Scientia Iranica
Volume:26 Issue: 1, Jan-Feb 2019

The majority of people have experienced pain in their low back or neck in their lives. In this paper a type-2 fuzzy rule based expert system is presented for diagnosing the spinal cord disorders. The interval type-2 fuzzy logic system permits us to handle the high uncertainty of diagnosing the type of disorder and its severity. The spinal cord disorders are studied in five categories using historical data and clinical symptoms of the patients. The main novelty of this paper lies in presentation of the interval type-2 fuzzy hybrid rule-based system, which is a combination of the forward and backward chaining approaches in its inference engine and avoids unnecessary medical questions. Using of parametric operations for fuzzy calculations increases the robustness of the system and the compatibility of the diagnosis with a wide range of physicians’ diagnosis. The outputs of the system are comprised of type of disorder, location and severity as well as the necessity of taking a M.R. Image. A comparison of the performance of the developed system with the expert shows an acceptable accuracy of the system in diagnosing the disorders and determining the necessity of the M.R. Image.

In this paper, we consider a two-echelon inventory system with a central warehouse and two identical retailers employing information sharing. Transportation times to each retailer and the warehouse are constant. Retailers face independent Poisson demand and apply continuous review policy, -policy. The warehouse initiates with m batches (of given size ) and places an order to an outside supplier when a retailer’s inventory position reaches , where is the inventory position considered by central warehouse and is a non-negative constant. So far, an approximate cost function as well as exact analysis of system for only one retailer has been proposed. However, the derivation of the exact value of the expected total cost of this system for more than one retailer, is still an open question. This paper attempts to meet this challenge and derive the exact cost function for two retailers. To achieve this purpose, we resort to conditional probability to split the problem into two simpler problems then we obtain the exact expected total cost of the system.

In this research the master surgical scheduling (MSS) problem at the tactical level of hospital planning and scheduling is studied. Before constructing the MSS, a strategic level problem, i.e. case mix planning problem (CMPP), shall be solved to allocate the capacity of operating room (OR) to each surgical specialty. In order to make an effective coordination between CMPP and MSS, the results obtained from solving the CMPP is used as an input for the respective MSS. In the MSS, frequently performed elective surgeries are planned in a cyclic manner for a pre-defined planning period. As a part of the planning process, it is required to level downstream limited resources such as intensive care unit (ICU) and ward beds with patient flow. In this study, a mathematical model is developed to construct an MSS. The proposed model is based on a lexicographic goal programming approach which is aimed at minimizing the OR spare time while considering the results of the CMPP. In this paper, data required to solve MSS, is collected from a medium-sized Iranian hospital. Hence, a robust estimation method is applied to reduce the effect of outliers in the decision making process. The results testify the performance of the proposed method against the solution put in practice in the hospital.

This study investigated a production-inventory model with defective items under a two-part trade credit where the agreement of conditionally freight concession is considered in the integration supply chain. We assume the inspection process is conducted by the retailer before selling incoming items. All the defective items are discovered, stored and then sold as a single batch to a secondary market at a decreased price. Furthermore, shortages are allowed and completely backlogged for the retailer. The purpose of this study is to determine the optimal number of shipments per production cycle for the supplier, and the optimal length of time wherein there is no inventory shortage and replenishment cycle for the retailer such that the total profit function has a maximum value. In theoretical analysis, the existence and uniqueness of the optimal solutions are shown and an algorithm is developed to find the optimal solutions. Furthermore, numerical examples are presented to demonstrate the solution procedures and a sensitivity analysis of the optimal solutions regarding all parameters are also carried out.

This paper presents a new Mixed-Integer Non-Linear Programming (MINLP) model for a Supply Vessel Planning (SVP) problem. The traditional SVP, which is a maritime transportation problem, is developed to a Maritime Fleet Sizing Mix Periodic Location-Routing Problem with Time Windows (MFSMPLRPTW) by considering suppliers, location of onshore-base(s) and some real life aspects. The objective of this model is to decide the composition of fleets, optimal voyages, schedules and also the optimal location(s) for onshore-base(s) in such a way that the total cost is minimized and the needs of operation regions are fulfilled. The MFSMPLPRTW model is solved by an exact two-phase solution approach for both small and medium cases. Also, two meta-heuristic algorithms are used to solve the large-sized instances. In order to justify and show how the model and solution can lead to significant economic improvements for real life instances, a case study by the IOOC is considered, which is the only offshore oil and gas producer in Iran that has lots of installations and operation regions in the Persian Gulf and the Sea of Oman.

