scholarly journals Tracking Predictive Gantt Chart for Proactive Rescheduling in Stochastic Resource Constrained Project Scheduling

2018 ◽  
Vol 42 (2) ◽  
pp. 179-192
Author(s):  
Mario Brčić ◽  
Danijel Mlinarić

Proactive-reactive scheduling is important in the situations where the project collaborators need to coordinate their efforts. The coordination is mostly achieved through the combination of the shared baseline schedule and the deviation penalties. In this paper, we present an extension of predictive Gantt chart to the proactive-reactive scheduling needs. It can be used to track the evolution of the relationship between dynamic and static elements through the time. The dynamic elements are evolving probability distributions due to the uncertainty and revealed information. The static elements are time-agreements in the baseline schedule. We demonstrate that in the state-of-the-art proactive-reactive scheduling, the baseline schedule is agnostic to the information received during the project execution. The sources of such inflexibility in the problem model and the scheduling methods are analyzed. The visualization is highlighted as a precursor to developing new methods that proactively change the baseline schedule in accordance with the gained information.

Author(s):  
Florian Mischek ◽  
Nysret Musliu ◽  
Andrea Schaerf

AbstractThe Test Laboratory Scheduling Problem (TLSP) is a real-world scheduling problem that extends the well-known Resource-Constrained Project Scheduling Problem (RCPSP) by several new constraints. Most importantly, the jobs have to be assembled out of several smaller tasks by the solver, before they can be scheduled. In this paper, we introduce different metaheuristic solution approaches for this problem. We propose four new neighborhoods that modify the grouping of tasks. In combination with neighborhoods for scheduling, they are used by our metaheuristics to produce high-quality solutions for both randomly generated and real-world instances. In particular, Simulated Annealing managed to find solutions that are competitive with the best known results and improve upon the state-of-the-art for larger instances. The algorithm is currently used for the daily planning of a large real-world laboratory.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongwei Zhu ◽  
Zhiqiang Lu ◽  
Chenyao Lu ◽  
Yifei Ren

Purpose To meet the requirement of establishing an effective schedule for the assembly process with overall detection and rework, this paper aims to address a new problem named resource-constrained multi-project scheduling problem based on detection and rework (RCMPSP-DR). Design/methodology/approach First, to satisfy both online and offline scheduling, a mixed integer programming model is established with a weighted bi-objective minimizing the expected makespan and the solution robustness. Second, an algorithm that combines a tabu search framework with a critical chain-based baseline generation scheme is designed. The tabu search framework focuses on searching for a reasonable resource flow representing the execution sequence of activities, while the critical chain-based baseline generation scheme establishes a buffered baseline schedule by estimating the tradeoff between two aspects of bi-objective. Findings The proposed algorithm can get solutions with gaps from −4.45% to 2.33% when compared with those obtained by the commercial MIP solver CPLEX. Moreover, the algorithm outperforms four other algorithms in terms of both objective performance and stability over instances with different weighting parameters, which reveals its effectiveness. Originality/value The represented RCMPSP-DR considering the overall detection and rework is an extension of the scheduling problem for large-scale equipment. An effective algorithm is proposed to establish the baseline schedule and determine the execution sequence of activities for the assembly process, which is significant for practical engineering applications.


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