Rerouting shortest paths in planar graphs

A rerouting sequence is a sequence of shortest st-paths such that consecutive paths differ in one vertex. We study the the Shortest Path Rerouting Problem, which asks, given two shortest st-paths P and Q in a graph G, whether a rerouting sequence exists from P to Q. This problem is PSPACE-hard in general, but we show that it can be solved in polynomial time if G is planar. To this end, we introduce a dynamic programming method for reconfiguration problems.Comment: submitte

Solving the unit-load pre-marshalling problem in block stacking storage systems with multiple access directions

Block stacking storage systems are highly adaptable warehouse systems with low investment costs. With multiple, deep lanes they can achieve high storage densities, but accessing some unit loads can be time-consuming. The unit-load pre-marshalling problem sorts the unit loads in a block stacking storage system in off-peak time periods to prepare for upcoming orders. The goal is to find a minimum number of unit-load moves needed to sequence a storage bay in ascending order based on the retrieval priority group of each unit load. In this paper, we present two solution approaches for determining the minimum number of unit-load moves. We show that for storage bays with one access direction, it is possible to adapt existing, optimal tree search procedures and lower bound heuristics from the container pre-marshalling problem. For multiple access directions, we develop a novel, two-step solution approach based on a network flow model and an A* algorithm with an adapted lower bound that is applicable in all scenarios. We further analyze the performance of the presented solutions in computational experiments for randomly generated problem instances and show that multiple access directions greatly reduce both the total access time of unit loads and the required sorting effort

Coil batching to improve productivity and energy utilization in steel production

This paper investigates a practical batching decision problem that arises in the batch annealing operations in the cold rolling stage of steel production faced by most large iron and steel companies in the world. The problem is to select steel coils from a set of waiting coils to form batches to be annealed in available batch annealing furnaces and choose a median coil for each furnace. The objective is to maximize the total reward of the selected coils less the total coil'coil and coil'furnace mismatching cost. For a special case of the problem that arises frequently in practical settings where the coils are all similar and there is only one type of furnace available, we develop a polynomial-time dynamic programming algorithm to obtain an optimal solution. For the general case of the problem, which is strongly NP-hard, an exact branch-and-price-and-cut solution algorithm is developed using a column and row generation framework. A variable reduction strategy is also proposed to accelerate the algorithm. The algorithm is capable of solving medium-size instances to optimality within a reasonable computation time. In addition, a tabu search heuristic is proposed for solving larger instances. Three simple search neighborhoods, as well as a sophisticated variable-depth neighborhood, are developed. This heuristic can generate near-optimal solutions for large instances within a short computation time. Using both randomly generated and real-world production data sets, we show that our algorithms are superior to the typical rule-based planning approach used by many steel plants. A decision support system that embeds our algorithms was developed and implemented at Baosteel to replace their rule-based planning method. The use of the system brings significant benefits to Baosteel, including an annual net profit increase of at least 1.76 million U.S. dollars and a large reduction of standard coal consumption and carbon dioxide emissions

Solutions to real-world instances of PSPACE-complete stacking

We investigate a complex stacking problem that stems from storage planning of steel slabs in integrated steel production. Besides the practical importance of such stacking tasks, they are appealing from a theoretical point of view. We show that already a simple version of our stacking problem is PSPACE-complete. Thus, fast algorithms for computing provably good solutions as they are required for practical purposes raise various algorithmic challenges. We describe an algorithm that computes solutions within 5/4 of optimality for all our real-world test instances. The basic idea is a search in an exponential state space that is guided by a state-valuation function. The algorithm is extremely fast and solves practical instances within a few seconds. We assess the quality of our solutions by computing instance-dependent lower bounds from a combinatorial relaxation formulated as mixed integer program. To the best of our knowledge, this is the first approach that provides quality guarantees for such problems

Optimales Sortieren von Objekten

This thesis is concerned with the problem of optimally rearranging objects, in particular, railcars in a rail yard. The work is motivated by a research project of the Institute of Mathematical Optimization at Technische Universität Braunschweig, together with our project partner BASF, The Chemical Company, in Ludwigshafen. For many variants of such rearrangement problems - including the real-world application at BASF - we state the computational complexity by exploiting their equivalence to particular graph coloring, scheduling, and bin packing problems. We present mathematical optimization methods for determining schedules that are either optimal or close to optimal, and computational results are discussed from both a theoretical and practical point of view. In addition to the railway industry, there are other fields of application in which efficiently rearranging, sorting, or stacking is an important issue. For instance, the results obtained in this thesis could also be applied to solving certain piling problems in warehouses or container terminals.Die Dissertation beschäftigt sich mit dem optimalen Sortieren von Objekten, insbesondere von Güterwagen in Rangierbahnhöfen. Motiviert wurde diese Arbeit durch ein BMBF-gefördertes Projekt mit der BASF, The Chemical Company, in Ludwigshafen. Zahlreiche Varianten derartiger Sortierprobleme werden mathematisch formuliert und komplexitätstheoretisch eingeordnet. Für viele Varianten wird deren Äquivalenz zu bestimmten Graphenfärbungs-, Scheduling- sowie Bin-Packing-Problemen gezeigt. Für mehrere als theoretisch schwer bewiesene Fälle werden schnelle approximative Algorithmen vorgeschlagen, die Lösungen mit einer beweisbaren Güte liefern. Neben heuristischen Methoden werden auch exakte Verfahren zur Bestimmung optimaler Lösungen vorgestellt. Unter anderem handelt es sich bei den eingesetzten exakten Ansätzen um LP- sowie Lagrange-basierte Branch-and-Bound-Verfahren, die auf verschiedenen binären Modellen beruhen. Die Lösungsmethoden werden durch die Auswertung von Rechenergebnissen für reale Daten evaluiert. Den Abschluss der Dissertation bildet eine Kompetitivitätsanalyse diverser Online-Varianten, die dadurch gekennzeichnet sind, dass nicht alle relevanten Informationen zu Beginn der Planung vorliegen. Es sei auf das Verwertungspotenzial der in dieser Arbeit vorgestellten Optimierungsverfahren innerhalb anderer Anwendungsbereiche, in denen Sortieren, Stapeln, Lagern oder Verstauen eine Rolle spielen, hingewiesen