A branch-and-price-and-cut method for computing an optimal bramble

Given an undirected graph, a bramble is a set of connected subgraphs (called bramble elements) such that every pair of subgraphs either contains a common node, or such that an edge $(i,j)$ exists with node $i$ belonging to one subgraph and node $j$ belonging to the other. In this paper we examine the problem of finding the bramble number of a graph, along with a set of bramble elements that yields this number. The bramble number is the largest cardinality of a minimum hitting set over all bramble elements on this graph. A graph with bramble number $k$ has a treewidth of $k-1$. We provide a branch-and-price-and-cut method that generates columns corresponding to bramble elements, and rows corresponding to hitting sets. We then examine the computational efficacy of our algorithm on a randomly generated data set.     

A new branch-and-price algorithm for the traveling tournament problem

The traveling tournament problem (ttp) consists of finding a distance-minimal double round-robin tournament where the number of consecutive breaks is bounded. For solving the problem exactly, we propose a new branch-and-price approach. The starting point is a new compact formulation for the ttp. The corresponding extensive formulation resulting from a Dantzig-Wolfe decomposition is identical to one given by Easton, K., Nemhauser, G., Trick, M., 2003. Solving the traveling tournament problem: a combined interger programming and constraint programming approach. In: Burke, E., De Causmaecker, P. (Eds.), Practice and Theory of Automated Timetabling IV, Volume 2740 of Lecture Notes in Computer Science, Springer Verlag Berlin/Heidelberg, pp. 100–109, who suggest to solve the tour-generation subproblem by constraint programming. In contrast to their approach, our method explicitly utilizes the network structure of the compact formulation: First, the column-generation subproblem is a shortest-path problem with additional resource and task-elementarity constraints. We show that this problem can be reformulated as an ordinary shortest-path problem over an expanded network and, thus, be solved much faster. An exact variable elimination procedure then allows the reduction of the expanded networks while still guaranteeing optimality. Second, the compact formulation gives rise to supplemental branching rules, which are needed, since existing rules do not ensure integrality in all cases. Third, non-repeater constraints are added dynamically to the master problem only when violated. The result is a fast exact algorithm, which improves many lower bounds of knowingly hard ttp instances from the literature. For some instances, solutions are proven optimal for the first time.     

Algorithms for some graph theoretical optimization problems

This is a summary of the main results presented in the author’s PhD thesis. This thesis, written in English, was supervised by Frits Spieksma and defended on September 23, 2005 at the Katholieke Universiteit Leuven. A copy of the thesis is available from the authors website (http://www.econ. kuleuven.be/linda.moonen/public/). The thesis can be roughly split into two parts. The first part is dedicated to the problem of partitioning partially ordered sets into chains of limited size. The second part deals with the structure and the connectivity of the Internet.     

Designing two-echelon supply networks

A modern distribution network design model needs to deal with the trade-offs between a variety of factors, including (1) location and associated (fixed) operating cost of distribution centers (DCs), (2) total transportation costs, and (3) storage holding and replenishment costs at DCs and retail outlets. In addition, network design models should account for factors such as (4) stockouts, by setting appropriate levels of safety stocks, or (5) capacity concerns, which may affect operating costs in the form of congestion costs. The difficulty of making such trade-offs is compounded by the fact that even finding the optimal two-echelon inventory policy in a fixed and uncapacitated distribution network is already a hard problem. In this paper, we propose a generic modeling framework to address these issues that continues and extends a recent stream of research aimed at integrating insights from modern inventory theory into the supply chain network design domain. Our approach is flexible and general enough to incorporate a variety of important side constraints into the problem.     

A branch-and-price algorithm for a vehicle routing with demand allocation problem

We investigate the vehicle routing with demand allocation problem where the decision-maker jointly optimizes the location of delivery sites, the assignment of customers to (preferably convenient) delivery sites, and the routing of vehicles operated from a central depot to serve customers at their designated sites. We propose an effective branch-and-price (B&P) algorithm that is demonstrated to greatly outperform the use of commercial branch-and-bound/cut solvers such as CPLEX. Central to the efficacy of the proposed B&P algorithm is the development of a specialized dynamic programming procedure that extends works on elementary shortest path problems with resource constraints in order to solve the more complex column generation pricing subproblem. Our computational study demonstrates the efficacy of the proposed approach using a set of 60 problem instances. Moreover, the proposed methodology has the merit of providing optimal solutions in run times that are significantly shorter than those reported for decomposition-based heuristics in the literature.     

