An Inexact-Fuzzy-Stochastic Optimization Model for a Closed Loop Supply Chain Network Design Problem

The development of optimization and mathematical models for closed loop supply chain (CLSC) design has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. The aim of this paper, therefore, is to propose a new mathematical model for designing a CLSC network that integrates the network design decisions in both forward and reverse supply chain networks. Moreover, another objective of this research is to introduce an inexact-fuzzy-stochastic solution methodology to deal with various uncertainties in the proposed model. Computational experiments are provided to demonstrate the applicability of the proposed model in the CLSC network design

Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters

Considering the increasingly high attention to quality, promoting the reliability of products during designing process has gained significant importance. In this study, we consider one of the current models of the reliability science and propose a non-linear programming model for redundancy allocation in the series-parallel systems according to the redundancy strategy and considering the assumption that the failure rate depends on the number of the active elements. The purpose of this model is to maximize the reliability of the system. Internal connection costs, which are the most common costs in electronic systems, are used in this model in order to reach the real-world conditions. To get the results from this model, we used meta-heuristic algorithms such as genetic algorithm and simulation annealing after optimizing their operators’ rates by using response surface methodology

Genetic Algorithm and Simulated Annealing for Redundancy Allocation Problem with Cold-standby Strategy

This paper presents a new mathematical model for a redundancyallocation problem (RAP) withcold-standby redundancy strategy and multiple component choices.The applications of the proposed model arecommon in electrical power, transformation,telecommunication systems,etc.Manystudies have concentrated onone type of time-to-failure, butin thispaper, two components of time-to-failures which follow hypo-exponential and exponential distributionare investigated. The goal of the RAP is to select available components and redundancy level for each subsystem for maximizing system reliability under cost and weight constraints.Sincethe proposed model belongs to NP-hard class, we proposed two metaheuristic algorithms; namely, simulated annealing and genetic algorithm to solve it. In addition, a numerical example is presented to demonstrate the application of the proposed solution methodology.</p

Using NSGA II Algorithm for a Three Objectives Redundancy Allocation Problem with k-out-of-n Sub-Systems

in the new production systems, finding a way to improving the product and system reliability in design is a very important. The reliability of the products and systems may improve using different methods. One of this methods is redundancy allocation problem. In this problem by adding redundant component to sub-systems under some constraints, the reliability improved. In this paper we worked on a three objectives redundancy allocation problem. The objectives are maximizing system reliability and minimizing the system cost and weight. The structure of sub-systems are k-out-of-n and the components have constant failure rate. Because this problem belongs to Np. Hard problems, we used NSGA II multi-objective Meta-heuristic algorithm to solving the presented problem

A New Method for Reliability Calculation of the Active Systems with Time-Dependent Failure Rates based on Weibull Distribution

Due to the high sensitivity in applying of electronic and mechanical equipment, creating any conditions to increase the reliability of a system is always one of the important issues for system designers. Hence, making academic models much closer to the real word applications is very attractive. In the most studies in the reliability area, it is assumed that the failure rates of the system components are constant and have exponential distributions. This distribution and its attractive memory less property provide simple mathematical relationships in order to obtain the system reliability. But in real word problems, considering time-dependent failure rates is more realistic to model processes. It means that, the system components do not fail with a constant rate during the time horizon; but this failure rate changes over the time. One of the most useful statistical distributions in order to model the time-dependent failure rates is the Weibull distribution. This distribution is not a memory less one, so it was impossible to apply simple and explicit mathematical relationships as the same as exponential distributions for the reliability of a system. Therefore, researchers in this field have used simulation technique in these circumstances which is not an exact method to get near-optimum solutions. In this paper, for the first time, it is tried to obtain a mathematical equation to calculate the reliability function of a system with time-dependent components based on Weibull distribution. Also, in order to validate the proposed method, the results compared with exact solution that exists in literature

Optimizing a fuzzy multi-objective closed-loop supply chain model considering financial resources using meta-heuristic

This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this pape

A Neural Network Model Based on Support Vector Machine for Conceptual Cost Estimation in Construction Projects

