Stochastic seat allocation models for passenger rail transportation under customer choice

We study the seat allocation problem for passenger rail revenue management, in which a rail operator attempts to determine the optimal quantity of seats to be allocated to each cabin class for each train service. We formulate the problem with single-stage and multi-stage decisions as two stochastic programming models that incorporate passengers’ choice behavior. We transform the stochastic models into equivalent deterministic mathematical programs that are easy to solve. Then, we form a variety of seat allocation polices from the optimal solutions to the seat allocation models. A number of simulation tests are offered to test the policies.     

A multi-objective sustainable load planning model for intermodal transportation networks with a real-life application

Growing importance of intermodal transportation necessitates modeling and solving load planning problems by taking into account various complex decisions simultaneously like transportation mode/service type selection, load allocation, and outsourcing. This paper presents a mixed-integer mathematical programming model for a multi-objective, multi-mode and multi-period sustainable load planning problem by considering import/export load flows to satisfy transport demands of customers and many other related issues. Several multiple objective optimization procedures are utilized in order to handle conflicting objectives simultaneously under crisp and fuzzy decision making environments. A real-life case study is also performed to present application and usefulness of the proposed model.     

Stochastic optimization for investment in facilities in emergency prevention

A coordinated approach is developed to integrate three preventive measures (i.e. building reinforcement, reinforcement of road networks, and facility location of relief supplies), with the objectives of minimizing budgets and risk-induced penalties. The Conditional Value-at-Risk is employed as a decision-making tool to evaluate diverse decisions of prevention based on the degree of risk aversion. Based on a real-world case of an earthquake, a series of scenarios were designed, and the applicability of the proposed model was studied. The coordinated approach for investing preventive measures is cost-efficient in helping reduce the impact of disaster on society.     

Structuring an effective human error intervention strategy selection model for commercial aviation

Flight safety cannot be compromised. Thus, commercial airlines should constantly develop safety management strategies to mitigate the diverse hazardous factors in flight operations. Given the constraint of organizational resource, a commercial airline may not have sufficient resources to implement all the necessary strategies simultaneously. This study uses a well-structured process to develop a qualitative evaluation model that will enable airlines to identify human errors and select an intervention strategy with the highest success potential. To clarify the decision problem, the Human Factors Intervention Matrix framework is utilized to construct the decision hierarchy. The Analytic Hierarchy Process is then used to attain the priorities of potential alternative strategies for various unsafe acts. Finally, Zero-One Goal Programming models are formulated to select an optimal portfolio based on the specific target and the available organizational resources. An empirical study is presented to illustrate the application of the proposed model. According to the results of the combined model, an optimal portfolio, including the intervention approaches of organizational/administrative, human/crew, and operational/physical environment, can remediate four unsafe acts, namely, decision errors, skill-based errors, perceptual errors, and violations, under resource constraints of the organization.     

Airline new route selection using compromise programming - The case of Taiwan-Europe

The decision to launch a new route has a huge effect on the outcomes of airlines in the market. In order to find potential destinations for airlines, this paper utilizes a compromise programming method for selecting direct flight routes. The selection framework comprises two objective functions, with one maximizing total revenue and the other minimizing total cost. A case studying Taiwan-European region destinations is interpreted in the analysis. The results indicate that four potential routes, including Madrid, Milan, Munich and Zurich should be added to the airlines’ service destinations. Therefore, this research suggested that the airport authority could apply incentives scheme to attract carriers to open new routes and add flight frequencies to strengthen its competitiveness.     

The effects of learning and signaling on money demand: With an application to heterodox inflation stabilization programs

This paper develops a nonlinear vector autoregression of inflation and money growth subject to changes in regime. The regimes are fully characterized by the mean and variance of inflation and are conjectured to be the result of alternative government policies. Agents are unable to observe directly whether government actions are indeed consistent with the inflation rate targeted as part of a stabilization program. However, as part of their money demand decision, agents construct probability inferences regarding the regime. Government announcements are assumed to provide agents with additional, possibly truthful information regarding the regime.  This specification is estimated using data from the Israeli and Argentine high-inflation periods. Results indicate that the successful stabilization program implemented in Israel in July 1985 was more credible than either the earlier Israeli attempt in November 1984 or the Argentine programs. Government's signaling might simplify the agents' inference problem and increase the speed of their learning but, under certain conditions, it might also increase inflation volatility. Welfare gains from a temporary increase in real balances might be high enough to induce agents to raise their money demand in the short-term even if they are uncertain about the nature of government policy and the eventual outcome of the stabilization attempt. Statistically, the model restrictions cannot be rejected at the 1% significance level. First version received: August 1998/Final version received: January 1999     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

Optimization approach to depot location and trip selection in one-way carsharing systems

In this paper, we present an optimization approach to depot location in one-way carsharing systems where vehicle stock imbalance issues are addressed under three trip selection schemes. The approach is based on mixed-integer programming models whose objective is to maximize the profits of a carsharing organization considering all the revenues and costs involved. The practical usefulness of the approach is illustrated with a case study involving the municipality of Lisbon, Portugal. The results we have obtained from this study provided a clear insight into the impact of depot location and trip selection schemes on the profitability of such systems.     

Analysis of intermodal freight from China to Indian Ocean: A goal programming approach

This paper presents an intermodal network optimization model to examine the competitiveness of 36 alternative routings for freight moving from China to and beyond Indian Ocean. The proposed model, which is built upon the principles of goal programming, is able to handle multiple and conflicting objective functions such as minimizing transportation cost, transit time and transit time variability while ensuring flow continuity and transit nodes compatibility among the rail, road, ocean vessel, airplane and inland waterway transports. Transportation time and cost obtained from comprehensive industry sources are then fed into the intermodal transport network connecting two important Chinese origins and four Indian destinations, from which the most attractive routes are identified. In addition, the paper investigates several non-dominated transportation cases through sensitivity analysis tests, and analyzes the potential competitiveness and possible influences of future route developments to current transportation patterns in Asia.