Journal of Advances in Industrial Engineering
Volume:52 Issue: 2, 2018

Planning of companies profits through customer satisfaction management according to 4 main steps as: 1. Recognize the constitutive components of customer satisfaction and measure them, 2 . The integration of components and measurement overall customer satisfaction , 3. reviews how the impact overall customer satisfaction on the level of performance and activity company And (4) identify variables and the main parameters capable of maneuver on customer satisfaction and finally compiled program the company's profits. The main focus of this research has been focused on the third step that's mean modeling the effects of customer satisfaction on the level of performance and the company's profitability and the method used to investigate this topic and modeling it econometric approach and its Case Study is Logan Pars Khodro. It is necessary to mention in modeling the subject in addition to variable of customer satisfaction 3 The other variable 1. Household income, 2. prices Logan and 3. price of rival car (Peugeot 206) considered. The results showed that the variables of customer satisfaction, household income and the price of Peugeot 206 with sales and a result of profits have direct relationship and price Logan with company profits has indirect relationship. Variable of customer satisfaction from after-sales service has the greatest amount of influence on its profitability. This means that each unit increase customer satisfaction, 26.5 unit sales increases. So company of Pars Khodro can with invest and pay more attention to these factors increase the the profit from the sale of car. To run this research of software EViews and Spss is used.

Planning of production, distribution, collection and recovery of used products plays an important role in reduction of environmental burdens of different industries. To this aim, many researchers have presented efficient models for planning of forward-reverse supply chains. The most of presented models have covered strategic issues in this area and neglected the tactical and operational level decision making problem. In this paper, a comprehensive and integrated mathematical programming model to production and distribution planning in a closed-loop supply chain is presented. In the proposed model, the customers are divided into three groups including new product customers, recovered product customers, and raw material customers. Due to economy of scale principal, hybrid production/recovery centers and hybrid distribution/redistribution centers are considered instead of separate facilities. The acquired results show the ability of the propose model to production and distribution planning in a closed-loop supply chain.

Project portfolio management consists of repetitive cycles of project evaluation, selection and execution. Project portfolio selection is the main part of project portfolio management. Organization identifies and prioritizes projects that are mostly aligned with stated strategic goals while considering real-world restrictions and considerations that are related to scheduling and resource allocation. In this paper, a new comprehensive model for the project portfolio selection problem over a planning horizon with multiple periods by maximizing profit is developed in which simultaneous selection, scheduling and resource allocation of projects are considered. The model incorporates project interdependency and strategies of project divisibility, reinvestment, external investment and resourcing in different ways at the same time in choosing the best execution schedule for the projects in real-life applications. In addition, resource and budget constraints, cardinality restriction, precedence relationship and scheduling, setup and resource costs are included in the model. Numerical examples under eight scenarios are presented to highlight the characteristics of the proposed model.

The increasing trend in happening natural disasters mandates developing appropriate contingency plans to deal with them. in this paper, a goal programming based model is developed for an integrated pre- and post-disaster operations management while considering the restoration of disrupted routed and warehouses. The model accounts for epistemic uncertainty in input data through a hybrid two-stage scenario-based possibilistic-stochastic programming model. To validate the proposed model and its practicality, an illustrative example is also presented and its numerical results are assessed.

Increasing competition, the extension of customer expectations and their frequent demand and rapid technological changes have been caused the rapid development of today's manufacturer’s obligations. So that any deficiencies and deviations in the performance of the products lead to loss of manufacturer’s market share. To remove the problems and improve the quality of products, identifying failures must take into consideration. In this study, potential failures are identified by implementing failure modes and effects analysis, with the help of the cross functional team in the car spare parts industry. Then, to achieve results in according to the facts and to remove problem in risk priority number computation, and the identified interrelationships between failures are taken into consideration. Because, the occurrence and control of any failure can affect other failures. In other words, the prioritization of failures based on the fuzzy cognitive map is done with regard to the three criteria including severity, occurrence and detection as well as interrelation between failures. A case study on auto parts manufacturing industry is used to show the abilities of integrating "failure modes and effects analysis-fuzzy cognitive map" for prioritizing failures.

A necessary condition for the achievement of organizational success is using of a new customized production system and to fit the new customized production system’s tools with old processes of the organization. In this paper we present a model for customization of Renault Production System (SPR), to an Iranian automaker company. After defining the framework of customization alternatives, including changes in the company's current processes, changes in the SPR or both, by using Nominal Group Technique, key criteria which are necessary to evaluate and prioritize alternatives, has been determined . The relationship map of alternatives and criteria were determined using DEMATEL techniques applied in MATLAB software. Finally weights of the criteria and alternatives were calculated using ANP method. The results show that the alternatives representing minimal changes in SPR and mild changes in the organization's current processes is selected.

"Taguchi" method is a conventional method for quality control in offline mode. This method is applied to design and select the best level of parameters for designing a better method to make high quality products. Taguchi method is one-response and it is a disadvantage for it. In the real world applications, there are several problems with some indicators of quality. Therefore, Taguchi method is not appropriate for optimizing multi-response problems and we need to an engineering and optimizing method to judge the best combination of parameters. On the other hand, due to some uncontrollable factors or the impossibility of experimental conditions, only some of experiments are implemented and a large number of them are incomplete. In this paper, to simulate the remaining experiments the Back-Propagation neural network is used. To overcome one-response problem in Taguchi method, the data envelopment analysis (DEA) is used. Since the results obtained from the neural network are uncertain, DEA model with interval grey data is used. To implement this approach and to identify effective factors, the wear characteristics of composite material PBT, the combined approach based on Taguchi method, neural network and DEA is used and the results will be analyzed.

