This paper presents a new algorithm for clustering a large amount of data. We improved the ant colony clustering algorithm that uses an ant’s swarm intelligence, and tried to overcome the weakness of the classical cluster analysis methods. In our proposed algorithm, improvements in the efficiency of an agent operation were achieved, and a new function “cluster condensation” was added. Our proposed algorithm is a processing method by which a cluster size is reduced by uniting similar objects and incorporating them into the cluster condensation. Compared with classical cluster analysis methods, the number of steps required to complete the clustering can be suppressed to 1% or less by performing this procedure, and the dispersion of the result can also be reduced. Moreover, our clustering algorithm has the advantage of being possible even in a small-field cluster condensation. In addition, the number of objects that exist in the field decreases because the cluster condenses; therefore, it becomes possible to add an object to a space that has become empty. In other words, first, the majority of data is put on standby. They are then clustered, gradually adding parts of the standby data to the clustering data. The method can be adopted for a large amount of data. Numerical experiments confirmed that our proposed algorithm can theoretically applied to an unrestricted volume of data.

Environmental problems have received a great deal of attention in recent years. In particular, CO₂ emissions worsen global warming and other environmental problems. The transport sector accounts for 20% of the total CO₂ emissions. Therefore, the CO₂ emission reduction of the transport sector is of great importance. In order to reduce emissions effectively, it is necessary to change the distribution and transportation processes. The purpose of this study is to minimize both the transportation costs and CO₂ emissions during transportation. Our model considers a transportation scheduling problem in which loads are transported from an overseas production base to three domestic demand centers. The need for time-space networks arises naturally to improve the model. It is possible to know the distance carriers are moving, and also consider the timetables of carriers during transportation. Carrier choice, less-than carrier load, and domestic transportation among demand centers are considered as the three target areas to reduce CO₂ emissions during the distribution process. The research model was formulated as a mixed integer programming (MIP) problem. It achieves cost reduction, and will contribute to improvement of the natural environment.

In a social system or production line, the restrictions of the cost and the due-time exist in each period. Generally, whether these restrictions are satisfied is dependent not only on the risks of this period, but also on the risks generated beforehand. We consider controlling the production process by switching the processing rate to a faster one at a given period. This paper deals with the optimal switching period to minimize the total expected cost of the production process. We first propose the optimal switching period model, and then the mathematic formulation of the total expectation is presented. Finally, the policy of optimal switching period is investigated in detail by numerical experiments.

In fixed order quantity systems, uncertainty in lead time is expressed as a set of scenarios with occurrence probabilities, and the mean and variance in demand distribution are supposed to be changeable according to a known pattern. A new concept of “dynamic robust optimal reorder point” is proposed in this paper and its value is calculated as a “robust optimal reorder point function with respect to reorder time”. Two approaches were employed in determining the dynamic optimal reorder point. The first is a shortage rate satisfaction approach and the second is a backorder cost minimization approach. The former aims at finding the minimum value of reorder point at each reorder time which satisfies the condition that the cumulative distribution function (CDF) of shortage rate under a given set of scenarios in lead time is greater than or equal to a basic CDF of shortage rate predetermined by a decision-maker. In the latter approach, the CDF of closeness of reorder point is defined at each reorder time to express how close to the optimal reorder points under the set of scenarios, and the dynamic optimal reorder point is defined according to stochastic ordering. Some numerical examples demonstrate the features of these dynamic robust optimal reorder points.

This research aims at developing a new fuzzy activity finish time estimation model for project scheduling management. With the application of the fuzzy quality function deployment (FQFD) and fuzzy analytic hierarchy process (FAHP) methods, the degree of fuzziness for every project activity is calculated in accordance with considerations of project uncertainties. These uncertainties are measured by the risk level of such project-related characteristics as time limit, activity start time, budget, manpower, technological difficulty, and facility requirements. In this paper, rather than applying the de-fuzzification technique to obtain the crisp activity duration for project scheduling, the fuzzy finish time estimation method for every activity is proposed based on the degree of fuzziness. The corresponding fuzzy activity duration time plot is also developed in a new fuzzy Gantt chart. The proposed model can provide a reasonable fuzzy finish time estimation for every activity, while most scheduling methods only provide the finish time of the entire project. Compared to existing models, this time estimation model and its corresponding Gantt chart are predicted to have higher reliability and practical application in project management and scheduling.

This paper dealt with composite scheduling problems which combine manufacturing scheduling problems and/or transportation routing problems. Two scheduling models were formulated as the elements of the composite scheduling model, and the composite model was formulated composing these models with indispensable additional constraints. A hybrid genetic algorithm was developed to solve the composite scheduling problems. An improved representation based on random keys was developed to search permutation space. A genetic algorithm based dynamic programming approach was applied to select resource. The proposed technique and a previous technique are compared by three types of problems. All results indicate that the proposed technique is superior to the previous one.

Biclustering is a method of grouping objects and attributes simultaneously in order to find multiple hidden patterns. When dealing with a long time series, there is a low possibility of finding meaningful clusters of whole time sequence. However, we may find more significant clusters containing partial time sequence by applying a biclustering method. This paper proposed a new biclustering algorithm for time series data following an autoregressive moving average (ARMA) model. We assumed the plaid model but modified the algorithm to incorporate the sequential nature of time series data. The maximum likelihood estimation (MLE) method was used to estimate coefficients of ARMA in each bicluster. We applied the proposed method to several synthetic data which were generated from different ARMA orders. Results from the experiments showed that the proposed method compares favorably with other biclustering methods for time series data.

