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This paper recommends new criteria for selecting seaports of embarkation during military deployments. Most importantly, this research compares the current port selection criterion, which is to select the seaport with the shortest inland transport time from the deploying installation, to the proposed port selection criteria, which are to select the seaport based on the shortest combined inland and oceanic transit time to the destination theater.

The authors construct an original integer program to select seaports that minimize the expected delivery timeline for a set of notional, but realistic, deployment requirements. The integer program is solved considering the current as well as the proposed port selection criteria. The solutions are then compared using paired-samples t-tests to assess the statistical significance of the port selection criteria.

This work suggests that the current port selection criterion results in a 10–13% slower delivery of deploying forces as compared to the proposed port selection criteria.

This work assumes deterministic inland transit times, oceanic transit times, and seaport processing rates. Operational fluctuations in transit times and processing rates are not expected to change the findings from this research.

This research provides evidence that the current port selection criterion for selecting seaports for military units deploying from the Continental United States is suboptimal. More importantly, logistics planners could use these recommended port selection criteria to reduce the expected delivery timelines during military deployments.

Several military doctrinal references suggest that planners select seaports based on habitual installation-to-port pairings, especially for early deployers. This work recommends a change to the military's current port selection process based on empirical analyses that show improvements to deployment timelines.

This study analyzes the impact of the information environment (IE) and credit default swap (CDS) transaction costs on information transmission between the stock and CDS markets. Using the daily regression analysis on the Korean firm’s stock and CDS data from 2004 to 2023, the results show that companies with superior IE in the stock market exhibit a larger and more sensitive total information flow from the stock market to the CDS market. Companies with lower transaction costs in the CDS market demonstrate faster information flow. In the case of companies with superior IE, fundamental information is reflected in stock prices with high weight and thus the CDS spreads change reflecting information about stock prices. According to this study’s findings, the primary factor influencing the information flow from the stock market to the CDS market is the information environment of the company in the stock market, rather than transaction costs in the CDS market.

The e-commerce boom presents new challenges for last-mile delivery (LMD), which may be mitigated by new delivery technologies. This paper evaluates the impact of mobile parcel lockers (MPL) on costs and CO₂ equivalent (CO₂e) emissions in existing LMD networks, which include home delivery and shipments to stationary parcel lockers.

To describe customers’ preferences, we design a multinomial logit model based on recipients’ travel distance to pick-up locations and availability at home. Based on route cost estimation, we define the operating costs for MPLs. We devise a mathematical model with binary decision variables to optimize the location of MPLs.

Our study demonstrates that integrating MPLs leads to additional cost savings of 8.7% and extra CO₂e emissions savings of up to 5.4%. Our analysis of several regional clusters suggests that MPLs yield benefits in highly populous cities but may result in additional emissions in more rural areas where recipients drive longer distances to pick-ups.

This paper designs a suitable operating model for MPLs and demonstrates environmental and economic savings. Moreover, it adds recipients’ availability at home to receive parcels improving the accuracy of stochastic demand. In addition, MPLs are evaluated in the context of several regional clusters ranging from large cities to rural areas. Thus, we provide managerial guidance to logistics service providers how and where to deploy MPLs.

Under the constraints of given passenger service level and coupling travel demand with train departure time, this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.

The authors optimize the train operational plan in a special network layout, i.e. an urban rail corridor with dead-end terminal yard, by decomposing it into two sub-problems: train timetable optimization and rolling stock circulation optimization. As for train timetable optimization, the authors propose a schedule-based passenger flow assignment method, construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm. For the optimization of rolling stock circulation, the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.

The case study shows that the train operational plan developed by the study's approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.

The example verifies the efficiency of the model and algorithm.

This study aims to investigate the existence of contagion between liquid and illiquid assets in the credit default swap (CDS) market around the recent financial crisis. The authors perform analyses based on vector autoregression model and the dynamic conditional correlation model. The estimation of vector autoregression models reveals that changes in liquid CDS (LCDS) spreads lead to changes in illiquid CDS spreads at least one week ahead during the financial crisis period, whereas the leading direction is reversed during the post-crisis period. Moreover, the results are robust after controlling for structural variables which are proven as determinants of CDS spreads and are empirically supported. This study interprets that information was incorporated first into the LCDSs because of the flight-to-liquidity during the recent crisis period but there is a default contagion effect by reflecting illiquidity-induced credit risk after the crisis. Finally, the dynamic conditional correlation analysis also confirms the main results.