No abstract

Recently, last mile delivery has emerged as an essential process that greatly affects the opportunity of obtaining delivery service market share due to the rapid increase in the business-to-consumer (B2C) service market. Express delivery companies are investing to expand the capacity of hub terminals to handle increasing delivery volume. As for securing massive delivery quantity by investment, companies must examine the profitability between increasing delivery quantity and price. This study proposes two strategies for a company’s decision making regarding the adjustment of market density and price by developing a pricing and collaboration model based on the delivery time of the last mile process. A last mile delivery time function of market density is first derived from genetic algorithm (GA)-based simulation results of traveling salesman problem regarding the market density. The pricing model develops a procedure to determine the optimal price, maximizing the profit based on last mile delivery time function. In addition, a collaboration model, where a multi-objective integer programming problem is developed, is proposed to sustain long-term survival for small and medium-sized companies. In this paper, sensitivity analysis demonstrates the effect of delivery environment on the optimal price and profit. Also, a numerical example presents four different scenarios of the collaboration model to determine the applicability and efficiency of the model. These two proposed models present managerial insights for express delivery companies.

In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.

Governments around the world are planning to ban sales of vehicles running on petroleum-based fuels as an effort to reduce greenhouse gas emissions, and electric vehicles surfaced as a solution to decrease pollutants produced by the transportation sector. As a result, wireless power transfer technology has recently gained much attention as a convenient and practical method for charging electric vehicles. In this paper, patent analysis is conducted to identify emerging and vacant technology areas of wireless power transfer. Topics are first extracted from patents by text mining, and the topics with similar semantics are grouped together to form clusters. Then, the process of identifying emerging and vacant technology areas is improved by applying a time series analysis and innovation cycle of technology to the clustering result. Lastly, the results of clustering, time series, and innovation cycle are compared to minimize the possibility of misidentifying emerging and vacant technology areas, thus improving the accuracy of the identification process and the validity of the identified technology areas. The analysis results revealed that one emerging technology area and two vacant technology areas exist in wireless power transfer. The emerging technology area identified is circuitries consisting of transmitter coils and receiver coils for wireless power transfer, and the two vacant technology areas identified are wireless charging methods based on resonant inductive coupling and wireless power transfer condition monitoring methods or devices.

With globalization increasing countries' trade volumes, resource management at container terminals has become more complex. To realistically address this problem, this paper proposes a methodology to solve the berth allocation and quay crane assignment problem (BACAP) while allowing the reassignment of vessels to other terminals in a multiuser container terminal. A filtered beam search (FBS)-based heuristic, a greedy randomized adaptive search procedure (GRASP)-based heuristic, and an iterative approach are integrated to provide a solution within a reasonable time frame. Numerical experiments are conducted with realistic instances from a container terminal located in Busan, Republic of Korea. The results show that the proposed approach yields a near-optimal solution with a significantly shorter computational time, compared with a commercial solver. Keywords Container terminals • Berth allocation problem (BAP) • Quay crane assignment problem (QCAP) • Filtered beam search (FBS) • Greedy randomized adaptive search procedure (GRASP)