In this paper, the economic production is considered by considering defective products during production, a percentage of which can be recycled at a fixed cost, also machine failure, and variable demand. It is assumed that the products are produced at a fixed rate, but the machine may break down during production. Machine failure during production is a random variable that follows an exponential distribution with a specified parameter. If the machine breaks down during production, production stops immediately and the machine is repaired, and this study assumes that the machine repair time is a fixed value. Also, unlike the classical models, the demand for the manufactured product is expressed as a non-incremental function of time. The main goal is to determine the optimal policies for reproducing the problem in such a way that the total annual cost is minimized. For this purpose, first, a mathematical model is presented, then, the average cost per unit of time is determined, and based on the concepts of global optimization, the optimal values are determined. Finally, by solving a numerical example, the problem is analyzed. The results show that, the repair time has a great impact on the cost of the entire system, and as it increases, the system cost increases exponentially.

In this paper, we try to provide a schedule for robotic cells in the event of a machine breakdown by using a proactive -reactive approach. We first develop a proactive model for a robotic m-machine cell under the condition of no disturbance. After the machine breakdown, we provide reactive programming in two time periods during the repair and after the repair of the broken machine. Finally, by stating the criteria of stability and Robustness in a robotic cell, we evaluate the efficiency of the presented model with a numerical example.

The classical economic production quantity (EPQ) model was developed to manage inventory costs in companies last decade ago. This model is extended in various directions in recent years. The classic EPQ has some unrealistic assumptions. The model assumes that all products are perfect, while the production of defective items is inevitable in the real-world environment. Another assumption relates to the continuous demand satisfaction, which ignores the commonly used multiple shipments policy in practice. Finally, the classic model does not consider the probabilistic breakdown of the machine and the required maintenance activities. The present work aims to develop a new imperfect EPQ model under probabilistic machine failure, corrective maintenance, and multiple shipments policy. Two cases are investigated: 1-Considering the production of a fixed percentage of imperfect items 2-Considering no production of defective items. Due to the complexity of the problem, a numerical bisection method is utilized to solve the problem and finding the best possible production time. This method's performance is evaluated by comparing it to the obtained solutions by MATLAB optimization toolbox for genetic and simulated annealing algorithms. Sensitivity analysis is performed, and finally, some directions for future research are suggested.

Purpose: One of the most important issues in the field of production scheduling, which has recently received much attention from researchers, is Dual Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP). To deal with unexpected disruptions such as machine breakdowns, the job schedule must be robust so that in the event of a malfunction, the job schedule works properly and deviate less from the optimal solution. The purpose of this paper is to study the DRCFJSP problem with possible scenarios of machine failure or workshop disruption. Methodology: In solving the under-studied problem, the assignment of jobs and the sequence of operations on each machine should be done in such a way that under any possible scenario, the maximum completion time is minimized so that the weight combination of system performance in average mode, system performance in worst mode, the penalty for violating the time window constraints of the due dates and the variance of the objective function value is optimal according to different scenarios. For this purpose, a Robust Scenario-based Stochastic Programming (RSSP) model based on a mixed integer linear programming model has been presented for this problem and has been solved by Gams software for validation in small and medium-sized problems. Also, due to the Np-hard nature of this problem, a meta-heuristic method based on Genetic Algorithm (GA) is proposed for solving the large-sized problems. Also, the results of a case study in Alborz Yadak company related to the problem of the research are reported in the article. Findings: The results of the proposed RSSP model indicate that GAMS software is able to solve these problems up to medium sizes in an acceptable time and achieve a controlled and robust solution. Numerical results also show the proper performance of the proposed GA as an alternative to solve the RSSP model in the large-sized problems. Originality/Value: In this paper, DRCFJSP problem is studied with possible scenarios of machine failure or disruption in the workshop. Also, a RSSP model according to the mixed integer linear programming formulation and a meta-heuristic Algorithm have been presented for mentioned problem in this article.

Cell formation problem mainly addresses how machines should be grouped and parts be processed in cells. In dynamic environments, product mix and demand are changed in each period of the planning horizon. Incorporating such assumption in the model increases flexibility of the system to meet customers’ requirements. In this model, to ensure the reliability of the system in presence of unreliable machines, alternative routing process as well as buffer storage are considered to reduce detrimental effects of machine failure. This problem is presented by a nonlinear mixed integer programming model attempting to minimize the overall cost of the system. To solve the model in large scale for practical purposes, a genetic algorithm approach is adopted as the model belongs to NP-hard class of problems. Sensitivity analysis is used to show the validity of the proposed model. Besides, numerical examples are used both, for small-sized and large-sized instances to show the efficiency of the method in finding near optimal solution.

Along with the development of laboratory methods for diagnosing addiction, concealment ways for creating false results, either physically or chemically, have been in progress. In this research, based on the Laser Induced Breakdown Spectroscopy (LIBS) technique and analyzing hair of addicted and normal people, a new method is proposed to overcome problems in conventional methods and reduce possibility of cheating in the process of diagnosing addiction significantly. For this purpose, at first, we sampled hair of 17 normal and 17 addicted people and recorded 5 spectrums for each sample, overall 170 spectrums. After analyzing the recorded LIBS spectra and detecting the atomic and ionic lines, as well as molecular bands, relative intensities of emission lines for Aluminum to Calcium (Al/Ca) and Aluminum to Sodium (Al/Na) were selected as the input variables for the Support Vector Machine (SVM) model. The radial basis and polynomial Kernel functions as well as a linear function were chosen for classifying the data in SVM model. The results of this research showed that by the combination of LIBS technique and SVM one can distinguish addicted person with precision of 100%. Because of several advantages of LIBS, such as high speed analysis and being portable, this method can be used individually or together with available methods as an automatic method for diagnosing addiction through hair analysis.

The recent advances in manufacturing systems motivate several studies to focus on Economic Production Quantity (EPQ) problem. Althuogh there are several extentions to the EPQ, this paper provides a new extension by considering some of the real world parameters like: (a) shortages in the form of partial backordering, (b) inventory can deteriorate stochastically, (c) machine can break down stochastically, and (d) machine repair time may change stochastically based on the failure status of machine. As far as we know, there is no study treated all these suppositions in an EPQ framework. In addition to this development, two forms of uniformly- and exponentially-distributed repair times are formulated and necessary convexity conditions are discussed. Then, the corresponding optimality conditions are written that lead to finding the roots of two equations. Due to difficulty of achieving a closed-form solution, the solution is obtained numerically by means of Newton-Raphson method. Finally, some sensitivity analyses are provided to explain the models’ applicability. The practicality and efficiency of the proposed method in this context lends weight to development of proposed EPQ with more complex elements and its application more broadly.

In this paper, we develop an economic production quantity (EPQ) model under machine breakdown and two types of repair (corrective and preventive). also, study the simultaneous effect of holding safety stock and purchasing policy. In order to avoid shortages occurring as a result of the random repair time, in addition to keep safety stock, we suppose that the manufacturer could purchase some quantities from an external supplier. This paper addresses the following question: how the manufacturer determine the optimal values of safety stock, production and purchasing lot sizes, simultaneously, to minimize the expected total cost? The introduced model is then compared with the situations in which the manufacturer only keeps safety stock or just uses an external supplier, respectively. The results through the analysis show that using the simultaneous policy when the system is prone to shortages due to long repair times, have more improvement in the performance of the system rather than using the safety stock or purchasing policies, separately.