Solution Repair/Recovery in Uncertain Optimization Environment

Operation management problems (such as Production Planning and Scheduling) are represented and formulated as optimization models. The resolution of such optimization models leads to solutions which have to be operated in an organization. However, the conditions under which the optimal solution is obtained rarely correspond exactly to the conditions under which the solution will be operated in the organization.Therefore, in most practical contexts, the computed optimal solution is not anymore optimal under the conditions in which it is operated. Indeed, it can be "far from optimal" or even not feasible. For different reasons, we hadn't the possibility to completely re-optimize the existing solution or plan. As a consequence, it is necessary to look for "repair solutions", i.e., solutions that have a good behavior with respect to possible scenarios, or with respect to uncertainty of the parameters of the model. To tackle the problem, the computed solution should be such that it is possible to "repair" it through a local re-optimization guided by the user or through a limited change aiming at minimizing the impact of taking into consideration the scenarios

Methods for Improving Robustness and Recovery in Aviation Planning.

In this dissertation, we develop new methods for improving robustness and recovery in aviation planning. In addition to these methods, the contributions of this dissertation include an in-depth analysis of several mathematical modeling approaches and proof of their structural equivalence. Furthermore, we analyze several decomposition approaches, the difference in their complexity and the required computation time to provide insight into selecting the most appropriate formulation for a particular problem structure. To begin, we provide an overview of the airline planning process, including the major components such as schedule planning, fleet assignment and crew planning approaches. Then, in the first part of our research, we use a recursive simulation-based approach to evaluate a flight schedule's overall robustness, i.e. its ability to withstand propagation delays. We then use this analysis as the groundwork for a new approach to improve the robustness of an airline's maintenance plan. Specifically, we improve robustness by allocating maintenance rotations to those aircraft that will most likely benefit from the assignment. To assess the effectiveness of our approach, we introduce a new metric, maintenance reachability, which measures the robustness of the rotations assigned to aircraft. Subsequently, we develop a mathematical programming approach to improve the maintenance reachability of this assignment. In the latter part of this dissertation, we transition from the planning to the recovery phase. On the day-of-operations, disruptions often take place and change aircraft rotations and their respective maintenance assignments. In recovery, we focus on creating feasible plans after such disruptions have occurred. We divide our recovery approach into two phases. In the first phase, we solve the Maintenance Recovery Problem (MRP), a computationally complex, short-term, non-recurrent recovery problem. This research lays the foundation for the second phase, in which we incorporate recurrence, i.e. the property that scheduling one maintenance event has a direct implication on the deadlines for subsequent maintenance events, into the recovery process. We recognize that scheduling the next maintenance event provides implications for all subsequent events, which further increases the problem complexity. We illustrate the effectiveness of our methods under various objective functions and mathematical programming approaches.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91539/1/mlapp_1.pd

A Rolling Horizon Based Algorithm for Solving Integrated Airline Schedule Recovery Problem

Airline disruption incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated airline schedule recovery problem, which considers flight recovery, aircraft recovery and crew recovery simultaneously. First we built an integer programming model which is based on traditional set partitioning model but including flight copy decision variables. Then a rolling horizon based algorithm is proposed to efficiently solve the model. Our algorithm decomposes the whole problem into smaller sub-problems by restricting swapping opportunities within each rolling period. All the flights are considered in each sub-problem to circumvent ‘myopic’ of traditional rolling horizon algorithm. Experimental results show that our method can provide competitive recovery solution in both solution quality and computation time.published_or_final_versio

A novel passenger recovery approach for the integrated airline recovery problem

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Schedule disruptions require airlines to intervene through the process of recovery; this involves modifications to the planned schedule, aircraft routings, crew pairings and passenger itineraries. Passenger recovery is generally considered as the final stage in this process, and hence passengers experience unnecessarily large impacts resulting from flight delays and cancellations. Most recovery approaches considering passengers involve a separately defined module within the problem formulation. However, this approach may be overly complex for recovery in many aviation and general transportation applications. This paper presents a unique description of the cancellation variables that models passenger recovery by prescribing the alternative travel arrangements for passengers in the event of flight cancellations. The results will demonstrate that this simple, but effective, passenger recovery approach significantly reduces the operational costs of the airline and increases passenger flow through the network. The integrated airline recovery problem with passenger reallocation is solved using column-and-row generation to achieve high quality solutions in short runtimes. An analysis of the column-and-row generation solution approach is performed, identifying a number of enhancement techniques to further improve the solution runtimes.Australian Research Council Centre of Excellence for Mathematics and Statistics of Complex Systems (MASCOS

Solving the integrated airline recovery problem using column-and-row generation

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordAirline recovery presents very large and difficult problems requiring high quality solutions within very short time limits. To improve computational performance, various solution approaches have been employed, including decomposition methods and approximation techniques. There has been increasing interest in the development of efficient and accurate solution techniques to solve an integrated airline recovery problem. In this paper, an integrated airline recovery problem is developed, integrating the schedule, crew and aircraft recovery stages, and is solved using column-and-row generation. A general framework for column-and-row generation is presented as an extension of current generic methods. This extension considers multiple secondary variables and linking constraints and is proposed as an alternative solution approach to Benders’ decomposition. The application of column-and-row generation to the integrated recovery problem demonstrates the improvement in the solution runtimes and quality compared to a standard column generation approach. Columnand-row generation improves solution runtimes by reducing the problem size and thereby achieving faster execution of each LP solve. As a result of this evaluation, a number of general enhancement techniques are identified to further reduce the runtimes of column-and-row generation. This paper also details the integration of the row generation procedure with branch-and-price, which is used to identify integral optimal solutions

Disruption management

The main objective of this project is to model the ARP (Aircraft Recovery Problem) from a constraint programming (CP) point of view. The information required for this project is extracted from previous papers that cope with the problem using heuristics, metaheuristics or using network-models. Also, two scenarios will be tested to verify that the implementation is correct