A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs

Abstract

Resource allocation is one of the core issues in project scheduling to ensure the effective use of scare renewable resource, and has been regularly encountered in production systems in the manufacturing and service industries. The transfers of renewable resources between activities generally incur certain scheduling costs and affect the robustness of a certain schedule in an uncertain environment. To address this issue, a bi-objective optimization model is proposed to make the resource transfer decisions, which aims to minimize the transfer cost and maximize solution robustness in the presence of activity duration variability. The proposed model employs a novel resourceoriented flow formulation that is different from those of the previous literature. A NSGA-II and a Pareto simulated annealing (PSA) algorithm have been applied as the solution methodologies. Besides, the effectiveness of the metaheuristics are evaluated in comparison with a  -constraint method. In detail, the algorithms are carried out on a set of benchmarks and are compared to test their efficiencies based on four performance metrics: number of nondominated solutions, general distance, hypervolume and spacing. Finally, a case study of a real project further indicates that the suggested model and algorithms are applicable and beneficial to the problem in practice.

A two‐layer approach for solving robust decentralized multiproject scheduling problem with multi‐skilled staff

“…Furthermore, the realized activity durations are assumed to follow a right‐skewed lognormal distribution with the mean equal to the deterministic duration and a standard deviation σ representing the duration variability level. The lognormal distribution has also been adopted by some other works (Hu et al., 2016; Wang et al., 2021). Three levels of σ (i.e., $$\sigma \in \lbrace 0.3,0.6,0.9\rbrace$$) represent a duration uncertainty that is low, medium, or high, respectively.…”

Tabu search for proactive project scheduling problem with flexible resources

“…Related to solution robustness, two main strands of research, i.e., the research on robust resource allocation and the research on time buffering, have been extensively explored in the literature (Liang et al, 2020). Regarding the research on robust resource allocation, interested readers are referred to Artigues et al (2003), Leus and Herroelen (2004), Deblaere et al (2007) and Wang et al (2021). Since the disruptions caused by uncertainties will eventually be reflected in the variability of the activity duration, many studies support inserting time buffers in front of activities to absorb potential delays coming from predecessors and to prevent the propagation of delays throughout the baseline schedule, thereby protecting the activity start times as well as possible.…”

Proactive Project Scheduling With Activity Splitting and Resource Transfer Times Under Uncertain Environments

“…For the environment of stochastic resource availabilities, resource buffers are beneficial to improving schedule robustness, and the combination of resource buffers and time buffers performs the best [15]. Thirdly, about resource allocation, resource flow network is introduced and many algorithms have been developed, such as branch-and-bound algorithm [16], integer programming-based heuristics [17], multi-objective algorithms [18], and heuristic algorithm of maximizing the use of precedence relations [19]. In addition, the scheduling problem of integrating resource allocation and time buffering together has also been investigated [20].…”