Journal of Applied Research on Industrial Engineering
Volume:7 Issue: 3, Summer 2020

This study presents a new mathematical optimization model using queuing theory to determine the hotel capacity in an optimal manner. For this purpose, a Knapsack model based on the queuing theory is proposed. In this regard, after simulating a hotel's reception system with the help of queuing models and using a limited two-dimensional Knapsack model, the capacity and an optimum number of rooms are obtained. Since the proposed model is too complex on large scales, a modified Genetic Algorithm (GA) approach enhanced by Taguchi method is employed to solve the problem. The obtained results indicate that unlike previous studies, the proposed models can be applied to different scenarios.

The scheduling of academic courses is a problem in which a weekly schedule is produced for educational purposes. Many different types of scheduling problems exist at various universities in accordance with their laws, needs, and constraints. These problems fall into the category of NP-hard problems and are incredibly complex. In this paper, an intelligent system for scheduling courses using the deep-belief network is proposed. The reason why the proposed system is intelligent is that it can learn the constraints, inputs, and other necessary parameters in one step by receiving the inputs as well as the training needed by the deep-belief network. The deep-belief network used has one output layer, four hidden layers, and four input layers. The experimental results of this research show that the deep-belief network proposed for the scheduling of academic courses provides a better score, less error, and execution time compared with Sequence-Based Selection Hyper-Heuristic (SSHH) approach.

The mathematical model of a multi-product multi-period multi-echelon closed-loop supply chain network design under uncertainty is designed in this paper. The designed network consists of raw material suppliers, plants, warehouses, distribution centers, and customer zones in forward chain and collection centers, repair centers, recovery/decomposition center, and disposal center in the reverse chain. The goal of the model is to determine the quantities of products and raw material transported between the supply chain entities in each period by considering different transportation mode, the number and locations of the potential facilities, the shortage of products in each period, and the inventory of products in warehouses and plants with considering discount and uncertainty parameters. The robust possibilistic optimization approach was used to control the uncertainty parameter. At the end to solve the proposed model, five meta-heuristic algorithms include genetic algorithm, bee colony algorithm, simulated annealing, imperial competitive algorithm, and particle swarm optimization are utilized. Finally, some numerical illustrations are provided to compare the proposed algorithms. The results show the genetic algorithm is an efficient algorithm for solving the designed model in this paper.

This study aims to identify and employ qualified individuals and assign different organizational positions. Accordingly, a data mining approach is proposed. This paper presents an empirical study which has important practical application in modern human resource management. Therefore, effective features on staff selection are extracted from literature and entered into the database after expert approval respectively. Further, the impact of each feature on staff selection is determined and the ability of applied classification algorithms is compared. The results represent that the organizational position feature has a great impact on forecasting of selection or rejection. Data mining algorithms used in this study have acceptable performance based on accuracy rate, and J48 algorithm performs better comparing to other algorithms based on accuracy rate, recall, F-measure and area under Receiver Operating Characteristic (ROC) curve. Three features of background, level of education, and major are identified as effective features in association rules. Finally, an approach is presented for applying data mining algorithms in employees hiring and organizational positions assignment procedure

This paper examines the petrochemical companies listed on the stock from the perspective of health indicators. Petrochemical companies are working to create health platforms to prevent accidents and reduce health costs. In this study, using two-stage data envelopment analysis technique, the efficiency and effectiveness of petrochemical companies, were investigated from a health point of view and was done by using health indicators. In this research, five inputs are used for two intermediate production and finally for the last three outputs of petrochemical companies from the aspect of human health.The results show that Maroon and Jam petrochemical companies have been more efficient than other well-known companies and the Shazand Petrochemical Company in the second part of achieving the final result.Of the seven petrochemical companies in total, none have had full productivity, but Maroon and Jam Petrochemicals have been targeted first and second in productivity, respectively.

A new approach to the dynamic Data Envelopment Analysis (DEA) referred to as the adjusted dynamic DEA, is proposed in this study. Adjusted dynamic DEA optimizes the production activity of DMUs by introducing adjustment variables to modify the interconnecting activities between consecutive terms, solving conflicts that arise between terms and between management and shareholders. The non-oriented Slack Based Model (SBM) is used as a base model and is extended to the adjusted dynamic framework, where adjustment variables are introduced. And also, in this paper, an attempt has been made to consider ratio data and extend traditional ratio DEA models to dynamic DEA-R model. In order to examine the applicability of the proposed method, the model is applied to evaluate the efficiency of ten branches of an Iranian bank during three consecutive terms. The adjusted dynamic SBM model under Variable Return to Scale (VRS) is solved and reference units for each inefficient DMU are identified. In addition, the slacks and adjustment variables are analyzed and further suggestions about the efficient conditions to the management are provided.

In this paper we have studied a numerical approximation to the solution of the nonlinear Burgers' equation. The presented scheme is obtained by using the Non-Standard Finite Difference Method (NSFD). The use of NSFD method and its approximations play an important role for the formation of stable numerical methods. The main advantage of the scheme is that the algorithm is very simple and very easy to implement.