A Column Generation Algorithm for Choice-Based Network Revenue Management

In the last few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges. One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a subset of the products provided by the firm that a customer views as options. Customers choose a particular product according to a multinomial-logit criterion, a model widely used in the marketing literature. In this paper, we consider the choice-based, deterministic, linear programming model (CDLP) of Gallego et al. [6], and the follow-up dynamic programming (DP) decomposition heuristic of van Ryzin and Liu [16], and focus on the more general version of these models, where customers belong to overlapping segments. To solve the CDLP for real-size networks, we need to develop a column generation algorithm. We prove that the associated column generation subproblem is indeed NP-Complete, and propose a simple, greedy heuristic to overcome the complexity of an exact algorithm. Our computational results show that the heuristic is quite effective, and that the overall approach has good practical potential and leads to high quality solutions.Operations Management Working Papers Serie

A Column Generation Algorithm for Choice-Based Network Revenue Management

In the last few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges. One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a subset of the products provided by the firm that a customer views as options. Customers choose a particular product according to a multinomial-logit criterion, a model widely used in the marketing literature. In this paper, we consider the choice-based, deterministic, linear programming model (CDLP) of Gallego et al. [6], and the follow-up dynamic programming (DP) decomposition heuristic of van Ryzin and Liu [16], and focus on the more general version of these models, where customers belong to overlapping segments. To solve the CDLP for real-size networks, we need to develop a column generation algorithm. We prove that the associated column generation subproblem is indeed NP-Complete, and propose a simple, greedy heuristic to overcome the complexity of an exact algorithm. Our computational results show that the heuristic is quite effective, and that the overall approach has good practical potential and leads to high quality solutions.Operations Management Working Papers Serie

EFFICIENT RESOURCE ALLOCATION IN A BILEVEL HIERARCHY WITH KNAPSACK AND ASSIGNMENT LOWER-LEVEL PROBLEMS

Bilevel optimization problems model a decision-making process with a two-level hierarchy of independent decision-makers, namely, the leader and the follower. The decisions are performed in a predetermined sequence with the leader acting first. Consequently, the follower solves an optimization problem which contains parameters (e.g., the right-hand sides of the follower's constraints) that are functionally dependent on the leader's decisions. On the other hand, the leader's objective and, possibly, constraints are also functions of both the leader's and follower's decision variables. Therefore, in the course of the decision-making process the leader should take into account the follower's rational response, i.e., optimal solutions to the follower's optimization problem. This dissertation is focused on the development of exact solution approaches for bilevel programs with combinatorial structures in the lower-level problems. In particular, we consider models arising in resource distribution systems that involve bilevel decision-making hierarchies with knapsack and assignment constraints. We discuss design and implementation of novel solution techniques, which exploit structural properties of the underlying optimization problems. The superiority of the proposed approaches is demonstrated through extensive computational experiments

Exact Methods In Fractional Combinatorial Optimization

This dissertation considers a subclass of sum-of-ratios fractional combinatorial optimization problems (FCOPs) whose linear versions admit polynomial-time exact algorithms. This topic lies in the intersection of two scarcely researched areas of fractional programming (FP): sum-of-ratios FP and combinatorial FP. Although not extensively researched, the sum-of-ratios problems have a number of important practical applications in manufacturing, administration, transportation, data mining, etc. Since even in such a restricted research domain the problems are numerous, the main focus of this dissertation is a mathematical programming study of the three, probably, most classical FCOPs: Minimum Multiple Ratio Spanning Tree (MMRST), Minimum Multiple Ratio Path (MMRP) and Minimum Multiple Ratio Cycle (MMRC). The first two problems are studied in detail, while for the other one only the theoretical complexity issues are addressed. The dissertation emphasizes developing solution methodologies for the considered family of fractional programs. The main contributions include: (i) worst-case complexity results for the MMRP and MMRC problems; (ii) mixed 0-1 formulations for the MMRST and MMRC problems; (iii) a global optimization approach for the MMRST problem that extends an existing method for the special case of the sum of two ratios; (iv) new polynomially computable bounds on the optimal objective value of the considered class of FCOPs, as well as the feasible region reduction techniques based on these bounds; (v) an efficient heuristic approach; and, (vi) a generic global optimization approach for the considered class of FCOPs. Finally, extensive computational experiments are carried out to benchmark performance of the suggested solution techniques. The results confirm that the suggested global optimization algorithms generally outperform the conventional mixed 0{1 programming technique on larger problem instances. The developed heuristic approach shows the best run time, and delivers near-optimal solutions in most cases