Stress-Relief Annealing (SRA), a polynomial-time simulation-free layout optimization algorithm that turns the task demand into a per-vertex stress field that predicts where traffic will concentrate in the warehouse; the field's peak provably caps the throughput.
Abstract
We study the problem of optimizing physical layouts for automated warehouses, where hundreds to thousands of robots are coordinated to transport packages. Previous works have shown that optimizing the warehouse layout (e.g., the physical location of the storage shelves) significantly improves throughput. However, state-of-the-art layout optimization approaches are based on evolutionary optimization methods, which treat the entire warehouse as a black box and rely on random mutation to search for high-quality layouts. While the optimization outcomes are promising, these methods require a massive number of simulations to evaluate candidate solutions, making them sample-inefficient. In this paper, we present Stress-Relief Annealing (SRA), a polynomial-time simulation-free layout optimization algorithm. SRA turns the task demand into a per-vertex \emph{stress field} that predicts where traffic will concentrate in the warehouse; the field's peak provably caps the throughput. Our experimental results show that (1) SRA improves both the throughput and the scalability of a human-designed warehouse, roughly doubling the number of robots it can sustain, (2) it matches or exceeds the throughput of the evolutionary baselines while taking only $19$ minutes on one CPU core, against their $25{,}000$ simulations and $25$ hours on a $64$-core machine, and (3) the gain generalizes across different Multi-Agent Path Finding algorithms, non-uniform task demands, and a warehouse with doubled dimensions.
The warehouse layout problem (WLP), also known as warehouse slotting problem, aims to determine the assignment of products to storage locations while minimizing the total travel distance for picking operations. It is combinatorial in nature and well-known to be NP-hard. To obtain high-quality solutions to medium and large WLP instances efficiently remains a computational challenge, which has motivated our work on applying quantum optimization algorithms for this problem. We reformulate the classical quadratic assignment programming (QAP) model of the WLP to a quadratic unconstrained binary optimization (QUBO) model. Our QUBO model avoids adding slack variables and reduces additional overheads as in the typical QUBO reformulation approach, which facilitates the application of the D-Wave Leap Hybrid solver. Computational study is performed on a use case with 30 stock-keeping-units (SKUs) and 32 storage slots, for which the QUBO formulation has 960 binary decision variables. We show that the D-Wave Leap Hybrid solver finds better solution to the base scenario than the exact IBM CPLEX mixed-integer quadratic programming (MIQP) solver operating under a 60-second wall-clock limit, one-tenth of the 600-second budget allocated to CPLEX in this experimental design. A sensitivity analysis across 30 scenarios, generated by crossing five annual pick volume profiles with six co-picking matrix density levels (0.5 to 1.0), shows that the D-Wave hybrid solver outperforms CPLEX MIQP solver in 26 of 30 cases, with solution quality improvements of 0.4 to 12.1%. D-Wave’s advantage generally increases as the density of co-picking matrix decreases. These results demonstrate quantum utility for WLP and motivate further investigation of hybrid quantum-classical methods for combinatorial logistics optimization at industrial strength scales.
This paper proposes a hybrid optimization algorithm that fuses multiple methods to address the weak global exploration ability, frequent local optima, and poor engineering adaptability in masonry layout optimization of building infill walls. The method builds a multilayer cooperative framework. It first uses the Genetic Algorithm to create a diverse population. It then applies Simulated Annealing to perform probabilistic jumping optimization. After that, it introduces sparse A search to verify topological feasibility. It finally relies on a cooperative mechanism of Adaptive Whale Optimization and iterative local search to explore the solution space in depth. Experiments on the simultaneous localization and mapping–building information modeling coupled dataset and the building information modeling component multimodal dataset show that the algorithm reaches a standard block utilization rate of 98.76 percent. It also keeps the cutting loss rate as low as 2.79%and achieves a peak stagger-joint compliance rate of 97.11%. In irregular wall scenarios, it reduces cost by up to 33.87%. The results show that this algorithm improves the optimization quality and engineering applicability of masonry layout and provides reliable technical support for precise construction and efficient material use of building infill walls.
Yuanzhe Chen, Feifei Chen· Research on Engineering Stru...· 0 citations
The assembly line balancing problem (ALBP) is an optimization problem which involves assigning tasks to stations to maximize line efficiency. In the context of Industry 4.0, assembly lines must be flexible and adaptable to stochastic and real-world conditions. However, in the literature, line balancing is mostly studied in deterministic environments. As a result, this paper presents a robust optimization approach to bridge the gap between theory and practice by addressing a real-world ALBP in the furniture industry. A mixed integer programming (MIP) model is proposed, considering uncertainty in task execution times, which are expressed as intervals of possible values, and the sequence of module assembly in addition to the standard ALBP constraints. Then, it is linearized by duality to be solved by Gurobi. Two objectives, (1) cycle time minimization and (2) tool efficiency, are optimized lexicographically using the proposed MILP models. Computational experiments and Monte Carlo simulations demonstrate that robust solutions outperform deterministic ones, ensuring stable throughput under variability. The study highlights the trade-off between efficiency and robustness, offering practical insights for furniture manufacturers.
