New Proactive Time Buffer Heuristics for Robust Project Scheduling

Elshaer, Raafat; Yamamoto, Hidehiko

doi:10.1299/jamdsm.6.559

Cited by 6 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deblaere et al [7] studied the resource-constrained proactive and reactive project scheduling problem, and used a proactive and reactive scheduling policy to predict interference incidents to minimize the negative impact of interference incidents on scheduling costs. Elshaer and Yamamoto [16] studied the time buffering problem of resource-constrained proactive and reactive project scheduling, which copes with multiple interruptions of project scheduling in uncertain environment. Schatteman et al [17] studied proactive scheduling and risk management in construction projects under uncertain environment and designed a heuristic algorithm to obtain the basic plan of proactive scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Proactive and Reactive Multi-Project Scheduling in Uncertain Environment

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

In this paper, a proactive and reactive multi-project scheduling problem is addressed. This problem is related to the influences of uncertain factors, which leads to a deviation between actual scheduling and baseline scheduling, and a recovery strategy is established in order to generate a baseline scheduling scheme. This paper introduces a proactive multi-project scheduling sub-model. When the activity is interrupted, the proactive scheduling scheme is used as the baseline scheduling scheme, which is embedded in the reactive scheduling, and then, the reactive scheduling sub-model is established. The proposed model can be used to generate alternative schedules, and to meet this need, a genetic simulated annealing algorithm is proposed. A buffer change operator (SC) and a crossover operator are designed in a genetic simulated annealing algorithm so that in the early stages of the algorithm, an optimum individual is produced and protected. The performance comparison shows that the genetic simulated annealing algorithm significantly outperforms the previous algorithms.INDEX TERMS Multi-project scheduling, proactive and reactive scheduling, genetic simulated annealing algorithm, optimization model.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Proactive and Reactive Multi-Project Scheduling in Uncertain Environment

Wang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Based on three heuristic algorithms, parametric heuristic (ParH), activity marginal stability (AMS) heuristic and activity stability contribution (ASC) heuristic, Elshaer and Yamamoto assigned the buffers to the baseline scheduling. While achieving the robustness of scheduling, the completion date is another important indicator that needs to be balanced [28]. Based on the application of buffer time,Ślusarczyk and Kuchta set a fuzzy number to determine the model of the compromise schedule, to ensure a better robustness of the schedule after the allocation of resources, and to maintain that the completion time of project is not too long at the same time.…”

Section: Introductionmentioning

confidence: 99%

Project Robust Scheduling Based on the Scattered Buffer Technology

Pang

Shi

2018

Applied Sciences

View full text Add to dashboard Cite

The research object in this paper is the sub network formed by the predecessor's affect on the solution activity. This paper is to study three types of influencing factors from the predecessors that lead to the delay of starting time of the solution activity on the longest path, and to analyze the influence degree on the delay of the solution activity's starting time from different types of factors. On this basis, through the comprehensive analysis of various factors that influence the solution activity, this paper proposes a metric that is used to evaluate the solution robustness of the project scheduling, and this metric is taken as the optimization goal. This paper also adopts the iterative process to design a scattered buffer heuristics algorithm based on the robust scheduling of the time buffer. At the same time, the resource flow network is introduced in this algorithm, using the tabu search algorithm to solve baseline scheduling. For the generation of resource flow network in the baseline scheduling, this algorithm designs a resource allocation algorithm with the maximum use of the precedence relations. Finally, the algorithm proposed in this paper and some other algorithms in previous literature are taken into the simulation experiment; under the comparative analysis, the experimental results show that the algorithm proposed in this paper is reasonable and feasible.

show abstract

“…Rafat. et al [12] develop a new time proactive buffer heuristics to generate a robust scheduling project. Vonder et al [14] study a proactive heuristic to improve project scheduling robustness.…”

mentioning

confidence: 99%

A Two-Stage Resource-Constrained Project Scheduling Model with Proactive and Reactive Strategies Under Uncertainty

Chen

Zhang

2014

Proceedings of the Eighth International Conference on Management Science and Engineering Management

View full text Add to dashboard Cite

The aim of this paper is to develop a two-stage model to obtain a proactive and reactive schedule in resource-constrained project scheduling problems (RCPSP) under uncertainty. In the proactive phase, the highest cumulative instability weight scheduling of resource buffering is selected to optimize the initial schedule, which is arrange the activities in decreasing order by the sum weights of the activity and its successors. For the reactive schedule, the tabu search is employed to ensure the scheduling process execution. Actually, in practice, the uncertain resource availabilities are inevitable in RCPSP. In this situation, the uncertain factors are considered as the fuzzy random variables in this paper, and some properties of fuzzy random variables are discussed. Subsequently, the Particle Swarm Optimization (PSO) algorithm that solve the two-stage model of RCPSP with proactive and reactive strategies under uncertainty is developed. Finally, by testing the example, the effectiveness of the proposed model and approach is validated by the computation results.Keywords Proactive and reactive scheduling · Two-stage · RCPSP · Fuzzy random variable IntroductionAs one of the most representative problems in the project management field, resourceconstrained project scheduling problems (RCPSP) is a classical NP-hard problem and most researchers assume that the generation of a baseline schedule is in a static and determined environment with complete crisp information, meanwhile the project can be executed smoothly and not disrupted if the initial schedule is determined. In

show abstract

New Proactive Time Buffer Heuristics for Robust Project Scheduling

Cited by 6 publications

References 8 publications

Proactive and Reactive Multi-Project Scheduling in Uncertain Environment

Proactive and Reactive Multi-Project Scheduling in Uncertain Environment

Project Robust Scheduling Based on the Scattered Buffer Technology

A Two-Stage Resource-Constrained Project Scheduling Model with Proactive and Reactive Strategies Under Uncertainty

Contact Info

Product

Resources

About