We present column elimination as a general framework for solving (large-scale) integer programming problems. In this framework, solutions are represented compactly as paths in a directed acyclic graph. Column elimination starts with a relaxed representation, that may contain infeasible paths, and solves a constrained network flow over the graph to find a solution. It then … Read more
Given a nominal combinatorial optimization problem, we consider a robust two-stages variant with polyhedral cost uncertainty, called Decision-Dependent Information Discovery (DDID). In the first stage, DDID selects a subset of uncertain cost coefficients to be observed, and in the second-stage, DDID selects a solution to the nominal problem, where the remaining cost coefficients are still … Read more
Dantzig-Wolfe decomposition is a well-known classical method for solving huge linear optimization problems with a block-angular structure. The most computationally expensive process in the method is pricing: solving block subproblems for a dual variable to produce new columns. Therefore, when we want to solve a slightly perturbated problem in which the block-angular structure is preserved … Read more
The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a … Read more
The length-constrained cycle partition problem (LCCP) is a graph optimization problem in which a set of nodes must be partitioned into a minimum number of cycles. Every node is associated with a critical time and the length of every cycle must not exceed the critical time of any node in the cycle. We formulate LCCP … Read more
This paper addresses the single crew scheduling and routing problem in the context of road network repair and restoration, which is critical in assisting complex post-disaster decisions in humanitarian logistics settings. We present three novel formulations for this problem, which are the first suitable for column generation and branch-and-price (BP) algorithms. Specifically, our first formulation … Read more
We tackle the problem of coordinating a three-echelon last-mile delivery system. In the first echelon, trucks transport parcels from distribution centres outside the city to public transport stops. In the second echelon, the parcels move on public transport and reach the city centre. In the third echelon, zero-emission vehicles pick up the parcels at public … Read more
This article delves into the early development of the Column Generation technique. It begins with Kantorovich’s classic 1939 work, correcting widespread misconceptions about his contributions to the Cutting Stock Problem. Then, it brings to light Kantorovich and Zalgaller’s lesser-known 1951 book, which is revealed to contain a complete Column Generation algorithm. The article also places … Read more
Motivated by applications in e-commerce logistics and production planning where orders (or items, or jobs) arrive at different times and must be dispatched or processed in batches, we consider a multi-vehicle dispatching problem that captures the tension between waiting for orders to arrive and the economies of scale due to batching. Our model extends the … Read more
Adjustable robust optimization (ARO) is a powerful tool to model problems that have uncertain data and that feature a two-stage decision making process. Computationally, they are often addressed using the column-and-constraint generation (CCG) algorithm introduced by Zhao and Zeng in 2012. While it was empirically shown that the algorithm scales well if all second-stage decisions … Read more