The discounted value of cash flows of assets is negatively related to interest rates
(i.e., the discount rate effect). However, economic activity is positively related to
interest rates and positively related to the cash flows of assets with tariffs that can
be adjusted to manage demand such as adjustable-rate toll roads, but uncorrelated
to assets that do not bear demand risk such as non-toll roads (i.e., the cash flow
effect). This effect arises in some types of assets from: (i) the positive correlation
between economic activity and demand for the infrastructure assets; and (ii) the
positive correlation between economic activity and inflation. We find that the cash
flow effect dominates the discount rate effect for assets with tariffs that can be
adjusted to manage demand and, therefore, the value of these assets increases in
periods of economic expansion. Nevertheless, the opposite occurs for assets that do
not bear demand risk.

This paper deals with joint production and corrective maintenance problem of a transported material network failure-prone manufacturing system along which two aspects are supposed. First, each non-identical machine are subject to degradation with failure phenomena. When a failure occurs, system is either repaired or replaced with new one, repairing activity not only degrades machine operating state, but also increases with the next repair time. Second, optimality production control policy called Modified Hedging Point Policy (MHPP) and Modified Hedging Corridor Policy (MHCP) are applied for given network machine. The aim of this article is to find decision variables so as to minimize incurred cost function, including repair costs, stock related costs (i.e., holding costs, backlog and shortage) and setup cost, over an finite-horizon time. Simulation experimental approach with meta-heuristic algorithm is applied to obtain near-optimum decision variables.

Background: Whether the international author collaboration and keywords on the topic of transportation management has been changed in the last several decades remains unclear. Along with the big data and API(allocation programming interface) emerged as a field of research with increasing attention being paid to it by scientific researchers and a rapid increase in related literature being reported using the bibliometric analysis, the international author collaboration and keywords should be explored to analyze the current state of research, including publication outputs, in-depth collaboration characteristics and keyword topics of transportation management research.

Methods: The authors collected two published papers in Medline library and downloaded their 206 similar articles without duplication since 1977. Various statistical techniques and bibliometric measures were employed, including publication growth analysis; journal distribution; and collaboration network analysis at the author country/area collaboration level. The visualization maps of international author collaboration and burst terms were drawn on Google maps using social network analysis(SNA) and cluster analysis. Gini coefficient(GC) was applied to measure inequality of density indices among clusters.

Results: A total of 208 bibliographic records on transportation management were collected. The earliest paper was published in 1977, with the number of papers sharply rising at the inflection point of the year 2014. We found that (1) the most number of papers on the topic of transportation management are from the U.S.( 43,27.04%), Spain(21,13.21%), and China(18, 11.32%); (2) the most linked keywords are organization & administration, analysis, education, and statistics & numerical data, and trends; (3) keyword networks presents lower GC that author collaborations among their respective clusters.   

Conclusions: The collaboration of international authors on transportation management is not tight and stable. The focus of research topics on transportation management is centralized(Gini=0.48) more than that of author collaboration(Gini=0.33). Our study might provide a potential guide for future research on the topic of transportation management.