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This article is proposing an appropriate approach to solve a constrained multi objective model by using the theory of utility functions in fuzzy form. One of the approaches to optimize a multi objective mathematical model is to employ utility functions for the objectives. Recent studies on utility based multi objective optimization concentrate on considering just one utility function for each objective. But, in reality it is not reasonable to have a unique utility function corresponding to each objective function. Here, a constrained multi objective mathematical model is considered in which several utility functions are associated for each objective. All of these utility functions are uncertain and in fuzzy form, so a fuzzy probabilistic approach is incorporated to investigate the uncertainty of the utility functions for each objective and the total utility function of the problem will be a fuzzy nonlinear mathematical model. Since there are not any conventional approaches to solve such a model, a defuzzification method to change the total utility function to a crisp nonlinear model is employed. Meanwhile, α-cut method is applied to defuzzify the conditional utility functions. This action results in changing the total utility function to a crisp single objective nonlinear model and will simplify the optimization process of the total utility function. The effectiveness of the proposed approach is shown by solving a test problem. [ABSTRACT FROM AUTHOR]

We develop a ranking process for multi-objective decision mak ing. For optimizing the multi-objective problem having both quantitative and qualitative objectives, weight assessment is important to convert the problem into the corresponding single objective problem. Therefore, a ranking process is proposed to simultaneously obtain the objective weights and the evaluation of alternatives with multiple objectives. Several new concepts are developed to handle the dynamism in distance computation and ranking of decisions in a multi-objective model having qualitative evaluations. The proposed process is illustrated in a numerical example [ABSTRACT FROM AUTHOR]

This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. [ABSTRACT FROM AUTHOR]

Automated guided vehicles (AGVs) are used as a material handling device in flexible manufacturing systems. Traditionally, AGVs were mostly used at manufacturing systems, but currently other applications of AGVs are extensively developed in other areas, such as warehouses, container terminals and transportation systems. This paper discusses literature related to different methodologies to optimize AGV systems for the two significant problems of scheduling and routing at manufacturing, distribution, transshipment and transportation systems. We categorized the methodologies into mathematical methods (exact and heuristics), simulation studies, meta-heuristic techniques and artificial intelligent based approaches. [ABSTRACT FROM AUTHOR]

In this paper, a new mathematical model is developed for a multi-depot vehicle routing problem with simultaneous pickup and delivery. A non-homogenous fleet of vehicles and a number of drivers with different levels of capabilities are employed to service customers with pickup and delivery demands. The capability of drivers is considered to have a balanced distribution of travels. The objective is to minimize the total cost of routing, penalties for overworking of drivers and fix costs of drivers' employment. Due to the problem's NP-hard nature, two meta-heuristic approaches based on Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) are employed to solve the generated problems. The parameter tuning is conducted by Taguchi experimental design method. The obtained results show the high performance of the proposed ICA in the quality of the solutions and computational time. [ABSTRACT FROM AUTHOR]

This paper concerns with proposing a heuristic statistical technique to compute total stochastic material handling time in an automated guided vehicle (AGV) equipped jobshop manufacturing system. With respect to stochastic times of AGVs material handling process, the material handling activities probability distributions are considered. Using the probability distributions, we model the AGV material handling problem using a heuristic statistical method when the activities' probability distribution functions are the same. Also, in the case that the activities' probability distribution functions are different, a cross-entropy approach is proposed and developed to model the problem. The effectiveness of the proposed model is illustrated in a numerical example and verified by a simulation study. The numerical experiments are worked out in two cases, namely having same probability distributions and different probability distributions for activities. Both cases are verified by simulation study using different simulation softwares showing the efficiency of the employed approaches. In case 1, the deviation of ARENA from the proposed statistical model is estimated to be 0.5 % while for other softwares detailed in the experiments the value of deviations are more than 30 %. On the other hand, in case 2 again ARENA performed better than others having the deviation of 0.3 % from the proposed cross-entropy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 rules and find the effective rules for cost estimation. The objective is to find a path minimizing an aggregate weighted unscaled time and cost criteria. A fuzzy dynamic programming approach is presented for computing a shortest path in the network. Then, a comprehensive economic and reliability analysis is worked out on the obtained paths to find the optimal producer's behavior. Finally, an application of the model is illustrated by a numerical example. The results show the effectiveness of our approach for finding an optimal path in a manufacturing system under uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: Creativity is a promoting factor in organizations. Having employees in structured and organized configurations in a creative manner, helps in improving the productivity. We investigate different structural aspects of teams’ network organization and the creativity within a knowledge development program (KDP). The proposed methodology being equipped with a heuristic clustering technique, classifies the employees with respect to creativity parameters and configures a creativity matrix. Applying the creativity matrix, clustering is performed via mathematical programming. For large problems, a genetic algorithm (GA) is developed to solve the mathematical model. We also employ the Taguchi method to evaluate the effects of different operators and parameters on the performance of GA. A case study conducted in Mazandaran Gas Company in Iran illustrates the applicability and effectiveness of the proposed methodology. [Copyright &y& Elsevier]

Abstract: Supply chain design problems have recently raised a lot of interest since the opportunity of an integrated management of the supply chain can reduce the propagation of undesirable events through the network and can affect decisively the profitability of the members. Often uncertainties may be associated with demand and relevant costs. In most of the existing models uncertainties are treated as randomness and are handled by appealing to probability theory. Here, we propose a fuzzy mathematical programming model for a supply chain which considers multiple depots, multiple vehicles, multiple products, multiple customers, and different time periods. In this work not only demand and cost but also decision variables are considered to be fuzzy. We apply two ranking functions for solving the model. The aim of the fuzzy mathematical program is to select the appropriate depots among candidate depots, the allocation of orders to depots and vehicles, also the allocation of the returning vehicles to depots, to minimize the total costs. To validate the model some numerical experiments are worked out and a comparative analysis is investigated. Also, a regression model is considered to analyze the applied fuzzy ranking methods. [Copyright &y& Elsevier]

