مجله مدیریت تولید و عملیات
سال دهم شماره 2 (پیاپی 19، پاییز و زمستان 1398)

Risk Adjusted Cumulative Sum (CUSUM) control charts are powerful statistical tools for early detection making of process changes. Unlike other industries, healthcare systems are of a wide range of variability and different levels of inputs. However, since variability in the output of healthcare process may result from different factors including environmental factors, doctor’s performance, or patient specifications; therefore, considering multiple outcomes facilitates and increases precision of the process control. Accordingly, in this paper, risk-adjusted CUSUM control chart with multiple outcomes is applied to monitor kidney transplantation surgery. It is assumed that transplantation surgery might result in full recovery of the patient, rejection of the organ, or after-surgery complications. Finally, the annual report of kidney transplant surgery in the U.K has been used to monitor 1779 surgeries between 2010 and 2011, and the associated CUSUM control charts have been presented.

Although statistical process monitoring methods were initially introduced for early detection of industrial and chemical process changes, soon Shewhart and Deming mentioned various applications of such methods including healthcare process monitoring. Cumulative sum (CUSUM) control charts are useful for rapid identification of tiny changes in process parameters (Montgomery, 2008; Altman & Royston, 1988). In this paper, the focus is on monitoring outputs of kidney transplantation surgery. Existing literature on monitoring surgery outputs indicates binary output as an assumption, i.e. failure or success for the process. However, there are possibly more than two outputs for a surgery. For example, kidney transplant surgery may lead to complete rejection of the kidney, infection or bleeding, deficiency of the organ, and acceptance of the kidney (Rossi et al., 2016). Furthermore, since each of the outputs may have a different origin, assuming multiple outcomes makes it possible to monitor the process more accurately. Therefore, in this paper a risk adjusted CUSUM chart is developed for monitoring kidney transplantation surgery assuming multiple outcomes for the process.

Three outputs are assumed for kidney transplantation surgery including: acceptance of the organ, complications, and full rejection. Then, a risk adjusted CUSUM chart is developed for monitoring surgery outputs based on the transplantation data of 1624 surgeries in U.K between 2008 and 2009 (Mumford & Brown, 2017). Then, the generated chart is used for monitoring 1779 transplant surgeries between 2010 and 2011 in the U.K.
Findings and

Monitoring the results indicated multiple out-of-control results. However, systems came back to the controlled mode. This implied an unstable performance. Overall, 57 signals were received by the chart out of which, 19 signals were due to complications and 38 signals were due to rejection. The 99.5 confidence interval for receiving signal from the process was [0.020, 0.44]. Also, chi square statistic was used to test independence of output levels from time and there was no evidence for the rejection of the null hypothesis at 0.005 significance level.

Monitoring processes with multiple outcomes helps better identification and categorization of the effective factors and better control of the process. In this paper, a risk adjusted CUSUM chart was developed for monitoring kidney transplant surgeries. The developed chart seems to be easily applicable in other healthcare processes.

Manufacturing strategic decisions and competitive priorities have effects on competitive advantage of firms. The focus of this study is on the relationship between the manufacturing strategic decisions and competitive priorities and its influence on the firm’s performance in the automotive supply industry of Iran. A survey has been conducted by the means of a questionnaire to collect data. Data was analyzed by descriptive and inferential statistics (bivariate correlation and multiple linear regression). In this study, after classifying the manufacturing strategic decisions (according to competitive priorities), its influence on the fulfillment of competitive priorities and business performance has been distinguished. Findings indicated that some of the decisions had more effects on profit, cost, quality, flexibility and delivery capabilities.