Surface roughness is an important parameter for ensuring that the dimension of geometry is within the permitted tolerance. The ideal surface roughness is determined by the feed rate and the geometry of the tool. However, several uncontrollable factors including work material factors, tool angle, and machine tool vibration, may also influence surface roughness. The objective of this study was to compare the measured surface roughness (from experiment) to the theoretical surface roughness (from theoretical calculation) and to investigate the surface roughness resulting from two types of insert, ‘C’ type and ‘T’ type. The experiment was focused on the turning process, using a lathe machine Colchester 6000. The feed rate was varied within the recommended feed rate range. We found that there were large deviations between the measured and theoretical surface roughness at a low feed rate (0.05 mm/r) from the application of both inserts. A work material factor of AISI D2 steel that affects the chip character is presumably responsible for this phenomenon. Interestingly, at a high feed rate (0.4 mm/r), the ‘C’ type insert resulted in 40% lower roughness compared to the ‘T’ type due to the difference in insert geometry. This study shows that the geometry of an insert may result in a different surface quality at a particular level of feed rate.

Fused deposition modelling (FDM) is one of rapid prototyping (RP) technologies which uses an additive fabrication approach. Each commercially available FDM model has different types of process parameters for different applications. Some of the desired parts require excellent surface finish as well as good tolerance. The most common parameters requiring setup are the raster angle, tool path, slice thickness, build orientation, and deposition speed. The purpose of this paper is to discuss the process parameters of FDM Prodigy Plus (Stratasys, Inc., Eden Prairie, MN, USA). Various selected parameters were tested and the optimum condition was proposed. The quality of the parts produced was accessed in terms of dimensional accuracy and surface finish. The optimum parameters obtained were then applied in the fabrication of the master pattern prior to silicone rubber moulding (SRM). These parameters would reduce the post processing time. The dimensional accuracy and surface roughness were analyzed using coordinate measuring machine (CMM) and surface roughness tester, respectively. Based on this study, the recommended parameters will improve the quality of the FDM parts produced in terms of dimensional accuracy and surface roughness for the application of SRM.

Designing reliable flight control for an autonomous helicopter requires a high performance dynamics model. In this paper, a nonlinear autoregressive with exogenous inputs (NLARX) model is selected as the mathematical structure for identifying and controlling the flight of a small-scale helicopter. A neural network learning algorithm is combined with the NLARX model to identify the dynamic component of the rotorcraft unmanned aerial vehicle (RUAV). This identification process is based on the well-known gradient descent learning algorithm. As a case study, the multiple-input multiple-output (MIMO) model predictive control (MPC) is applied to control the pitch motion of the helicopter. Results of the neural network output model are closely match with the real flight data. The MPC also shows good performance under various conditions.

An assembly robot needs to be capable of executing an assembly task robustly under various uncertainties. To attain this goal, we use a task sequence tree model originally proposed for manual assembly. This model regards an assembly task under uncertainties as a transformation of the contact state concept. The concept may contain several contact states with probabilities but these are transformed through a series of task elements into the contact state concept having only the goal state at the end. The transformed contact state concept can be classified according to the terminal condition of each task element. Thus, the whole assembly task can be designed as a tree-shaped contingent strategy called a task sequence tree. This paper proposes a systematic approach for reconfiguring a task sequence tree model for application to a robotic assembly task. In addition, by taking a 2D peg-in-hole insertion task to be performed by a robot equipped with a force sensor as an example, we confirm that the proposed approach can provide a robust motion strategy for the task and that the robot can actually execute the task robustly under bounded uncertainty according to the strategy.

This study discusses a yard planning system, which considers various resources such as storage space, yard cranes, and traffic areas in container terminals. The system is based on the function for estimating resource requirements of yard plans. For a given yard plan, the proposed system allows planners to check the feasibility of the plan which requires a certain amount of workload of resources in related blocks during a planning horizon. The yard planning system in this study is aimed at balancing workloads among the blocks and providing the ability to modify current yard plans by detecting blocks and periods with overloaded workloads. The system implements its planning function in a distributed manner in which planners construct yard plans under their individual control and send and receive only limited necessary information for the negotiation.

Since the assumption of plane sections cannot be applied to the strain of unbonded tendons in prestressed concrete beams subjected to loadings, a moment-curvature nonlinear analysis method is used to develop analytical programs from stress increases in unbonded tendons at the ultimate limit state. Based on the results of model testing and simulation analysis, equations are proposed to predict the stress increase in tendons at the ultimate state in simple or continuous beams of partially prestressed concrete, considering the loading type, non-prestressed reinforcement index β_p, prestressing reinforcement index β_s, and span-depth ratio L/h as the basic parameters. Results of 380 beams studied here and test results for 35 simple beams obtained by the China Academy of Building Research were compared with those from prediction equations given in codes and other previous studies. The comparison reveals that the values predicted by the proposed equations agree well with experimental results.

The integration of water quality analysis simulation program (WASP) with a geographical information system (GIS) is presented. This integration was undertaken to enhance the data analysis and management ability of the widely used water quality model. Different types of data involved in WASP modeling were converted and integrated into GIS using a database method. The spatial data modeling and analysis capability of GIS were used in the operation of the model. The WASP water quality model was coupled with the environmental fluid dynamics code (EFDC) hydrodynamic model. A case study of the Lower Charles River Basin (Massachusetts, USA) water quality model system was conducted to demonstrate the integration process. The results showed that high efficiency of the data process and powerful function of data analysis could be achieved in the integrated model, which would significantly improve the application of WASP model in water quality management.