Nonlinear formulations and improved randomized approximation algorithms for multiway and multicut problems

Cover title.Includes bibliographical references (p. 21-22).D. Bertsimas, C. Teo and R. Vohra

Nonlinear Formations and Improved Randomized Approximation Algorithms for Multiway and Multicut Problems

We introduce nonlinear formulations of the multiway cut and multicut problems. By simple linearizations of these formulations we derive several well known formulations and valid inequalities as well as several new ones. Through these formulations we establish a connection between the multiway cut and the maximum weighted independent set problem that leads to the study of the tightness of several LP formulations for the multiway cut problem through the theory of perfect graphs. We also introduce a new randomized rounding argument to study the worst case bound of these formulations, obtaining a new bound of 2a(H)(1 - ) for the multicut problem, where ac(H) is the size of a maximum independent set in the demand graph H

On approximability and LP formulations for multicut and feedback set problems

Graph cut algorithms are an important tool for solving optimization problems in a variety of areas in computer science. Of particular importance is the min $s$-$t$ cut problem and an efficient (polynomial time) algorithm for it. Unfortunately, efficient algorithms are not known for several other cut problems. Furthermore, the theory of NP-completeness rules out the existence of efficient algorithms for these problems if the $P\neq NP$ conjecture is true. For this reason, much of the focus has shifted to the design of approximation algorithms. Over the past 30 years significant progress has been made in understanding the approximability of various graph cut problems. In this thesis we further advance our understanding by closing some of the gaps in the known approximability results. Our results comprise of new approximation algorithms as well as new hardness of approximation bounds. For both of these, new linear programming (LP) formulations based on a labeling viewpoint play a crucial role. One of the problems we consider is a generalization of the min $s$-$t$ cut problem, known as the multicut problem. In a multicut instance, we are given an undirected or directed weighted supply graph and a set of pairs of vertices which can be encoded as a demand graph. The goal is to remove a minimum weight set of edges from the supply graph such that all the demand pairs are disconnected. We study the effect of the structure of the demand graph on the approximability of multicut. We prove several algorithmic and hardness results which unify previous results and also yield new results. Our algorithmic result generalizes the constant factor approximations known for the undirected and directed multiway cut problems to a much larger class of demand graphs. Our hardness result proves the optimality of the hitting-set LP for directed graphs. In addition to the results on multicut, we also prove results for multiway cut and another special case of multicut, called linear-3-cut. Our results exhibit tight approximability bounds in some cases and improve upon the existing bound in other cases. As a consequence, we also obtain tight approximation results for related problems. Another part of the thesis is focused on feedback set problems. In a subset feedback edge or vertex set instance, we are given an undirected edge or vertex weighted graph, and a set of terminals. The goal is to find a minimum weight set of edges or vertices which hit all of the cycles that contain some terminal vertex. There is a natural hitting-set LP which has an $\Omega(\log k)$ integrality gap for $k$ terminals. Constant factor approximation algorithms have been developed using combinatorial techniques. However, the factors are not tight, and the algorithms are sometimes complicated. Since most of the related problems admit optimal approximation algorithms using LP relaxations, lack of good LP relaxations was seen as a fundamental roadblock towards resolving the approximability of these problems. In this thesis we address this by developing new LP relaxations with constant integrality gaps for subset feedback edge and vertex set problems

Block-based Outpatient Clinic Appointments Scheduling Under Open-access Policy

Outpatient clinic appointment scheduling is an important topic in OR/IE studies. Open-access policy shows its strength in improving patient access and satisfaction, as well as reducing no-show rate. The traditional far-in-advance scheduling plays an important role in handling chronic and follow-up care. This dissertation discusses a hybrid policy under which a clinic deals with three types of patients. The first type of patients are those who request their appointments before the visit day. The second type of patients schedule their appointment on the visit day. The third type of patients are walk-in patients who go to the clinic without appointments and wait to see the physician in turn. In this dissertation, the online scheduling policy is addressed for the Type 2 and Type 3 patients, and the offline scheduling policy is used for the Type 1 patients. For the online scheduling policy, two stochastic integer programming (SIP) models are built under two different sets of assumptions. The first set of assumptions ignores the endogenous uncertainty in the problem. An aggregate assigning method is proposed with the deterministic equivalent problem (DEP) model. This method is demonstrated to be better than the traditional one-at-a-time assignment through both overestimation and underestimation numerical examples. The DEP formulations are solved using the proposed bound-based sampling method, which provides approximated solutions and reasonable sample size with the least gap between lower and upper bound of the original objective value. On the basis of the first set of assumptions and the SIP model, the second set of assumptions considers patient no-shows, preference, cancellations and lateness, which introduce endogenous uncertainty into the SIP model. A modified L-shaped method and aggregated multicut L-shaped method are designed to handle the model with decision dependent distribution parameter. Distinctive optimality cut generation schemes are proposed for three types of distribution for linked random variables. Computational experiments are conducted to compare performance and outputs of different methods. An alternative formulation of the problem with simple recourse function is provided, based on which, a mixed integer programming model is established as a convenient complementary method to evaluate results with expected value. The offline scheduling aims at assigning a certain number of Type 1 patients with deterministic service time and individual preferences into a limited number of blocks, where the sum of patients’ service time in a block does not exceed the block length. This problem is associated with bin packing problem with restrictions. Heuristic and metaheuristic methods are designed to adapt the added restrictions to the bin packing problem. Zigzag sorting is proposed for the algorithm and is shown to improve the performance significantly. A clique based construction method is designed for the Greedy Randomized Adaptive Search Procedure and Simulated Annealing. The proposed methods show higher efficiency than traditional ones. This dissertation offers a series of new and practical resolutions for the clinic scheduling problem. These methods can facilitate the clinic administrators who are practicing the open-access policy to handle different types of patients with deterministic or nondeterministic arrival pattern and system efficiency. The resolutions range from operations level to management level. From the operations aspect, the block-wise assignment and aggregated assignment with SIP model can be used for the same-day request scheduling. From the management level, better coordination of the assignment of the Type 1 patients and the same-day request patients will benefit the cost-saving control