Journal of Quality Engineering and Production Optimization
Volume:4 Issue: 2, Winter Spring 2019

The multi-objective optimization problem is the main purpose of generating an optimal set of targets known as Pareto optimal frontier to be provided the ultimate decision-makers. The final selection of point of Pareto frontier is usually made only based on the goals presented in the mathematical model to implement the considered system by the decision-makers. In this paper, a mathematical model is presented and analyzed to design manufacturing cells by considering two non-contiguous objectives and switch among cellular pieces. Cooperation among workers in a cell can have a significant effect on the operation completion time. Therefore, one of the important points of using the cellular manufacturing system is to control all system pieces during the manufacturing process. It implies that the number of labors, sorts of apparatus, and parts given to a cell must be at administrative level.  It means that the number of workers, types of machinery, and parts devoted to a cell must be at managerial level. To analyze and evaluate the robustness of the manufactured solutions, Monte Carlo simulation as robustness analysis technique has been used. Finally, the result of solving and analyzing the problem is presented in the frame of the case study of Emersun Company.

– Sustainable evaluation of construction projects in strategy-focused condition is the main issue for municipalities to appropriately improve public sector services. In this respect, the group decision-making methods could help experts to select suitable sustainable projects and to schedule them regarding their ranking results. Therefore, the objective of this study is to present a hybrid group decision-making approach based on hesitant fuzzy sets theory to select the best strategic project for Tehran municipality. Hesitant fuzzy sets theory regarding the other modern fuzzy sets could assist the experts in assessing the candidate strategic projects based on evaluation criteria by assigning some membership degrees under a set to decrease the judgments’ errors in vague environments. In this proposed approach, the weight of each decision maker (DM) is determined according to the proposed hesitant fuzzy collective wisdom weighting (HFCWW) method. Besides, the evaluation criteria weights are determined based on the presented hesitant fuzzy preference weighting (HFPW) technique. Hence, hesitant fuzzy utility index method is defined to rank the candidate strategic projects. Finally, a real case study about the sustainable strategic project selection for Tehran municipality is provided to represent the feasibility and applicability of the proposed framework.

In this paper, a newsboy model is developed under uniformly distributed lead-time and demand that is an appropriate assumption in obtaining optimal relief inventory of humanitarian disasters. It is noteworthy that limited historical data are in hand on relief operations. Hence, analytical and approximate solutions for optimal relief order quan tity were derived. The effect of lead-time uncertainty on the optimal solution was analytically tested. The approximate solution was numerically evaluated and proper agreement with analytical data was achieved with a low variation coefficient of lead-time. The analytical results showed that lead-time uncertainty might increase or decrease relief order quantity, depending on the variation coefficient of lead-time.

Real time and accurate fault detection has attracted an increasing attention with a growing demand for higher operational efficiency and safety of industrial gas turbines as complex engineering systems. Current methods based on condition monitoring data have drawbacks in using both expert knowledge and quantitative information for detecting faults. On account of this reason, this paper proposes an online fault detection and isolation method based on belief rule base (BRB), which can deal with modeling behavior of complex systems when semi-quantitative information is available. Although it is difficult to obtain accurate and complete quantitative information, some expert knowledge can be collected and represented by a BRB, which is essentially an expert system. As such, a new BRB based diagnosis model is proposed to detect and isolate faults of the system in real-time when aiming at isolating various damages and determining the severity of each. Moreover, a recursive algorithm is developed for online updating the parameters of the fault diagnosis model. Equipped with the recursive algorithm, the proposed diagnosis model can determine the severity of fault in real-time when two types of faults are dependent and competitive. To prove its potential application, experimental results demonstrate that the proposed model can track the fault severity very well, and the faults can be diagnosed accurately in real time. Thus, R2 values of 0.99782 and 0. 99782 were obtained for the fault estimation of fouling and erosion, respectively, indicating the accurate performance of the proposed model.

Operating room scheduling is an important task in healthcare sector. This study proposes a Mixed Integer Nonlinear Programming (MINLP) mathematical model for the scheduling of the operating rooms. In the presented model, apart from scheduling the patients’ surgery process, shifting of the medical staff is also carried out. The innovation considered in the proposed model is aimed at prioritizing patients in the operation process, according to the priority and level of the patient’s emergencies, and operating room treatment processes. Ultimately, the proposed model is assessed with random data, and in addition to scheduling patients based on the level of service delivery priority, the medical staff has been scheduled, as well. Furthermore, the sensitivity analysis results reveal that the proposed model is very sensitive to preoperative preparation times, and this bottleneck needs to be improved in the hospitals. On the other hand, this study presents solutions to improve the operating room scheduling and improving the status of patient services to manage and optimize operating room scheduling that result in satisfied patients.

Double sampling plan is an examination with a certain parameter, so it cannot decide about manufactured products whose portion parameter ( ) is not certain. The main goal of this survey is to introduce double variable plan when  is indefinite to examine manufacturing products when concerned characteristics are normally distributed. Plan parameters are achieved by an optimization manner. Sum of fuzzy customer and producer’s risks and contract’s commitments are assumed as a goal function and restrictions, respectively, in this manner. Optimum values of parameters are provided to be employed in industry for variant compositions of demands. A simulation study is also conducted to represent that the presented approach becomes traditional one as  is not imprecise. In addition, conclusions display that the proposed method is more economical than the existing scheme. At the end, an industrial example is given in real situations.

