International Journal of Industrial Engineering & Production Research

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Response surface methodology is a common tool in optimizing processes. It mainly concerns situations when there is only one response of interest. However, many designed experiments often involve simultaneous optimization of several quality characteristics. This is called a Multiresponse Surface Optimization problem. A common approach in dealing with these problems is to apply desirability function approach combined with an optimization algorithm to determine the best settings of control variables. As the response surfaces are often nonlinear and complex a number of meta-heuristic search techniques have been widely for optimizing the objective function. Amongst these techniques genetic algorithm, simulated annealing, tabu search and hybridization of them have drawn a great deal of attention so far. This study presents the use of harmony search algorithm for Multiresponse surface optimization. It is one of the recently developed meta heuristic algorithms that has been successfully applied to several engineering problems. This music inspired heuristic is conceptualized from the musical process of searching for a perfect state of harmony. The performance of the algorithm is evaluated by an example from the literature. Results indicate the efficiency and outperformance of the method in comparison with some previously used methods.

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control of production processes in an industrial environment needs the correct setting of input factors, so that output products with desirable characteristics will be resulted at minimum cost. Moreover, such systems havetomeetset of qualitycharacteristicstosatisfycustomer requirements.Identifyingthemosteffectivefactorsindesignoftheprocesswhichsupportcontinuousandcontinualimprovement isrecentlydiscussedfromdifferentviewpoints.Inthisstudy, we examined the quality engineering problems in which several characteristics and factors are to be analyzed through a simultaneous equations system. Besides, the several probabilistic covariates can be included to the proposed model. The main purpose of this model is to identify interrelations among exogenous and endogenous variables, which give important insight for systematic improvements of quality. At the end, the proposed approach is described analytically by a numerical example.

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The quality of manufactured products is characterized by many controllable quality factors. These factors should be optimized to reach high quality products. In this paper we try to find the controllable factors levels with minimum deviation from the target and with a least variation. To solve the problem a simple aggregation function is used to aggregate the multiple responses functions then an imperialist competitive algorithm is used to find the best level of each controllable variable. Moreover the problem has been better analyzed by Pareto optimal solution to release the aggregation function. Then the proposed multiple response imperialist competitive algorithm (MRICA) has been compared with Multiple objective Genetic Algorithm. The experimental results show efficiency of the proposed approach in both aggregation and non aggregation methods in optimization of the nonlinear multi-response programming.

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Abstract It is the purpose of this article to introduce a linear approximation technique for solving a fractional chance constrained programming (CC) problem. For this purpose, a fuzzy goal programming model of the equivalent deterministic form of the fractional chance constrained programming is provided and then the process of defuzzification and linearization of the problem is started. A sample problem is presented for clarification purposes.

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The paper is devoted to solution of some problems in nuclear power station generating unit intellectual control systems using genetic algorithms on the basis of control system model development, optimizations methods of their direct quality indices and improved integral quadratic estimates. Some mathematical vector models were obtained for control system multicriterion quality indices with due consideration of stability and quality indices criteria, this increasing the reliability of optimal control system synthesis. Optimal control systems with fuzzy controllers were synthesized for nuclear reactor, steam generator and steam turbine, thus allowing comparison between fuzzy controllers and traditional PID controllers. Mathematical models built for nuclear power station generating unit control systems, including nuclear reactor, steam generator, steam turbine and their control systems interacting under normal operational modes, which permitted to perform parametrical synthesis of system and to study various power unit control laws. On the basis of power unit control system models controllers were synthesized for normal operational modes.

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Response surface methodology (RSM) is a statistical method useful in the modeling and analysis of problems in which the response variable receives the influence of several independent variables, in order to determine which are the conditions under which should operate these variables to optimize a corrugated box production process. The purpose of this research is to create response surface models through regression on experimental data which has been reduced using DA to obtain optimal processing conditions. Studies carried out for corrugated sheet box manufacturing industries having man machine system revealed the contribution of many independent parameters on cycle time. The independent parameters include anthropometric data of workers, personal data, machine specification, workplace parameters, product specification, environmental conditions and mechanical properties of corrugated sheet. Their effect on response parameter cycle time is totally unknown. The developed model was simulated and optimized with the aid of MATLAB R2011a and the computed value for cycle time is obtained and compared with experimental value. The results obtained showed that the correlation R, adjusted R2 and RMS error were valid.

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Abstract One of the basic assumptions in hub covering problems is considering the covering radius as an exogenous parameter which cannot be controlled by the decision maker. Practically and in many real world cases with a negligible increase in costs, to increase the covering radii, it is possible to save the costs of establishing additional hub nodes. Change in problem parameters during the planning horizon is one of the key factors causing the results of theoretical models to be impractical in real world situations. To dissolve this problem in this paper a mathematical model for dynamic single allocation hub covering problem is proposed in which the covering radius of hub nodes is one of the decision variables. Also Due to NP-Hardness of the problem and huge computational time required to solve the problem optimally an effective genetic algorithm with dynamic operators is proposed afterwards. Computational results show the satisfying performance of the proposed genetic algorithm in achieving satisfactory results in a reasonable time. Keywords: hub location problem, dynamic hub covering problem, flexible covering radius, dynamic genetic algorithm.

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There are many numerous methods for solving large-scale problems in which some of them are very flexible and efficient in both linear and non-linear cases. League championship algorithm is such algorithm which may be used in the mentioned problems. In the current paper, a new play-off approach will be adapted on league championship algorithm for solving large-scale problems. The proposed algorithm will be used for solving large-scale solving support vector machine model which is a quadratic optimization problem and cannot be solved in a non polynomial time using exact algorithms or optimally using traditional heuristic ones in large scale sizes. The efficiency of the new algorithm will be compared to traditional one in terms of the optimality and time measures. The superiority of the algorithm can be compared versus older version.

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Empirical studies show that there is stronger dependency between large losses than large profit in financial market, which undermine the performance of using symmetric distribution for modeling these asymmetric. That is why the assuming normal joint distribution of returns is not suitable because of considering the linier dependence, and can be lead to inappropriate estimate of VaR. Copula theory is basic tool for multivariate modeling, which is defined by using marginal and dependencies between variables joint distribution function. In addition, Copulas are able to explain and describe of complex multiple dependencies structures such as non-linear dependence. Therefore, in this study, by combining symmetric and asymmetric GARCH model for modeling the marginal distribution of variables and Copula functions for modeling financial data and also use of DCC model to determine the dynamic correlation structure between assets, try to estimate the Value at Risk of investment portfolio consists of five active index In Tehran Stock Exchange. The results demonstrate excellence of GJR-GARCH(1,1) with the distribution of t-student for marginal distribution. t-Copula model, estimates the Value at Risk model less than the Gaussian Copula in all cases.

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    Given the increasing number of documents, sites, online sources, and the users’ desire to quickly access information, automatic textual summarization has caught the attention of many researchers in this field. Researchers have presented different methods for text summarization as well as a useful summary of those texts including relevant document sentences. This study selects extractive method out of different summarizing methods (e.g. abstract method). Extractive method involves summarizing text through objective extraction of some parts of a text like word, sentence, and paragraph. A summarization issue would be unsolvable by exact methods in a reasonable time with considering documents with high amount of information (NP complete). These kinds of issues are usually solved using metaheuristic methods. A biogeography-based optimization algorithm (BBO), which is a new metaheuristic method in the domain of extractive text summarization, is used in this article. 

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Natural disasters and crisis are inevitable and each year impose destructive effects on human as injuries and damage to property. In natural  disasters and after the outbreak of the crisis, demand for logistical goods and services increase. Effective distribution of emergency aid could have a significant role in minimizing the damage and fatal accident. In this study, a three-level relief chain including a number of suppliers in fixed locations, candidate distribution centers and affected areas at certain points are considered. For this purpose a mixed integer nonlinear programming model is proposed for open transportation location routing problem by considering split delivery of demand. In order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed mode. The objectives of the proposed model include total cost minimization, minimization of the maximum travel time of
vehicles and minimization of unmet demands. In order to solve the problem of the proposed model, fuzzy multi-objective planning is used. For efficiency and effectiveness of the proposed model and solution approach, several numerical examples are studied. Computational results show the effectiveness and efficiency of the model and the proposed approach.

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CNC turning is widely used as a manufacturing process through which unwanted material is removed to get the high degree of surface rough. In this research article, Taguchi technique was coupled with grey relation analysis (GRA) to optimize the turning parameters for simultaneous improvement of productivity, average surface roughness (R_a), and root mean square roughness (R_q).Taguchi technique L27 (3⁴) orthogonal array was used in this experimental work. Feed, speed, and depth of cut were considered as the controllable process parameters. average roughness (R_a), root mean square roughness (R_q),and material removal rate (MRR) were considered as the performance characteristic and from TGRA result, it was revealed that the optimum combinational parameters for multi-performance, based on mean response values and confirmation experiments with Taguchi-based GRA is A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values obtained from experimental investigations for R_a was 6.86 μm, and MRR was 20690.31 mm³/s,further analysis of variance(ANOVA) were applied and it was identified that the depth of cut having most significant effect followed by speed and feed for multiresponse optimization. The percentage contribution of depth of cut was 38.28.71 %, speed was 11.89 % and feed was 8.466 %.
CNC turning is widely used as a manufacturing process through which unwanted material is removed to get a high degree of surface roughness. In this research article, Taguchi technique was coupled with grey relation analysis (GRA) to optimize the turning parameters for simultaneous improvement of productivity, the average surface roughness (R_a), and root means square roughness (R_q). Taguchi technique L27 (3⁴) orthogonal array was used in this experimental work. Feed, speed, and depth of cut were considered as the controllable process parameters. average roughness (R_a), root mean square roughness (R_q), and material removal rate (MRR) were considered as the performance characteristic and from TGRA result, it was revealed that the optimum combinational parameters for multi-performance, based on mean response values and confirmation experiments with Taguchi-based GRA is A1B1C1 (Vc=400 rpm, f=0.06 mm/rev, and DOC=0.5 mm). The optimum values obtained from experimental investigations for R_a was 6.86 μm, and MRR was 20690.31 mm³/s, further analysis of variance(ANOVA) were applied and it was identified that the depth of cut having most significant effect followed by speed and feed for multiresponse optimization. The percentage contribution of the depth of cut was 38.28.71 %, speed was 11.89 % and feed was 8.466 %.

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Practically, Islamic banking in Iran is not much different from conventional banking principles. Many paradigms of the commercial banking are considered in the Islamic-Iranian banking. Owing to the fact that asset and liability optimization is an important issue in the banking industry, the present paper investigates the balance sheet and income statement to constitute a structure for measuring each asset’s risk. The author uses the method of multiple objective programming to solve the problem of commercial bank's diversified pursuit of low risk and high profit by considering the so-called duration constraint. To test the proposed model, the data were collected from an Iranian commercial bank named Mellat bank from June 2009 to December 2016. The results suggest that Mellat bank, as the biggest private bank in Iran, should reform its asset-liability allocation to achieve the optimal level.
 

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In this paper, an inventory-routing problem for a network of appliance repair service is discussed including several repair depots and customers. The customer in this network makes a demand to have his/her faulty appliance repaired. Then, the repairman is assigned to the demand based on the skill needed for repairing of appliance differing for each one. The assigned repairman picks up the faulty appliance from the customer place using the vehicle for transferring faulty appliances to repair depot. The vehicle for picking up and delivering the appliances has a maximum capacity. Additionally, the repair depot needs spare parts to repair the faulty appliances that is supplied either by the supplier or lateral transshipment from the other depots. The capacitated vehicle inventory-routing problem with simultaneous pickup and delivery is NP-hard which needs special optimization procedure. Regarding the skill of repairman, it becomes more complex. Many solution approaches have been provided so far which have their pros and cons to deal with. In this study, an augmented angle-based sweep method is developed to cluster nodes for solving the problem. Finally, the heuristic is used in the main body of genetic algorithm with special representation.

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Multi-manned assembly line balancing problems (MALBPs) can be usually found in plants producing large-sized high-volume products such as automobiles and trucks. In this paper, a cost-oriented version of MALBPs, namely, CMALBP, is addressed. This class of problems may arise in final assembly lines of products in which the manufacturing process is very labor-intensive. Since CMALBP is NP-Hard, a heuristic approach based on a tabu search algorithm is developed to solve the problem. The proposed algorithm uses two neighborhood generation mechanisms, namely swap and mutation, that effectively collaborate with each other to build new feasible solutions; moreover, two separate tabu lists (associated with the two generation mechanisms) are used to check if moving to a new generated neighbor solution is forbidden or allowed. To examine the efficiency of the proposed algorithm, some experimental instances are collected from the literature and solved. The obtained results show the effectiveness of the proposed tabu search approach.

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In this paper, a novel data driven approach for improving the performance of wastewater management and pumping system is proposed, which is getting knowledge from data mining methods as the input parameters of optimization problem to be solved in nonlinear programming environment. As the first step, we used CART classifier decision tree to classify the operation mode -number of active pumps- based on the historical data of the Austin-Texas infrastructure. Then SOM is applied for clustering customers and selecting the most important features that might have effect on consumption pattern. Furthermore, the extracted features will be fed to Levenberg-Marquardt (LM) neural network which will predict the required outflow rate of the period for each operation mode, classified by CART. The result show that F-measure of the prediction is 90%, 88%, 84% for each operation mode 1,2,3, respectively. Finally, the nonlinear optimization problem is developed based on the data and features extracted from previous steps, and it is solved by artificial immune algorithm. We have compared the result of the optimization model with observed data, and it shows that our model can save up to 2%-8% of outflow rate and wastewater, which is significant improvement in the performance of pumping system.

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Abstract: Wire Electrical Discharge Machining (WEDM) is a non-traditional thermal machining process used to manufacture irregularly profiled parts. Machining of ductile cast iron (ASTM A536) under several machining factors, which affect the WEDM process, is presented. The considered machining factors are pulse on time (T_on), pulse off (T_off), peak current (Ip), voltage (V), and wire speed (S). To optimize the machining factors, their setting is performed via an experimental design using the Taguchi method. The optimization objective is to achieve maximum Material Removal Rate (MRR) and minimum Surface Roughness (SR). Additionally, the analysis of variance (ANOVA) is used to identify the most significant factor. Also, a regression analysis is carried out to forecast the MRR and SR dependent on defined machining factors. Depending on consequences, the best regulation factors for reaching the maximum MRR are T_on = 32 μs, T_off = 8 μs, Ip = 4 A, S = 40 mm/min. and V = 70 volt. Whereas, the optimal control factors that achieve the minimum SR is T_on = 8 μs, T_off = 8 μs, Ip = 2 A, S = 20 mm/min, and V= 30 volt. It is hypothesized that the perfect combination of control factors that achieves minimum SR and maximum MRR is T_on = 8 μs, T_off = 8 μs, Ip=5 A, S=50 mm/min. The microstructure of the machined surface in the optimal machining conditions shows a very narrow recast layer at the top of the machined surface.

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In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy numbers, a similarity criterion based on the intersection region of the fuzzy numbers is used.  The performance of the suggested clustering method has been experimented on both benchmark and artificial datasets. These datasets are used in the fuzzy form. The experiential results represent that the suggested clustering method with fuzzy cluster centers can cluster triangular fuzzy datasets like other standard uncertain data clustering methods. Experimental results demonstrate that, in almost all datasets, the proposed clustering method provides better results in accuracy when compared to Uncertain K-Means and Uncertain K-medoids algorithms.

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Previous studies have reported that trust is the main issue that needs to be resolving. (McKnight & Chervany, 2001). Trust proficiently leads people or organizations to acquire maximized benefits and potentially gives an organization a competitive advantage in markets, communities, and hierarchies (Robbins (2016), Semuel & Chandra (2014). The extent of this study revolves around develop consumer trust in the quality of cosmetic product scope.  Researchers have shown an increased interest in the cosmetics field as the average annual growth in the last twenty years is 4.5% and the rate of growth presume to continue over 3%.   The objectives of this research are to (1) understand factor involves in the process of build consumers’ confidence and trust virtually in offline and online business, (2) to determine the prominent information need to be an underline in marketing strategy, and (3) to understand how trust can affect consumer preference on cosmetics product. This study underlined cosmetic price, cosmetic brand name, and cosmetic country of origin are the prominent information that needs to underline in marketing strategy. Important issues were addressed and recommendations were made for prospect research.