An integrated machine scheduling and personnel allocation problem for large-scale industrial facilities using a rolling horizon framework

Santos, Fernando Afonso; Fukasawa, Ricardo; Ricardez‐Sandoval, Luis A.

doi:10.1007/s11081-020-09542-7

Cited by 4 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is primarily because one avoids the formulation of mixed integer nonlinear programs (MINLP). However, it should be noted that optimization approaches fair well with regard to integrated problems when the resultant formulation is mixed integer linear programming (MILP) [18,19]. In addition, simulation based approaches are generally expensive to conduct online because of the sample inefficiency associated with Monte Carlo methods and stochastic search algorithms.…”

Section: Online Production Scheduling: Optimization and Simulationmentioning

confidence: 99%

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes

Mowbray¹,

Zhang²,

Chanona³

2022

Preprint

View full text Add to dashboard Cite

Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling decision in the presence of uncertainty. In this work, we present a RL methodology tailored to efficiently address production scheduling problems in the presence of uncertainty. We consider commonly imposed restrictions on these problems such as precedence and disjunctive constraints which are not naturally considered by RL in other contexts. Additionally, this work naturally enables the optimization of risk-sensitive formulations such as the conditional value-at-risk (CVaR), which are essential in realistic scheduling processes. The proposed strategy is investigated thoroughly in a parallel batch production environment, and benchmarked against mixed integer linear programming (MILP) strategies. We show that the policy identified by our approach is able to account for plant uncertainties in online decision-making, with expected performance comparable to existing MILP methods. Additionally, the framework gains the benefits of optimizing for risk-sensitive measures, and identifies online decisions orders of magnitude faster than the most efficient optimization approaches. This promises to mitigate practical issues and ease in handling realizations of process uncertainty in the paradigm of online production scheduling.

show abstract

Section: Online Production Scheduling: Optimization and Simulationmentioning

confidence: 99%

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes

Mowbray¹,

Zhang²,

Chanona³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Group collaboration Update the position of drosophila according to formula (19) Visual search process Update and record the historical global optimal individual according to formula ( 23)…”

Section: G > Maxgenmentioning

confidence: 99%

“…However, the existing personnel optimization research focuses on the enterprise or industrial background [18] and does not consider the maintenance task of the equipment. e established model is mainly aimed at nonmaintainable professional technical personnel, which is not suitable for the optimization of maintenance personnel of military equipment [19].…”

Section: Introductionmentioning

confidence: 99%

Maintenance Personnel Optimization Model of Vehicle Equipment Based on Support Task

Song

Lei²,

Qian³

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Vehicle equipment maintenance support tasks have problems such as low maintenance efficiency and unreasonable allocation of maintenance personnel. In order to further strengthen the theoretical research of vehicle equipment maintenance support, an optimization model of vehicle equipment maintenance personnel based on support task is proposed in this paper. Firstly, the maintenance workload model of vehicle equipment is constructed by analyzing the three task sources of vehicle equipment: scheduled maintenance, natural random failure, and combat damage. Then, considering the technical professional level, maintenance efficiency, and other factors of maintenance personnel, two optimization models of maintenance personnel are constructed. In view of the situation where there are enough human resources, the prediction model of the number of personnel with the minimum total number as the goal is constructed to achieve the purpose of saving human resources. Using MATLAB mixed integer nonlinear programming problem (MINP) toolbox to solve the prediction model of the number of personnel, in view of the shortage of maintenance personnel, a maintenance personnel allocation model aiming at minimizing maintenance time is constructed to maximize maintenance efficiency. In order to solve the model, the fruit fly optimization algorithm (FOA) is improved, and the group cooperation is used to update the fruit fly position. The new algorithm not only retains the essential advantages of the FOA but also solves the problem that the algorithm is easy to fall into local extreme value and improves the global optimization ability of the algorithm. Finally, two example simulations verify the effectiveness of the optimization method in this paper and provide a certain theoretical basis for maintenance personnel to optimize decision-making.

show abstract

Distributional reinforcement learning for inventory management in multi-echelon supply chains

Servia

Mowbray

2023

Digital Chemical Engineering

View full text Add to dashboard Cite

An integrated machine scheduling and personnel allocation problem for large-scale industrial facilities using a rolling horizon framework

Cited by 4 publications

References 33 publications

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes

Distributional Reinforcement Learning for Scheduling of Chemical Production Processes

Maintenance Personnel Optimization Model of Vehicle Equipment Based on Support Task

Distributional reinforcement learning for inventory management in multi-echelon supply chains

Contact Info

Product

Resources

About