Browsing by Subject "stochastic modeling"

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Access status: Open Access ,
Operations Research in Municipal Solid Waste Management: Decision-Making Problems, Applications, and Research Gaps
(Wydawnictwa AGH, 2021) Gdowska, Katarzyna
Municipal Solid Waste Management (MSWM) represents a complex, multi-level decision domain that involves strategic, tactical, and operational planning under economic, environmental, and social constraints. This paper reviews the state of Operations Research (OR) applications to MSWM. The analysis encompasses optimization, simulation, metaheuristic, and hybrid approaches that address decision problems ranging from facility siting and capacity expansion to routing and scheduling. The study classifies OR contributions across decision levels, identifying methodological patterns and dominant model types such as mixed-integer programming, metaheuristics, and simulation-optimization frameworks. Despite significant progress in optimization and the integration of sustainability, critical gaps remain in uncertainty modeling, system-wide integration, and data-driven decision support. Deterministic formulations prevail at the strategic and tactical levels, while uncertainty is mainly explored in operational routing. Cross-level coordination among infrastructure planning, fleet design, and daily operations remains underdeveloped. Furthermore, persistent data scarcity and the limited incorporation of behavioral factors constrain the practical applicability of OR models. The review concludes with a research agenda that advocates for multi-level, uncertainty-aware, and dynamic optimization frameworks, supported by standardized data infrastructures and behavioral insights.
Access status: Open Access ,
Zastosowanie losowej metody elementów skończonych do analizy losowej zmienności nośności granicznej fundamentu bezpośredniego
(Wydawnictwa AGH, 2009) Pieczyńska, Joanna; Puła, Wojciech
Accepting specified soil properties to a designing process plays a vital role in safety of foundations. Taking this problem into consideration the authors tried to analyse bearing capacity predictions, involving random soil properties, by the random finite element method (RFEM). The analysis has been confined itself to a strip surface footing on the weightless cohesive subsoil. The soil properties have been modeled by lognormal cohesion random field and specially selected friction angle random field of bounded distributions. The numerical computations have been carried out by the finite element method in conjunction with Monte Carlo simulations. They have resulted by the first two statistical moments of bearing capacity. Moreover this analysis have shown the importance of the correlation length values as well as its changes in horizontal and vertical direction on bearing capacity predictions. At the end the authors try to relate to the worst case of correlation length to which bearing capacity is the lowest.