An artificial neural network based heuristic for flow shop scheduling problems

The objective of this paper is to find a sequence of jobs in the flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the problem. The network is trained with the optimal sequences of completely enumerated five, six and seven jobs, ten machine problem and this trained network is then used to solve the problem with greater number of jobs. The sequence obtained using artificial neural network (ANN) is given as the initial sequence to a heuristic proposed by Suliman and also to genetic algorithm (GA) as one of the sequences of the population for further improvement. The approaches are referred as ANN-Suliman heuristic and ANN-GA heuristic respectively. Makespan of the sequences obtained by these heuristics are compared with the makespan of the sequences obtained using the heuristic proposed by Nawaz, Enscore and Ham (NEH) and Suliman Heuristic initialized with Campbell Dudek and Smith (CDS) heuristic called as CDS-Suliman approach. It is found that the ANN-GA and ANN-Suliman heuristic approaches perform better than NEH and CDS-Suliman heuristics for the problems considered.     

Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm

Production scheduling plays an important role in the intelligent decision support system and intelligent optimization decision technology. In the context of the globalization trend, the current production and management may extend from a single factory to a distributed production network. In this paper, we study the distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem in the distributed environment. Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder. The first five heuristics are developed based on the well-known NEH2 heuristic [B. Naderi, R. Ruiz, The distributed permutation flowshop scheduling problem, Computers & Operations Research, 37 (4) (2010) 754–768.] and the last heuristic is presented by extending the PW heuristic [Q.K. Pan, L. Wang, Effective heuristics for the blocking flowshop scheduling problem with makespan minimization, Omega, 40 (2) (2012) 218–229.] to DBFSP in an effective way. The composite heuristics that combining constructive heuristics and local searches are also studied. The proposed composite heuristics are chosen to generate an initial solution with a high level of quality. Keeping the simplicity of the IG algorithm, three local search procedures, two destruction procedures, an improved reconstruction procedure, and a simulated annealing-like acceptance criterion are well designed based on the problem-specific knowledge to enhance the IG algorithm. The computational experiments are carried out based on the 720 benchmark instances from the literature. The results show that the proposed heuristics are very effective for solving the problem under consideration and the presented IG algorithm performs significantly better than the other state-of-the-art metaheuristics from the literature.     

An efficient stochastic hybrid heuristic for flowshop scheduling

The paper addresses the problem of flowshop scheduling in order to minimize the makespan objective. Three probabilistic hybrid heuristics are presented for solving permutation flowshop scheduling problem. The proposed methodology combines elements from both constructive heuristic search and a stochastic improvement technique. The stochastic method used in this paper is simulated annealing (SA). Experiments have been run on a large number of randomly generated test problems of varying jobs and machine sizes. Our approach is shown to outperform best-known existing heuristics, including the classical NEH technique (OMEGA, 1983) and the SA based on (OMEGA, 1989) of Osman and Potts . Statistical tests of significance are performed to substantiate the claims of improvement.     

An iterated local search algorithm for the permutation flowshop problem with total flowtime criterion

An ILS algorithm is proposed to solve the permutation flowshop sequencing problem with total flowtime criterion. The effects of different initial permutations and different perturbation strengths are studied. Comparisons are carried out with three constructive heuristics, three ant-colony algorithms and a particle swarm optimization algorithm. Experiments on benchmarks and a set of random instances show that the proposed algorithm is more effective. The presented ILS improves the best known permutations by a significant margin.     

Flow shop scheduling with blocking using modified harmony search algorithm with neighboring heuristics methods

The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuristics methods (MHSNH). To improve the initial harmony memory, we apply two heuristic techniques: nearest neighbor (NN) and constructive modified NEH (MNEH). A modified version of harmony search algorithm evolves to explore and generates a new solution. The newly generated solution is then enhanced by using neighboring heuristics. Lastly, another neighboring heuristic is applied to improve the obtained solution. The proposed algorithm is evaluated using 12 real-world problem instances each with 10 samples. The experimental evaluation is accomplished using two factors: CPU computational time and the number of iterations. For the first factor, comparative evaluation against six well-established methods shows that the proposed method achieves almost the best overall results in six problem instances out of the twelve and yields fruitful results for others. For the second factor, comparative evaluation against twelve well-regarded methods shows that the proposed method achieves the best overall results in three problem instances and obtains very good results in other instances. In a nutshell, the proposed MHSNH is an effective strategy for solving the job shop scheduling problem.     

