Runway Operations Management: Models, Enhancements, and Decomposition Techniques

Air traffic loads have been on the rise over the last several decades and are expected to double, and possibly triple in some regions, over the coming decade. With the advent of larger aircraft and ever-increasing air traffic loads, aviation authorities are continually pressured to examine capacity expansions and to adopt better strategies for capacity utilization. However, this growth in air traffic volumes has not been accompanied by adequate capacity expansions in the air transport infrastructure. It is, therefore, predicted that flight delays costing multi-billion dollars will continue to negatively impact airline companies and consumers. In airport operations management, runways constitute a scarce resource and a key bottleneck that impacts system-wide capacity (Idris et al. 1999). Throughout the three essays that form this dissertation, enhanced optimization models and effective decomposition techniques are proposed for runway operations management, while taking into consideration safety and practical constraints that govern access to runways. Essay One proposes a three-faceted approach for runway capacity management, based on the runway configuration, a chosen aircraft assignment/sequencing policy, and an aircraft separation standard as typically enforced by aviation authorities. With the objective of minimizing a fuel burn cost function, we propose optimization-based heuristics that are grounded in a classical mixed-integer programming formulation. By slightly altering the FCFS sequence, the proposed optimization-based heuristics not only preserve fairness among aircraft, but also consistently produce excellent (optimal or near optimal) solutions. Using real data and alternative runway settings, our computational study examines the transition from the (Old) Doha International Airport to the New Doha International Airport in light of our proposed optimization methodology. Essay Two examines aircraft sequencing problems over multiple runways under mixed mode operations. To curtail the computational effort associated with classical mixed-integer formulations for aircraft sequencing problems, valid inequalities, pre-processing routines and symmetry-defeating hierarchical constraints are proposed. These enhancements yield computational savings over a base mixed-integer formulation when solved via branch-and-bound/cut techniques that are embedded in commercial optimization solvers such as CPLEX. To further enhance its computational tractability, the problem is alternatively reformulated as a set partitioning model (with a convexity constraint) that prompts the development of a specialized column generation approach. The latter is accelerated by incorporating several algorithmic features, including an interior point dual stabilization scheme (Rousseau et al. 2007), a complementary column generation routine (Ghoniem and Sherali, 2009), and a dynamic lower bounding feature. Empirical results using a set of computationally challenging simulated instances demonstrate the effectiveness and the relative merits of the strengthened mixed-integer formulation and the accelerated column generation approach. Essay Three presents an effective dynamic programming algorithm for solving Elementary Shortest Path Problems with Resource Constraints (ESPPRC). This is particularly beneficial, because the ESPPRC structure arises in the column generation pricing sub-problem which, in turn, causes computational challenges as noted in Essay Two. Extending the work by Feillet et al. (2004), the proposed algorithm dynamically constructs optimal aircraft schedules based on the shortest path between operations while enforcing time-window restrictions and consecutive as well as nonconsecutive minimum separation times between aircraft. Using the aircraft separation standard by the Federal Aviation Administration (FAA), our computational study reports very promising results, whereby the proposed dynamic programming approach greatly outperforms the solution of the sub-problem as a mixed-integer programming formulation using commercial solvers such as CPLEX and paves the way for developing effective branch-and-price algorithms for multiple-runway aircraft sequencing problems

Heuristics and meta-heuristics for runway scheduling problems

This chapter addresses the state-of-the-art heuristic and meta-heuristic approaches for solving aircraft runway scheduling problem under variety of settings. Runway scheduling has been one of the emerging challenges in air traffic control as the congestion figures continue to rise. From a modeling point of view, mixed-integer programming formulations for single and multiple dependent and independent runways are presented. A set partitioning reformulation of the problem is demonstrated which suggests development of a column generation scheme. From a solution methodology viewpoint, generic heuristic algorithms, optimization-based approaches, and a dynamic programming scheme within the column generation algorithm are presented. Common meta-heuristic approaches that model variant problem settings under static and dynamic environments are discussed

