FRF)和最早完工优先(first complete first, FCF)两种机器分配策略。仿真结果表明, 所提出的方法求解混合约束下柔性流水车间调度问题是可行的、有效的。     

Optimizing blocking flow shop scheduling problem with total completion time criterion

The blocking flow shop scheduling problem has found many applications in manufacturing systems. There are a few exact methods for solving this problem with different criteria. In this paper, efforts will be made to optimize the total completion time criterion for this problem. We present two mixed binary integer programming models, one of which is based on the departure times of jobs from machines, and the other is based on the idle and blocking times of jobs. An initial upper bound generator and some lower bounds and dominance rules are also developed to be used in a branch and bound algorithm. The algorithm solves 17 instances of the Taillard's benchmark problem set in less than 20 min.     

A multi-restart iterated local search algorithm for the permutation flow shop problem minimizing total flow time

A variety of metaheuristics have been developed to solve the permutation flow shop problem minimizing total flow time. Iterated local search (ILS) is a simple but powerful metaheuristic used to solve this problem. Fundamentally, ILS is a procedure that needs to be restarted from another solution when it is trapped in a local optimum. A new solution is often generated by only slightly perturbing the best known solution, narrowing the search space and leading to a stagnant state. In this paper, a strategy is proposed to allow the restart solution to be generated from a group of solutions drawn from local optima. This allows an extension of the search space, while maintaining the quality of the restart solution. A multi-restart ILS (MRSILS) is proposed, with the performance evaluated on a set of benchmark instances and compared with six state of the art metaheuristics. The results show that the easily implementable MRSILS is significantly better than five of the other metaheuristics and comparable to or slightly better than the remaining one.     

双目标流水线调度的动态双子群离散果蝇算法

提出了一种基于动态双子群的离散果蝇优化算法,求解以最大完工时间和机床空闲时间的最小化为目标的无等待流水线调度问题。与传统的果蝇算法不同,该算法采用基于工序的编码方式,并用改进的NEH方法进行初始化,提高初始解的质量;根据算法在进化过程中个体的进化水平,动态地将整个群体划分为先进子群和后进子群,简单但有效地插入方法在先进个体邻域内进化精细搜索,贪婪迭代进化机制用于优化后进个体,以此平衡算法的全局开发能力和局部搜索能力;为了提高算法效率,快速算法用于计算函数目标值和判断更新非支配解。仿真试验表明了所提果蝇算法的有效性和高效性。     

An extended teaching-learning based optimization algorithm for solving no-wait flow shop scheduling problem

The no-wait flow shop scheduling problem (NWFSSP) performs an important function in the manufacturing industry. Inspired by the overall process of teaching-learning, an extended framework of meta-heuristic based on the teaching-learning process is proposed, which consists of four parts, i.e. previewing before class, teaching phase, learning phase, reviewing after class. This paper implements a hybrid meta-heuristic based on probabilistic teaching-learning mechanism (mPTLM) to solve the NWFSSP with the makespan criterion. In previewing before class, an initial method that combines a modified Nawaz-Enscore-Ham (NEH) heuristic and the opposition-based learning (OBL) is introduced. In teaching phase, the Gaussian distribution is employed as the teacher to guide learners to search more promising areas. In learning phase, this paper presents a new means of communication with crossover. In reviewing after class, an improved speed-up random insert local search based on simulated annealing (SA) is developed to enhance the local searching ability. The computational results and comparisons based on Reeves, Taillard and VRF’s benchmarks demonstrate the effectiveness of mPTLM for solving the NWFSSP.     

On the connection between a cyclic job shop and a reentrant flow shop scheduling problem

Hall et al. (J. Sched. 2002; 5:307–327) investigated the cycle time minimization problem in a two-machine job shop, where each job consists of at most three operations. In this note, we reduce the problem to a two-machine reentrant flow shop problem and briefly discuss some consequences.     

A tabu search algorithm for the job shop problem with sequence dependent setup times

This paper addresses the classic job shop scheduling problem where sequence dependent setup times are present. Based on a modified disjunctive graph, we further investigate and generalize structural properties for the problem under study. A tabu search algorithm with a sophisticated neighbourhood structure is then developed. Compared to most studies in this research area, we are interested in moving internal critical operations rather than merely focusing on non-internal ones. Moreover, neighbourhood functions are defined using insertion techniques instead of simple swaps. Test results show that our algorithm outperforms a simulated annealing algorithm which is recently published. We have also conducted experiments considering the efficiency of developed propositions.     

An improved heuristic to minimize total flow time for scheduling in the m-machine no-wait flow shop

In this paper, we present a constructive heuristic to minimize total flow time criterion for the well-known NP-hard no-wait flow shop scheduling problem. It is based on the assumption that the priority of a job in the initial sequence is given by the sum of its processing times on the bottleneck machines. The initial sequence of jobs thus generated is further improved using a new job insertion technique. We show, through computational experimentation, that the proposed method significantly outperforms the best-known heuristics while retaining its time complexity of O(n²). Statistical tests of significance are used to confirm the improvement in solution quality.     

批量无等待调度问题的微粒群蛙跳混合优化算法*

将离散微粒群与蛙跳算法相结合解决以最大完工时间为指标的批量无等待流水线调度问题.结合微粒群算法较强的全局收敛能力和蛙跳算法较强的深度搜索能力,设计了三种混合算法,平衡了算法的全局开发能力和局部探索能力.对随机生成不同规模的实例进行了广泛的实验,仿真实验结果的比较表明了所得混合算法的有效性和高效性.     

An improved heuristic for no-wait flow shop to minimize makespan

No-wait flow shop production has been widely applied in manufacturing, where no waiting time is allowed between intermediate operations. However, minimization of makespan for no-wait flow shop production is NP-hard. In this paper, we propose an average idle time (AIT) heuristic to minimize makespan in no-wait flow shop production. First, we take the current idle times and future idle times into consideration, proposing an initial sequence algorithm, and then use the insertion and neighborhood exchanging methods to further improve solutions. Compared with three existing best-known heuristics, our AIT heuristic can achieve the smallest deviations of 0.23% from optimum, based on Taillard’s benchmarks and 600 randomly generated instances, in the same computational complexity.     

Flow shop scheduling to minimize makespan with decreasing time-dependent job processing times

In this study, we consider an n-job, m-machine flow shop scheduling problem with decreasing time-dependent job processing times. By the decreasing time-dependent job processing times, we mean that the processing time is a decreasing function of its execution starting time. When some dominant relationships between m − 1 machines can be satisfied, we show that the makespan minimization problem can be solved in polynomial time.