In this paper, a new interval-valued fuzzy multi-criteria group decision-making model is proposed to evaluate each of the energy plans with sustainable development criteria for proper energy plan selection. The purpose of this study is divided into two parts: first, it is aimed at determining the weights of evaluation criteria for sustainable energy planning and second at rating sustainable energy alternatives by a group decision model under uncertainty. In the proposed method, given the concept of asymmetric data, possibilistic statistical concepts are used to make a more appropriate decision with less uncertainty consideration. A new rating system based on the reference point and a new improved version of Entropy method are introduced as the leading features of this model to determine the weight of criteria and possibilistic statistical concepts, including mean, standard deviation and cube root of skewness in the interval-valued fuzzy form, by considering positive and negative ideal points. Moreover, a practical example in the field of energy is presented and discussed, taking into account the experts experience in different fields and inaccurate concepts of information, efficiency and results of the proposed model.

In healthcare systems, one of the important actions is related to perishable products such as red blood cells (RBCs) units that its consumption management in different periods can contribute greatly to the optimality of the system. In this paper, main goal is to enhance the ability of medical community to organize the RBCs units’ consumption in way to deliver the unit order timely with a focus on minimizing total costs of the system. In each medical center such as hospitals or clinics, decision makers consider a one-day period for their policy making about supply and demand of RBCs. Based on the inventory status of the previous day, decisions are made for following day. In this paper, we use Markov decision process (MDP) as a sequential decision-making approach for blood inventory problem considering red blood cells consumption. The proposed MDP model for RBCs consumption management is solved using sequential approximation algorithm. We perform a case study for the proposed model using blood consumption data of Zanjan, Iran. Results for several blood types are discussed accordingly. In terms of total cost of the system, LIFO-LIFO policy is best policy for RBCs consumption among all other policies. In order to analyze the importance of some parameters in the model, a sensitivity analysis is done over shortage cost.

This paper presents an economic production quantity (EPQ) model with a periodic order quantity (POQ) policy, product reliability and periodic demand. The machine reliability has decreased over time; therefore, the rates of perfect and defective products reduce and increase over time, respectively. A fixed percentage of these products are reworked while the rest is wasted. Some equipment in their early days operates with excellent efficiency, but its performance deteriorates over time; therefore, operating costs increase. It is assumed the machine is inspected and repaired at the end of each production cycle; then, the machine returns to its original state. The demand for the final products is discrete, periodic and constant for each period. We use the POQ policy to meet customers' demand. In POQ procedures, orders for replenishment occur at fixed intervals. A Mixed Integer Non-Linear Program (MINLP) model is suggested. Then, a computational experiment is presented to discuss the optimally of the profit function. By analyzing the data, it is found that under different conditions, the manufacturer can use either Single Setup Single Period (SSSP) or Single Setup Multi-Period (SSMP) policy.

An integrated optimization framework, including location assignment under grouping class-based storage policy and schedule of dual shuttle cranes, is offered by presenting a new optimization programming model. The objective functions, which are considered at this level, are the minimization of total costs and energy consumption. Scheduling of dual shuttle cranes among specified locations, which were determined in the upper-level, is conducted in the lower-level by considering time windows and balance constraints under multi-period planning conditions. A modified nested differential evolution-based algorithm is introduced to solve the proposed model because it is an Np-hard bi-level bi-objective optimization model. Eventually, with the intention of illustrating the validation of the presented optimization model and solution methodology, various numerical experiments are tailored, and different comparative numerical examples are provided based on two current algorithms in the literature. Sensitivity analyses illustrate that grouping class-based storage policy could be rendered superior planning of operations in both levels of the investigated problem.

This study proposes a new approach for single-sampling plan by determining sample size and acceptance number. The proposed approach is based on a two-step methodology. In the first step: quality management step, different single sampling inspection plans were generated by running an optimization model for different possible acceptance numbers. While, in the second step: Multi-Attribute Decision Making (MADM) step, Shannon Entropy Approach (SEA) and Linear Assignment Method (LAM) were applied for ranking the inspection plans, generated in the previous step and selecting an appropriate plan. In the MADM step, single-sampling inspection plans defined as alternatives and Expected Non-conforming Cost (ENC), Inspection Cost (IC), and Average Outgoing Quality (AOQ) were introduced as main criteria. The proposed approach is able to determine acceptance number or maximum allowable defective number, besides the sample size for inspection lot in manufacturing lines. An example is given for illustration. The results reveal that the proposed approach could provide insightful implications for quality management.

Transportation in economic systems such as services, production and distribution enjoys a special and important position and provides a significant portion of the country's gross domestic product. Improvements in transportation system mean improvements in the traveling routes and the elimination of unnecessary distances in any system. The Vehicle Routing Problem (VRP) is one of the practical concepts in the field of investigation and many attempts have been made by researchers in this area. Due to the importance of transportation issues in the real world and the status of these issues in the types of existing systems. In this paper, we investigate the Vehicle Routing Problem with Time Window (VRPTW) and provide a solution for it. The problem of routing vehicles with a time window is an extension of the problem of routing vehicles with limited capacity (CVRP) in which servicing must be done in a specific time window. The purpose of this problem is to optimize the route for each vehicle so as to minimize the total cost of the route and the number of vehicles used, and ultimately maximize customer satisfaction. In the paper, a hybrid method based on cuckoo search and greedy algorithm is proposed to solve the problem of VRPTW. For the cost function, different criteria have been used that are within the framework of the VRPTW problem within hard and soft constraints. In order to evaluate the proposed method, the dataset is used in different sizes. The proposed method is significantly higher compared to similar methods.