A Review of Stope Layout Optimization Algorithms: Linking Resource Efficiency, Circular Mining, and Sustainable Production

Medium-Term Planning of Mining Complexes with Explicit Shovel Tracking and Processing Plant Uncertainty

Simultaneous and stochastic optimization of open-pit mining complexes at the medium-term level aims to maximize expected profits, manage technical risk for integrated value chains, and enhance the operational feasibility of the long-term plan while still meeting its targets to achieve long-term value. To address two key operational feasibility challenges over a twelve-month horizon, the proposed framework integrates two features that provide a more detailed operational evaluation during decision-making than existing methods. The first involves explicit tracking of shovel movements to align optimized extraction sequences with the operational capabilities of loading equipment. The second uses high-order simulation to produce probability distributions for processing plant responses based on geometallurgical properties of the material being scheduled and selected operating modes. To efficiently optimize schedules with this detailed evaluation, a solution method is proposed using an online machine learning model to rank moves in a metaheuristic search procedure. The framework is demonstrated using a gold mining complex and results show realistic extraction sequences with an increase in metal production and cashflow when compared to a regression-based processing model.

Earthwork Optimization in Road Construction Projects Using Linear Programming, Metaheuristic, Geographic Information System and Artificial Intelligence Approaches

Optimization-Based and Optimization-Linked Decision Methods for Building Construction Safety: A Systematic Review

Building construction sites are dynamic systems in which safety decisions interact with time, cost, productivity, equipment movement, and spatial constraints. This systematic review examines how building-construction-stage safety is represented in optimization-based and optimization-linked decision studies published between 1 January 2016 and 30 June 2026. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-informed workflow used searches of the Web of Science Core Collection and IEEE Xplore, supplemented by Google Scholar and backward-citation checks. Seventy-nine studies met the core inclusion criterion, which required safety to appear as a quantified objective, constraint, evaluation metric, decision criterion, or prediction target. Studies were classified by problem type, safety role, method family, digital integration, and validation evidence. The synthesis identifies a problem-type-dependent formulation pattern: site-layout and scheduling studies mainly optimize safety or exposure objectives; crane/lifting studies distribute safety across constraints, objectives, and decision criteria; risk-decision studies use criteria or metrics; and prediction studies tune models whose targets are safety or risk outcomes. The core corpus is concentrated in site-layout and crane/lifting studies, whereas temporary works, monitoring-to-intervention, and construction-stage emergency response are less often formulated as optimization problems. Strict real-site/field evidence was identified in 7 of 79 studies, with an upper sensitivity bound of 11. Future research should prioritize transparent metrics, benchmarks, field validation, and closed-loop workflows.

Stress-Relief Annealing: Polynomial-Time Simulation-Free Layout Optimization for Automated Warehouses

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.

Layout Optimization of Irregular Construction Sites Based on SLP and Improved NSGA-II

Previous studies on construction site layout often simplified the site as a rectangle, with little consideration of adaptability to complex terrain and multiple functional constraints. An optimization method was developed for irregular construction sites, based on Systematic Layout Planning (SLP) and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), to address the limited adaptability of traditional methods in multi-objective and multi-constraint scenarios. A mathematical model for site layout was constructed using a rasterization method, with transportation time, transportation cost, and noise level as the optimization objectives. High-quality initial populations were generated by quantifying logistics and non-logistics relationships using the SLP method. The NSGA-II algorithm was enhanced with an adaptive penalty function, two-point crossover encoding, dynamically adjusted crossover and mutation probabilities, and a population restart mechanism. This improved its global search efficiency and convergence performance in complex solution spaces. Case validation results indicate that SLP-INSGA-II outperforms NSGA-II and SLP-NSGA-II while maintaining comparable performance to INSGA-II on some indicators. Without degrading overall optimization performance, incorporating SLP-based engineering priors can enhance search guidance, leading to layout solutions that are both feasible and engineering-interpretable. This study provides a modeling and solution approach for layout optimization in irregular construction sites.

Integrating Automotive Production and Intralogistics Planning: A Systematic Literature Review of Optimization Problems and Research Directions

The increasing complexity of automotive production systems, driven by mass customization, the transition from internal combustion engine vehicles to electric vehicles, competitive markets, and cost pressure, has intensified the need for advanced optimization across production and intralogistics planning. Optimization refers to the process of determining the best feasible solution to a decision problem according to a defined objective function, subject to given constraints. Whereas most reviews focus on individual problem classes, this systematic literature review adopts an integrated production and intralogistics perspective. Following the PRISMA statement, four research questions on problem types, their classification, solution methods, and trends are addressed by searching four databases (Web of Science, IEEE Xplore, ACM Digital Library, and Science Direct), yielding 194 publications from 1983 to 2025. The analysis shows that production planning, scheduling, resource management, and uncertainty and robustness are the dominant problem types, while metaheuristics, exact methods, and simulation are the most common, frequently hybridized solution methods. Publication activity has risen sharply, with 73.7% of studies appearing since 2019 and material feeding, sustainability, human–robot collaboration, and machine learning showing increased recent publication activity. A taxonomy classifying optimization problems among five dimensions, problem type, decision level, objectives, solution methodology, and real-data usage is proposed to guide researchers and practitioners towards an integrated, industrially deployed optimization.