Two-stage Mixed-integer Programming Approach Research Articles

Collaborative optimization of depot location, capacity and rolling stock scheduling considering maintenance requirements

Generally, when optimizing a rolling stock schedule, the locations of the depots, or places in the network where the composition changes and maintenance occurs, are assumed known. The locations where maintenance is performed naturally influence the quality of any resulting rolling stock schedules. In this paper, the problem of selecting new depot locations and their corresponding capacities is considered. A two-stage mixed integer programming approach for rolling stock scheduling with maintenance requirements is extended to account for depot selection. First, a conventional flow-based model is solved, ignoring maintenance requirements, to obtain a variety of rolling stock schedules with multiple depot locations and capacity options. Then, a maintenance feasible rolling stock schedule can be obtained by solving a series of assignment problems by using the schedules found in the first stage. The proposed methodology is tested on real-life instances, and the numerical experiments of different operational scenarios are discussed.

A Two-Stage Mixed Integer Programming Approach for Optimizing the Skill Mix and Training Schedules For Aircraft Maintenance

A Two-Stage Mixed Integer Programming Approach for Optimizing the Skill Mix and Training Schedules For Aircraft Maintenance

A METHODOLOGICAL COMPARISON BETWEEN STANDARD AND TWO STAGE MIXED INTEGER APPROACHES FOR DISCRIMINANT ANALYSIS

A standard mixed integer programming (MIP) approach is compared with a two-stage MIP approach. A computational difference between the two DA (discriminant analysis) approaches is that the former uses a single MIP model to solve various classification problems and the latter uses two MIP models that classify all observations into one of two groups or an overlap at the first stage and then reclassify the overlapped observations at the second stage. In this study, the two MIP approaches are methodologically compared and then applied to a published data set related to Japanese banks. These classification performances are assessed by four hit rates. Based upon the comparison, it is confirmed that the two-stage approach performs at least as well as the standard approach.