The manufacturing strategy seeks to answer the question “How to compete” (Voss, 2005). Competitive priorities and manufacturing strategic decisions are the most important components of the manufacturing strategy. The importance of making manufacturing strategic decisions should be sought in resource constraints. In other words, organizations have to choose the goals and priorities, and in order to fulfill them, they should be able to choose the most effective measures that on the one hand fulfill their priorities with least using resource, and, on the other hand, by doing so, improve their total business performance, such as profitability (Größler, 2010). In this study, after clarifying the competitive priorities in the automotive supplier industry and its relevance to the decisions made by the companies, two fundamental questions are answered. First, whether decisions made to fulfill competitive priorities have the maximum influence on generating manufacturing competence and competitive potentials, and that there is a combination of strategic manufacturing measures that if organizations pay attention to them, they will be able to better fulfill competitive capability. After answering the first question, the second question seeks to answer whether decisions that have the most influence on the fulfillment of competitive priorities have the maximum influence on total performance of companies.
Literature on studies performed on manufacturing strategy can be distinguished in different categories. In the first category, the relationship between competitive priorities (or capabilities) and the importance and type of relationship they have with each other has been examined. In the second category, companies have been classified (clustered) according to competitive priorities and the performance of each cluster has been examined. In the third category, structural and infrastructural decisions have not been omitted and in fact, they have been considered as a part of the manufacturing strategy (McCarthy, 2004). In addition, some researchers have studied the best practice companies (Shah & Ward, 2003; Voss, 2005).

Considering the field's relationship with the research question and access to information, active companies in the automotive supplier industry of Iran are selected as the statistical community. With regard to the subject and the possibility of better access to the suppliers, a list of companies in Sapco and Sazehgostar was prepared. The questionnaire was sent to all 215 companies in the list and 48 companies responded, which means a response rate of over 22%. In this study, measurement is used to collect data and information about competitive priorities, manufacturing strategic decisions and performance. For this purpose, a questionnaire is used. Spearman correlation coefficient is used to find the relationship between competitive priorities and manufacturing strategic decisions. Also, to investigate the effect of manufacturing strategic decisions on companies’ performance  (including cost, quality, flexibility, delivery) and business performance (including profitability, return of investment rate, sales growth and market share), multiple linear regression is employed. In this study, a stepwise approach is used.

In this study, after identifying the common manufacturing strategic decisions in the automotive supply industry to meet their competitive priorities, the influence of such decisions on the fulfillment of competitive priorities was studied. Since the influence of such decisions on the fulfillment of competitive priorities was less than what expected to be, efforts were made to identify those decisions that had the most influence on the fulfillment of competitive priorities. After identifying such decisions, they were referred to as the best manufacturing strategic decisions (Table 1).

This research presents a mathematical model for information integrating manufacturers, distributors, and intermediate warehouses and transportation systems in the supply chain over multi period. Goods are spoilable and Capacity, production costs, warehousing costs and transportation of each transportation systems costs are limited. Rate of corruption of goods in the warehouse and transportation systems is known or predictable by experts. The amount of demand for goods is constant. The objective function of the proposed model is single-objective and the costs of production, transportation, warehousing, corruption, shortage and unpackaged goods are integrated minimally. The proposed model is non-linear and of a strict type and has been confirmed by solving a few small-scale problems. For validation, a case study was performed and genetic Meta heuristic algorithms were used for solving the problem. The results of the solved model showed that Decision making integrated is better than the case that sections are decided separately.

One of the most important issues in supply chain is integrated production-distribution planning. Integration of two production-distribution and three-supply, production and distribution loops are important optimization issues in the supply chain. Research in this area involves locating new or special facilities and a combination of allocation locating. The current research is a type of allocation and examines the impact of supply chain integration with several manufacturers, warehouses and distributors and the transportation system. The goods in this chain are perishable and with a lifetime limitation. Production capacity, warehouses, and transportation systems are limited. The mathematical model developed is single-purpose and minimizes the cost of the whole chain. For the first time in the current study, distributor middle warehouses have been modeled taking into account corruption in the warehouse and during shipping. The proposed model is solved once considering the cost of corruption and once without it and the results are compared.

 In this study, a single-objective model is presented for the integrated production-distribution problem considering the lifetime limitation of the goods. The supply chain in question consists of three levels of manufacturers, distribution centers, and end customers. The model is offered for several product types over multiple time periods. The integrated problem-solving model minimizes the entire cost of the chain, including: warehousing, distribution, commodity rotation, and shipment, which maximizes product quality when the product reaches the consumer. In order to evaluate the performance of the proposed model and to validate it, several different sample problems have been solved in various dimensions. A Proposed Genetic Algorithm for Solving Models Genetic Algorithm and CPLEX10.2 software have been solved, the results of which show the accuracy of the models. The proposed genetic algorithm is coded in the MATLAB R2015a programming environment and its functions are used within the algorithm.

