Design and Construction of Zana Robot for Modeling Human Player in Rock-paper-scissors Game using Multilayer Perceptron, Radial basis Functions and Markov Algorithms

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies

A simulated annealing approach for multi-manned assembly line balancing problem type II

Multi-manned assembly lines are often designed to produce large-sized products, such as automobiles, trucks and buses. In this type of production lines, usually there are multimanned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones in order to meet the market demand. In this paper, such a class of assembly line balancing problems, named multi-manned assembly line balancing problems type II, has been addressed. Since the problem is NP-hard, a meta-heuristic approach based on a simulated annealing algorithm has been developed to solve the problem. The performance of the proposed algorithm has been tested on a set of test problems taken from the literature; the results show that the algorithm performs well

A Mathematical Programming Formulation for Cost-oriented Multi-manned Assembly Line Balancing Problem

Multi-manned assembly line balancing problems are usually occurred in plants producing large-sized high-volume products such as automobiles and trucks. In this paper, a cost- oriented objective function is presented for a multi-manned assembly line balancing problem. This kind of objective function may be used to balance final assembly lines of products in which manufacturing process is very labor intensive. A mixed-integer mathematical programming model is proposed to solve the problem optimally. The proposed formulation has been used to solve some small size problems by considering both time-oriented and cost-oriented objective functions. The experiments show that, given the same precedence graph of multi-manned assembly line with a same cycle time, two different optimal solutions can be actually found when switching from time-oriented to cost-oriented objective functions, and vice versa. This difficulty increases the complexity of the cost-oriented multi-manned assembly line balancing problems with respect to the multi-manned assembly line balancing problems addressed in the literature

Mixed model multi-manned assembly line balancing problem: a mathematical model and a simulated annealing approach

Purpose – This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line. Design/methodology/approach – To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time. Findings – The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy. Originality/value – This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm

A simulated annealing approach for the capacitated dynamic lot sizing problem in a closed remanufacturing system

The capacitated dynamic lot sizing problem with product returns in a closed remanufacturing system is addressed in this paper. The system is designed to satisfy the demands of different classes of single level products by remanufacturing end-life returned products. A single machine with a limited capacity in each time period is used to perform the remanufacturing operations. A mathematical programming formulation is proposed for the considered problem: the proposed model minimizes the sum of remanufacturing costs over a finite planning horizon. The problem is a generalized version of the classical capacitated dynamic lot sizing problem, and thus it is NP-hard itself. Therefore, a simulated annealing algorithm, with an efficient neighborhood generation which takes into account the constraints of the problem, is proposed as a solution approach. To evaluate the efficiency of the proposed algorithm, a set of experimental instances are generated and solved. The comparison between the results obtained with the proposed simulated annealing approach and the ones generated by the CPLEX solver shows the effectiveness of the proposed algorithm

Multi-manned Assembly Line Balancing Problem with Skilled Workers: A New Mathematical Formulation

Multi-manned assembly lines are usually found in plants producing large-sized high-volume products such as automotive sector. In this paper, the balancing problem of this kind of assembly lines with skilled workers is addressed. A new mixed integer programming formulation is presented to solve this problem optimally with the objective of minimizing the total operating cost of the assembly line. The main advantage of the proposed model is to allow the workers in each multi-manned workstation to perform the different assembly tasks of same product simultaneously. The proposed formulation has been used to solve some experimental problems found in the literature. The comparison between the results obtained with the proposed model and those obtained with the model proposed by Moon, Logendran, and Lee (2009) shows that the proposed model can improve the operating cost of the system by reducing the number of workers and workstations

A hybrid adaptive variable neighbourhood search approach for multi-sided assembly line balancing problem to minimise the cycle time

Multi-sided assembly line balancing problems usually occur in plants producing big-sized products such as buses, trucks, and helicopters. In this type of assembly line, in each workstation, it is possible to install several workplaces, in which a single operator performs his/her own set of tasks at an individual mounting position. In this way, the operators can work simultaneously on the same product without hindering each other. This paper considers for the rst time the multi-sided assembly line balancing problem with the objective of minimizing the cycle time, proposing a new mathematical formulation to solve small-sized instances of this problem. Besides, a metaheuristic algorithm based on variable neighborhood search hybridized with simulated annealing is developed to solve large-sized instances. The algorithm is called adaptive because of the adopted neighborhood selection mechanism. A novel three-string representation is introduced to encode the problem solutions and six dierent neighborhood generation structures are presented. The developed approach is compared to other meta-heuristics, considering some well-known in literature test instance and a real world assembly line balancing problem arising in a car body assembly line. The experimental results validate the eectiveness of the proposed algorithm

The capacitated lot-sizing and energy efficient single machine scheduling problem with sequence dependent setup times and costs in a closed-loop supply chain network

In this paper, the capacitated lot-sizing and scheduling problem with sequence dependent setup times and costs in a closed loop supply chain is addressed. The system utilizes the closed-loop supply chain strategy so that the multi-class single-level products are produced through both manufacturing of raw materials and remanufacturing of returned recovered products. In this system, a single machine with a limited capacity in each time period is used to perform both the manufacturing and remanufacturing operations. The sequence-dependent setup times and costs (both between two lots of products of different classes and between two lots belonging to the same class of products produced through different methods) are considered. A large-bucket mixed integer programming formulation is proposed for the problem. This model minimizes not only the manufacturing and remanufacturing costs, the setup costs and the inventory holding and backlogging costs over the planning horizon, but also the energy costs paid for the utilization of machine and the compression of processing times. Since the problem is NP-hard, a matheuristic and a grey wolf optimization algorithm are proposed to solve it. To evaluate the efficiency of the proposed algorithm, some experimental instances are generated and solved. The obtained results show the effectiveness of the proposed algorithms