arXiv subjects

Stefan Nickel

Publications and source records attributed to Stefan Nickel.

5 recordsLinked to original sources

arXiv2026

Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization

Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing. While integrated models capture interactions between these decisions, practical warehouse systems often require decomposed approaches due to organizational boundaries, differing responsibilities, or limited data availability. Existing studies primarily evaluate algorithms for isolated subproblems or fixed subproblem combinations for specific warehouse settings, but lack a general mechanism to determine applicable algorithm configurations, compose them into valid solution pipelines, and assess their performance. With Context-Aware Synthesis of Optimization Pipelines (CASOP), we propose a framework for constructing and evaluating context-specific optimization pipelines and apply these to order fulfillment. The framework comprises: (1) a modular repository of algorithms for common order fulfillment problems; (2) semantic data and algorithm cards to describe warehouse context and algorithm requirements; (3) a taxonomy that structures order fulfillment problems into relevant subproblems; (4) a pipeline synthesizer that identifies applicable algorithms for a given warehouse context and composes all valid optimization pipelines; and (5) a pipeline evaluator that assesses all resulting pipelines. We demonstrate the framework on 7 benchmark instance sets covering four problem classes, resulting in 1,063,044 valid pipelines. The framework supports researchers and practitioners in designing, automatically synthesizing, and selecting valid, high-performing algorithmic pipelines for warehouse operations. The software is open-source and available at https://github.com/kit-dsm/ware_ops_pipes and https://github.com/kit-dsm/ware_ops_algos. Keywords: Warehouse optimization, Algorithm selection, Pipeline synthesis, Order fulfillment

cs.AI↗

arXiv2026

Revisiting transportation problems under Monge costs with applications to location problems

We investigate the transportation problem under a Monge cost structure and derive compact formulas for optimal dual solutions based on the northwest-corner rule. As an application illustrating how these formulas yield structural insight while enhancing computational performance, we consider a broad class of facility location problems. In particular, the expressions are used within a Benders decomposition framework to derive novel formulations for the Discrete Ordered Median Problem with non-increasing weights. Numerical experiments validate that the resulting formulations achieve state-of-the-art performance and exhibit strong robustness across a wide range of instances.

math.OC↗

arXiv2026

Quantum Optimization in Loc(Q)ation Science: QUBO Formulations, Benchmark Problems, and a Computational Study

Recent advances in quantum computing and the increasing availability of quantum hardware have substantially enhanced the practical relevance of quantum approaches to discrete optimization. Among these, the Quadratic Unconstrained Binary Optimization (QUBO) formulation provides a unifying modeling framework for a broad class of $\mathbf{NP}$-hard problems and is naturally suited to quantum computing and quantum-inspired algorithms. Location science, network design, and logistics represent core application domains of discrete optimization, combining high practical impact with substantial computational challenges. In this work, we develop QUBO formulations for several fundamental problems in these domains, including a nonlinear integer formulation of the Discrete Ordered Median Problem (DOMP). Beyond their modeling relevance, these QUBO formulations serve as representative benchmark problems for assessing quantum algorithms and quantum hardware. We further derive a tight bound for the penalty parameter ensuring equivalence between the QUBO formulation and its underlying integer program. Finally, we conduct a comprehensive computational study using QAOA, WS-QAOA, and classical heuristics for QUBO instances of the $p$-Median Problem and the Fixed-Charge Facility Location Problem (FCFLP), and introduce two effective warm-start strategies for WS-QAOA based on its linear programming relaxation.

quant-ph↗

arXiv2025

Enhancing Kernel Search with Pattern Recognition: the Single-Source Capacitated Facility Location Problem

We introduce Pattern-based Kernel Search (PaKS), a two-phase matheuristic for the solution of the Single-Source Capacitated Facility Location Problem (SSCFLP). In the first phase, PaKS employs a pattern recognition technique to identify an implicit spatial separation of potential locations and customers into subsets, called regions, within which location and assignment decisions are strongly interdependent. In the second phase, PaKS employs an enhanced Kernel Search (KS) heuristic that leverages the interdependencies among the decision variables identified in the first phase. On a set of 112 benchmark instances, consisting of up to 1,000 locations and 1,000 customers, computational results show that PaKS consistently outperforms both a standard KS implementation and the current state-of-the-art heuristic for solving the SSCFLP, as well as CPLEX when run with a time limit. For these instances, PaKS achieved an average gap compared to the best known solution of 0.02%. Experimental results conducted on a large set of new very large test problems, comprising up to 2,000 locations and 2,000 customers, demonstrate that PaKS outperforms both the standard KS heuristic and CPLEX in terms of quality of the solution found, finding the largest number of best solutions, and achieving the smallest average gap.

math.OC↗

arXiv2014

Simulation-Based Risk Reduction for Planning Inspections

Organizations that develop software have recognized that software process models are particularly useful for maintaining a high standard of quality. In the last decade, simulations of software processes were used in several settings and environments. This paper gives a short overview of the benefits of software process simulation and describes the development of a discrete-event model, a technique rarely used before in that field. The model introduced in this paper captures the behavior of a detailed code inspection process. It aims at reducing the risks inherent in implementing inspection processes and techniques in the overall development process. The determination of the underlying cause-effect relations using data mining techniques and empirical data is explained. Finally, the paper gives an outlook on our future work.

cs.SE↗