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A utility function is an important tool for representing a DM’s preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model. © 2017, The Author(s)  

We propose an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, we consider... 

Here, an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost is proposed. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, time is considered to be a triangular fuzzy number and apply an expert system to infer the cost. The expert system based on fuzzy rule backpropagation network to configure the rules for estimating the cost under uncertainty is proposed. A multiple linear regression model is applied to analyze the... 

We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty  

We propose a cost estimation model based on a fuzzy rule back-propagation network (BPN), configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach we determine the optimal path in the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty  

We propose an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology, and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An Automated Guided Vehicle (AGV) functions as a material handling device through the manufacturing network. To account for uncertainty, we... 

Availability of a system is a crucial factor for planning and optimization. The concept is more challenging for modern systems such as robots and autonomous systems consisting of a complex configuration of components. In this paper, a reliability evaluation framework is developed for a system of binary state autonomous robots in an automated manufacturing environment. In this framework, the concepts in functional block diagram, table of truth, and sum of state are employed simultaneously to develop a binary state reliability model. Due to inefficacy of the method for larger number of components involved in complex systems, an extension of the Bernoulli trials is proposed. In an... 

Current design practice is usually to produce a safety system which meets a target level of performance that is deemed acceptable by the regulators. Safety systems are designed to prevent or alleviate the consequences of potentially hazardous events. In many modern industries the failure of such systems can lead to whole system breakdown. In reliability analysis of complex systems involving multiple components, it is assumed that the components have different failure rates with certain probabilities. This leads into extensive computational efforts involved in using the commonly employed generating function (GF) and the recursive algorithm to obtain reliability of systems consisting of a... 

In this paper, minimal paths and cuts technique is developed to handle fault tree analysis (FTA) on the critical components of industrial robots. This analysis is integrated with the reliability block diagram (RBD) approach in order to investigate the robot system reliability. The model is implemented in a complex advanced manufacturing system having autonomous guided vehicles (AGVs) as material handling devices. FTA grants cause and effects and hierarchical properties to the model. On the other hand, RBD simplifies the complex system of the AGVs for reliability evaluation. The results show that due to the filtering of the paths in a manufacturing system for AGVs, the reliability is highly... 

Purpose - This paper aims to conduct a comprehensive fault tree analysis (FTA) on the critical components of industrial robots. This analysis is integrated with the reliability block diagram (RBD) approach to investigate the robot system reliability. Design/methodology/approach - For practical implementation, a particular autonomous guided vehicle (AGV) system was first modeled. Then, FTA was adopted to model the causes of failures, enabling the probability of success to be determined. In addition, RBD was used to simplify the complex system of the AGV for reliability evaluation purpose. Findings - Hazard decision tree (HDT) was configured to compute the hazards of each component and the... 

Current design practice is usually to produce a safety system which meets a target level of performance that is deemed acceptable by the regulators. Safety systems are designed to prevent or alleviate the consequences of potentially hazardous events. In many modern industries the failure of such systems can lead to whole system breakdown. In reliability analysis of complex systems involving multiple components, it is assumed that the components have different failure rates with certain probabilities. This leads into extensive computational efforts involved in using the commonly employed generating function (GF) and the recursive algorithm to obtain reliability of systems consisting of a... 

We concern with both time and cost optimization in an automated manufacturing system. In our proposed system, material handling is carried out by automated guided vehicles and robots for the manufacturing functions. In this way, defect rates, breakdown times, waiting time, processing time, and certain other parameters are not expected to be deterministic. Therefore, we apply stochastic programming to optimize the production time and material handling cost. The proposed stochastic program is a nonlinear model, for this reason we apply a successive linear programming (SLP) technique for its optimization. Numerical test results point to the inefficacy of the proposed optimization method for... 

