Air Taxi Skyport Location Problem for Airport Access

Witnessing the rapid progress and accelerated commercialization made in recent years for the introduction of air taxi services in near future across metropolitan cities, our research focuses on one of the most important consideration for such services, i.e., infrastructure planning (also known as skyports). We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior into the decision process. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in an urban city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, and varying transfer times at skyports. A case study of New York City is conducted considering a network of 149 taxi zones and 3 airports with over 20 million for-hire-vehicles trip data to the airports to discuss insights around the choice of skyport locations in the city, and demand allocation to different skyports under various parameter settings. Results suggest that a minimum of 9 skyports located between Manhattan, Queens and Brooklyn can adequately accommodate the airport access travel needs and are sufficiently stable against transfer time increases. Findings from this study can help air taxi providers strategize infrastructure design options and investment decisions based on skyport location choices.Comment: 25 page

Distance, Lending Relationships, and Competition

A recent string of theoretical papers has highlighted the importance of geographical distance in explaining loan rates for small firms.Lenders located in the vicinity of small firms face significantly lower transportation and monitoring costs, and hence wield considerable market power, if competing financiers are located relatively far from the borrowing firms.We study the effect on loan conditions of geographical distance between firms, the lending bank, and all other banks in the vicinity.For our study we employ detailed contract information from more than 15,000 bank loans to small firms comprising the entire loan portfolio of a large Belgian bank.We control for relevant relationship, loan contract, bank branch, firm, and regional characteristics.We report the first comprehensive evidence on the occurrence of spatial price discrimination in bank lending.Loan rates decrease with the distance between the firm and the lending bank and similarly increase with the distance between the firm and competing banks.The effect of distance on the loan rate is statistically significant and economically relevant.Robust to changes in model specifications and variable definitions, the effect is seemingly not driven by the modest changes over time in lending technology that we infer.We deduce that transportation costs cause the spatial price discrimination we observe.prices;credit;banks;competition;bank lending

Bank branch presence and access to credit in low-to-moderate income neighborhoods

Banks specialize in lending to informationally opaque borrowers by collecting soft information about them. Some researchers claim that this process requires a physical presence in the market to lower information collection costs. The author provides evidence in support of this argument in the mortgage market for low-income borrowers. Mortgage originations increase and interest spreads decline when there is a bank branch located in a low-to-moderate income neighborhood.Mortgage loans ; Branch banks

Distance, lending relationships and competition.

Competition;

Tactical fixed job scheduling with spread-time constraints

We address the tactical fixed job scheduling problem with spread-time constraints. In such a problem, there are a fixed number of classes of machines and a fixed number of groups of jobs. Jobs of the same group can only be processed by machines of a given set of classes. All jobs have their fixed start and end times. Each machine is associated with a cost according to its machine class. Machines have spread-time constraints, with which each machine is only available for L consecutive time units from the start time of the earliest job assigned to it. The objective is to minimize the total cost of the machines used to process all the jobs. For this strongly NP-hard problem, we develop a branch-and-price algorithm, which solves instances with up to 300 jobs, as compared with CPLEX, which cannot solve instances of 100 jobs. We further investigate the influence of machine flexibility by computational experiments. Our results show that limited machine flexibility is sufficient in most situations

Simultaneous column-and-row generation for large-scale linear programs with column-dependent-rows

In this paper, we develop a simultaneous column-and-row generation algorithm for a general class of large-scale linear programming problems. These problems typically arise in the context of linear programming formulations with exponentially many variables. The defining property for these formulations is a set of linking constraints. These constraints are either too many to be included in the formulation directly, or the full set of linking constraints can only be identified, if all variables are generated explicitly. Due to this dependence between columns and rows, we refer to this class of linear programs as problems with column-dependent-rows. To solve these problems, we need to be able to generate both columns and rows on the fly within an efficient solution method. We emphasize that the generated rows are structural constraints and distinguish our work from the branch-and-cut-and-price framework. We first characterize the underlying assumptions for the proposed column-and-row generation algorithm and then introduce the associated set of pricing subproblems in detail. The proposed methodology is demonstrated on numerical examples for the multi-stage cutting stock and the quadratic set covering problems

Relationship Lending, Distance and Efficiency in a Heterogeneous Banking System

During the last decades banks have progressively moved towards centralized and hierarchical organizational structures. Therefore, the investigation of the determinants of bank efficiency and relationships with the functional distance between the bank head-quarter and operational units have become increasingly important. This paper extends the literature on bank efficiency examining the impact of different bank business models on the efficiency of the Italian banks, distinguished by size and type over the period 2006-2009. Using a stochastic frontier approach, the intertemporal relationships between bank efficiency and some key variables, as distance and income diversification (used as proxies of different organizational banking models) are investigated. Results suggest that organizational structure significantly affects cost efficiency, being different between bank groups.relationship lending; bank groups; credit risk; stochastic frontiers; panel data