An adjustable robust optimization model for the resource-constrained project scheduling problem with uncertain activity durations

Bruni, Maria Elena; Pugliese, Luigi Di Puglia; Beraldi, Patrizia; Guerriero, Francesca

doi:10.1016/j.omega.2016.09.009

Cited by 108 publications

(64 citation statements)

References 55 publications

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“…Their proposed approach guarantees the feasibility of solutions and produces high-quality solutions. Bruni et al [7] propose an adaptive robust optimization model to derive the resource allocation decisions that minimize the worst-case makespan, under general polyhedral uncertainty sets assuming that the activity durations are subject to interval uncertainty. Also, a general decomposition approach is proposed by them to solve the robust counterpart of the RCPSP, further tailored to address the uncertainty set with the protection factor.…”

Section: Robust Project Scheduling Problemmentioning

confidence: 99%

“…Any of the resource conflicts can be removed by adding extra precedence relationships to the primary precedence graph for postponing some activities in such a way that the makespan can be determined by applying an early start policy (ES-Policy) on an extended graph. So, the extra precedence relationships ( ) \ E X V V  should be found in such a way that the extended graph [7]. According to Balas [37] the set X, containing pairs of activities that leads to one feasible ES-policy, can be called a sufficient selection.…”

Section: Problem Statementmentioning

confidence: 99%

“…Bruni et al [7] presented a numerical example for two different selections in the project with 5 activities. They illustrated that when the activities durations are deterministic, the project makespan would be the same for two different selections.…”

Section: Problem Statementmentioning

confidence: 99%

“…In other words, due to unavailable resources, delays in delivery of materials, absent employees, bad weather conditions, accident and many other uncontrollable factors, some project activities may last longer than expected, threatening the operational viability of the planned schedule [7]. Therefore, the obtained results of the project scheduling model with deterministic parameters are no longer valid.…”

Section: Introductionmentioning

confidence: 99%

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Robust Optimization for the Resource Constrained Multi-Project Scheduling Problem with Uncertain Activity Durations

2018

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This paper studies the multi-project scheduling problem which involves multiple projects with different importance weight; with predefined assigned due dates; with activities that have uncertain durations; and with renewable resources that are constrained. The resource sharing policy is applied to share the resources among projects. Due to the environmental rapid changes and also the uniqueness of projects, the probability distribution function of uncertain durations cannot be estimated with confidence. Besides, the multi-project scheduling problem with its large scale investment dictates a conservative approach to deal with the existing uncertainty. Therefore, the Robust Resource-Constrained Multi-Project Scheduling Problem (RRCMPSP) is studied in this paper while the maximum total weighted tardiness of the projects should be minimized. A scenario-relaxation algorithm is implemented which results in optimal solutions for the RRCMPSP. The aim is to find an optimal structure containing all the projects in such a way that it transfers the resources between the activities based on the resource sharing policy while the maximum weighted differences between the projects finish times and their assigned due dates will be minimum.

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Section: Robust Project Scheduling Problemmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Optimization for the Resource Constrained Multi-Project Scheduling Problem with Uncertain Activity Durations

2018

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“…They presented a linear optimization model that could regulate conservation degree of solutions. The robust optimization method is widely used in many different fields to solve uncertainty problems, such as in finance [27], energy [28,29] and scheduling [30,31].…”

Section: Introductionmentioning

confidence: 99%

The robust analysis of supply chain based on uncertainty computation: insight from open innovation

et al. 2017

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In this study, the effects of open innovation practices on enterprise value were discussed based on the supply chain perspective. A nonlinear programming mode was constructed considering the uncertainty of open innovation effect. On this basis, the prototype was analyzed using a robust optimization approach with comprehensive considerations to the infeasible probability of constraint and goal accuracy. Findings show that parameter could convert the nonlinear programming model with uncertainty into a robust model with a strong stability. Furthermore, could be used to regulate the preference of the manufacturer to profit and the uncertainties in innovative practices. A small indicates manufacturers can refer to more information, and the manufacturers are more inclined to a mass production, so the profit is larger. Meanwhile, a high means that manufacturers can refer to less information, so that they pay more attention to making the product output adapt to the uncertainty of innovative effect. Manufacturers could select the appropriate value and reasonably arrange the production outputs of different goods according to the technological level of open innovation subject and their preference to profit and uncertainty. These conclusions were verified by a case study. In this work, the uncertainty of the open innovation effect was investigated from the supply chain perspective, which is of important significance to the decision making on optimal B Qingchun Meng

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A generalized cutting‐set approach for nonlinear robust optimization in process systems engineering

et al. 2021

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We propose a novel computational framework for the robust optimization of highly nonlinear, non‐convex models that possess uncertainty in their parameter data. The proposed method is a generalization of the robust cutting‐set algorithm that can handle models containing irremovable equality constraints, as is often the case with models in the process systems engineering domain. Additionally, we accommodate general forms of decision rules to facilitate recourse in second‐stage (control) variables. In particular, we compare and contrast the use of various types of decision rules, including quadratic ones, which we show in certain examples to be able to decrease the overall price of robustness. Our proposed approach is demonstrated on three process flow sheet models, including a relatively complex model for amine‐based CO2 capture. We thus verify that the generalization of the robust cutting‐set algorithm allows for the facile identification of robust feasible designs for process systems of practical relevance.

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An adjustable robust optimization model for the resource-constrained project scheduling problem with uncertain activity durations

Cited by 108 publications

References 55 publications

Robust Optimization for the Resource Constrained Multi-Project Scheduling Problem with Uncertain Activity Durations

Robust Optimization for the Resource Constrained Multi-Project Scheduling Problem with Uncertain Activity Durations

The robust analysis of supply chain based on uncertainty computation: insight from open innovation

A generalized cutting‐set approach for nonlinear robust optimization in process systems engineering

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