Metric estimation approach for managing uncertainty in resource leveling problemстатья
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Дата последнего поиска статьи во внешних источниках: 11 декабря 2024 г.
Аннотация:Real-life applications in project planning often involve grappling with inaccurate data orunexpected events, which can impact the project duration and cost. The delay in the projectexecution can be overcome by investing in additional resources to avoid compromising theproject duration. The goal of the resource leveling problems (RLP) is to determine the optimalamount of resources to invest in, aiming to minimize the associated complementary costsand adhere to the fixed deadline. To tackle data uncertainty in the RLP, the literature haspredominantly focused on developing robust and stochastic approaches. In contrast, sensitivityanalysis and reactive approaches have received comparatively less attention, especiallyconcerning the generalized RLP with flexible job durations. In this problem, the durationof each job depends on the amount of resources available for its execution. Therefore, utilizingmore resources may help reduce the project duration but at an additional cost. Thispaper introduces a novel approach that addresses the generalized RLP with uncertain joband resource parameters, incorporating reactive and sensitivity-based methodologies. Theproposed approach extends the concept of evaluation metrics from machine scheduling tothe domain of the RLP with flexible job durations. It is based on a metric-based function thatestimates the impact of changes in input data on the solution quality, considering both optimalityand feasibility for the newproblem instance. The approach is tested through numericalexperiments conducted on benchmark instance sets to investigate the impact of variations indifferent problem parameters. The obtained results demonstrated a meaningful accuracy inestimating the impact on the value of the objective function. Additionally, they underscoredthe importance of utilizing optimality/feasibility preservation conditions, as for a significantportion of the tested instances, the use of these conditions gave a satisfactory outcome.