The main purpose of this research is to manage and make the right decisions on the amount of transport, production, inventory and distribution in a supply chain network. In order to make better and more practical decisions in the field of inventory and transportation for distribution of perishable products at supply chain levels, taking into account the real world conditions. Determining the optimum amount of production for each manufacturer, the amount of shipment carried out by each warehouse transport system, and the optimum inventory availability with respect to demand and the perishability factor of the warehouse are the objectives of the study. To simplify the real-world problem with the model in question, some simplifying assumptions have been used. In general, the optimal storage conditions vary depending on the type of goods and depend on many parameters. Generally, oranges can be stored at 7-2°C for 12 to 8 weeks. The distances are straight lines. The planning horizon is intended for three periods. There are 60 middle warehouses (30 traditional warehouses and 30 cold storage warehouses). The results showed that in the integration of production-distribution departments without limiting the product life span, the most important criterion in terms of cost is distance. Volumes account for up to 35% of corrupt products due to the lack of a cold supply chain. But in view of the factor of corruption in the model, the integration of the chain leads to the use of new technologies in the maintenance and transport of goods and reduces the cost of corruption. Although maintenance and shipping costs increase slightly, the overall cost of the chain eventually declines. In a model that does not include the costs of corruption.

  As a result of the decision to use traditional supply chains, in addition to the negative environmental consequences, high costs of corruption on the chains are imposed. Integrated decision making is better than the case of each sector being decided individually. This research as a back-up model can help managers make better decisions with regard to real-world conditions and constraints and make use of the potential of the food and agriculture industry. There are still many areas for the development of future research. Consider real-world assumptions in problem modeling, such as: Demand for the product as a possible rate of decay based on the quality of raw material demand dependent on the price of the product and considering the problem in an uncertain environment.

In this paper, a new integrated mathematical model of the production planning and dynamic cellular manufacturing system (DCMS) wherein the product mix and/or volume is different from one period to another has been developed. So far, literature review indicates that the key role of Material Handling Equipment (MHE) has not been considered in the developed model, while ignoring such role will lead to wrong results. In other words, ignoring characteristics such as MHE capacity and inter and intra-cell movement times cannot be justifiable especially in shops in which, the movement times for parts are considerable compared to their processing times. The proposed model covers concepts such as inter/intra-cell movement, reconfiguration, subcontracting, inventory and backorder, lead time for subcontracted parts, optimal lot sizing in each period, number of inter/intra-cell MHE assigned to manufacturing system, number of MHE purchased and sold in each period and price of purchasing/selling for each inter/intra-cell MHE. A numerical example and sensitivity analysis have been used to verify the proposed mathematical model.  

Due to the global market competition, the manufacturing systems are changing from traditional configurations such as flow shop and job shop toward structures such as Cellular Manufacturing System (CMS). On the other hand, customer demands are different from one period to another. In such conditions, companies that use CMS should change their cell configurations every period. In other words, a new Dynamic Cell Formation Problem (DCFP) is needed to be performed for each period. The objective is to handle a DCFP together with a production planning policy by manufacturers. This integrated problem was proposed by Bulgak and Bektas (2009) for the first time. They developed a mixed integer nonlinear mathematical model and solved several computational examples by CPLEX. In another study, Safaee and Tavakkoli Moghaddam (2009) studied an integrated model of DCFP and production planning. Their model included the outsourcing and lead time concepts together. Then, other studies proposed DCFP and production planning together with others subjects such as worker assignment, machine breakdown, company layout, etc. In this paper, the roles of inter/intra cell Material Handling Equipment (MHE) and DCFP and production planning are studied, simultaneously.  

First, a new mixed integer nonlinear mathematical model is proposed considering DCFP, production planning and the role of inter/intra cell MHE. Due to the complexity of nonlinear models, a transformation is occurred from the nonlinear developed model to a linear one. Then, the linear model is coded in commercial software named ‘GAMS’. Several examples are run on GAMS to validate the proposed model. Finally, the sensitivity analysis is performed on a number of important parameters.  