Heuristics and meta-heuristics for runway scheduling problems

This chapter addresses the state-of-the-art heuristic and meta-heuristic approaches for solving aircraft runway scheduling problem under variety of settings. Runway scheduling has been one of the emerging challenges in air traffic control as the congestion figures continue to rise. From a modeling point of view, mixed-integer programming formulations for single and multiple dependent and independent runways are presented. A set partitioning reformulation of the problem is demonstrated which suggests development of a column generation scheme. From a solution methodology viewpoint, generic heuristic algorithms, optimization-based approaches, and a dynamic programming scheme within the column generation algorithm are presented. Common meta-heuristic approaches that model variant problem settings under static and dynamic environments are discussed

How to Make Lean Cellular Manufacturing Work? Integrating Human Factors in the Design and Improvement Process

There are three components involved in lean implementation at any institute, and only when considered cooperatively, they can guarantee a sustainable deployment: technical components, human factors, and the organizational elements. In this paper, we propose a comprehensive model of these components to assist managers in transitioning from a traditional manufacturing facility to a cellular lean manufacturing unit. This model can also be employed to enhance the performance of a lean cell. We integrate the mechanical factors including the number of processes, types of tasks, and demand levels with the human behavioral factors including learning, forgetting, and motivation levels, to enhance productivity. The role of organizational and cultural change is also discussed within this transformation process

A column generation approach for aircraft sequencing problems: A computational study

This paper investigates the computational tractability of aircraft sequencing problems over multiple runways under mixed mode operations, contrasting an enhanced mixed-integer programme (MIP) and an accelerated column generation approach. First, we examine the benefit of augmenting a base MIP with valid inequalities, preprocessing routines, and symmetry-defeating hierarchical constraints in order to improve the performance of branch-and-bound (B&B)/cut techniques as implemented in commercial solvers. Second, we alternatively reformulate the problem as a set partitioning model that prompts the development of a specialized column generation approach. The latter is accelerated by incorporating an interior point dual stabilization scheme and a complementary column generation routine. Empirical results using a set of new, computationally challenging instances and classical instances in the OR Library reveal the potential and limitations of the two methodologies

The job rotation scheduling problem considering human cognitive effects: an integrated approach

Purpose: This paper aims to unfold the role that job rotation plays in a lean cell. Unlike many studies, the authors consider heterogeneous operators with dynamic performance factor that is impacted by the assignment and scheduling decisions. The purpose is to derive an understanding of the underlying effects of job rotations on performance metrics in a lean cell. The authors use an optimization framework and an experimental design methodology for sensitivity analysis of the input parameters. Design/methodology/approach: The approach is an integration of three stages. The authors propose a set-based optimization model that considers human behavior parameters. They also solve the problem with two meta-heuristic algorithms and an efficient local search algorithm. Further, the authors run a post-optimality analysis by conducting a design of experiments using the response surface methodology (RSM). Findings: The results of the optimization model reveal that the job rotation schedules and the human cognitive metrics influence the performance of the lean cell. The results of the sensitivity analysis further show that the objective function and the job rotation frequencies are highly sensitive to the other input parameters. Based on the findings from the RSM, the authors derive general rules for the job rotations in a lean cell given the ranges in other input variables. Originality/value: The authors integrate the job rotation scheduling model with human behavioral and cognitive parameters and formulate the problem in a lean cell for the first time in the literature. In addition, they use the RSM for the first time in this context and offer a post-optimality analysis that reveals important information about the impact of the job rotations on the performance of operators and the entire working cell

Balancing, sequencing, and job rotation scheduling of a U-shaped lean cell with dynamic operator performance

