Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion

Recently, iterated greedy algorithms have been successfully applied to solve a variety of combinatorial optimization problems. This paper presents iterated greedy algorithms for solving the blocking flowshop scheduling problem (BFSP) with the makespan criterion. Main contributions of this paper can be summed up as follows. We propose a constructive heuristic to generate an initial solution. The constructive heuristic generates better results than those currently in the literature. We employ and adopt well-known speed-up methods from the literature for both insertion and swap neighborhood structures. In addition, an iteration jumping probability is proposed to change the neighborhood structure from insertion neighborhood to swap neighborhood. Generally speaking, the insertion neighborhood is much more effective than the swap neighborhood for the permutation flowshop scheduling problems. Instead of considering the use of these neighborhood structures in a framework of the variable neighborhood search algorithm, two powerful local search algorithms are designed in such a way that the search process is guided by an iteration jumping probability determining which neighborhood structure will be employed. By doing so, it is shown that some additional enhancements can be achieved by employing the swap neighborhood structure with a speed-up method without jeopardizing the effectiveness of the insertion neighborhood. We also show that the performance of the iterated greedy algorithm significantly depends on the speed-up method employed. The parameters of the proposed iterated greedy algorithms are tuned through a design of experiments on randomly generated benchmark instances. Extensive computational results on Taillard’s well-known benchmark suite show that the iterated greedy algorithms with speed-up methods are equivalent or superior to the best performing algorithms from the literature. Ultimately, 85 out of 120 problem instances are further improved with substantial margins.     

Initialization strategies to enhancing the performance of genetic algorithms for the p-median problem

Finding solutions to the p-median problem is an important research topic in location science. A number of meta-heuristic methods have been developed in the literature to find optimal or near optimal solutions to large-scale p-median problems within an acceptable computational time. Among these methods, the recent literature has demonstrated the effectiveness of genetic algorithms (GAs) and hybrid GAs. In this paper, we focus on the strategies of generating the initial population of a genetic algorithm and examine the impact of such strategies on the overall GA performance in terms of solution quality and computational time. Our initialization approach first produces a near optimal solution with low computational complexity, and then uses this solution as a seed to generate a set of solutions as the initial GA population, which is then used in an existing hybrid GA to test the performance of the proposed approach. Experiments based on the forty p-median problems in the OR Library are conducted. Results demonstrate that the proposed approach can significantly reduce computational time without compromising the quality of resulting solutions in almost all cases, and the excellence of the proposed approach increases with the problem scale. Furthermore, a geo-referenced dataset is also tested and the resulting solution maps visualize and validate the principle of the proposed approach.     

Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem

This paper addresses a novel distributed assembly permutation flowshop scheduling problem that has important applications in modern supply chains and manufacturing systems. The problem considers a number of identical factories, each one consisting of a flowshop for part-processing plus an assembly line for product-processing. The objective is to minimize the makespan. To suit the needs of different CPU time and solution quality, we present a mixed integer linear model, three constructive heuristics, two variable neighborhood search methods, and an iterated greedy algorithm. Important problem-specific knowledge is obtained to enhance the effectiveness of the algorithms. Accelerations for evaluating solutions are proposed to save computational efforts. The parameters and operators of the algorithms are calibrated and analyzed using a design of experiments. To prove the algorithms, we present a total of 16 adaptations of other well-known and recent heuristics, variable neighborhood search algorithms, and meta-heuristics for the problem and carry out a comprehensive set of computational and statistical experiments with a total of 810 instances. The results show that the proposed algorithms are very effective and efficient to solve the problem under consideration as they outperform the existing methods by a significant margin.     

Rescheduling rail networks with maintenance disruptions using Problem Space Search

The creation of train timetables for long-haul single track networks is a challenging process. This task is more difficult if track maintenance disruptions are to be taken into account. This paper describes how the Problem Space Search (PSS) meta-heuristic can be used for large scale problems to create quality timetables in which both train movements and scheduled track maintenance are simultaneously considered. We show that the PSS meta-heuristic can rapidly generate a large number of alternative train timetables and then describe how the technique is generalized to construct an integrated timetable which includes track maintenance. We show how the technique can also be used as an operational tool where a revised schedule can be quickly generated to take into account the new state of a disrupted system. A case study for a single track rail network in Queensland Australia, which spans a distance of 480 km, has 57 crossing loops and typically carries over 50 trains per day is discussed.     