In order to illustrate the effect of MHE on the DCFP and production planning, two numerical examples were investigated with and without MHE. The first difference between these two examples was in objective function value as represented in Table 3 regardless of MHE and in Table 5 regarding MHE. The second difference was in production planning as it addressed in Table 4 regardless of MHE and Table 6 regarding MHE. The third difference was in cell configuration as represented in Figure 2. Finally the forth difference was in the number of MHE used in manufacturing system regardless and regarding MHE as addressed in Tables 7 and 8, respectively. All Tables and Figures proved that MHE management can play an effective role in a manufacturing system.  

In this paper, the integrated model of DCFP, production planning and MHE was investigated. A mixed integer nonlinear mathematical model was developed and then transformed into a linear one. To validate the proposed model, a numerical example was presented and this example was solved without and with MHE. Finally, a sensitivity analysis was performed on a number of important parameters. 

The upward growth of steel industry has led to an increase in demand for raw materials and the release of about 7% of global greenhouse gases (An et al., 2018; Griffin and Hammond, 2019). Blast furnace (BF) is the most essential section of a steel company (Liu et al., 2016). Costs of production in steel companies are contributive to the competitiveness of such plants (Zhang et al., 2011). Due to the shortage of domestic lump and concerns about CO2 emission, Iranian steel industry has encountered serious challenges of supplying ferrous raw materials and coke for blast furnaces, while the overproduced direct reduced iron (DRI) and the vast sources of domestic natural gas and pulverized coal have made it possible to replace coke with these sources of energy and using DRI as ferrous raw material in the blast furnaces. High differences in the price of coke with natural gas and pulverized coal along with big price gap between DRI and lump, the influence of replacing complexity on the cost of ferrous raw materials, coke, and energy consumption, BF productivity, technical constraints, and carbon dioxide emissions level are the main reasons for conducting this research.

A non-linear optimization model, extracted from thermodynamic equations, process relations, and mass and energy balances, has been applied in this study. This model can be applied as a decision support system for purchasing and supplying coke-energy, ferrous burden materials, and examining the effect of consuming different raw materials on the CO2 emission and evaluating the production profit.

Results indicated that this model can decrease CO2 emission and is highly effective in gaining company benefits. Based on the research sensitivity analysis it was found that despite the advantages of the model, as long as there are no tough restrictions on CO2 emission like in Japan and in the developed European countries, and there is subsidized domestic lump charging DRI as BF burden materials, it is not economic. As a result, it was concluded that available ferrous raw materials options for Iranian blast furnaces are only lump, sinter and pellet.

BF thermal reserve zone is assumed 1200k, which may vary from 1100 K up to 1300 K in practice; hot metal and slag temperatures are assumed fixed; chemical elements distribution is assumed fixed; and the state of gas rising from the bottom segment into the up segment of BF is ignored.

The proposed model was implemented in MATLAB and validated using the data of Esfahan Steel Company. A comparison between the model results and the experience-based results for supplying ferrous materials blending indicated a good compromise between the model and real situation, and it leads to an increase in production benefit around 16% for ferrous raw material and 19% for energy when using the model to purchase them. Another advantage of this model is the ability of prediction of raw materials which affects production parameters. In this regard, the effect of DRI on the CO2 emission, energy consumption and the benefit were studied and validated.

 Some of the innovation aspects of this study include:i) compared to available studies, optimal decision making on the supply and replacement of raw materials and energy, together with new constraints, were analyzed;ii) applying scrap and direct reduction iron (DRI) as environmental friendly ferrous raw materials for Iranian blast furnaces became possible, which contributed to a decrease in energy consumption;iii) the coke consumption rate in a BF as a function of the blending of ferrous burden materials and other production variables was assumed to change; andiv) for the first time in this study, the simultaneous consumption of carbon-bearing materials such as pulverized coal, natural gas, oil and coke were modeled.

The aim of this study is to propose a comprehensive performance evaluation model with emphasis on service performance metrics in the service-product supply chain rather than the production supply chain in the home appliance industry and using neural-fuzzy networks for performance evaluation. The present study is typically a descriptive-exploratory research with survey approach in which, data analysis has been conducted using quantitative method and exploratory and confirmatory factor analysis. For the purpose of this study, a sample of 58 home appliance companies has been selected and Smart-PLS, SPSS and Matlab software have been used for data analysis. Findings indicated 10 main constructs and 29 performance criteria obtained for evaluating the performance of service supply chain and fuzzy neural networks of several home appliance companies.