Performance of a manufacturing cell is dependent on an efficient layout design, and optimal work schedules. However, the operator-dependent factors such as learning, forgetting, motivation, and boredom, can considerably impact the output of the system. In this study, we consider heterogeneous operators with dynamic performance metrics and integrate the job assignment, and job rotation scheduling problems, with the balancing and production sequencing in a U-shaped lean manufacturing cell. We present a novel multi-period nonlinear mixed-integer model to minimize the deviations from takt time, and the number of operators, in a finite planning horizon. An efficient meta-heuristic approach is developed to solve the problem and the results are compared to a static case where no human factor is included. Our computational results demonstrate that including the operator-dependent metrics can improve the performance of the cell design. We conduct a sensitivity analysis of the scheduling parameters including, rotation frequencies, takt time, cell size, and task types, and derive that the obtained solutions with the static settings, are not sufficient for an efficient lean cell design in the presence of dynamic human factors

An accelerated branch-and-price algorithm for multiple-runway aircraft sequencing problems

This paper presents an effective branch-and-price (B&P) algorithm for multiple-runway aircraft sequencing problems. This approach improves the tractability of the problem by several orders of magnitude when compared with solving a classical 0-1 mixed-integer formulation over a set of computationally challenging instances. Central to the computational efficacy of the B&P algorithm is solving the column generation subproblem as an elementary shortest path problem with aircraft time-windows and non-triangular separation times using an enhanced dynamic programming procedure. We underscore in our computational study the algorithmic features that contribute, in our experience, to accelerating the proposed dynamic programming procedure and, hence, the overall B&P algorithm

Patient appointment scheduling at hemodialysis centers: An exact branch and price approach

Scheduling patient appointments at a hemodialysis center presents a unique setting. Unlike other appointment scheduling problems in healthcare systems, patients are scheduled for a series of dialysis treatment appointments instead of a single appointment. In this study, we formulate this multiple-appointment system as a set-partitioning problem that allows partial schedules to be feasible. We employ a Branch and Price (BP) algorithm to solve the problem, however, the pricing sub-problem proves to be exceedingly challenging for state-of-the-art dynamic programming algorithms. Therefore, we propose a novel decomposition of the sub-problem and design an efficient embedded Column Generation (CG) algorithm to find the optimal solution. We further design a greedy heuristic that enhances the computational efficiency of the BP algorithm. Our proposed BP algorithms efficiently solve challenging instances that are simulated based on the data from a collaborating hemodialysis center. Specifically, the Enhanced CG-embedded BP algorithm accelerates the CPU time on average by 78% compared to the Base BP algorithm (32% and 59% compared to the Enhanced and the CG-embedded BP algorithms, respectively). We also compare the optimal results with the current scheduling policy at the center. Our proposed Enhanced CG-embedded BP algorithm improves the percentage of leftover appointments by 98% on average and the hours of deviations per patient by 46% on average, compared to the current policy

Optimization models for patient and technician scheduling in hemodialysis centers

Patient and technician scheduling problem in hemodialysis centers presents a unique setting in healthcare operations as (1) unlike other healthcare problems, dialysis appointments have a steady state and the treatment times are determined in advance of the appointments, and (2) once the appointments are set, technicians will have to be assigned to two types of jobs per appointment: putting on and taking off patients (connecting to and disconnecting from dialysis machines). In this study, we design a mixed-integer programming model to minimize technicians’ operating costs (regular and overtime costs) at large-scale hemodialysis centers. As this formulation proves to be computationally challenging to solve, we propose a novel reformulation of the problem as a discrete-time assignment model and prove that the two formulations are equivalent under a specific condition. We then simulate instances based on the data from our collaborating hemodialysis center to evaluate the performance of our proposed formulations. We compare our results to the current scheduling policy at the center. In our numerical analysis, we reduced the technician operating costs by 17% on average (up to 49%) compared to the current practice. We further conduct a post-optimality analysis and develop a predictive model that can estimate the number of required technicians based on the center’s attributes and patients’ input variables. Our predictive model reveals that the optimal number of technicians is strongly related to the time flexibility of patients and their dialysis times. Our findings can help clinic managers at hemodialysis centers to accurately estimate the technician requirements