Developing three-phase modified bat algorithms to solve medical staff scheduling problems while considering minimal violations of preferences and mean workload

2021 ◽  
pp. 1-22
Author(s):  
Ping-Shun Chen ◽  
Chia-Che Tsai ◽  
Jr-Fong Dang ◽  
Wen-Tso Huang

BACKGROUND: This research studies a medical staff scheduling problem, which includes government regulations and hospital regulations (hard constraints) and the medical staff’s preferences (soft constraints). OBJECTIVE: The objective function is to minimize the violations (or dissatisfaction) of medical staff’s preferences. METHODS: This study develops three variants of the three-phase modified bat algorithms (BAs), named BA1, BA2, and BA3, in order to satisfy the hard constraints, minimize the dissatisfaction of the medical staff and balance the workload of the medical staff. To ensure workload balance, this study balances the workload among medical staff without increasing the objective function values. RESULTS: Based on the numerical results, the BA3 outperforms the BA1, BA2, and particle swarm optimization (PSO). The robustness of the BA1, BA2, and BA3 is verified. Finally, conclusions are drawn, and directions for future research are highlighted. CONCLUSIONS: The framework of this research can be used as a reference for other hospitals seeking to determine their future medical staff schedule.

2019 ◽  
Vol 10 (2) ◽  
pp. 3-31
Author(s):  
Kirill Vladimirovich Pushkaryov

A hybrid method of global optimization NNAICM-PSO is presented. It uses neural network approximation of inverse mappings of objective function values to coordinates combined with particle swarm optimization to find the global minimum of a continuous objective function of multiple variables with bound constraints. The objective function is viewed as a black box. The method employs groups of moving probe points attracted by goals like in particle swarm optimization. One of the possible goals is determined via mapping of decreased objective function values to coordinates by modified Dual Generalized Regression Neural Networks constructed from probe points. The parameters of the search are controlled by an evolutionary algorithm. The algorithm forms a population of evolving rules each containing a tuple of parameter values. There are two measures of fitness: short-term (charm) and long-term (merit). Charm is used to select rules for reproduction and application. Merit determines survival of an individual. This two-fold system preserves potentially useful individuals from extinction due to short-term situation changes. Test problems of 100 variables were solved. The results indicate that evolutionary control is better than random variation of parameters for NNAICM-PSO. With some problems, when rule bases are reused, error progressively decreases in subsequent runs, which means that the method adapts to the problem.


2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Tzu-An Chiang ◽  
Z. H. Che ◽  
Zhihua Cui

This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM),VMaxmethod (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.


Author(s):  
METİN TOZ

Thispaperproposesanimprovedformoftheantlionoptimizationalgorithm(IALO)tosolveimageclustering problem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recently proposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and K- means algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies– Bouldin and Xie–Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering.


Computation ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 80
Author(s):  
John Fernando Martínez-Gil ◽  
Nicolas Alejandro Moyano-García ◽  
Oscar Danilo Montoya ◽  
Jorge Alexander Alarcon-Villamil

In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) structure is used. As an objective function, minimization of the investment costs in conductors together with the technical losses of the network for a study period of one year is considered. Additionally, the model will be implemented in balanced and unbalanced test systems and with variations in the connection of their loads, i.e., Δ− and Y−connections. To evaluate the costs of the energy losses, a classical backward/forward three-phase power-flow method is implemented. Two test systems used in the specialized literature were employed, which comprise 8 and 27 nodes with radial structures in medium voltage levels. All computational implementations were developed in the MATLAB programming environment, and all results were evaluated in DigSILENT software to verify the effectiveness and the proposed three-phase unbalanced power-flow method. Comparative analyses with classical and Chu & Beasley genetic algorithms, tabu search algorithm, and exact MINLP approaches demonstrate the efficiency of the proposed optimization approach regarding the final value of the objective function.


2021 ◽  
Vol 13 (12) ◽  
pp. 6615
Author(s):  
Tri Sulistyaningsih ◽  
Achmad Nurmandi ◽  
Salahudin Salahudin ◽  
Ali Roziqin ◽  
Muhammad Kamil ◽  
...  

This paper, which is focused on evaluating the policies and institutional control of the Brantas River Basin, East Java, Indonesia, aims to review government regulations on watershed governance in Indonesia. A qualitative approach to content analysis is used to explain and layout government regulations regarding planning, implementation, coordination, monitoring, evaluation, and accountability of the central and local governments in managing the Brantas watershed, East Java, Indonesia. Nvivo 12 Plus software is used to map, analyze, and create data visualization to answer research questions. This study reveals that the management regulations of the Brantas watershed, East Java, Indonesia, are based on a centralized system, which places the central government as an actor who plays an essential role in the formulation, implementation, and accountability of the Brantas watershed management. In contrast, East Java Province’s regional government only plays a role in implementing and evaluating policies. The central government previously formulated the Brantas watershed. This research contributes to strengthening the management and institutional arrangement of the central government and local governments that support the realization of good governance of the Brantas watershed. Future research needs to apply a survey research approach that focuses on evaluating the capacity of the central government and local governments in supporting good management of the Brantas watershed.


2015 ◽  
Vol 785 ◽  
pp. 495-499
Author(s):  
Siti Amely Jumaat ◽  
Ismail Musirin

The paper presents a comparison of performance Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) with objective function to minimize the transmission loss, improve the voltage and monitoring the cost of installation. Simulation performed on standard IEEE 30-Bus RTS and indicated that EPSO a feasible to achieve the objective function.


Author(s):  
Abbas Al-Refaie ◽  
Hala Abedalqader

This research proposes two optimization models to deal with the berth allocation problem. The first model considers the berth allocation problem under regular vessel arrivals to minimize the flow time of vessels in the marine container terminal, minimize the tardiness penalty costs, and maximize the satisfaction level of vessels’ operators on preferred times of departure. The second model optimizes the berth allocation problem under emergency conditions by maximizing the number of assigned vessels, minimizing the vessel’s waiting time, and maximizing the satisfaction level on the served ships. Two real examples are provided for model illustration under regular and emergent vessel arrivals. Results show that the proposed models effectively provide optimal vessel scheduling in the terminal, reduce costs at an acceptable satisfaction level of vessels’ operators, decrease the waiting time of vessels, and shorten the delay in departures under both regular and emergent vessel arrivals. In conclusion, the proposed models may provide valuable assistance to decision-makers in marine container terminals on determining optimal berth allocation under daily and emergency vessel arrivals. Future research considers quay crane assignment and scheduling problems.


2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Guo-Rong Cai ◽  
Shui-Li Chen

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.


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