This article addresses the problem of subcontractor selection in an uncertain decision-making environment by proposing a new hybrid method and improving a classical multicriteria decision-making (MCDM) method. First, the proposed hybrid method combines subjective and objective weighting methods to estimate the weights of criteria that are represented as grey numbers. The subjective weighting method is the PA weighting method. The objective weighting method is the rank order centroid with slacks (ROCS) weighting method used to compensate for the limitation of the rank order centroid weighting method. Second, grey relational analysis (GRA) is improved by introducing both positive and negative reference (PNR) alternatives instead of a single reference alternative as in the classical GRA. Subsequently, the proposed hybrid grey-point allocation-ROCS weighting method and the GRA-PNR evaluation method are applied to select the most suitable subcontractor for the supply of heliostats for photothermal power station construction. Sensitivity analysis is conducted to verify the robustness of the results. Finally, the technique for order preferences by similarity to an ideal solution with grey values, simple additive weighting with grey relations, and additive ratio assessment with grey criteria scores is applied to validate the participation of the selected subcontractor in the project.Index Terms-Grey relational analysis (GRA), grey system theory (GST), multicriteria decision-making (MCDM), photothermal power station, rank order centroid (ROC), subcontractor selection.
I. INTRODUCTIONT HE seventh sustainable development goal is to ensure access to affordable, reliable, sustainable, and modern energy for all [1]. Although meeting the demand for energy is not an easy requirement, the People's Republic of China (PRC) is now Manuscript
PurposeThis paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects.Design/methodology/approachThis paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed.FindingsThe results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted.Originality/valueThe paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers.
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