Korea ocean research stations manage the weather and environmental data collected from coastal and ocean areas to provide short-term and long-term ocean forecasts. The purpose of this paper is to analyze and quantify economic benefits of the ocean research stations with sensors to observe physical, chemical, and biological data. The construction and operation of an integrated ocean observation station is expected to reduce uncertainty about ocean and coastal areas and to improve the quality of ocean forecasts. The economic benefits are mainly come from improved search and rescue operations, ocean pollution management, yellow dust management, and improved productivity in ocean-related industries. In addition, an input-output analysis is performed to evaluate the economic impacts of ocean research stations nationwide. The analysis shows that the system can contribute to industries such as fishing, maritime and air cargo, medical and health care.

Purpose – The purpose of this paper is to partition a last-mile delivery network into zones and to determine locations of last mile delivery centers (LMDCs) in Bangkok, Thailand. Research design, data, and methodology – As online shopping has become popular, parcel companies need to improve their delivery services as fast as possible. A network partition has been applied to evaluate suitable service areas by using METIS algorithm to solve this scenario and a facility location problem is used to address LMDC in a partitioned area. Research design, data, and methodology – Clustering and mixed integer programming algorithms are applied to partition the network and to locate facilities in the network. Results – Network partition improves last mile delivery service. METIS algorithm divided the area into 25 partitions by minimizing the inter-network links. To serve short-haul deliveries, this paper located 96 LMDCs in compact partitioning to satisfy customer demands. Conclusions –The computational results from the case study showed that the proposed two-phase algorithm with network partitioning and facility location can efficiently design a last-mile delivery network. It improves parcel delivery services when sending parcels to customers and reduces the overall delivery time. It is expected that the proposed two-phase approach can help parcel delivery companies minimize investment while providing faster delivery services.

Purpose – The purpose of this paper is to improve last mile delivery capability and ensure customers’ satisfaction by approaching an analytic hierarchy process(AHP) and identifying criteria framework to determine locations of last mile delivery centre(LMDC). Research design, data, and methodology – Traffic congestion and emission policy in cities are barriers of last mile delivery in dense areas. The urban consolidation centre(UCC) cannot increase last mile delivery efficiency in dense cities because of their space and traffic limitation. In this paper, we develop a case to improve last mile delivery efficiency and to ensure customers’ satisfaction by concentrating on LMDC. In addition, AHP has been applied to identify criteria framework and determine LMDC locations. The weighted priorities are derived from parcel delivery industry experts and have been calculated using Expert Choice software. Results – The framework criteria have assisted decision makers to place LMDC in a dense area to enhance customer’s satisfaction with last mile delivery service. Conclusions – AHP has provided ranking framework criteria of LMDC potential for parcel delivery industry. The LMDC helps by improving last mile delivery efficiency to final destination amids conditions of CO2 emissions, traffic congestion, and pollution problems. It especially concerns delivery service activities when delivering parcels to customers rather than UCC.