In the shipping area of a cross-docking center, it is important to decide the chutes to be assigned each store and the docks each truck should be assigned to in order to reduce the transportation time of items between chutes and docks. However, there are cases that consume more time than anticipated because of congestion. Most of the existing literature does not consider the relaxation of congestion. Some researchers have been studying it, but there is still insufficient knowledge about it under specific situations. In this study, we propose a mathematical model for a chute and dock assignment problem based on a traditional model that not only reduces transportation time of items, but also relaxes congestion. To verify the characteristics of the proposed model, we compare this model with the traditional model in terms of objective function and results of assignment. To solve a large-scale problem that cannot be solved by an exact method, we propose a hybrid metaheuristic based on a standard genetic algorithm and local search algorithm. To evaluate the performance of the proposed metaheuristic, we compare it with the optimal solution and a standard genetic algorithm in a small-scale problem, as well as with a random search algorithm and multi-point local search algorithm in a large-scale problem.
Existing literature has claimed the need of theoretical research regarding the Agile development method (Agile), since project success or failure can be easily impacted if Agile is not introduced properly. While Agile can enable us to effectively find more upfront symptoms of the rework due to the ambiguous requirements and changes, overhead workload, such as planning and coordination activities, for iterative developments should be reduced. This paper proposes a fundamental mathematical model to illustrate the effective area of Agile by analyzing the reduction of possible rework workload and possible overhead workload in a previous study. Our numerical experiments show that the bigger the reduction rate of the probability of rework in each iteration, the wider the effective area of Agile is. However, they also indicate that the effective area should be impacted more significantly by the overhead workload.
In this paper, the point of service encounters was considered based on the difference with the production area. Based on the servuction system which captures service at a production-like standpoint, the research theme of industrial engineering is discussed. As a result, three sub-systems in the servuction system and five factors in the service system are discussed. Finally, an example of the research theme in each system is shown.
The developments in information technology have highlighted the importance of analyzing big data stored in various databases. With this as a background, the importance of distributed data mining (DDM), which is the technique of implementing data mining while databases are not transmitting raw data to each other, has been advocated. As one of the methods, Forrero et al. proposed the method of optimal learning with a support vector machine (SVM) that uses the alternating direction method of multipliers (ADMM) in the context of DDM. The apparatus is called a consensus-based distributed support vector machine (D-SVM). This method can learn the optimal hyperplane with a relatively small number of iterations and minimal communication cost for an arbitrary network structure without sharing data. However, when the statistical characteristics of data stored in each database are quite different, this method requires many iterations until convergence. Needless to say, it is better that the number of iterations and total communication cost for the learning classifier are minimized. In this study, we propose a new and effective learning method that reduces the number of iterations considering the network structure, provided that all of the nodes are connected to each other. To verify the effectiveness of the proposed method, a simulation experiment using the UCI machine learning repository and artificial data is conducted.
There is an ongoing effort to consolidate existing elements in various fields such as government, medicine, and education. At the same time, society is facing considerable challenges, such as a projected aging of the population, a decline in the younger population, and a shortage of workers. To respond to these issues, it is necessary to classify the features of the objects in each field and to propose efficient improvements. In this research, we propose a structure and flow for a classification analysis foundation, as well as an environment in which classifications can be performed on any scale. A data envelopment analysis (DEA) is used for the classification in order to clarify the features of each element. We then use the analysis foundation to classify medical and educational institutions in order to investigate the type of categories produced. Various approaches have been used for classification. Methods used in cluster analyses include the farthest neighbor method and the k-means method. However, because these methods use average values, it is possible that the features of individual elements may remain unclear. Moreover, classification foundations include a geographically specialized foundation, which does not lend itself to general use. Furthermore, many classification analyses restrict the number of objects, casting doubt on their ability to cope with large-scale classification analyses. In this research, we propose an analysis foundation that is capable of general-purpose, large-scale classifications. The classification analysis foundation is structured to enable the classification of many objects, including medical institutions, educational institutions, and patient analyses. Here, APIs are used to provide the subject data for the classifications, and a DEA is used for the calculations to clarify the features of the objects.
In recent years, economic globalization has been advancing and many companies have started foreign operations for the purpose of cost reduction and business expansion. On the other hand, some companies have withdrawn from foreign countries for various reasons. In accordance with the situation, some companies intend to develop human resources through OJT and Off-JT. Some companies have high expectations of educational institutions, especially universities, to innovate education methodologies and curriculum. This paper aims to develop and propose an educational method using a board game entitled “Global Manufacturing Game (GMG)” for university students studying industrial engineering, where players can learn effectively while simulating global manufacturing. An experiment was conducted to verify the educational effect of the education method proposed using GMG compared to a conventional method using books. The method proposed resulted in providing more correct answers for quizzes on global manufacturing than the conventional method. Therefore, the educational effects of using GMG, the educational method proposed were verified.
In recent years, under the limited information that the means and variances of the failure and repair times are just given, the method of evaluating MTTF in the two-component standby redundancy system with priority has been proposed. In this study, by expanding the idea to evaluate MTTF proposed in the previous research, an evaluation method for the variance of the failure times in the aforementioned system is established under the premise that the means and variances of the failure and repair times are just given. Through numerical verification, the usefulness of the evaluation method constructed in this study is reconfirmed.