Diego Klabjan

Warehousing and Analyzing Massive RFID Data Sets

Radio Frequency Identification (RFID) applications are set to play an essential role in object tracking and supply chain management systems. In the near future, it is expected that every major retailer will use RFID systems to track the movement of products from suppliers to warehouses, store backrooms and eventually to points of sale. The volume of information generated by such systems can be enormous as each individual item (a pallet, a case, or an SKU) will leave a trail of data as it moves through different locations. As a departure from the traditional data cube, we propose a new warehousing model that preserves object transitions while providing significant compression and path-dependent aggregates, based on the following observations: (1) items usually move together in large groups through early stages in the system (e.g., distribution centers) and only in later stages (e.g., stores) do they move in smaller groups, and (2) although RFID data is registered at the primitive level, data analysis usually takes place at a higher abstraction level. Techniques for summarizing and indexing data, and methods for processing a variety of queries based on this framework are developed in this study. Our experiments demonstrate the utility and feasibility of our design, data structure, and algorithms.

Airline Crew Scheduling

An airline must cover each flight leg with a full complement of cabin crew in a manner consistent with safety regulations and award requirements. Methods are investigated for solving the set partitioning and covering problem. A test example illustrates the problem and the use of heuristics. The Study Group achieved an understanding of the problem and a plan for further work.

Airline Crew Scheduling with Time Windows and Plane-Count Constraints

Airline planning consists of several problems that are currently solved separately. We address a partial integration of schedule planning, aircraft routing, and crew scheduling. In particular, we provide more flexibility for crew scheduling while maintaining the feasibility of aircraft routing by adding plane-count constraints to the crew-scheduling problem. In addition, we assume that the departure times of flights have not yet been fixed and we are allowed to move the departure time of a flight as long as it is within a given time window. We demonstrate that such a model yields solutions to the crew-scheduling problem with significantly lower costs than those obtained from the traditional model.

Single machine multi-product capacitated lot sizing with sequence-dependent setups

In production planning in the glass container industry, machine-dependent setup times and costs are incurred for switch overs from one product to another. The resulting multi-item capacitated lot-sizing problem has sequence-dependent setup times and costs. We present two novel linear mixed-integer programming formulations for this problem, incorporating all the necessary features of setup carryovers. The compact formulation has polynomially many constraints, whereas the stronger formulation uses an exponential number of constraints that can be separated in polynomial time. We also present a five-step heuristic that is effective both in finding a feasible solution (even for tightly capacitated instances) and in producing good solutions to these problems. We report computational experiments.

Robust Airline Crew Pairing: Move-up Crews

Due to irregular operations, the crew cost at the end of a month is typically substantially higher than the crew cost projected in planning. We assume that the fleeting and the aircraft routing decisions have already been made. We present a model and a solution methodology that produces robust crew schedules in planning. Besides the objective of minimizing the crew cost, we introduce the objective of maximizing the number of move-up crews, i.e., the crews that can potentially be swapped in operations. To solve the resulting large-scale integer program, we use a combination of delayed column generation and Lagrangian relaxation. The restricted master problem is solved by means of Lagrangian relaxation and the “duals” of the restricted master problem, which are used in delayed column generation, and correspond to the Lagrangian multipliers. We report computational experiments that demonstrate the benefits of using the robust crew schedule instead of the traditional one. We evaluate various crew schedules by generating random disruptions and then running a crew recovery module. We compare solutions with respect to the direct crew cost and indirect costs such as uncovered legs, reserved crews, and deadheading. The main conclusion is that robustness leads to reduced operational crew cost; however, in planning the trade-off between the inflated direct crew cost and robustness needs to be exploited judicially.

An agent-based decision support system for electric vehicle charging infrastructure deployment

The current scarcity of public charging infrastructure is one of the major barriers to mass household adoption of electric vehicles (EVs). Many drivers are reluctant to purchase EVs without convenient charging access away from home, but investors are also hesitant to build charging stations without underlying knowledge of EV demand realization. In this paper, an agent-based decision support system is presented for identifying patterns in residential EV ownership and driving activities to enable strategic deployment of new charging infrastructure. The Chicagoland area is used as a case study to demonstrate the model.

Large-Scale Models in the Airline Industry

Operations research models are widely used in the airline industry. By using sophisticated optimization models and algorithms many airlines are able to improve profitability. In this paper we review these models and the underlying solution methodologies. We focus on models involving strategic business processes as well as operational processes. The former models include schedule design and fleeting, aircraft routing, and crew scheduling, while the latter models cope with irregular operations.

Integrated Airline Fleeting and Crew-Pairing Decisions

The tactical planning process of an airline is typically decomposed into several stages among which fleeting, aircraft routing, and crew pairing form the core. In such a decomposed and sequential approach, the output of fleeting forms the input to aircraft routing and crew pairing. In turn, the output to aircraft routing is part of the input to crew pairing. Due to this decomposition, the resulting solution is often suboptimal. We propose a model that completely integrates the fleeting and crew-pairing stages and guarantees feasibility of plane-count feasible aircraft routings, but neglects aircraft maintenance constraints. We design two solution methodologies to solve the model. One is based on a combination of Lagrangian relaxation and column generation, while the other one is a Benders decomposition approach. We conduct computational experiments for a variety of instances obtained from a major carrier.

Finding minimum-cost paths for electric vehicles

Modern route-guidance software for conventional gasoline-powered vehicles does not consider refueling since gasoline stations are ubiquitous and convenient in terms of both accessibility and use. The same technology is insufficient for electric vehicles (EVs), however, as charging stations are much more scarce and a suggested route may be infeasible given an EVs initial charge level. Recharging decisions may also have significant impacts on the total travel time and longevity of the battery, which can be costly to replace, so they must be considered when planning EV routes. In this paper, the problem of finding a minimum-cost path for an EV when the vehicle must recharge along the way is modeled as a dynamic program. It is proven that the optimal control and state space are discrete under mild assumptions, and two different solution methods are presented.

Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach

Massive radio frequency identification (RFID) data sets are expected to become commonplace in supply chain management systems. Warehousing and mining this data is an essential problem with great potential benefits for inventory management, object tracking, and product procurement processes. Since RFID tags can be used to identify each individual item, enormous amounts of location-tracking data are generated. With such data, object movements can be modeled by movement graphs, where nodes correspond to locations and edges record the history of item transitions between locations. In this study, we develop a movement graph model as a compact representation of RFID data sets. Since spatiotemporal as well as item information can be associated with the objects in such a model, the movement graph can be huge, complex, and multidimensional in nature. We show that such a graph can be better organized around gateway nodes, which serve as bridges connecting different regions of the movement graph. A graph-based object movement cube can be constructed by merging and collapsing nodes and edges according to an application-oriented topological structure. Moreover, we propose an efficient cubing algorithm that performs simultaneous aggregation of both spatiotemporal and item dimensions on a partitioned movement graph, guided by such a topological structure.