Scientia Iranica
Volume:29 Issue: 4, Jul-Aug 2022

In this paper,a novel bi-objectivemathematical model is proposed to designa four-dimensional (i.e.,part, machine, operator, and tool) cellular manufacturing system (CMS) in a dynamic environment. The main objectives of this model are to 1) minimize total costs including tools processing cost, costs of transporting cells between various cells, machine setup cost, and operators’ educational costs, and 2) maximizing skill level of operators. The developedmodel is strictly NP-hard and exact algorithms cannot find globally optimal solutions in reasonably computational time. So, a multi-objective vibration damping optimization algorithm (MOVDO) with a new solution structure that satisfies all the constraints and generates feasible solutions is proposed to find near-optimal solutions in reasonablycomputational time. Since there is no benchmark available inthe literature, three other meta-heuristic algorithms (i.e., non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO) and multi-objective invasive weeds optimization (MOIWO)) with the similar solution structure are developed to validate theperformance of the proposedMOVDOalgorithm for solving various instances of the developed model. A Taguchi method is employed to calibrate the main parameters ofthese fouralgorithms. The result of comparing theirperformances based on statistical tests and different measuring metrics reveals that theproposed MOVDO algorithm outperforms remarkably better than other meta-heuristics used in this paper.

Determining the most important criteria to evaluate Humanitarian Relief Supply Chain (HRSC) performance is aimed in this study. For this purpose, Supply Chain Operations Reference (SCOR) model is adapted to HRSC. A trapezoidal type-2 fuzzy Analytic Hierarchy Process (T2F-AHP) methodology is proposed to evaluate the criteria influencing the performance of the HRSC, and also evaluate different Non-Governmental Organizations’ performances operating in Turkey as a real case study. Results of this methodology is aimed to be used by both governmental and non-governmental organizations to improve their HRSC strategies. Lastly, results are discussed with sensitivity analysis to demonstrate the feasibility and applicability of the proposed methodology.

Two-sided Assembly Line Balancing (2S-ALB) is important in assembly plants that produce large-sized high-volume products, such as in automotive production. The 2S-ALB problem involves different assembly resources such as worker skills, tools, and machines required for the assembly. This research modelled and optimised the 2S-ALB with resource constraints. In the end, besides having good workload balance, the number of resources can also be optimised. For optimisation purpose, Particle Swarm Optimisation was modified to reduce the dependencies on a single best solution. This was conducted by replacing the best solution with top three solutions in the reproduction process. Computational experiment result using 12 benchmark test problems indicated that the 2S-ALB with resource constraints model was able to reduce the number of resources in an assembly line. Furthermore, the proposed modified Particle Swarm Optimisation (MPSO) was capable of searching for minimum solutions in 11 out of 12 test problems. The good performance of MPSO was attributed to its ability to maintain the particle diversity over the iteration. The proposed 2S-ALB model and MPSO algorithm were later validated using industrial case study. This research has a twofold contribution; novel 2S-ALB with resource constraints model and also modified PSO algorithm with enhanced performance.

This paper investigates a multistage production–inventory model for deteriorating items, including raw materials and finished goods, based on collaborative preservation technology investment, hitherto not treated in the previous researches. The major purpose is to determine the optimal materials supply, production delivery, replenishment and investment policies for maximizing the joint total profit of the integrated system. Considering the proposed model, this paper uses mathematical programming analysis to ascertain the optimal solutions. Furthermore, several numerical examples are presented to demonstrate the solution process and verify the concavity of the proposed model. Sensitivity analyses with respect to major parameters are also performed. The numerical results shows that market demand, fixed shipping cost, production rate, manufacturer’s sales price and holding cost of finished goods may affect the optimal number of shipments. Besides, when collaborative preservation technology investment becomes an option, whether the effect of the deterioration rate of raw materials or finished goods on the shipping and ordering quantity will be reduced by preservation technology investment. Finally, the increase in the amount of raw materials used to produce a finished product implies the amount of finished goods produced by the original material quantity is reduced, so the preservation technology investment will be increased.

Several auxiliary information-based estimators of the population variance are available in the existing literature of survey sampling. Mostly, these estimators are based on conventional dispersion measures of the auxiliary variable. In this study, a generalized class of ratio-product type exponential estimators of the population variance is proposed which integrates the auxiliary information on non-conventional dispersion measures under simple random sampling in the ratio-type exponential class of estimators. The performance of the proposed estimators is compared, theoretically and numerically, with the several existing estimators of the population variance. It is established that the proposed class of estimators outperforms the existing estimators in terms of the lower mean square and relative root mean square errors. Moreover, the percentage relative efficiency of the proposed estimators is much higher as compared to their counterparts.

In statistical process control, measurement error plays an important role which is usually ignored. Measurement error can lead to incorrect conclusions about the performance of the process. In this paper, we examined the effect of measurement error on the shift detection ability of the mixed exponentially weighted moving average-cummulative sum (EWMA-CUSUM) control chart. We investigate the performance of mixed EWMA-CUSUM chart in case of mean shift by using (i) covariate method (ii) multiple measurement method (iii) linearly increasing variance method. The performance measuring tools such as average run length (ARL) and standard deviation of run length (SDRL) are estimated by using the Monte-Carlo simulation method. It is concluded that the performance of the mixed EWMA-CUSUM control chart is adversely effected by considering the measurement error. It is revealed from the comparative study that the mixed EWMA-CUSUM control chart is performing better than EWMA and CUSUM control charts in the presence of measurement error. An illustrative example is presented to demonstrate the performance of control charts in case of measurement error.

