Optimization of manpower allocation by considering customer relationship management criteria and uncertainty conditions in car dealerships

Purpose A mathematical mixed integer model was used in this research in order to optimize manpower allocation in car industry. The objective function of proposed model subjected to minimization of the maximum waiting time for customers in service queue and limitations included manpower allocation and time calculation for each service in each center. Methodology: Therefore, mathematical optimization methods were employed in this research. To solve the problem at small dimensions, BARON solver was used through GAMS software. Metaheuristic algorithms were used to solve the large dimensions of problem due to NP-hard nature of allocation problem. However, these algorithms have been designed based on the natural elements; hence, a stochastic procedure is applied to generate initial responses and to improve the process to obtained the final response. Therefore, proper comparisons should be done to make sure of accurate performance of such procedure. To this end, three metaheuristic algorithms of Genetic, Harmony Search and Gray Wolf were used to solve the final problem. Findings: According to the obtained computational results, gray wolf algorithm had the highest performance efficiency compared to other algorithms so it is more practical in solving the real numerical samples. Originality/Value: The objective function of proposed model subjected to minimization of the maximum waiting time for customers in service queue and limitations included manpower allocation and time calculation for each service in each center. We used three metaheuristic algorithms, Genetic, Harmony Search and Gray Wolf, to solve the final problem

Culture affects consumer behavior: Theoretical reflections and an illustrative example with Germany and Iran

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NISQ-Compatible Error Correction of Quantum Data Using Modified Dissipative Quantum Neural Networks

Using a dissipative quantum neural network (DQNN) accompanied by conjugate layers, we upgrade the performance of the existing quantum auto-encoder (QAE) network as a quantum denoiser of a noisy m-qubit GHZ state. Our new denoising architecture requires a much smaller number of learning parameters, which can decrease the training time, especially when a deep or stacked DQNN is needed to approach the highest fidelity in the Noisy Intermediate-Scale Quantum (NISQ) era. In QAE, we reduce the connection between the hidden layer's qubits and the output's qubits to modify the decoder. The Renyi entropy of the hidden and output qubits' states is analyzed with respect to other qubits during learning iterations. During the learning process, if the hidden layer remains connected to the input layers, the network can almost perfectly denoise unseen noisy data with a different underlying noise distribution using the learning parameters acquired from training data.Comment: 9 pages, 14 figure

Cell Production System Design: A Literature Review

Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously. Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified. Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed