International Journal of Industrial Engineering & Production Research

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  Although control charts are very common to monitoring process changes, they usually do not indicate the real time of the changes. Identifying the real time of the process changes is known as change-point estimation problem. There are a number of change point models in the literature however most of the existing approaches are dedicated to normal processes. In this paper we propose a novel approach based on clustering techniques to estimate Shewhart control chart change-point when a sustained shift is occurrs in the process mean. For this purpose we devise a new clustering mechanism, a new similarity measure and a new objective function. The proposed approach is not only capable of detecting process change-points, but also automatically estimates the true values of the out-of-control parameters of the process. We also compare the performance of the proposed approach with existing methods.

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Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrated via real data of a purchase system in order to simulate the system’s behavior.

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In nowadays world competitive market, on account of the development of electronic media and its influence on shortening distances, companies require some core competencies in order to be able to compete with numerous competitors in industry and sustain their situation in such a market. In addition companies achieve this target are those which their processes perform great and exploit from competitive price, quality, guarantee, etc. Since some parameters such as price and quality are so dependent on the performance of company supply chain management, so the results can highly impress the final price and quality of products. One of the main processes of supply chain management is supplier selection process which its accurate implementation can dramatically increase company competitiveness. In presented article two layers of suppliers have been considered as a chain of suppliers. First layer suppliers are evaluated by two groups of criteria which the first one encompasses criteria belongs to first layer suppliers features and the second group contains criteria belong to the characteristics of second layer suppliers. One of the criteria is the performance of second layer suppliers against environmental issues. Then the proposed approach is solved by a method combined of concepts of fuzzy set theory (FST) and linear programming (LP) which has been nourished by real data extracted from an engineering design and supplying parts company. At the end results reveal the high importance of considering second layer suppliers features as a criteria for selecting the best supplier.

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This paper presents a new formulation for warehouse inventory management in a stochastic situation. The primary source of this formulation is derived from FP model, which has been proposed by Fletcher and Ponnambalam for reservoir management. The new proposed mathematical model is based on the first and the second moments of storage as a stochastic variable. Using this model, the expected value of storage, the variance of storage, and the optimal ordering policies are determined. Moreover, the probability of within containment, surplus, and shortage are computable without adding any new variables. To validate the optimization model, a Monte Carlo simulation is used. Furthermore, to evaluate the performance of the optimal FP policy, It is compared to (s*,S*) policy, as a very popular policy used in the literature, in terms of the expected total annual cost and the service level. It is also demonstrated that the FP policy has a superior performances than (s*,S*) policy.

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Identification of a real time of a change in a process, when an out-of-control signal is present is significant. This may reduce costs of defective products as well as the time of exploring and fixing the cause of defects. Another popular topic in the Statistical Process Control (SPC) is profile monitoring, where knowing the distribution of one or more quality characteristics may not be appropriate for discussing the quality of processes or products. One, rather, uses a relationship between a response variable and one or more explanatory variable for this purpose. In this paper, the concept of Maximum Likelihood Estimator (MLE) applied to estimate of the change point in binary profiles, when the type of change is drift. Simulation studies are provided to evaluate the effectiveness of the change point estimator.

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The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA.

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Abstract
Production and distribution network (PDN) planning in multi-stage status is commonly complex. These conditions cause significant amount of uncertainty relating to demand and lead time. In this study, we introduce a PDN to deliver the products to customers in the least time and optimize the total cost of the network, simultaneously. The proposed network is four stage PDN including suppliers, producers, potential entrepots, retailers and customers with multi time period horizon with allowable shortage. A mixed integer programming model with minimizing total cost of the system and minimizing total delivery lead time is designed. We present a novel heuristic method called selective firefly algorithm (SFA) in order to solve several sized especially real world instances. In SFA, each firefly recognizes all better fireflies with more brightness and analyses its brightness change before moving, tacitly. Then, the firefly that makes best change is selected and initial firefly moves toward the selected firefly. Finally, the performance of the proposed algorithm is examined with solving several sized instances. The results indicate the adequate performance of the proposed algorithm.

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In this study, the electrochemical machining (ECM) of the 304 stainless steel with the response surface methodology (RSM) approach for designing, analyzing and mathematical modeling was used. The electrolyte type, concentration and current parameters were considered as the machining parameters. The mathematical model for the responses was presented and based on the type of electrolyte including NaCl, NaNO₃ and KCl. The results showed that the current has the highest effect on Surface Roughness (SR) and Material Removal Rates (MRR) and respectively it improves them to 0.465μm and 0.425gr/min. The electrolyte concentration has the highest effect on Over Cut (OC) and causes to increase its values. Under the conditions of NaCl electrolyte, 1 molarity concentration and 55 A current, the optimum condition 0.4006 gr/min MRR, 0.75 mm OC and 0.465mm SR was achieved. 

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This study proposes an outsourcing mechanism for marketing plans in small and medium-sized enterprises (SMEs) using knowledge sharing.  SMEs may not be able to establish a marketing department due to operational expenditures. Therefore, organizing a marketing agency to handle marketing concerns of SMEs is significant. First, SMEs are clustered regarding their activity area, products, services, and etc. Then, for SMEs in a same cluster, the marketing agency should collect the required information to process marketing actions. The challenge is how to gather and deposit information in common among SMEs in a cluster. Knowledge sharing is one of the stages of knowledge management helping to distribute information among elements of a system. Thus, the process of knowledge sharing is investigated in outsourcing marketing activities. Accordingly, a questionnaire was prepared based on research hypotheses. After confirmation of validity and reliability, the questionnaire was given to managers and employees of furniture companies in Tehran province, Iran. The collected questionnaires were analyzed using SPSS software version 24.0.  According to the statistical sample of the research, descriptive statistics, and inferential statistics were analyzed. Descriptive statistics were used to describe the demographic characteristics of respondents. The inferential statistics, Kolmogorov-Smirnov test was used first for the test of normality of data. Considering normality of the data, T-student test was used to obtain the relationship between variables. Finally, the results of the research showed that there is a positive and significant relationship between outsourcing marketing in SMEs using knowledge sharing. Therefore, it is suggested that SMEs pay particular attention to outsourcing their marketing activities using knowledge sharing.

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Nowadays, supply chain management (SCM) is an interesting problem that has attracted the attention of many researchers. Transportation network design is one of the most important fields of SCM. In this paper, a logistics network design is considered to optimize the total cost and increase the network stability and resiliency. First, a mixed integer nonlinear programming model (MINLP) is formulated to minimize the transportation time and transportation cost of products. The proposed model consists of two main stages.
One is a normal stage that minimizes the transportation and holding costs, all manufacturers are also assumed to be healthy and in service. In this stage, the quantity of customer demand met by each manufacturer is eventually determined.
The second is the resilience stage. A method is presented by creating an information network in this supply chain for achieving the resilient and sustainable production and distribution chain that, if some manufacturers break down or stop production, Using the Restarting and load sharing scenarios in the reactive approach to increase resilience with accepting the costs associated with it in the supply network and return to the original state in the shortest possible time, the consequences of accidental failure and shutdown of production units are managed.
Two capacities are also provided for each manufacturer

• Normal capacity to meet the producer's own demand
• Load sharing capacity, Determine the empty capacity and increase the capacity of alternative units to meet the out-of-service units demand

In order to solve the model, we used GAMS & Matlab software to find the optimal solutions. A hybrid priority-based Non-dominated Sorting Genetic Algorithms (NSGA-II) and Sub-population Genetic Algorithm (SPGA- II) is provided in two phases to find the optimal solutions. The solutions are represented with a priority matrix and an Allocated vector. To compare the efficiency of two algorithms several criteria are used such as NPS, CS and HV. Several Sample problems are generated and solved that show the Sub-population Genetic Algorithm (SPGA- II) can find good solutions in a reasonable time limit.

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Brand loyalty is driven by share, comments, online review, like, and dislike on the social media platform of specific brands. The study empirically assessed with the influence of the dimensions of brand's personality as a moderator on SMBC and brand loyalty among customers’ popular fashion brands. The Aaker Brand Personality Scale used to measure the personality of fashion brands. Online brand personality can exist in the same way as offline brands. This means that social media has brand personalities, and these can influence consumer perceptions in different ways. This research utilized a quantitative approach in which questionnaires was distributed to SMBC users as the research population. The research was performed Structural Equation Modeling using IBM SPSS Statistics 23 software and Smart PLS 3.2.9 to analyze the data. The findings were help brands to make marketing plans to influence any type of unsatisfactory situations.
 

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To develop a system for specific purpose, it needs to estimate its parameters (parameterization). It can be used in different fields like engineering, industry etc. In this work, authors used adaptive algorithm to model a system that is applicable in industry for control. This adaptive model is non-linear where its estimation is based on kernel based Least-mean square (LMS) algorithm. The kernel used as Polynomial and Gaussian. As the system is nonlinear polynomial kernel-based algorithm fails to prove its efficacy, though it is of low complexity approach. Gaussian kernel-based application for nonlinear system control performance better as compared to polynomial kernel. Further its complexity is reduced and used for faster performance. The result shows its performance in form of MSE, MAE, RMSE for identification and control that is very useful in industrial application.
 

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