Purpose – The purpose of this paper is to propose a Meta modeling based on regression, neural network, and clustering to analyze the job satisfaction factors and improvement policy making. Design/methodology/approach – Since any job satisfaction evaluation supposes to improve the status by prescribing specific strategies to be performed in the organization, proposing applicable strategies is decisively important. Task demand, social structure and leader-member exchange (LMX) are general applications easily conceptualized while proposing job satisfaction improvement strategies. Findings – On the basis of these empirical findings, the authors first aim to identify relationships between LMX,... 

This work considers scheduling problems minding the setup and removal times of jobs rather than processing times. For some production systems, setup times and removal times are so important to be considered independent of processing times. In general, jobs are performed according to the automatic machine processing in production systems, and the processing times are considered to be constant regardless of the process sequence. As the human factor can influence the setup and removal times, when the setup process is repetitive the setup times decreases. This fact is considered as learning effect in scheduling literature. In this study, a bi-criteria m-identical parallel machines scheduling... 

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems when suppliers have capacity or other different constraints, two questions persist: which suppliers are the best and how much should be purchased from a selected supplier? Here, we propose an integrated approach of analytic hierarchy process (AHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and multi-objective nonlinear programming to consider both tangible and intangible factors in choosing the best suppliers and define the optimum quantities among selected suppliers to maximize the total value of purchasing and minimize the budget, total... 

Four 5-, 10-, 20- and 30-story moment frames, representing low-, mid-, and two high-rise structures, were subjected to a great number of idealized directivity pulses. The amplitudes and periods of pulses vary from 0.02 g to 1.0 g and 0.5 to 12 sec, respectively. Over 1400 nonlinear dynamic analyses of low- to high-rise moment frames were performed which were feasible through using modified fish-bone model. The distribution of interstory drift along the height was studied and two applied contours were proposed: (i) the maximum interstory drift contour, and (ii) the critical story contour. These contours were demonstrated versus the ratio of natural period of the structure to the pulse period... 

In this article singular points of a parallel manipulator are obtained based on a novel geometrical method. Here we introduce the constrained plain method (CPM) and some of its application in parallel mechanism. Given the definition of constraint plane (CP) and infinite constraint plane (ICP) the dependency conditions of constraints is achieved with the use of a new theorem based on the Ceva geometrical theorem. The direction of angular velocity of a body is achieved by having three ICPs with the use of another theorem. Finally, with the use of the above two novel theorems singularities of the 3UPF_PU mechanism are obtained. It should be emphasized that this method is completely geometrical,... 

An analytical model is proposed to study the effect of particle size on melting enthalpy and entropy of metallic nanoparticles (NPs). The Mott's and Regel's equations for melting entropy in the combination of core average coordination number (CAC) and surface average coordination number (SAC) of freestanding NPs are considered. Clusters of icosahedral (IC), body centered cubic (BCC), and body centered tetragonal (BCT) structure without any vacancies and defects are modeled. Using the variable coordination number made this model to be in good agreement with experimental and molecular dynamic (MD) results of different crystal structures. The model predicts melting entropy and enthalpy of... 

Grinding is widely used for manufacturing of components that require fine surface finish and good dimensional accuracy. In this study a thermo-mechanical finite element analysis is conducted to find out how grinding parameters can affect temperature and residual stress distribution in the workpiece. Results of parametric study presented in this work indicate, by carefully selecting the grinding parameters, minimum thermal and mechanical damage can be achieved. Higher workpiece velocities produce higher surface residual stress. By increasing depths of cut, depth of tensile residual stresses increases. Convection heat coefficient does not have any considerable effect on surface residual stress... 

Adaptive Noise Cancellation (ANC) is a well-known technique for background noise reduction in automobile and vehicular environments. The noise fields in automobile and other vehicle interior obey the diffuse noise field model closely. On the other hand, the ANC does not provide sufficient noise reduction in the diffuse noise fields. In this paper, a new multistage post-filter is designed for ANC as a solution to diffuse noise conditions. The designed post-filter is a single channel Linear Prediction (LP) based speech enhancement system. The LP is performed by an adaptive lattice filter and attempts to extract speech components by using intermediate ANC signals. The post-filter has no...