A novel approach applied to Particle Swarm optimization (PSO) and antcolonyoptimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO a...
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A novel approach applied to Particle Swarm optimization (PSO) and antcolonyoptimization is presented. The main contribution of this work is the use of fuzzy systems to dynamically update the parameters for the ACO and PSO algorithms. In the case of ACO, two fuzzy systems are designed for the antcolony System (ACS) algorithm variant. The first system adjusts the value for the pheromone evaporation parameter from the global pheromone trail update equation and the second system adjusts the values for the pheromone evaporation parameter from the local pheromone trail update equation. In the case of PSO, a fuzzy system is designed to find the values for the inertia weight parameter from the velocity equation. Fuzzy logic controllers (FLCs) are optimized with ACO and PSO, respectively, to prove the performance of the proposed approach. The particular benchmark problems considered to test the proposed methods are the water level control in a tank and temperature control in a shower. Therefore, PSO and ACO algorithms are applied in the optimization of the parameters of the FLCs. The achievement of the proposed fuzzy ACO and PSO algorithms is compared with the original results of each benchmark control problem.
Operating room (OR) surgery scheduling is a challenging combinatorial optimization problem that determines the operation start time of every surgery to be performed in different surgical groups, as well as the resourc...
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Operating room (OR) surgery scheduling is a challenging combinatorial optimization problem that determines the operation start time of every surgery to be performed in different surgical groups, as well as the resources assigned to each surgery over a schedule period. One of the main challenges in health care systems is to deliver the highest quality of care at the lowest cost. In real-life situations, there is significant uncertainty in several of the activities involved in the delivery of surgical care, including the duration of the surgical procedures. This paper tackles the operating room surgery scheduling problem with uncertain surgery durations, where uncertainty in surgery durations is represented by means of fuzzy numbers. The problem can be considered as a Fuzzy Flexible Job-shop Scheduling Problem (FFJSP) due to similarities between operating room surgery scheduling with uncertain surgery durations and a multi-resource constraint flexible job-shop scheduling problem with uncertain processing times. This research handles both the advanced and allocation scheduling problems simultaneously and provides an antcolonyoptimization (ACO) metaheuristic algorithm which utilized a two-level ant graph to integrate sequencing jobs and allocating resources at the same time. To assess the performance of the proposed method, a computational study on five test surgery cases is presented, considering both deterministic and fuzzy surgery durations to enhance the significance of the study. The results of this experiment demonstrated the effectiveness of the proposed metaheuristic algorithm.
This paper provides a new intelligent technique for semisupervised data clustering problem that combines the ant System (AS) algorithm with the fuzzy c-means (FCM) clustering algorithm. Our proposed approach, called F...
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This paper provides a new intelligent technique for semisupervised data clustering problem that combines the ant System (AS) algorithm with the fuzzy c-means (FCM) clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in antcolonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.
This research study focuses on the optimization of multi-item multi-period procurement lot sizing problem for inventory management. Mathematical model is developed which considers different practical constraints like ...
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This research study focuses on the optimization of multi-item multi-period procurement lot sizing problem for inventory management. Mathematical model is developed which considers different practical constraints like storage space and budget. The aim is to find optimum order quantities of the product so that total cost of inventory is minimized. The NP-hard mathematical model is solved by adopting a novel antcolonyoptimization approach. Due to lack of benchmark method specified in the literature to assess the performance of the above approach, another metaheuristic based program of genetic algorithm is also employed to solve the problem. The parameters of genetic algorithm model are calibrated using Taguchi method of experiments. The performance of both algorithms is compared using ANOVA analysis with the real time data collected from a valve manufacturing company. It is verified that two methods have not shown any significant difference as far as objective function value is considered. But genetic algorithm is far better than the ACO method when compared on the basis of CPU execution time.
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