Asian Journal of Shipping and Logistics最新文献

Truck appointment systems facilitate coordination between container terminals and drayage trucks in container pick-up operation reservation. However, in many cases, trucks with a reservation do not arrive at the scheduled appointment. As the number of no-shows increases, the container terminal’s productivity will plummet, and drayage trucks that failed to get reservations will lose their opportunity to get service. This research proposes an overbooking reservation mechanism (ORM) to alleviate the negative impact of these no-shows. This research scrutinizes the detailed process mapping of the existing reservation mechanism, proposes an ORM, and conducts agent-based simulations to evaluate the ORM's performance against the regular and go-show reservation mechanisms at different levels of no-shows and working occupancies. The application of an ORM can improve productivity and service levels while minimizing such negative externalities as queue length, overtime, and greenhouse gas emissions. High overtime intensities only appear when the container terminal's workload is exceptionally high, at 200% of maximum capacity, with a low level of no-shows. Even in exceptionally high demand conditions, the drayage trucks wait only up to 16 min before receiving service.

We estimate intentional and unintentional herding in the dry-bulk, ocean-going segment that spans from August 1998 to January 2020 and measure how this can impact vessel orders. As the results suggest, while intentional herding has a large effect on the orders of the newbuilding vessels, the impact is very short-lived. On the contrary, unintentional herding, related to common environmental factors, has a smoother but more time persistent effect on the newbuilding vessels. The findings suggest that the key players in the market do not only affect the dry-bulk market with their decisions but also the logistics trade and ultimately the world economy. Thus, the inclusion of the dry bulk key shipowners by the policymakers in the discussions before reaching an important decision could minimise the volatility of the overall sector.

One of the serious inquiries for industry experts and academics is whether and how corporate social responsibility (CSR) can help create better workplace environments. However, this has not been studied enough so far from employees’ perspective. To fill this gap, this study aims to examine the relationship between employees’ perception on corporate social responsibility (CSR), their job satisfaction, organisational trust and ultimately, organisational commitment to suggest academic and practical implications on sustainable management for international freight forwarders in Korea. For this study, 166 global logistics companies in Busan, one of the world’s leading maritime cities, were targeted, and questionnaires were surveyed from 10 April to 30 May 2019. A total of 265 questionnaires were analysed to verify the relevance of individual hypothesis in the research model through confirmatory factor analysis and structural equation model analysis using AMOS 18.0. The results of five hypotheses testing demonstrated a positive and significant effect except that Hypothesis 4 on ‘job satisfaction will have a significant effect on organisational commitment’ was rejected. Overall, this paper provides important insights into the significance of CSR in global freight forwarding companies with theoretical development and the empirical test of CSR model, highlighting the trust to increase the organisational commitment of employees.

Hub-and-spoke (HS) networks are cost-effective because they allow the realization of economies of density. However, the cost of point-to-point (PP) networks may be lower than that of HS networks when certain conditions, such as cargo demand, bunker price, vessel size, and the shippers’ value of time change. This study explores the factors that influence the cost-effectiveness of HS and PP networks. We developed a mixed-integer programming model that allows for bi-level optimization between shipping lines and shippers. As a case study, we applied it to Chinese and Japanese ports, with both HS and PP networks. We found that high cargo demand increases the use of PP networks while enlarging vessel size increases the use of HS networks. These findings enable us to predict the occurrence of hubbing—shifting from a PP to an HS network—and de-hubbing—shifting from an HS to a PP network.

Foreign Direct Investment (FDI) plays an important part in the economic growth of developing Asia and logistics capability is a crucial factor in FDI location choice. Till today, research investigating Logistics Performance (LP) and FDI in Asia are still lacking and rather inconclusive and not standardised. Using a total of 31 Asian countries covering 2007 through 2017, the study critically investigates the impact of LP on FDI using static panel regression method. The results demonstrate that while LP significantly impact FDI, there is a threshold effect of institutional quality in the LP-FDI relationship. Specifically, this paper found that the impact of LP on FDI varies across the perceived level of institutional quality i.e., the effect is positive only before certain threshold level of institutional quality, after which the effect of LP on FDI is reversed. This finding suggests that the LP-FDI nexus is contingent on the perceived level of institutional quality, thus supporting the idea that innovations (i.e., creativity and flexibility embedded within a sound institutional framework) is potent in delivering long-run LP-FDI development in Asia.

Decision-making for shelter location-allocation influences the success of disaster response and affects the security of victims. This paper proposes a multi-objective optimization model for determining shelter location-allocation in response to humanitarian relief logistics. Three objective functions are formulated to improve both efficiency and effectiveness. The first objective is to minimize total costs, including fixed costs for opening the shelters, transportation costs, and service costs. The second objective is to minimize the total time for evacuating victims from all affected areas to allocated shelters. The third objective is to minimize the number of shelters required to provide thorough service to victims. The Epsilon Constraint method (EC) and Goal Programming (GP) are employed for solving the proposed model. The applicability of the proposed model is validated through a case study of flooding in Surat Thani, Thailand. The Pareto efficiency obtained from solving the proposed model is compared with current shelter location-allocation plans determined by the government sector. The comparisons reveal that the results obtained from solving the proposed model outperform current shelter location-allocation plans. Furthermore, the results of this study could provide an advantage to decision-makers considering appropriate strategies for disaster response.

Designating the ideal shipping route can spare expenses, enlarge profits and improve the competitiveness of shipping companies. Liner shipping route choice is mainly contingent on fuel cost, which always contributes the major proportion of the ship's operating cost. Although many studies on this topic have been carried out, none are based on the fuel consumption forecast model designed by the advanced machine learning method. This paper provides a platform idea for selecting the optimal operating route for container ships to minimize fuel cost by using an asymmetric traveling salesman problem (ATSP) algorithm solution, in which the fuel consumption model for the route is estimated based on the deep-machine learning method. Five input variables are given in the model including average velocity, sailing time, ship's capacity, wind speed, and wind direction. The mean absolute percentage error (MAPE) of the model is 5.89%, indicating that the predictive result obtains a very high accuracy, close to 95%. The optimal model is thus applied in combination with ATSP to address the optimal solution for a certain route.