Branch-and-price for staff rostering: An efficient implementation using generic programming and nested column generation

We present a novel generic programming implementation of a column-generation algorithm for the generalized staff rostering problem. The problem is represented as a generalized set partitioning model, which is able to capture commonly occurring problem characteristics given in the literature. Columns of the set partitioning problem are generated dynamically by solving a pricing subproblem, and constraint branching in a branch-and-bound framework is used to enforce integrality. The pricing problem is formulated as a novel three-stage nested shortest path problem with resource constraints that exploits the inherent problem structure. A very efficient implementation of this pricing problem is achieved by using generic programming principles in which careful use of the C++ pre-processor allows the generator to be customized for the target problem at compile-time. As well as decreasing run times, this new approach creates a more flexible modeling framework that is well suited to handling the variety of problems found in staff rostering. Comparison with a more-standard run-time customization approach shows that speedups of around a factor of 20 are achieved using our new approach. The adaption to a new problem is simple and the implementation is automatically adjusted internally according to the new definition. We present results for three practical rostering problems. The approach captures all features of each problem and is able to provide high-quality solutions in less than 15 minutes. In two of the three instances, the optimal solution is found within this time frame.     

Models and algorithms for three-stage two-dimensional bin packing

We consider the three-stage two-dimensional bin packing problem (2BP) which occurs in real-world applications such as glass, paper, or steel cutting. We present new integer linear programming formulations: models for a restricted version and the original version of the problem are developed. Both only involve polynomial numbers of variables and constraints and effectively avoid symmetries. Those models are solved using CPLEX. Furthermore, a branch-and-price (B&P) algorithm is presented for a set covering formulation of the unrestricted problem, which corresponds to a Dantzig-Wolfe decomposition of the polynomially-sized model. We consider column generation stabilization in the B&P algorithm using dual-optimal inequalities. Fast column generation is performed by applying a hierarchy of four methods: (a) a fast greedy heuristic, (b) an evolutionary algorithm, (c) solving a restricted form of the pricing problem using CPLEX, and finally (d) solving the complete pricing problem using CPLEX. Computational experiments on standard benchmark instances document the benefits of the new approaches: The restricted version of the integer linear programming model can be used to quickly obtain near-optimal solutions. The unrestricted version is computationally more expensive. Column generation provides a strong lower bound for 3-stage 2BP. The combination of all four pricing algorithms and column generation stabilization in the proposed B&P framework yields the best results in terms of the average objective value, the average run-time, and the number of instances solved to proven optimality.     

The distance constrained multiple vehicle traveling purchaser problem

In the Distance Constrained Multiple Vehicle Traveling Purchaser Problem (DC-MVTPP) a fleet of vehicles is available to visit suppliers offering products at different prices and with different quantity availabilities. The DC-MVTPP consists in selecting a subset of suppliers so to satisfy products demand at the minimum traveling and purchasing costs, while ensuring that the distance traveled by each vehicle does not exceed a predefined upper bound. The problem generalizes the classical Traveling Purchaser Problem (TPP) and adds new realistic features to the decision problem. In this paper we present different mathematical programming formulations for the problem. A branch-and-price algorithm is also proposed to solve a set partitioning formulation where columns represent feasible routes for the vehicles. At each node of the branch-and-bound tree, the linear relaxation of the set partitioning formulation, augmented by the branching constraints, is solved through column generation. The pricing problem is solved using dynamic programming. A set of instances has been derived from benchmark instances for the asymmetric TPP. Instances with up to 100 suppliers and 200 products have been solved to optimality.     

A branch-and-price approach for integrating nurse and surgery scheduling

A common problem at hospitals is the extreme variation in daily (even hourly) workload pressure for nurses. The operating room is considered to be the main engine and hence the main generator of variance in the hospital. The purpose of this paper is threefold. First of all, we present a concrete model that integrates both the nurse and the operating room scheduling process. Second, we show how the column generation technique approach, one of the most employed exact methods for solving nurse scheduling problems, can easily cope with this model extension. Third, by means of a large number of computational experiments we provide an idea of the cost saving opportunities and required solution times.     

Branch-and-price and constraint programming for solving a real-life technician dispatching problem

We consider a real problem faced by a large company providing repair services of office machines in Santiago, Chile. In a typical day about twenty technicians visit seventy customers in a predefined service area in Santiago. We design optimal routes for technicians by considering travel times, soft time windows for technician arrival times at client locations, and fixed repair times. A branch-and-price algorithm was developed, using a constraint branching strategy proposed by Ryan and Foster along with constraint programming in the column generation phase. The column generation takes advantage of the fact that each technician can satisfy no more than five to six service requests per day. Different instances of the problem were solved to optimality in a reasonable computational time, and the results obtained compare favorably with the current practice.