More companies focus on open innovation (OI) practices that enable them to acquire external resources by collaborating with external parties (e.g., companies in different industries and universities). Even though OI has received much attention in recent years, several companies are struggling to coordinate with external parties or select them. The purpose of this study is to clarify what kinds of opportunities contribute to accumulating know-how to acquire external resources through more effective OI. To achieve this, this study mainly focuses on the collaboration with suppliers that may enable such opportunities. Although this type of collaboration is a long-term relationship for companies, through this relationship, organizations can build an adequate system for coordinating with or assessing external parties. As a result, organizations having experienced collaboration with suppliers can utilize their know-how to acquire external resources. Based on this discussion, a theoretical framework is constructed and the framework is analyzed using structural equation modeling (SEM). In the analytical process, this study utilized questionnaire data obtained from Japanese manufacturing companies. As a consequence of the SEM analysis, the relationship between the degree how companies collaborate with suppliers and the degree of OI practices is evaluated.
In recent years, companies have been challenged with the growing demand to design more effective airline schedules both domestically and internationally. Typically, the airline scheduling problem has been sequentially decomposed into the following four stages: (1) Flight Scheduling Problem (FSP), (2) Fleet Assignment Problem (FAP), (3) Aircraft Maintenance Routing Problem (AMRP), and (4) Crew Scheduling Problem (CSP). Unfortunately, in most of the previous research, these problems have been solved independently for reasons of computational tractability. Since the decisions from one stage impose upon the decision-making process in subsequent stages, this approach may result in sub-optimal solutions, and there is growing consensus of the necessity for an integrated view. In this study, we propose a model that integrates departure re-timing, fleet assignment, and aircraft maintenance routing decision-making. Allowing flexibility in the departure times of scheduled flight legs determined in a FSP can increase connection opportunities in the FAP and CSP, thus saving fleet assignment costs. There are a few papers that integrate these three decisions, but our model is differentiated by the following two aspects. First, we consider the detailed schedule of each fleet, while previous papers only considered the number and type of fleet assigned to each flight leg. Second, in our model, we impose the constraint that maintenance must be conducted at pre-specified airports that have maintenance capability. By doing so, the problem becomes much more complicated and an exact algorithm cannot be applied. We propose an ant colony optimization-based algorithm that exploit the special structure of the problem. A numerical example is utilized to illustrate the model, and a sensitivity analysis of the re-timing parameters is conducted to demonstrate the effectiveness of the model proposed.
Planning the shift schedule of nurses is a time-consuming task. Senior staff members, (e.g., head nurses) spend considerable time and effort performing the activity; this scheduling problem is referred to as the nurse scheduling problem (NSP). Automating this scheduling process would be considerably beneficial. NSP is a complicated combinatorial optimization problem wherein multiple constraints of various levels are entangled vertically and horizontally. Various researchers have employed different optimization methods to solve NSP. Among these, heuristics methods, (e.g., genetic algorithms) have exhibited promising results; however, they have not satisfied all of the problem constraints. Several commercial products have also been offered to solve NSP, but they tend to be considerably expensive. A few such products are also not stable with regard to search performance. Herein, we develop an NSP system that can derive stable solutions utilizing a genetic algorithm. The NSP system proposed also satisfies the requirements of each shift and shift pattern constraint while considering the shift dulation-shift type balance for each nurse.
General waste disposal is closely related to the lives of local residents, so the effective use of thermal energy generated at the time of incineration helps to prevent global warming and becomes a major return for local residents. In this research, to effectively utilize the thermal energy generated by burning general waste, we propose a method for predicting the heat value from occurrence factors using two-stage multiple regression analysis for a case study using Chiba Prefecture. In this two-stage multiple regression analysis, the objective variables of the first stage are used for the explanatory variables of the second stage. In the first stage, regression analysis is performed separately for household and business units, and in the second stage, regression analysis is performed for each facility unit. Pooled OLS and a random effects model were used for the analysis since fixed effects were not significant. To verify the validity of the prediction method, we calculated the correlation coefficient and performed the paired t test to compare the heat value obtained from the prediction method with the actual heat value measured. As a result, the correlation coefficient was 0.4216, which exceeds that which was considered to be correlated, 0.4. The results of the paired t test showed that the null hypothesis revealed no difference in mean at a significance level of 5%, and was therefore not rejected since the P-value was 0.5548. Therefore, our research verified the effectiveness of the method proposed for predicting the heat value from occurrence factors.
The Self-organizing Map (SOM) is one of the learning models widely used in market segmentation, and Growing Hierarchical SOM (GHSOM), which is a model extended to a hierarchical structure, is also used for the task. However, GHSOM cannot increase the map size due to the limitation of the number of data allocated to the underlying map. To aim for visual understanding of market data, we newly propose construction of a model through interacting with GHSOM analysts. In the analysis, we extract the newly defined indexes that show the customers behavior from the dataset as the feature vectors. Furthermore, market segments hidden in data set are visualized based on the method we propose.