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2022 ◽  
Vol 12 (2) ◽  
pp. 659
Quoc Nhat Han Tran ◽  
Nhan Quy Nguyen  ◽  
Hicham Chehade ◽  
Lionel Amodeo ◽  
Farouk Yalaoui

In this paper, we study a complex outpatient planning problem in the chemotherapy department. The planning concerns sequences of patients’ treatment sessions subject to exact in-between resting periods (i.e., exact time-lags). The planning is constrained by the hospital infrastructure and the availability of medical staff (i.e., multiple time-varying resources’ availability). In order to maximize the patients’ service quality, the objective of the function considered is to minimize the total wait times, which is equivalent to the criteria for minimizing the total completion time. Our main contribution is a thorough analysis of this problem, using the Hybrid Flow Shop problem as a theoretical framework to study the problem. A novel Mixed Integer Linear Programming (MILP) is introduced. Concerning the resolution methods, priority-based heuristics and an adapted genetic algorithm (GA) are presented. Numerical experiments are conducted on historical data to compare the performances of the approximate resolution methods against the MILP solved by CPLEX. Numerical results confirm the performances of the proposed methods.

2022 ◽  
Vol 12 (2) ◽  
pp. 607
Fredy Juárez-Pérez ◽  
Marco Antonio Cruz-Chávez ◽  
Rafael Rivera-López ◽  
Erika Yesenia Ávila-Melgar ◽  
Marta Lilia Eraña-Díaz ◽  

In this paper, a hybrid genetic algorithm implemented in a grid environment to solve hard instances of the flexible flow shop scheduling problem with sequence-dependent setup times is introduced. The genetic algorithm takes advantage of the distributed computing power on the grid to apply a hybrid local search to each individual in the population and reach a near optimal solution in a reduced number of generations. Ant colony systems and simulated annealing are used to apply a combination of iterative and cooperative local searches, respectively. This algorithm is implemented using a master–slave scheme, where the master process distributes the population on the slave process and coordinates the communication on the computational grid elements. The experimental results point out that the proposed scheme obtains the upper bound in a broad set of test instances. Also, an efficiency analysis of the proposed algorithm indicates its competitive use of the computational resources of the grid.

2022 ◽  
Vol 7 (1) ◽  
pp. 1-12 ◽  
Muberra Allahverdi

Since scheduling literature has a wide range of uncertainties, it is crucial to take these into account when solving performance measure problems. Otherwise, the performance may severely be affected in a negative way. In this paper, an algorithm is proposed to minimize the total completion time (TCT) of a two-machine no-wait flowshop with uncertain setup times within lower and upper bounds. The results are compared to the best existing algorithm in scheduling literature: the programming language Python is used to generate random samples with respect to various distributions, and the TCT of the proposed algorithm is compared to that of the best existing one. Results reveal that the proposed one significantly outperforms the best one given in literature for all considered distributions. Specifically, the average percentage improvement of the proposed algorithm over the best existing one is over 90%. A test of hypothesis is conducted to further confirm the results.

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