Decision Support Models for Waste Management
Istanbul Metropolitan Municipality, 01/2007 - 01/2009, 10,300 TL ($8,000).
Joint with Ilker Birbil, Tonguc Unluyurt, Gurdal Ertek. Researcher.

Robust Airline Crew Pairing: Models, Solution Techniques and Applications
The Scientific and Technological Research Council of Turkey, 02/2007 - 02/2010, 188,150 TL ($145,000).
Joint with Ilker Birbil, Husnu Yenigun, Dilek Tuzun, Ilker Topcu. Researcher.

Dynamic Multi-Project Scheduling under Uncertainty
The Scientific and Technological Research Council of Turkey, 04/2010 - 07/2012, 143,360 TL ($96,000).
Joint with Gunduz Ulusoy, Umit Bilge. Researcher.

Models and Algorithms for Risk Averse Machine Scheduling Problems
The Scientific and Technological Research Council of Turkey, 04/2013 - 04/2016, 173,495 TL ($96,000).
Joint with Nilay Noyan. Researcher.

 

Decision Support Models for Waste Management

Abstract:

In the scope of this project, decision support models for solid waste management will be developed for the Istanbul Metropolitan Municipality (IMM). Possible uses of these models may include determining the locations and capacities of waste collection centers, calculating the collection frequencies based on vehicle capacities and waste production, and forming effective routes for garbage trucks. In addition, the potential impacts of more advanced collection methods, such as separating organic, metal, plastic, glass and paper waste at the source, or the value of collecting real time information about the fill rate of collection bins via sensors, will be examined. In the first phase of the project, the existing system will be analyzed, and the areas of potential improvement will be identified. Studies about similar issues elsewhere will be examined carefully. In the second phase, mathematical models and associated effective solution approaches will be developed based on tools such as integer programming, heuristics, network optimization, simulation, and data processing and visualization. The proposed solution approaches will be implemented in a pilot study.

 

Robust Airline Crew Pairing: Models, Solution Techniques and Applications

Abstract:

The airlines have been applying operations research techniques to their planning problems at strategic, tactical and operational levels since the 1950’s. In particular, the American airline companies have been using mathematical techniques heavily in order to increase their profitability and market shares after the Airline Deregulation Act of 1978 intensified the competition. The demand for mathematical approaches from the airlines prompted a rapid development of many existing and new areas in operations research; such as, revenue and yield management, network planning and design, and so on. One of the major areas of focus has been the airline crew scheduling problem as the crew costs come only second to the fuel costs for a typical airline. Generally, the crew scheduling problem is divided into crew pairing and crew assignment problems. In the crew pairing problem, the objective is to determine a crew for each flight leg in the schedule, and in this problem a crew is considered as an entity, i.e., its composition and the identities of its members are not taken into account. In the crew assignment problem, a schedule for each crew member is determined given the solution of the crew pairing problem. In this project, our objective is to develop models and solution approaches to the robust airline crew pairing problem, including a pilot software implementing these models and algorithms which may potentially lead to a commercial software in the long run. In addition, our methodology will be verified and the performance of the proposed approaches will be demonstrated on real data as well as on benchmark instances taken from the literature.

The airline crew pairing problem is a tactical level planning problem determining a crew to operate each flight leg in a planning period of typically 1 to 3 months. Although many approaches have been developed for this problem over the years, heuristic approaches without performance guarantees dominated the field until the 1990’s when great progress was achieved by using mathematical programming techniques based on column generation. State-of-the-art solvers can obtain feasible solutions to large real life instances within1% of optimality. In recent years, incorporating robustness into the crew pairing solution has been identified as a new research direction. In the classical crew pairing problem, the flight schedule is assumed to be given and fixed. In practice, however, the pairings are modified depending on the disruptions, e.g., delays, cancellations, service additions, occurring during the operation. Such updates to the pairings increase the crew costs from 1 to 3-4% and from 3 to 8% above the minimum possible cost for large and small airlines, respectively. Clearly, building flexibility into the pairings upfront bears great importance in order to keep the effects of the operational disruptions within acceptable limits. Such concerns are incorporated into the robust crew pairing problem, and related models have started to appear in the literature in the last few years. However, there are still many open research questions in this area, and the models and solution approaches to be developed in the scope of this project will have an original contribution to the literature.

 

Dynamic Multi-Project Scheduling under Uncertainty

Abstract:

Beyond being a major way of doing business both in manufacturing and services, “Dynamic Multi-Project Scheduling under Uncertainty” is an important research area as well. The research environment investigated here is a dynamic and uncertain multi-mode multi-project scheduling environment with renewable and nonrenewable resources. In a dynamic setting, at any time t one or more new projects might join the already existing n projects and/or one or more projects might terminate. In these projects, there are uncertainties concerning activity durations, resource levels, and network structure (such as activity insertion and deletion).

The objective of this project is to build an integrated set of realistic models representing the dynamic and uncertain multi-project environment. The theoretical basis for such an integrated set, which can be employed in practice and serves the effective management of projects and risks, will be established.

 

Models and Algorithms for Risk Averse Machine Scheduling Problems

Abstract:

In its more than half a century old history the machine scheduling literature has mostly ignored the risk that may arise from the uncertainty inherent in the problem parameters. Research has either adopted a completely deterministic point of view or a risk-neutral approach where in most cases distributional assumptions are put in place for uncertain parameters and the goal is to optimize the expected value of the objective function. In practice, the performance of either approach is closely related to the degree of uncertainty. Clearly, with increased uncertainty the solution of a deterministic or a risk-neutral scheduling model may produce undesirable performance for some realizations of the random data and be considered as “risky.” Building upon this perspective, our primary goal in this project is to construct risk-averse machine schedules by explicitly factoring in the uncertainty in the problem parameters into the scheduling models. The literature on this topic is at best scarce except for some related work on robust scheduling models. In the scope of this project, we intend to pioneer research on risk-averse machine scheduling problems, models and associated solution algorithms by contributing new, novel, interesting, and challenging problems to the literature under two popular risk measures.