Based on predictions, services are a key component of the growth of the global economy in future (Arnold et al. 2011). Acording to Jane and Kumar (2012), services play a critical role in a supply chain. Also, according to Wang et al. (2015), a "product" or "service" must exist in each supply chain which is produced by the upstream sectors and delivered to downstream. Recently due to increasing customer expectations, companies’ competition has been replaced by the supply chains competition and as a result, competition has been increased in the simultaneous supply of products and services. This has led to challenges in integrating companies and in coordinating the materials, information and financial flow that were previously overlooked. Accordingly, a new managerial philosophy has been developed known as Product-Service Supply Chain (PSSC) (Stanley & Wisner, 2002). This study seeks to develop a performance evaluation model for the product-service supply chain in the home appliance industry, which is finally solved using Adaptive Neuro-Fuzzy Inference System (ANFIS).

In this paper, performance evaluation constructs and criteria of service supply chain are identified by reviewing the literature and exploratory and confirmatory factor analysis and then, the performance evaluation of service supply chains in Iran's home appliance industry has been performed using these constructs, criteria and ANFIS.
Findings and

Based on the findings, ten main extracted constructs can be suggested for the performance evaluation of the supply chain. They include "Operational Performance (OP)", "Strategic Performance (SP)", "Financial Performance (FP)", "Performance of Information and Communication Technology (PICT)", “Return Performance” (REP), “Risk Performance (RIP)”, “Logistic Performance (LP)”, “Market Performance (MP)”, “Internal Structure Performance (PIS)” and “Growth and Innovation Performance (PGI)”, among which, the Strategic Performance (SP) and Return Performance (REP) are the most important and the least important constructs, respectively.

Based on the findings, the following practical recommendations are suggested to the companies:Enhancing the demand forecasts performance and utilizing more appropriate methods and software to improve forecasts in demand and order management areas.Improving the return management status by increased attention and more investment in return management processes. Effective investment in service development management to enhance the R&D services performance.Utilizing risk management approaches and methods to identify and take preventive actions on the risks in the companies’ service supply chain.

The aim of this study is to develop a dynamic and flexible procedure for designing the spectrum of quality management maturity as well as measuring and determining the membership degree of each quality management characteristic to different levels of this spectrum. The model is essentially based on the evolution of the quality management systems and provides a basis for calculating the organizational maturity in quality management and determining its position in the maturity spectrum.

The four levels of the spectrum of the quality management maturity have been defined and designed. Then, the fuzzy Kano questionnaire has been developed, followed by examining changes in the membership degree of each attribute to different levels of the maturity spectrum. Based on the analysis of quantitative results from the experts’ point of view, relative importance of the degree of quality management characteristics to various levels of the maturity spectrum was determined; in other words, for organizations at higher levels of the quality management maturity, tools/techniques have been considered as fundamental or functional, while for lower levels, they have been considered as motivational and attractive.

The analysis of quantitative results indicated the relative membership degree of the quality management attributes to different levels of maturity and these differences varied from the motivational aspect to the questionable dimension depend on the competitive position of the organization.

The results of this study was used to prioritize the capabilities associated with the characteristics of each maturity level in those organizations that intended to improve competitiveness while adhering to the prerequisite principle. This study also provided a basis for assessing the maturity of quality management by focusing on the deployment of such characteristics. This study also provided a basis for prioritizing and establishing the needed and relevant capabilities associated with such characteristics based on their interdependencies. In the case study, 28 well known characteristics of quality management were exploited in the competitive environment of Iran. Obviously, the proposed model was found to have the capability of applying different characteristics in higher levels of competitiveness.

According to the climatological features of quality management systems at the national level, the terms 'infancy', 'stripling', 'hobbledehoy' and 'adolescent' were also used to classify the levels in the maturity spectrum. Since deploying any of the characteristics and developing the associated capabilities is an improvement project, firms can refer to their maturity level of quality management to invest in and to deploy the quality management characteristics.