A tabu search procedure based on a random Roulette diversification for the weighted maximal planar graph problem

An efficient and effective tabu search implementation for the weighted maximal planar graph problem is proposed. The search incorporates a number of novel features including: the introduction of a new set of two-move operators; a move-cache-memory structure for efficiently searching the neighbourhood space; and a random roulette selection from a ranked list of best moves for diversification. The effects of these and other features on solution quality are investigated. Computational results are reported on a set of 100 benchmark instances of sizes varying from 20 to 100 vertices. The results demonstrate that the new probabilistic tabu search algorithm is very competitive with state of the art algorithms in the literature in terms of both solution quality and computational effort.Scope     

置换流水线车间调度问题的研究

随着科学技术的发展和生产规模的不断扩大,置换流水线车间调度问题已经越来越受到广大学者的关注。目前,该领域已经出现了许多优化算法,其大大提高了生产的效率。尽管有一些文献已经进行了比较和综述,但是它们没有包括最新的求解方法和研究结果。因此,文中从一个崭新的视角对该问题进行综述,并详细地对比各种算法,使广大学者对其有一个更新、更全面的了解。     

A greedy random adaptive search procedure for the weighted maximal planar graph problem

The weighted maximal planar graph (WMPG) problem seeks to find a subgraph from a given weighted complete graph such that the subgraph is planar—it can be embedded on the plane without any arcs intersecting. The subgraph is maximal—no additional arc can be added to the subgraph without destroying its planarity and it also has the maximal sum of arc weights. In this paper, the main objective is to develop, implement and empirically analyse a new greedy random adaptive search procedure (GRASP) to solve the WMPG problem. A dynamic strategy to update the restricted candidate list is proposed. An efficient data structure is developed for the Green&Al-Hakim (GH) construction heuristic. The data structure reduces the GH complexity from O(n³) to O(n²). The GH heuristic with the data structure is then integrated with advanced moves neighbourhood to develop an efficient GRASP implementation. Further, we investigate the behaviour of GRASP parameters in relation to the problem's characteristics. Finally, the developed algorithms are compared with the best-known procedures in the literature on a set of 100 test instances of sizes varying from 20 to 100 nodes.     

用于编制热轧生产流程的新型Meta-heuristic算法

带钢热轧具有特殊的生产工艺约束, 其生产流程的编制是钢铁企业生产的关键, 因此提出采用并行策略的基于多旅行商问题(MTSP)热轧轧制模型. 该模型不但考虑了板坯在宽度、厚度和硬度跳变时的约束, 还考虑了同一轧制单元内轧制板坯数量的约束. 并设计了新的Meta-heuristics算法求解此模型. 通过对某热轧带钢厂生产数据的仿真实验,表明模型和算法能有效地给出满意的排产结果, 并且具有较高的执行效率.     

Repository and Mutation based Particle Swarm Optimization (RMPSO): A new PSO variant applied to reconstruction of Gene Regulatory Network

Particle Swarm Optimization (PSO) is a meta-heuristic approach based on swarm intelligence, which is inspired by the social behaviour of bird flocking or fish schooling. The main disadvantage of the basic PSO is that it suffers from premature convergence. To prevent the process of search from premature convergence as well as to improve the exploration and exploitation capability as a whole, here, in this paper, a modified variant, named Repository and Mutation based PSO (RMPSO) is proposed. In RMPSO variant, apart from applying five-staged successive mutation strategies for improving the swarm best as referred in Enhanced Leader PSO (ELPSO), two extra repositories have been introduced and maintained to store personal best and global best solutions having same fitness values. In each step, the personal and global best solutions are chosen randomly from their respective repositories which enhance exploration capability further, retaining the exploitation capability. The computational experiment on benchmark problem instances shows that in most of the cases, RMPSO performs better than other algorithms in terms of the statistical metrics taken into account. Moreover, the performance of the proposed algorithm remains consistent in most of the cases when the dimension of the problem is scaled up. RMPSO is further applied to a practical scenario: the reconstruction of Gene Regulatory Networks (GRN) based on Recurrent Neural Network (RNN) model. The experimental results ensure that the RMPSO performs better than the state-of-the-art methods in the synthetic gene data set (gold standard) as well as real gene data set.     

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

Sensor node localization is considered as one of the most significant issues in wireless sensor networks (WSNs) and is classified as an unconstrained optimization problem that falls under NP-hard class of problems. Localization is stated as determination of physical co-ordinates of the sensor nodes that constitutes a WSN. In applications of sensor networks such as routing and target tracking, the data gathered by sensor nodes becomes meaningless without localization information. This work aims at determining the location of the sensor nodes with high precision. Initially this work is performed by localizing the sensor nodes using a range-free localization method namely, Mobile Anchor Positioning (MAP) which gives an approximate solution. To further minimize the location error, certain meta-heuristic approaches have been applied over the result given by MAP. Accordingly, Bat Optimization Algorithm with MAP (BOA-MAP), Modified Cuckoo Search with MAP (MCS-MAP) algorithm and Firefly Optimization Algorithm with MAP (FOA-MAP) have been proposed. Root mean square error (RMSE) is used as the evaluation metrics to compare the performance of the proposed approaches. The experimental results show that the proposed FOA-MAP approach minimizes the localization error and outperforms both MCS-MAP and BOA-MAP approaches.