An Improved Discrete PSO for Tugboat Assignment Problem under a Hybrid Scheduling Rule in Container Terminal

In container terminal, tugboat plays vital role in safety of ship docking. Tugboat assignment problem under a hybrid scheduling rule (TAP-HSR) is to determine the assignment between multiple tugboats and ships and the scheduling sequence of ships to minimize the turnaround time of ships. A mixed-integer programming model and the scheduling method are described for TAP-HSR problem. Then an improved discrete PSO (IDPSO) algorithm for TAP-HSR problem is proposed to minimize the turnaround time of ships. In particular, some new redefined PSO operators and the discrete updating rules of position and velocity are developed. The experimental results show that the proposed IDPSO can get better solutions than GA and basic discrete PSO

Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

The conventional k-modes algorithm and its variants have been extensively used for categorical data clustering. However, these algorithms have some drawbacks, e.g., they can be trapped into local optima and sensitive to initial clusters/modes. Our numerical experiments even showed that the k-modes algorithm could not identify the optimal clustering results for some special datasets regardless the selection of the initial centers. In this paper, we developed an integer linear programming (ILP) approach for the k-modes clustering, which is independent to the initial solution and can obtain directly the optimal results for small-sized datasets. We also developed a heuristic algorithm that implements iterative partial optimization in the ILP approach based on a framework of variable neighborhood search, known as IPO-ILP-VNS, to search for near-optimal results of medium and large sized datasets with controlled computing time. Experiments on 38 datasets, including 27 synthesized small datasets and 11 known benchmark datasets from the UCI site were carried out to test the proposed ILP approach and the IPO-ILP-VNS algorithm. The experimental results outperformed the conventional and other existing enhanced k-modes algorithms in literature, updated 9 of the UCI benchmark datasets with new and improved results

A variable neighborhood search with an effective local search for uncapacitated multilevel lot-sizing problems

In this study, we improved the variable neighborhood search (VNS) algorithm for solving uncapacitated multilevel lot-sizing (MLLS) problems. The improvement is two-fold. First, we developed an effective local search method known as the Ancestors Depth-first Traversal Search (ADTS), which can be embedded in the VNS to significantly improve the solution quality. Second, we proposed a common and efficient approach for the rapid calculation of the cost change for the VNS and other generate-and-test algorithms. The new VNS algorithm was tested against 176 benchmark problems of different scales (small, medium, and large). The experimental results show that the new VNS algorithm outperforms all of the existing algorithms in the literature for solving uncapacitated MLLS problems because it was able to find all optimal solutions (100%) for 96 small-sized problems and new best-known solutions for 5 of 40 medium-sized problems and for 30 of 40 large-sized problems

A study on human-task-related performances in converting conveyor assembly line to cellular manufacturing

In this paper, we propose a study to analyse the human-task-related performances in converting a Conveyor Assembly Line (CAL) to cellular manufacturing, which include the possible added operational tasks (which is considered a negative factor for the conversion), the skill level and the cross-training of workers. Three theoretical models (CAL, cellular manufacturing and a joint type, CAL+CM) are constructed involving those constraints respectively. A human-factor-based training approach is also represented for the system performance improvement in cellular manufacturing. Assuming the product mix and the skill level of workers are probability variables, simulation experiments based on the data collected from the previous documents are then used to estimate the marginal impact each factor change had on the estimated performance improvement resulting from the conversion. [Received 18 January 2007; Revised 13 July 2007; Accepted 26 July 2007]assembly line conversion; conveyor assembly lines; cellular manufacturing; labour efficiency; skill levels; cross-training; simulation; performance evaluation; manufacturing cells; human factors; performance improvement.

A reduced variable neighborhood search algorithm for uncapacitated multilevel lot-sizing problems

Multilevel lot-sizing (MLLS) problems, which involve complicated product structures with interdependence among the items, play an important role in the material requirement planning (MRP) system of modern manufacturing/assembling lines. In this paper, we present a reduced variable neighborhood search (RVNS) algorithm and several implemental techniques for solving uncapacitated MLLS problems. Computational experiments are carried out on three classes of benchmark instances under different scales (small, medium, and large). Compared with the existing literature, RVNS shows good performance and robustness on a total of 176 tested instances. For the 96 small-sized instances, the RVNS algorithm can find 100% of the optimal solutions in less computational time; for the 40 medium-sized and the 40 large-sized instances, the RVNS algorithm is competitive against other methods, enjoying good effectiveness as well as high computational efficiency. In the calculations, RVNS updated 7 (17.5%) best known solutions for the medium-sized instances and 16 (40%) best known solutions for the large-sized instances.Meta-heuristics Uncapacitated multilevel lot-sizing (MLLS) problem Material requirement planning (MRP) Reduced variable neighborhood search (RVNS) algorithm Production planning