Applied & Interdisciplinary
Mohammad Javad Pahlevanzadeh; Mohammad Reza Fathi; Amirhossein Saffarinia
Abstract
This study presents a multi-objective mixed-integer linear programming (MILP) model for optimizing rotational shift scheduling of personnel at gas pressure reduction stations (CGSs) and administrative offices of the Gas Company of Qom Province, Iran. The model simultaneously addresses organizational ...
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This study presents a multi-objective mixed-integer linear programming (MILP) model for optimizing rotational shift scheduling of personnel at gas pressure reduction stations (CGSs) and administrative offices of the Gas Company of Qom Province, Iran. The model simultaneously addresses organizational requirements and human-centric factors, including employee shift preferences, coworker compatibility, sensitivity to mercaptan gas, public communication skills, commuting distances, and regulatory workload constraints. A two-phase solution framework is employed: the multi-objective problem is first scalarized using the ε-constraint method, and the resulting single-objective subproblems are then solved using the Gray Wolf Optimizer (GWO) and the Crow Search Algorithm (CSA). An MILP-solver-based ε-constraint baseline is also reported for benchmarking. A real-world case study involving 49 employees, 9 service locations, and a 30-day planning horizon is used to evaluate the framework. Results from 30 independent runs show that CSA obtains lower average overtime (44.2 h versus 48.6 h), higher shift-preference satisfaction (90.1% versus 87.3%), higher coworker-preference satisfaction (86.4% versus 83.6%), and shorter average runtime (109.8 s versus 121.4 s) than GWO. Wilcoxon signed-rank tests indicate statistically significant differences at the 5% level. Sensitivity analysis reveals the trade-off between individual shift preferences and coworker compatibility under alternative weight settings. The framework therefore provides a practical and adaptable decision-support approach for workforce planning in continuous and safety-critical gas distribution operations.
Applied & Interdisciplinary
Hossein Jahangiri; Pooria Rashvand; Ahmad Shokouhfar; Reza Farokhzad; Nima Amani
Abstract
Accurate forecasting of final construction costs is important for project planning and cost control in inflation-sensitive construction markets. This study develops a BIM-integrated artificial neural network (ANN) workflow that links item-level Fehrestbaha data and official construction adjustment indices ...
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Accurate forecasting of final construction costs is important for project planning and cost control in inflation-sensitive construction markets. This study develops a BIM-integrated artificial neural network (ANN) workflow that links item-level Fehrestbaha data and official construction adjustment indices to BIM components and forecasts unit costs at a user-specified completion date. Historical cost information for sixteen materials over 2013--2022 was used to develop the deterministic ANN predictor, and the resulting unit-cost forecasts were returned to the BIM quantity-takeoff workflow. The framework was evaluated on a single nine-story residential building in Tehran by comparing the April 2022 initial estimate, the ANN-based December 2023 forecast, and the actual final project cost. The December 2023 case lies beyond the 2013--2022 historical development horizon and is therefore used as the principal project-level temporal check, whereas the internal training/test diagnostics are interpreted only as model-fit diagnostics because the monthly resampling procedure can induce dependence among adjacent observations. For this case, the absolute project-level deviation was 11.30% for the ANN forecast compared with 23.89% for the initial no-update estimate. The contribution is the automated linkage of national item coding, time-indexed cost information, ANN forecasting, Revit plug-ins, and BIM quantity aggregation rather than a new optimization algorithm or a probabilistic uncertainty model. The results support the feasibility of the workflow as a proof of concept, subject to stricter chronological validation, improved categorical encoding, benchmark comparisons, and multi-project external validation.
Applied & Interdisciplinary
Reza Rajabi; Farzin Modarres Khiabania; Saba Sadeghi Gavganib
Abstract
Candidate route sets in the Urban Transit Network Design Problem (UTNDP) must provide adequate network coverage and connectivity while complying with prescribed route-length and route-cardinality requirements. This study develops a demand-aware heuristic-initialized NSGA-II framework for the UTNDP. The ...
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Candidate route sets in the Urban Transit Network Design Problem (UTNDP) must provide adequate network coverage and connectivity while complying with prescribed route-length and route-cardinality requirements. This study develops a demand-aware heuristic-initialized NSGA-II framework for the UTNDP. The method uses demand and travel-time information to construct feasible initial route sets, then applies Pareto ranking, crowding-distance selection, crossover, problem-specific mutation, feasibility repair, and duplicate filtering during the evolutionary search. The optimization model minimizes two explicitly defined objectives: the demand-weighted passenger generalized travel time (F1) and the total route weight, which serves as an operator-cost proxy (F2). In the reported Mandl/Mumford-style benchmark evaluation, every transfer edge of the transfer-expanded graph carries a fixed 5-min penalty, consistent with the standard convention for the urban transit routing problem (UTRP). Candidate route sets that leave any positive-demand origin–destination pair disconnected are first subjected to feasibility repair and, if the repair fails, are rejected before environmental selection. The framework is assessed on established UTNDP benchmark networks ranging from small to large instances. Hypervolume and spacing are formally defined as Pareto-set performance indicators, whereas the reported numerical comparisons are restricted to benchmark objective values directly supported by the available computational records; no run-level inferential comparison is claimed. Duplicate filtering and parameter settings are treated as algorithmic design components rather than being assigned independent gains without a dedicated ablation study. On the reported benchmark data, the framework yields passenger-cost values of 21.34 min for Mumford0 (22.18 min in the original study by Mumford) and 52.18 min for Edinburgh200 (55.34 min in the original study by Mumford). We treat frequency and vehicle capacity as exogenous quantities so the experiments isolate route-set optimization. The resulting framework provides a reproducible basis for passenger–operator trade-off analysis and a clearly delimited platform for future extensions to joint frequency, capacity, uncertainty, and parallel-computing formulations.