Estimation of the conceptual costs in construction projects can be regarded as an important issue in feasibility studies. This estimation has a major impact on the success of construction projects. Indeed, this estimation supports the required information that can be employed in cost management and budgeting of these projects. The purpose of this paper is to introduce an intelligent model to improve the conceptual costaccuracy during the early phases of the life cycle of projects in construction industry. A computationally efficient model, namely support vector machine model, is developed to estimate the conceptual costs of construction projects. The proposed neural network model is trained by a cross validation technique in order to produce the reliable estimations. To demonstrate the performance of the proposed model, twopowerful intelligent techniques, namely nonlinear regression and back-propagation neural networks (BPNNs), are provided. Their results are compared on the basis of the available dataset from the related literature in construction industry. The computational results illustrate that the presented intelligent model performs better than the other two powerful techniques

Neuromuscular efficiency in relation to muscle blood flow and oxygen consumption in isolated muscle work: is there any age-related effect?

Abstract Background: It is well-established that aging is associated with declined muscle mass and force. However, the impacts of age on neuromuscular efficiency, muscle blood flow and oxygenation, and their association are less clear. Objective: The present study investigates whether advanced age affects neuromuscular efficiency, muscle blood flow, and mV̇O2 in isolated muscle work and if neuromuscular efficiency is associated with O2 delivery and extraction. Methodology: 11 young (26.45±7.38 years) and 12 older (64.41±3.94 years) adults participated. All were healthy and recreationally active. An incremental handgrip test was conducted to determine maximal handgrip load. Bouts of dynamic handgrip exercise at work rates 10, 30, 50, 70, and 90% of maximal handgrip load were performed. Force/EMGRMS was used as a measure of neuromuscular efficiency. Blood flow and mV̇O2 were evaluated using NIRS and venous occlusion. All measurements were done in two forearm muscles, flexor digitorum superfacialis and brachioradialis. Results: Young showed higher neuromuscular efficiency than the older group, only for brachioradialis (borderline significant effect, F(1,21)=4.04, p=0.05). There was also a significant group-by-WR interaction effect for blood flow in brachioradialis (F(4,82)=3.27, p=0.01). Except for three significant associations out of 40 tested (p0.05) with muscle blood flow and mV̇O2. Conclusion: The findings of this study demonstrate some differences in the rate of muscle aging within forearm musculature since flexor digitorum superfacialis function appears to be preserved with age while there were declines in brachioradialis. Moreover, muscle O2 delivery and extraction do not seem to affect neuromuscular efficiency. Keywords: muscle aging, forearm musculature, dynamic exercis

Reliability optimization of a series-parallel k-out-of-n system with failure ratedepends on working system components

This paper presents a mathematical model for a redundancy allocation problem (RAP) for the series-parallel system with k-out-of-n subsystems and failure rate depends on working components of system. It means that failure rate of components increases when a component fails. The subsystems may use either active or cold-standby redundancy strategies, which considered as a decision variable for individual subsystems. Thus, the proposed model and solution methods are to select the best redundancy strategy among active or cold-standby, component type, and levels of redundancy for each subsystem. The objective function is to maximize the system reliability under cost and weight constraints. To solve the model, since RAP belongs to Np-Hard class of the problems, one effective meta-heuristic algorithm named genetic algorithm (GA)is proposed. Then, response surface methodology is applied for algorithm parameter tuning.Finally, we consider the results of solving presented model with a numerical exampl

Development of multi-objective simulated annealing based decision support system for course timetabling with consideration preferences of teachers and students

Course timetabling problem is a weekly assignment a set of course and teacher to the time and space with considering a lot of hard and soft constraints in universities. In each semester, heads of educational institutes take too much time and effort to prepare a timetable by using trial and error method or last semester's timetable, although the rapid changing needs, resources and rules of each semester causes this method are not the perfect solutions. In this study, we design and develop a novel multi objective mathematical model which taking into account the preferences of students and teachers, Due to the complexity, we have benefited the metaheuristic algorithm to solve nonlinear model. Simulated Annealing algorithm is used to solve the mathematical model in two stages. In the first stage, the system automatically generates feasible solutions that will meet all the hard constraints. Then, the solutions are improved with spotting different neighborhood's structures. This collection is in the form of computer software application which is implemented the C# language programing and SQL database. This system is tested the data gathered by Azad University data and the results compared to the manual process showed the great progress is achieved. The entire system is flexible and easy to test different scenario