Achieving excellence in quality is an incremental process that will often be achieved by adoption and deployment of a set of attributes the quality management (values, methodologies, and tools). Given the fact that achieving higher degrees of quality depends on increase in the adoption of products and services in response to the changing needs of customers, the maturity of quality management is also incremental and can be illustrated and visualized through a spectrum. The dynamics of the quality management systems and the complexity and ambiguity of their measurement have led to challenges in providing the scientific and executive methodologies for measuring quality management maturity, which in turn resulted in some limitations in the theoretical framework. To fill this theoretical gap, in this study, the focus on the indicators of quality improvement was changed for the purpose of investigating maturity. In other words, in order to determine the degree of organization maturity in the field of quality management, the basic focus was on applying and deploying quality management characteristics, i.e. values, techniques and tools, while the proposed methodology opened a new window for future studies.

One of the most important risks of organizations is technology risk. In gas companies, due to the expansion of activities, increase in the number of subscribers, and increase share of gas in energy basket, technology has a very important role in delivering appropriate service. Risk assessment in gas technology development projects is very vital. In fact, the existence of numerous risks in the gas industry is one of the main obstacles to the technology development in the country's gas industry. In other words, the implementation of plans and projects of the gas industry are highly risky due to the uncertainty of the specific elements of this industry. The purpose of this study is to provide a suitable framework for identifying and ranking the risks of gas companies using the integrative technique of FMEA and TOPSIS. The distinguished aspect of this paper compared to previous studies is the new method developed based on failure modes and effects analysis (FMEA), Shannon Entropy approach, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking technological risks of the gas company.

In this paper, technology risks in gas distribution have been determined based on literature and expert’s viewpoints. Then, the identified risks were sent to 33 of the company's experts via the FMEA worksheet. After rating the risks by the experts in the FMEA worksheet, instead of obtaining the Risk Priority Number (RPN) number for each risk, the risks were prioritized using the TOPSIS technique. The FMEA method considers three kinds of attributes, namely, occurrence, detection rate, and severity. Occurrence is the probability of the risk, detection rate is the ability of detecting risk, and severity is applied as severity of the effect of risk. The judgment about determination of indicators has been proposed by experts. In this paper, TOPSIS has been used instead of applying an RPN to assess potential failure modes by multiplying indicators of occurrence, detection rate, and severity. TOPSIS is a ranking method with the aim of selecting alternatives that simultaneously have the shortest and farthest distances from the positive and negative ideal solutions, respectively.

Findings indicated that the most important technology risks in gas distribution are i) variation in macroeconomics index (exchange and inflation rate) in country; ii) inability to access required equipment and machinery; iii) inability to access manufacturing technologies; and iv) limited financing for technology development. Research limitations/implications: One of the limitations of this study was separate access to the projects of this industry. In this study, the gas distribution project was defined generally and included all projects in the gas distribution industry. In fact, it was not possible to individually access the gas distribution projects. Analyzing and presenting solutions for each risk separately was another limitation of this study. In other words, considering each risk separately according to the structure of the industry was another limitation of this study.

The results were valid based on the reasonable method and experts’ confirmation and could be suitable for this industry. The technique presented in this study was based on information obtained from the Chaharmahal and Bakhtiari Province Gas Company, while due to the similar structure of provincial gas companies in gas technology and distribution, the method and results obtained in this study can be applied in all gas companies in the field of gas distribution.

The results of this study could decrease the cost of gas distribution industry by determining the most important technological risks of the gas company.

The aim of this study was to propose a new method of FMEA for ranking technological risks of the gas company by integrating Shannon Entropy approach and TOPSIS. The contribution of this study was the investigation of the technological risks of the gas company. In addition, in this paper, a new method was applied by the integration of FMEA and TOPSIS.

The main purpose of this paper is to propose a multi-objective model for assigning service/product to clustered customers. The main practical objectives of this model from the perspective of the bank are reduced cost and risk and increased customer satisfaction. Design/methodology/approach: In this paper, five indicators of recency, frequency, monetary, loan and deferred have been identified and customers have been clustered, accordingly using K-means approach. Then, a three-objective mathematical model has been designed to assign optimal service/product as response to customer. Finally the model has been solved by simulation based optimization.