Design of an appropriate cellular manufacturing system (CMS) leads to system flexibility and production efficiency by using the similarities in the manufacturing process of products. One of the main issues in these systems is to consider product quality level and worker’s skill level in the production process. This study proposes a comprehensive bi-objective possibilistic nonlinear mixed-integer programming model under uncertain environment to design a suitable CMS with aims of minimizing the total costs and total inaction of workers and machines, simultaneously. In this respect, the demand of each product with a specific quality level and linguistic parameters such as product quality level, worker’s skill level and job hardness level on machines are considered under fuzzy environment. To this end, the robust possibilistic programming approach is tailored to cope with fuzzy impute parameters. Finally, a real case study is provided to show the efficiency and applicability of the proposed model. In this respect, the proposed approach could be improved the total costs by 23.6% and the total inaction of workers and machines by 11.7% regarding the real practice. In addition, the performance of the presented model is demonstrated by comparing between the results obtained from the proposed model and actual practice.

In this paper, we consider a deficient production system with permissible shortages. The production system consists of a unique machine that manufactures a number of products that a part of them are imperfect in form of rework or scrap. These defective products are identified by 100% inspection during production, then, they are whether reworked or disposed of after normal production process. Like real-world production systems, there are diverse kinds of errors creating dissimilar breakdown severity and rework. Moreover, reworks have non-zero setup times that makes the problem closer to real-world instances where machines require some preparations before starting a new production cycle. Thus, we introduce an economic production quantity (EPQ) problem for an imperfect manufacturing system with non-zero setup times for rework items. The rework items are classified into several categories based on their type of failure and rework rate. The aim of this study is to obtain optimum production time and shortage in each period that minimizes inventory system costs including setup costs in reworks, holding costs, production costs, inspection costs, disposal costs, and shortage. Finally, a numerical example is proposed to assess efficiency and validation of the proposed algorithm.

Discrete phase-type (DPH) distributions have one property that is not shared by continuous phase-type (CPH) distributions, i.e., representing a deterministic value as a DPH random variable. This property distinguishes the application of DPH in stochastic modeling of real-life problems such as stochastic scheduling where service time random variables should be compared with a deadline that is usually a constant value. In this paper, we consider a restricted class of DPH distributions, called Mixed Shifted Negative Binomial (MSNB) and show its flexibility in producing a wide range of variances as well as its adequacy in fitting fat-tailed distributions. These properties render MSNB applicable to represent data on certain types of service time. Therefore, we adapt an expectation-maximization (EM) algorithm to estimate the parameters of MSNB distributions that accurately fit trace data. To present the applicability of the proposed algorithm, we use it to fit real operating room times as well as a set of benchmark traces generated from continuous distributions as case studies. Finally, we illustrate the efficiency of the proposed algorithm by comparing its results to that of two existing algorithms in the literature. We conclude that our proposed algorithm outperforms other DPH algorithms in fitting trace data and distributions.

User satisfaction and loyalty are very important in the mobile communications market because mobile is frequently updated. It is very necessary work that builds up a scientific assessment method to assist product in understanding and knowing well the trend of customers. This paper is intend to build up a scientific assessment method for measuring user satisfaction and loyalty. First, combining the group decision making and TOPSIS (technique for order preference by similarity to ideal solution) technique, a theoretical framework of evaluation method is established. Second, the respondents are allowed to express their opinions by using some simple symbols or by leaving the lack of answers to some measurement questions, even whole questionnaire. Then the symbol information along with the nonresponses in questionnaires are fused into an intuitionistic fuzzy information. Third, the levels of user satisfaction are ranked based on TOPSIS technique and projection measure in an intuitionistic fuzzy environment. Finally, the theoretical and practical implications of current model are discussed, the important limitations are recognized and future research directions are suggested.

In this article, we have proposed some modifications in the maximum likelihood estimation for estimating the parameters of the Pareto distribution and evaluated performance of these modified estimators in comparison to the existing maximum likelihood estimators. Total Relative Deviation (TRD) and Mean Square Error (MSE) have been used as performance indicators for goodness of fit analysis. The modified and traditional estimators have been compared for different sample sizes and different parameter combinations using a Monto Carlo simulation in R-language. We have concluded that the modified maximum likelihood estimator based on expectation of empirical Cumulative Distribution Function (CDF) of first-order statistic performs much better than the traditional ML estimator and other modified estimators based on median and coefficient of variation. The superiority of the said estimator is independent of sample size and choice of true parameter values. The simulation results were further corroborated by employing the proposed estimation strategies on two real-life data sets.