This study adopts an integrated performance measurement and prediction model based on a combination of earned value management approach and the learning curve theory under risk condition. The present research to be presented has two main parts: One that concerns the project performance measurement and a second that focuses on forecasting performance indicators in terms of time and cost of the project subject to the errors and risks. The contributions of the present study are threefold. First, this study extends to the traditional performance measurement models, which focuses only on forecasting time at completion, by extending the performance measurement domain to analyse both time and cost. Second, the learning curve models are explicitly used as a basis to assess the nonlinear effect of learning on the performance. Novel risk performance metrics are proposed and adopted for knowledge-based projects. Third, compared with classic deterministic and static performance measurement models, the proposed performance assessment analysis utilizes the Kalman-filter to predict the final time and cost performance accurately by taking into account the probabilistic risk factors and the errors in the performance forecasting procedures. The validity of the integrated performance measurement model is justified based on a case study. The computational results demonstrate that the developed performance measurement framework affords more accurate forecasts for the future performance compared with the traditional deterministic earned value methodology. The integrated performance measurement model developed in this study affords probabilistic prediction bounds and generated low errors than those achieved in classic EVM.

In this paper, a new model for managing multi-product multi-level supply chain using predictive control of Scenario-based model is presented which it can handle supply chain with Stochastic programming of the uncertainty, which is caused, by demand and random lead time. In addition, it guarantees a certain level of customer service in the form of horizon with 95% probability. Probabilistic waiting time may lead to lash effect throughout the entire chain And causes shortages at different levels therefore cost of facing shortages is considered in the model. After modeling problem is solved in deterministic and Probabilistic state and to solve larger problems Imperialist competitive algorithm is used. The results indicate that SCMPC is computationally very efficient and offers significant advantage over the robust and stochastic optimization.

Competitive location deals with the problem of locating facilities to provide the service to the customers where other competing facilities offering the same services. Many competitive location models are presented in the literature. However, the literature on competitive location considering reverse logistic in closed-loop supply chain is rather scarce. Also, there are two main approaches in the related literature; increasing profit, and increasing market share of the service centers. Most of the studies in the literature consider one of these issues as the objective function. But, in this study it is shown that these objectives may have conflict with each other. Therefore, addressing these issues simultaneously is important for a successful design of supply chain. This study addresses a novel bi-objective competitive facility location problem in a closed-loop supply chain in a manner that increasing profit and market share are considered simultaneously. On the other hand, in the real world, the customer may choose facilities that are not necessarily close to them, because of the greater attractiveness of other facilities. Hence, in this study, a new relationship is introduced to determine the attractiveness of each potential center for customers based on the distance and quality of service centers. To solve the proposed model and tackle the computational complexity of the proposed model, two approaches are developed: LP-metric and NSGAII. Furthermore, multiple numerical instances are defined and solved with exact approach of LP-metric through GAMS and the results are evaluated in order to validate the accuracy of the proposed model. The best value of “P” in LP-metric approach is also obtained via analyzing the results. Furthermore, the performance of the NSGAII is analyzed by comparing with exact solution of LP-metric through GAMS. The results indicate that the proposed NSGAII is more appropriate than LP-metric and consequently solving the large size problems through GAMS in a logical time is impossible.

After deregulation in electricity markets, huge amount of studies were done especially in designing new systems and energy pricing in order to improve efficiency of power systems and increase investors’ profit. Investment’s profit could be increased by better contracts and better price bidding for buying and selling energy in electricity market, as a consequence price forecasting is essential. The main goal of this paper is to predict price of electricity in Iran’s electricity market, using combined of fuzzy-neural network with Particle Swarm Optimization (PSO). In this paper, past prices, past loads, working and nonworking days, day hours and effect of seasons in 1394 have been taken into account as effective factors in forecasting mechanism. Combined model, is more precise in contrast to other methods like ARIMA,neural network, neural-fuzzy network and combination of fuzzy-neural with genetic algorithm. In following, process of price fluctuations has discussed for improving and increasing effectiveness of bidding. Results of simulation revealed that price forecasting is much more precise with price process mechanism.

Multi-project scheduling problem is one of the most important problems in the project scheduling applications which has attracted considerable attention in the past decades. Due to the importance of resources in the multi-project scheduling problem, the resource sharing policy is applied in this research. In addition, in each project, the activities durations are subject to the considerable uncertainty. According to the rapid changing of the environment and also the uniqueness of the project, one cannot set the probability distribution for the activity durations with certainty. In addition, the problem in multi-project scale needs more conservative approach while facing with uncertainty. Therefore, the robust optimization approach is applied in this paper in such a way that the maximum total weighted tardiness of the projects would become minimum. The robust resource-constrained multi-project scheduling problem (RRCMPSP) is investigated in this paper as a two stage models. The scenario relaxation algorithm is applied while resulting the exact solution for the RRCMPSP and tested on the examples produces by the RanGen. So in this paper, one optimal structure containing all of the projects for the multi-project problem is obtained in such a way that the maximum differences between the finish time of projects and their due date would become minimum.