A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop

In this paper we address a hybrid flow shop scheduling problem considering the minimization of the sum of the total earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, we propose an ant colony optimization method to deal with this problem. Our proposed method has several features, including some heuristics that specifically take into account both earliness and tardiness penalties to compute the heuristic information values. The performance of our algorithm is tested by numerical experiments on a large number of randomly generated problems. A comparison with solutions performance obtained by some constructive heuristics is presented. The results show that the proposed approach performs well for this problem.     

A beam-search-based constructive heuristic for the PFSP to minimise total flowtime

In this paper we present a beam-search-based constructive heuristic to solve the permutation flowshop scheduling problem with total flowtime minimisation as objective. This well-known problem is NP-hard, and several heuristics have been developed in the literature. The proposed algorithm is inspired in the logic of the beam search, although it remains a fast constructive heuristic.The results obtained by the proposed algorithm outperform those obtained by other constructive heuristics in the literature for the problem, thus modifying substantially the state-of-the-art of efficient approximate procedures for the problem. In addition, the proposed algorithm even outperforms two of the best metaheuristics for many instances of the problem, using much lesser computation effort. The excellent performance of the proposal is also proved by the fact that the new heuristic found new best upper bounds for 35 of the 120 instances in Taillard’s benchmark.     

求解置换流水车间调度问题的改进遗传算法

针对置换流水车间调度问题的基本特征和传统遗传算法易早熟的缺陷,设计了改进遗传算法来求解此问题。采用NEH和Palmer启发式算法进行种群初始化,以提高初始解的质量;根据Metropolis准则对染色体进行选择操作,避免陷入局部最优;在变异过程中引入禁忌算法,避免迂回搜索;在算法迭代过程中引入了保优机制,避免丢失优秀染色体的基因信息;采用自适应终止准则,以保证解的质量。基于典型Benchmark算例的仿真实验结果表明,算法在求解质量和收敛速度方面明显优于NEH算法和种群经过初始优化的传统遗传算法。     

差异工件流水车间批调度问题的求解

针对流水车间批调度问题,提出一种基于群智能算法的求解思路。结合问题具体特点,给出工件集合的分批策略,设计了将Palmer和Best Fit（BF）分批规则相结合的分批方法;在批排序阶段,提出了一种改进的微粒群算法;在粒子初始生成阶段,通过引入NEH启发式算法改进了粒子的初始化质量;在全局最佳位置更新前,通过变邻域搜索优化了算法的局部搜索能力,避免了算法陷入局部最优。仿真实验表明,改进后的算法优于传统的微粒群算法和NEH启发式算法。     

求解置换流水车间调度问题的混合鸟群算法

针对置换流水车间调度问题（PFSP）,提出了一种混合鸟群算法（HBSA）以更加有效地最小化最大完工时间。首先,为了改善初始种群的质量和多样性,结合一种基于NEH（Nawaz-Enscore-Ham）的启发式算法和混沌映射提出了一种新的种群初始化方法;其次,为了使算法能够处理离散的调度问题,采用最大排序值（LRV）规则将连续的位置值转换为离散的工件排序;最后,为了强化算法对解空间的探索能力,借鉴变邻域搜索（VNS）和迭代贪婪（IG）算法的思想针对个体最佳工件排序和种群最佳工件排序分别提出了局部搜索方法。针对广泛使用的Rec标准测试集进行了仿真测试,并与目前有效的元启发式算法——刘等提出的混合差分进化算法（L-HDE）、混合共生生物搜索算法（HSOS）、离散狼群算法（DWPA）、多班级教学优化算法（MCTLBO）相比较,结果表明,HBSA取得的最佳相对误差（BRE）、平均相对误差（ARE）的平均值比上述四种算法至少下降了73.3%、76.8%,从而证明HBSA具有更强的寻优能力和更好的稳定性。尤其是针对测试算例Rec25和Rec27,仅HBSA的求解结果达到了目前已知最优解,进一步证明了其优越性。     

Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan

The multistage hybrid flow shop (HFS) scheduling problems are considered in this paper. Hybrid flowshop scheduling problems were proved to be NP-hard. A recently developed cuckoo search (CS) metaheuristic algorithm is presented in this paper to minimize the makespan for the HFS scheduling problems. A constructive heuristic called NEH heuristic is incorporated with the initial solutions to obtain the optimal or near optimal solutions rapidly in the improved cuckoo search (ICS) algorithm. The proposed algorithm is validated with the data from a leading furniture manufacturing company. Computational results show that the ICS algorithm outperforms many other metaheuristics.     

求解置换流水线调度问题的改进萤火虫优化算法

针对最小化最大完成时间的置换流水线调度问题,提出了一种改进的离散萤火虫优化算法。在传统萤火虫优化算法的基础上,采用基于升序排序的随机键编码方式对萤火虫种群进行离散化处理,使用NEH算法对萤火虫种群进行初始化处理,结合遗传算法的交叉变异思想改进位置更新策略,采用个体变异方式解决孤立个体问题,提高算法的寻优能力。最后通过典型算例对改进算法进行仿真测试,实验结果表明该算法求解置换流水线调度问题时具备很强的寻优能力和鲁棒性,明显优于传统萤火虫优化算法和遗传算法,是解决置换流水线调度问题的一种有效算法。     

A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem

This paper proposes a hybrid modified global-best harmony search (hmgHS) algorithm for solving the blocking permutation flow shop scheduling problem with the makespan criterion. First of all, the largest position value (LPV) rule is proposed to convert continuous harmony vectors into job permutations. Second, an efficient initialization scheme based on the Nawaz-Enscore-Ham (NEH) heuristic is presented to construct the initial harmony memory with a certain level of quality and diversity. Third, harmony search is employed to evolve harmony vectors in the harmony memory to perform exploration, whereas a local search algorithm based on the insert neighborhood is embedded to enhance the local exploitation ability. Moreover, a new pitch adjustment rule is developed to well inherit good structures from the global-best harmony vector. Computational simulations and comparisons demonstrated the superiority of the proposed hybrid harmony search algorithm in terms of solution quality.     

基于新邻域结构的Memetic 算法求解流水车间调度问题

流水车间调度是一类典型的生产调度问题,属于NP-难问题.针对传统的最优化方法难以求解大规模问题,提出了一个Memetic算法,在算法的局部搜索中使用一种新型的基于NEH的邻域结构,并且其邻域规模随着搜索的进行能够动态变化,可以大大提高算法的搜索能力.通过对标准Benchmark问题的测试,所得结果表明提出的基于新邻域结构的Memetic算法具有较好的性能,并且优于已有文献中的粒子群算法.     

Efficient heuristic algorithms for the blocking flow shop scheduling problem with total flow time minimization

This paper proposes two constructive heuristics, i.e. HPF1 and HPF2, for the blocking flow shop problem in order to minimize the total flow time. They differ mainly in the criterion used to select the first job in the sequence since, as it is shown, its contribution to the total flow time is not negligible. Both procedures were combined with the insertion phase of NEH to improve the sequence. However, as the insertion procedure does not always improve the solution, in the resulting heuristics, named NHPF1 and NHPF2, the sequence was evaluated before and after the insertion to keep the best of both solutions. The structure of these heuristics was used in Greedy Randomized Adaptive Search Procedures (GRASP) with variable neighborhood search in the improvement phase to generate greedy randomized solutions. The performance of the constructive heuristics and of the proposed GRASPs was evaluated against other heuristics from the literature. Our computational analysis showed that the presented heuristics are very competitive and able to improve 68 out of 120 best known solutions of Taillard’s instances for the blocking flow shop scheduling problem with the total flow time criterion.     