In the case study, all information about five characteristics of customers was extracted from the database, 31953 customers were placed in seven clusters and the validity of these clusters was measured. A three-objective mathematical model was designed based on the characteristics of 13 types of bank products/services. Then, the simulation modeling solutions were improved using the simulated annealing algorithm. In this study, Weka and R-Studio, Arena and Longo were used for data mining, simulation and optimization, respectively.

The limitations of this study include inability of simulation instruments for drawing, solving all probable states (more scenarios) and solving the model for those states. It is recommended to develop the mathematical model with respect to customer, so that after problem solving, the bank would be able to make decision on providing services and products to its customers. Simultaneously, the objective functions would be fitted within their most reasonable states and ultimately, using a model, the parameters related to each product can be set for the new customer referring to the bank.

Products/services were assigned according to customer needs in a way that cost and risk were reduced and the utility of assignment was increased through the proposed model and simulating the behavior of each cluster of customers.

Paradigm shift in the banking industry is changing from e-banking to digital banking. In digital banking, assigning/customizing products/services, regarding the needs of customers, is very difficult .The banking industry is not well equipped to respond to the digital banking expectations of most consumers. One of the most important challenges of banks is recognizing customers, clustering and assigning a service/product to each of the different clusters. The main policy in the banking industry is to increase customer satisfaction and reduce cost and risk in sales service. Therefore, each customer should have a dedicated service/product.

In this paper, authors attempted to use one of the clustering approaches in multi-objective programming. In addition, they proposed an approach for assigning product/service to customer by simulating and analyzing the behavior of each customer cluster.

In this paper, the problem of scheduling jobs with non-identical sizes has been studied on a single-batch processing machine, in order to minimize the makespan. Using new lower bounds, a branch and bound algorithm has been proposed to solve the problem. In this algorithm, two new methods have been used to generate lower bounds and results have been compared with the existing lower bound in literature. In order to evaluate the performance of the proposed method, test problems have been randomly generated and branch and bound algorithm has been tested with different lower bounds on these cases. Findings indicated that when the size of the jobs is large compared to the capacity of the machine, the branch and bound algorithm with the new lower bound has the best performance. When the size of the jobs is small compared to the capacity of the machine (up to half the capacity of the machine), the algorithm with existing lower bound has better performance. In addition, when the size of the jobs is neither large nor small, the lower bounds provide the best performance.

Based on predictions, services are a key component of the growth of the global economy in future (Arnold et al. 2011). Acording to Jane and Kumar (2012), services play a critical role in a supply chain. Also, according to Wang et al. (2015), a "product" or "service" must exist in each supply chain which is produced by the upstream sectors and delivered to downstream. Recently due to increasing customer expectations, companies’ competition has been replaced by the supply chains competition and as a result, competition has been increased in the simultaneous supply of products and services. This has led to challenges in integrating companies and in coordinating the materials, information and financial flow that were previously overlooked. Accordingly, a new managerial philosophy has been developed known as Product-Service Supply Chain (PSSC) (Stanley & Wisner, 2002). This study seeks to develop a performance evaluation model for the product-service supply chain in the home appliance industry, which is finally solved using Adaptive Neuro-Fuzzy Inference System (ANFIS).

In this paper, performance evaluation constructs and criteria of service supply chain are identified by reviewing the literature and exploratory and confirmatory factor analysis and then, the performance evaluation of service supply chains in Iran's home appliance industry has been performed using these constructs, criteria and ANFIS. Findings and

Based on the findings, ten main extracted constructs can be suggested for the performance evaluation of the supply chain. They include "Operational Performance (OP)", "Strategic Performance (SP)", "Financial Performance (FP)", "Performance of Information and Communication Technology (PICT)", “Return Performance” (REP), “Risk Performance (RIP)”, “Logistic Performance (LP)”, “Market Performance (MP)”, “Internal Structure Performance (PIS)” and “Growth and Innovation Performance (PGI)”, among which, the Strategic Performance (SP) and Return Performance (REP) are the most important and the least important constructs, respectively.

Based on the findings, the following practical recommendations are suggested to the companies: Enhancing the demand forecasts performance and utilizing more appropriate methods and software to improve forecasts in demand and order management areas. Improving the return management status by increased attention and more investment in return management processes. Effective investment in service development management to enhance the R&D services performance. Utilizing risk management approaches and methods to identify and take preventive actions on the risks in the companies’ service supply chain.