In this paper, we propose an integer programming model for Capacitated Multi-Allocation Median Hub Location Problem, which is applied in a both cooperative and competitive environment among airlines. We divide the hubs into six independent categories by comparing the parameters of the ticket price, travel time, and the service quality of hub airports are controlled by follower and leader airlines. In this paper, the degree of importance of time and cost parameters determine by a multivariate Lagrange interpolation method, which can play an important role in allocating travelers to follower airline hubs. Then, based on the seasonal demand of travelers, we consider travel demand as uncertain parameters. To determine the deterministic equivalent forms of this category of hub location models, robust optimization method and chance-constrained programming model are used. Finally, the proposed model test in a case study. Based on the results, a coalition of follower airlines can absorb nearly 2% of travelers of leader airline due to lower travel cost and travel time compared to that of leader airline.

In this paper, we focus on the matrices representing the inverse fuzzy soft sets over both the universal object set and the universal parameter set. Some basic operations and properties of these inverse fuzzy soft matrices are investigated. Moreover, two adjustable approaches to multi-criteria group decision making (MCGDM), namely inverse fuzzy soft sum-product decision making (IFSSPDM) and inverse fuzzy soft distributive If-difference decision making (IFSDIf-dDM), are proposed. The IFSSPDM approach achieves the optimal choice for the MCGDM problem based on the inverse fuzzy soft structures consisting of multiple-discrete parameter sets and common universal object sets. The objective of IFSDIf-dDM approach is to present a solution for the MCGDM problem based on the inverse fuzzy soft structures consisting of a common universal parameter set and twodiscrete universal object sets. Thus, the solutions can be obtained using the practicality of inverse fuzzy soft matrices for two different types of decision making problems. Besides, the comparisons are presented showing that the proposed approaches produce more convincing outputs than the current fuzzy soft approaches.

In this paper, sustainability-related factors driving success of healthcare system management include a group of hospitals are considered. A three-pronged approach is considered based on the internal functions of the hospital, which are affecting the social responsibility as well as functions related to the service recipients from health centers. A novel comprehensive multi-period evaluation of hospitals’ performance is considered by the proposed dynamic network. This hybrid data envelopment analysis-based fuzzy multi-criteria decision-making model incorporates fuzzy DEMATEL and best-worst method provides several useful managerial insights using a realistic case study from Iran for sustainable management of the healthcare system. Obtained results indicate the importance of considering potential inter-related relations between network nodes on comprehensive performance assessment of healthcare service system.

In statistical process control one objective is to control the stability of a process. A process is stable when its mean is in control and variance bounded. Different control charts were introduced for monitoring the mean and variance of a process by plotting suitable test statistics on the chart. In this research design of a system which converts the sample mean to a test statistics was proposed. The second order filter, a special class of the Linear Time Invariant (LTI) systems, was used to design the converting system. It was shown that design of a low pass filter was better for detecting a level (mean) change in the process. Markov chain approach was also followed to construct appropriate control chart and to estimate its control limits. Simulated data under normality assumption for different scenarios were used to compare the proposed control chart with Shewhart and Exponentially Weighted Moving Average charts by means of ARL and PFS criteria. Existing data from the Central Bank of Iran was also applied to evaluate the suggested method. The signal to noise ratio was used to assess the performance of this method at different stages. Results indicate that the proposed method detects shifts more rapidly.

Many applications of optimisation require the final value of the decision variables tobe integer. In many cases the relaxed optimal solution does not satisfy the integral-ity constraint therefore, the problem must be solved by integer or mix-integer pro-gramming algorithms at a significant computational effort and most likely a worsenobjective function value. The contribution of this paper is twofold: The identificationof a type of problems in which the relaxed optimal objective function value can bekept in the implementation by a change in the planning horizon; and the identifica-tion of a multi-period based solution procedure. Three small instances are providedin order to illustrate the methodology as well as the economic impact involved. Inaddition, a fourth industrial size case is included for the benefit of practitioners.This work shows that business profit can be increased for pseudo-continuous-integerperiodical linear problems by identifying optimal decision-making periods.

In this manuscript, we present a repetitive group sampling plan and a multiple dependent state sampling plan based on the EWMA (exponentially weighted moving average) yield index for product acceptance. The proposed plans utilize the current and the previous information through EWMA statistic to reach a decision of lot sentencing. A non-linear optimization model is developed to determine the plan parameters of the proposed plans for various specified conditions. The performance of the proposed plans over several existing sampling plans is analyzed, which shows that the proposed plans are efficient in reducing the sample size for lot sentencing. For industrial application, a real example is given to demonstrate the implementation of the proposed plans.

According to the high importance of the university in the growth and development of the country, the evaluation performance of educational and research groups in universities is very important. The black-box Data Envelopment Analysis (DEA) model is mathematical programming for measuring the relative efficiency of a set of DMUs, without considering the operations of the component processes that may have misleading results. To overcome this defect, network models are recommended. This paper intends to propose a hybrid Intuitionistic Fuzzy Analysis Network Process (IFANP) and Network DEA (NDEA) technique to evaluate the efficiency of the faculty of basic sciences of Islamic Azad University. The IFANP method is used to evaluate the overall weights among all the criteria and sub-criteria and these weights are used in the NDEA model to measure the relative efficiency of a system. The hypothetical example shows that the efficiency of all DMUs is equal to 1 by using the DEA, and there is no ranking between DMUs. The results of the IFANP-NDEA are more meaningful because of the full ranking of DMUs, considering component process operations. Also, it can prioritize efficient DMUs, provide the efficiencies of the DMU’s functions which enables managers to identify areas of weakness.