Parallel hyper heuristic algorithm based on reinforcement learning for the corridor allocation problem and parallel row ordering problem

Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported that hyper heuristics are well applied in combinatorial optimisation problems. As a classic combinatorial optimisation problem, the row layout problem has not been publicly reported on applying hyper heuristics to its various sub-problems. To fill this gap, this study proposes a parallel hyper-heuristic approach based on reinforcement learning for corridor allocation problems and parallel row ordering problems. For the proposed algorithm, an outer layer parallel computing framework was constructed based on the encoding of the problem. The simulated annealing, tabu search, and variable neighbourhood algorithms were used in the algorithm as low-level heuristic operations, and Q-learning in reinforcement learning was used as a high-level strategy. A state space containing sequences and fitness values was designed. The algorithm performance was then evaluated for benchmark instances of the corridor allocation problem (37 groups) and parallel row ordering problem (80 groups). The results showed that, in most cases, the proposed algorithm provided a better solution than the best-known solutions in the literature. Finally, the meta-heuristic algorithm applied to three low-level heuristic operations is taken as three independent algorithms and compared with the proposed hyper-heuristic algorithm on four groups of parallel row ordering problem instances. The effectiveness of Q-learning in selection is illustrated by analysing the comparison results of the four algorithms and the number of calls of the three low-level heuristic operations in the proposed method.     

基于改进离散和声算法的批量流水线调度研究

针对批量流水线调度问题,提出了以总流经时间为目标的改进离散和声算法。与基本的和声算法相比,该算法首先采用了基于工件序列的编码方式,使其直接应用于调度问题,同时运用NEH和SWAP方法产生初始和声库,保证了初始种群具有较高的质量和多样性。使用自适应和声微调概率参数和INSERT方法产生新解,提高了算法的优化性能。为了提高算法的局部搜索能力,结合交换扰动策略和插入邻域搜索算法给出了两种混合求解策略。仿真实验表明所提算法的有效性。     

The 2-stage assembly flowshop scheduling problem with total completion time: Efficient constructive heuristic and metaheuristic

In this paper, we address the 2-stage assembly scheduling problem where there are m machines in the first stage to manufacture the components of a product and one assembly station (machine) in the second stage. The objective considered is the minimisation of the total completion time. Since the NP-hard nature of this problem is well-established, most previous research has focused on finding approximate solutions in reasonable computation time. In our paper, we first review and derive a number of problem properties and, based on these ideas, we develop a constructive heuristic that outperforms the existing constructive heuristics for the problem, providing solutions almost in real-time. Finally, for the cases where extremely high-quality solutions are required, a variable local search algorithm is proposed. The computational experience carried out shows that the algorithm outperforms the best existing metaheuristic for the problem. As a summary, the heuristics presented in the paper substantially modify the state-of-the-art of the approximate methods for the 2-stage assembly scheduling problem with total completion time objective.     

求解一类并行多机调度问题的混合启发式算法

该文研究了一类工件具有不同释放时间的并行多机调度问题，调度目标为使总流程时间最小。针对该类调度问题具有强NP—hard的特点，首先构造了的一种启发式算法，该算法能够在很短的时间内找到次优解。由于通常启发式算法会随着问题规模的扩大导致求解的质量有所下降，结合遗传算法的全局搜索能力，提出了一种混合启发式算法进一步改善解的质量。仿真结果表明该算法很好地结合了启发式算法和遗传算法的特点，能够在较短的时间内求解较大规模的调度问题，算法的计算量小，鲁棒性好。     

A biased random key genetic algorithm for the field technician scheduling problem

This paper addresses a problem that service companies often face: the field technician scheduling problem. The problem considers the assignment of a set of jobs or service tasks to a group of technicians. The tasks are in different locations within a city, with different time windows, priorities, and processing times. Technicians have different skills and working hours. The main objective is to maximize the sum of priority values associated with the tasks performed each day. Due to the complexity of this problem, constructive heuristics that explore specific characteristics of the problem are developed. A customized Biased Random Key Genetic Algorithm (BRKGA) is also proposed. Computational tests with 1040 instances are presented. The constructive heuristics outperformed a heuristic of the literature in 90% of the instances. In a comparative study with optimal solutions obtained for small-sized problems, the BRKGA reached 99% of the optimal values; for medium- and large-sized problems, the BRKGA provided solutions that are on average 3.6% below the upper bounds.