Hybrid flow shop scheduling with assembly operations and key objectives: A novel neighborhood search

This paper addresses hybrid flow shop scheduling problem (HFSP) with assembly operations, in which parts of each product are produced in a hybrid flow shop and then assembled at an assembly stage. The goal is to minimize total tardiness, maximum tardiness and makespan simultaneously. Tardiness objectives are regarded as key ones because of their relative importance and this situation is seldom considered. A simple strategy is applied to handle the optimization with key objectives. A novel neighborhood search with global exchange (NSG) is proposed, in which a part-based coding method is adopted and global exchange is cooperated with neighborhood search to produce high quality solution. Extensive experiments are conducted and the results show that the strategy on key objectives is reasonable and effective and NSG is a very competitive method for the considered HFSP.     

Two-phase neighborhood search algorithm for two-agent hybrid flow shop scheduling problem

In this paper hybrid flow shop scheduling problem with two agents is studied and its feasibility model is considered. A two-phase neighborhood search (TNS) algorithm is proposed to minimize objectives of two agents simultaneously under the given upper bounds. TNS is constructed through the combination of multiple variable neighborhood mechanisms and a new perturbation strategy for new current solution. A new replacement principle is also applied to decide if the current solution can be updated. TNS is tested on a number of instances and compared with the existing methods. The computational results show the promising advantage of TNS on the considered problem.     

A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem

This paper proposes a hybrid variable neighborhood search (HVNS) algorithm that combines the chemical-reaction optimization (CRO) and the estimation of distribution (EDA), for solving the hybrid flow shop (HFS) scheduling problems. The objective is to minimize the maximum completion time. In the proposed algorithm, a well-designed decoding mechanism is presented to schedule jobs with more flexibility. Meanwhile, considering the problem structure, eight neighborhood structures are developed. A kinetic energy sensitive neighborhood change approach is proposed to extract global information and avoid being stuck at the local optima. In addition, contrary to the fixed neighborhood set in traditional VNS, a dynamic neighborhood set update mechanism is utilized to exploit the potential search space. Finally, for the population of local optima solutions, an effective EDA-based global search approach is investigated to direct the search process to promising regions. The proposed algorithm is tested on sets of well-known benchmark instances. Through the analysis of experimental results, the high performance of the proposed HVNS algorithm is shown in comparison with four efficient algorithms from the literature.     

求解流水线调度问题的万有引力搜索算法

研究了以最大完工时间为目标的流水线调度问题,使用万有引力算法求解调度问题,提出了一种最大排序规则,利用物体间各个位置分量值存在的大小次序关系,并结合随机键编码的方法产生,将物体的连续位置转变成了一个可行的调度方案;提出了一种边界变异的策略使得越界的物体不再聚集在边界上,而是分布在边界附近的可行空间内,从而增加种群的多样性;结合交换算子和插入算子提出了一种新的局部搜索算法,有效地避免了算法陷入局部最优值,进一步提高了解的质量.最后证明了算法的收敛性,并且计算了算法的时间复杂度和空间复杂度,仿真实验说明了所得算法的有效性.     

求解流水车间调度问题的混合粒子群算法

针对最小化流水车间调度总完工时间问题,提出了一种混合的粒子群优化算法(Hybrid Particle Swarm Algorithm,HPSA),采用启发式算法产生初始种群,将粒子群算法、遗传操作以及局部搜索策略有效地结合在一起。用Taillard’s基准程序随机产生大量实例,实验结果显示:HPSA通过对种群选取方法的改进和搜索范围的扩大提高了解的质量,在性能上均优于目前较有效的启发式算法和混合的禁忌搜索算法,产生最好解的平均百分比偏差和标准偏差均显著下降,最优解所占比例大幅度提高。     

An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers

This paper proposes an effective hybrid algorithm based on differential evolution (DE), namely HDE, to solve multi-objective permutation flow shop scheduling problem (MPFSSP) with limited buffers between consecutive machines, which is a typical NP-hard combinatorial optimization problem with strong engineering background. Firstly, to make DE suitable for solving scheduling problems, a largest-order-value (LOV) rule is presented to convert the continuous values of individuals in DE to job permutations. Secondly, after the DE-based exploration, an efficient local search, which is designed based on the landscape of MPFSSP with limited buffers, is applied to emphasize exploitation. Thus, not only does the HDE apply the parallel evolution mechanism of DE to perform effective exploration (global search) in the whole solution space, but it also adopts problem-dependent local search to perform thorough exploitation (local search) in the promising sub-regions. In addition, the concept of Pareto dominance is used to handle the updating of solutions in sense of multi-objective optimization. Moreover, the convergence property of HDE is analyzed by using the theory of finite Markov chain. Finally, simulations and comparisons based on benchmarks demonstrate the effectiveness and efficiency of the proposed HDE.     

Evolutionary multi-objective blocking lot-streaming flow shop scheduling with interval processing time

A blocking lot-streaming flow shop scheduling problem with interval processing time has a wide range of applications in various industrial systems, however, not yet been well studied. In this paper, the problem is formulated as a multi-objective optimization problem, where each interval objective is converted into a real-valued one using a dynamically weighted sum of its midpoint and radius. A novel evolutionary multi-objective optimization algorithm is then proposed to solve the re-formulated multi-objective optimization problem, in which non-dominated solutions and differences among parents are taken advantage of when designing the crossover operator, and an ideal-point assisted local search strategy for multi-objective optimization is employed to improve the exploitation capability of the algorithm. To empirically evaluate the performance of the proposed algorithm, a series of comparative experiments are conducted on 24 scheduling instances. The experimental results show that the proposed algorithm outperforms the compared algorithms in convergence, and is more capable of tackling uncertainties.     

