Column generation approaches to a robust airline crew pairing model for managing extra flights

A typical airline crew pairing problem aims at selecting a set of flight sequences (pairings) for crews such that each flight in the regular schedule is covered by one crew. In this thesis, we consider the management of potential extra flights that can possibly be introduced to the regular flight schedule during operation at a later point in time. Without delaying or canceling any existing flight, we try to handle these extra flights within the regular schedule and refer to the resulting mathematical model as a robust airline crew pairing model. The objective function of the robust model involves not only the regular pairing costs but also the opportunity costs for failing to cover the extra flights. Due to the large number of variables (pairings), a typical crew pairing model is usually solved by column generation methods. Before applying column generation to the proposed robust model, we first discuss several procedures to cover the extra flights by a given set of feasible pairings. However, these procedures introduce extra column-dependent constraints to the model. That is, as new columns are added by column generation to the model, the number of constraints may also increase. Similarly if a column is removed from the model, then some of these extra constraints may be deleted. To handle this dynamic change both in the number of constraints and variables we propose two approaches. The main idea behind these approaches is to generate a set of pairings (column pool) so that the number of constraints can be fixed. To this end, we flag the pairings that can be used for covering the extra flights and keep them in a special pool. We illustrate the proposed column generation approaches on a set of actual data acquired from a local airline

Comparing ASP, CP, ILP on two challenging applications: wire routing and haplotype inference

We study three declarative programming paradigms, Answer Set Programming (ASP), Constraint Programming (CP), and Integer Linear Programming (ILP), on two challenging applications: wire routing and haplotype inference. We represent these problems in each formalism in a systematic way, compare the formulations both from the point of view of knowledge representation (e.g., how tolerant they are to elaborations) and from the point of view of computational efficiency (in terms of computation time and program size). We discuss possible ways of improving the computational efficiency, and other reformulations of the problems based on different mathematical models

Comparing ASP, CP, ILP on two challenging applications: wire routing and haplotype inference

We study three declarative programming paradigms, Answer Set Programming (ASP), Constraint Programming (CP), and Integer Linear Programming (ILP), on two challenging applications: wire routing and haplotype inference. We represent these problems in each formalism in a systematic way, compare the formulations both from the point of view of knowledge representation (e.g., how tolerant they are to elaborations) and from the point of view of computational efficiency (in terms of computation time and program size). We discuss possible ways of improving the computational efficiency, and other reformulations of the problems based on different mathematical models

Blood supply chain management and future research opportunities

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In this chapter, we discuss the challenges and research opportunities in the blood collection operations and explore the benefits of recent advances in the blood donation process. According to the regulations, donated blood has to be processed in a processing facility within 6 h of donation. This forces blood donation organizations to schedule continuous pickups from donation sites. The underlying mathematical problem is a variant of well-known Vehicle Routing Problem (VRP). The main differences are the perishability of the product to be collected, and the continuity of donations. We discuss the implications of such differences on collection routes from donation centers. Recent advances such as multicomponent apheresis (MCA) allow the donation of more than one component and/or more than one transfusable unit of each blood product. MCA provides several opportunities including (1) increasing the donor utilization, (2) tailoring the donations based on demand, and (3) reducing the infection risks in the transfusion. We also discuss MCA, its potential benefits and how to best use MCA in order to improve blood products availability and manage donation/disposal costs.TÜBİTA

Logic-based benders decomposition for planning and scheduling: a computational analysis

Logic-based Benders decomposition (LBBD) has improved the state of the art for solving a variety of planning and scheduling problems, in part by combining the complementary strengths of constraint programming and mixed integer programming (MIP). We undertake a computational analysis of specific factors that contribute to the success of LBBD, to provide guidance for future implementations. We study a problem class that assign tasks to multiple resources and poses a cumulative scheduling problem on each resource. We find that LBBD is at least 1000 times faster than state-of-the-art MIP on larger instances, despite recent advances in the latter. Further, we conclude that LBBD is most effective when the planning and scheduling aspects of the problem are roughly balanced in difficulty. The most effective device for improving LBBD is the inclusion of a subproblem relaxation in the master problem. The strengthening of Benders cuts also plays an important role when the master and subproblem complexity are properly balanced. These findings suggest future research directions

Les étapes de Hodler

Exposition à la Kunsthalle de Bâle. Deux salles réservées à Hodler : résumé de 40 ans d'activité. Brève étude des oeuvres exposées, bonne critique