Ana Beatriz Costa, C. Pimentel, João C. O. Matias et al.· Mathematics· 0 citations
Robotic Fulfillment Centers (FCs) store inventory on shelves (pods) arranged in dense blocks. Retrieving a target pod that is buried deep in a block requires moving obstructing pods out of the way (i.e., digout). Multi-robot planners use parameterized cost functions to control digout behavior, producing a spectrum of strategies: at one extreme, obstructing pods are sent to other blocks (using more robots in travel lanes); at the other, pods are shuffled within the block (avoiding lane congestion but increasing extraction time). Each point on this spectrum has different downstream consequences for floor congestion and throughput. The optimal operating point depends on the specific facility configuration and shifts with operational conditions such as varying station demand and congestion patterns, making offline tuning impractical. We present an adaptive parameter tuning framework based on Extremum Seeking Control (ESC) that continuously adjusts planner parameters in response to measured throughput. ESC performs model-free optimization by perturbing parameters with sinusoidal dither signals and correlating perturbations with performance changes to estimate gradients, making it robust to the multi-minute delayed effects and credit assignment challenges inherent in large FC operations. Simulation studies demonstrate that the adaptive policy improves upon fixed policies across several conditions. We observe an improvement in throughput by an average of 5.0% across map and robot fleet size variations, and by 8.4% under dynamic operating conditions. This work eliminates manual parameter provisioning and enables real-time adaptation, providing a self-tuning paradigm for FC storage operations.
Pratap Tokekar, M. Benosman, Rahul Chandan et al.· 0 citations
Facility Layout Planning (FLP) remains a complex task for manufacturers seeking to improve productivity, reduce daily operating costs, and stay competitive in fast-changing markets. Traditional methods such as Systematic Layout Planning (SLP) offer useful guidelines for designing department layouts but still rely heavily on judgment and experience. At the same time, modern optimization and simulation techniques provide valuable quantitative insights. These techniques are often used separately rather than as part of an integrated process. In this work, a hybrid layout-planning approach that combines these techniques is developed and validated through an industrial case study, providing a practical decision-support process for facility layout planning. The process starts with SLP, which develops an initial layout using activity relationship charts, material-flow analysis, and handling-cost estimates. A simulation model then evaluates throughput, machine utilization, and work-in-progress, providing early indications of the layout’s real-world performance. A Genetic Algorithm (GA) is used to find improved configurations that reduce distances and costs. The optimized layouts are further tested through simulation. To demonstrate practical use, the framework was applied at a transformer manufacturing plant. It resulted in an approximately 35% reduction in material-handling costs. The results show that the optimized layout reduced material-handling costs from 7062.5 to approximately 4560 L.E. per transformer while increasing monthly throughput by 2.46% (approximately 11 transformers per month). Additionally, a what-if analysis was performed to identify opportunities for improvement, such as increasing production by using an automatic laser-cutting machine. The findings support data-driven decisions in facility layout design and long-term operational planning.
I. Fikry, N. Zamzam· Applied System Innovation· 0 citations
Background: Planning power distribution networks is crucial in contemporary infrastructure development. Current distribution paradigms require not only cost minimization but also reliability and fault tolerance. However, designing meshed network topologies is a computationally demanding combinatorial optimization problem, especially for large instances. Methods: We reframe this problem as a multi-depot vehicle routing problem in which electrical substations act as depots and power lines represent routes. We develop a four-phase Large Neighborhood Search (LNS) that combines geographically-based destroy operators with a topology-specific MILP repair operator. Each repair subproblem is solved to optimality under the adopted topological, flow conservation, and line capacity constraints. Results: Experiments on realistic medium-voltage distribution network instances with up to 1150 nodes show that the proposed method handles cases that are beyond the reach of exact global optimization. Compared with a greedy constructive heuristic, the best LNS solution achieves an average cost reduction of 28.5%. Ablation and sensitivity analyses support the algorithmic design and show stable behavior under reasonable parameter variations. AC power flow analyses on the largest instance confirm electrical consistency under the tested single-branch outage scenarios, with a maximum voltage deviation of 5.1%. Conclusions: The proposed optimal-repair LNS provides a scalable approach for planning large fault-tolerant distribution networks under topology and line capacity constraints.
Unknown authors· Logistics· 0 citations
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