求解批量流水线调度问题的和声算法

针对以最大完工时间和总流经时间为目标的批量流水线调度问题,提出了改进的和声调度算法。该算法采用基于最大位置值(LPV)规则的编码方式,使具有连续性质的和声算法应用于求解调度问题;提出新的初始化方法,应用了多种群进化的思想更新和声库,并结合和声算法和模拟退火算法各自的特点,给出了两种混合调度算法。仿真实验表明所提算法的可行性和有效性。     

Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Factory management plays an important role in improving the productivity and quality of service in the production process. In particular, the distributed permutation flow shop scheduling problem with multiple factories is considered a priority factor in the factory automation. This study proposes a novel model of the developed distributed scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop (DRPFS) scheduling. This problem is described as a given set of jobs with a number of reentrant layers is processed in the factories, which compromises a set of machines, with the same properties. The aim of the study is to determine the number of factory needs to be used, jobs assignment to certain factory and sequence of job assigned to the factory in order to simultaneously satisfy three objectives of minimizing makespan, total cost and average tardiness. To do this, a novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed for finding the near optimal solutions based on the Pareto front. Various destroy and repair operators are presented to balance between intensification and diversification of searching process. The numerical examples of computational experiments are carried out to validate the proposed model. The analytical results on the performance of proposed algorithm are checked and compared with the existing methods to validate the effectiveness and robustness of the proposed potential algorithm in handling the DRPFS problem.     

Variable neighborhood search for two-agent flow shop scheduling problem

Multi-agent scheduling in flow shop environment is seldom considered. In this paper flow shop scheduling problem with two agents is studied and its feasibility model is considered, in which the goal is to minimize the makespan of the first agent and the total tardiness of the second agent simultaneously under the given upper bounds. A simple variable neighborhood search (VNS) algorithm is proposed, in which a learning neighborhood structure is constructed to produce new solutions and a new principle is applied to decide if the current solution can be replaced with the new one. VNS is tested on a number of instances and the computational results show the promising advantage of VNS when compared to other algorithms of the problem.     

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.     

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.     

求解置换流水车间调度问题的混合蚁群算法

针对最大—最小蚂蚁系统在解决置换流水车间调度问题时易陷入局部最优的问题,引入最好—最差蚂蚁系统中的信息素变异和重置规则,提出了一种混合蚁群算法。使信息素矩阵变异并在搜索过程停滞时重置信息素矩阵以在搜索过程中引入多样性。在基准问题集上的对比实验表明,该算法比传统的蚁群算法具有更好的搜索全局最优解的能力。     

Parallel tabu search algorithm for the hybrid flow shop problem

The paper deals with the parallel variant of the scheduling algorithm dedicated to the hybrid flow shop problem. The problem derives from practice of automated manufacturing lines, e.g. for printed packages. The overall goal is to design a new algorithm which merges the performance of the best known sequential approach with the efficient exploitation of parallel calculation environments. In order to fulfill the above aim, there are two methods proposed in this paper: the original fast method of parallel calculation of the criterion function and the local neighborhood parallel search method embedded in the tabu search approach. The theoretical analysis, as well as the original implementation, with the use of vector processing instructions SSE2 supported by suitable data organization, are presented below. Numerical properties of the proposed algorithm are empirically verified on the multi-core processor.     

求解零空闲流水线调度问题的改进蚁群算法*

在研究蚁群算法的基础上,解决零空闲流水线调度问题的最大完工时间。改进了蚁群算法中信息素密度的初始化方法和更新规则,结合快速邻域搜索算法,解决算法易陷入局部收敛的缺点,提出了该算法解决零空闲调度问题的最佳求解策略。仿真实验表明,该算法具有高效性和优越性。     

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.     

Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm

This paper proposes a hybrid metaheuristic for the minimization of makespan in permutation flow shop scheduling problems. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on a greedy randomized constructive heuristic, a genetic algorithm (GA) for solution evolution, and a variable neighbourhood search (VNS) to improve the population. The hybridization of a GA with VNS, combining the advantages of these two individual components, is the key innovative aspect of the approach. Computational experiments on benchmark data sets demonstrate that the proposed hybrid metaheuristic reaches high-quality solutions in short computational times. Furthermore, it requires very few user-defined parameters, rendering it applicable to real-life flow shop scheduling problems.     

An effective neighborhood search algorithm for scheduling a flow shop of batch processing machines

This paper considers scheduling problem of flow shop with many batch processing machines and objective of maximum lateness. An effective neighborhood search algorithm (NSA) is proposed for the problem, in which a job permutation and a batch permutation are used to indicate the solution of two sub-problems, respectively. Each job permutation consists of several family-permutations for the representation of jobs from the same family. Two swaps are applied to two permutations to produce new solutions. NSA is applied to a number of instances and compared with some methods, and computational results validate the good performance of NSA.     

Flow shop问题的嵌套分区优化调度方法

介绍了嵌套分区算法(NP)的基本思想, 并用于求解流水作业优化调度问题. 算法用嵌套分区树来描述流水作业调度问题, 对可行域进行系统性分区, 然后集中搜索有优良解的区域. 在每一步迭代中, 算法跟踪最有希望的分区, 并结合启发式算法和邻域搜索来实现分区转移. 仿真实验表明, 该算法比单纯的启发式算法和邻域搜索有较好的寻优能力.     

Flexible flow shop scheduling: optimum, heuristics and artificial intelligence solutions

Abstract: For the past three decades or so the flexible flow shop (FFS) scheduling problem has attracted many researchers. Numerous research articles have been published on this topic. This study reviews research on the FFS scheduling problem from the past and the present. The solution approaches reviewed range from the optimum to heuristics and to artificial intelligence search techniques. I not only discuss the details from the selected methods and compare them, but also provide insights and suggestions for future research.