We investigate different structural aspects teams' network organization and their creativity within a knowledge development program (KDP). Initially, a pilot group of employees in an organization is selected. This group is evaluated through creativity parameters using a questionnaire. Considering the questionnaires' data, a creativity matrix is configured by a binary scoring. Applying the creativity matrix, clustering is performed via mathematical programming. The pilot group is divided into some research teams. The research subjects are submitted to the teams. Finally, an allocated problem is solved and some new research subjects are evolved to be assigned to the next configured teams. This procedure is repeated dynamically for different time periods. [ABSTRACT FROM AUTHOR]

In spite of successful research on new agricultural practices concerning crop cultivation, the majority of farmers is not getting upper-bound yield due to several reasons. One of the reasons is that expert/scientific advice regarding crop cultivation is not reaching farming community in a timely manner. It is true that we possess a valuable agricultural knowledge and expertise. However, a wide information gap exists between the research level and practice. Farmers need timely expert advice to make them more productive and competitive. In this paper, we made an effort to present a solution to bridge the information gap by exploiting advances in Information Technology (IT). It is interesting to notice a major difference between developed countries and developing countries, serves as a distinguished representative, in their roadmaps to agriculture modernization to developing counties. IT assists to achieve agricultural mechanization efficiently and intelligently. We proposed a framework of strategic plan based on SWOT analysis using IT in Agriculture. [ABSTRACT FROM AUTHOR]

This study explores implementation of value engineering (VE) in gas pipeline projects, focusing on the link among transfer, feed, and distribution lines. For any feed line, there is a City Gate Station (CGS), and for any distribution line, there are some Town Broad Stations (TBS). We assess value in two cases. The first case concerns the location of the TBS's, allocation of consumption zones to TBS's, and the routing of distribution lines among the TBS's and consumption zones. The second case focuses on analyzing activities during the implementation process to identify any extra or malfunctioned activities in order to propose a change of strategy to gain a higher value. For the first case, we apply a combination of a location-allocation mathematical model and the minimum spanning tree (MST) method, and for the second case, we use the Functional Analysis System Technique (FAST). We conduct a case study of a gas company to assess the validity and effectiveness of the value engineering approach. [ABSTRACT FROM AUTHOR]

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 penalized earliness and tardiness, and defect rate. The priorities are calculated for each supplier by use of AHP. TOPSIS is applied to rank the suppliers. Finally, using the obtained weights, the optimal quantities of order to the suppliers are clarified in multi-period horizon. An application study presents the validity and efficiency of the proposed model. Moreover, a performance analysis has been worked out on the numerical example to investigate the capability and effectiveness of the results. [ABSTRACT FROM AUTHOR]

embly line balancing has gotten a considerable place in industrial importance. Hence, a lot of researchers are interested in this subject and several papers have been published until now. Many exact, heuristic, meta-heuristic, and hybrid approaches have been used to solve this type of problems. Different rules are applied for line balancing such as rank positional weight (RPW). Assembly line balancing problems are considered mostly in definite status, while the nature of manufacturing systems is accompanied with uncertainty. In this paper, we propose a model based on RPW algorithm considering uncertain time parameters for stochastic assembly line balancing. Also, two other methodologies of normal distribution integration and Monte Carlo simulation are developed. The effectiveness of the proposed model and validity testing with respect to other two approaches are illustrated using numerical examples. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop an intelligent information system in a multilayer electronic supply chain network. Merging supply chain and internet is a key area of concern for contemporary managers and researchers. They have realized that the internet can enhance SCM by making real time information available and enabling collaboration between trading partners. Here, we propose a multi-agent information system to cluster the elements of a supply network based on the similarity in information flow. Each layer consists of elements which are differentiated by their performance throughout the supply network. We apply intelligent agents as decision aids in different layers of our supply network. The proposed agents measure and record the performance flow of elements considering their web interactions. [ABSTRACT FROM AUTHOR]

In this paper, we discuss about the hierarchical Bayesian (HB) estimation concerning system reliability for a waste‐to‐energy (WTE) process. The main goal of this approach is to obtain this estimation WTE process reliability under different loss functions related series, parallel, and k‐out‐of‐m systems. In this case, we can drive system outcome by estimating each individual component. It is assumed that components are independent and identically distributed exponential random variables. Properties of the HB estimations under different loss functions are also provided, and comparisons are made between Bayesian and HB estimators via Monte Carlo simulation. The implementation of the proposed procedure was illustrated in detail by employing numerical practical examples extracted from a WTE process at the final part of this paper. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present an integrated strategy for inventory control and preventive maintenance planning for a single-machine production system with increasing failure rates. Design/methodology/approach: There are three scenarios for solving presented model. The strategy is such that the production component is placed under maintenance as soon as it reaches the m level or in the event of a malfunction earlier than m. Maintenance completion time is not predictable. As a result of periodic maintenance, a buffer stock h is held and the production component starts to produce from period A with the maximum throughput to satisfy demand and handle the shortage. A numerical algorithm to find the optimal policy is developed. The algorithm is implemented using MATLAB software. Findings: The authors discovered that joint optimization mainly reduces production system costs. Cs is holding cost of a product unit during a unit of time. The authors consider two values for Cs, consist of, Cs = 1 and Cs = 2. By comparing the two cases, it is concluded that by reducing the cost from Cs = 2 to Cs = 1, the optimal scenario does not differ. The amount of decision variables decreases. Originality/value: This paper is the provision of a model in which the shortage of back order type is considered, which greatly increases the complexity of the problem compared to similar issues. The methods for solving such problems are provided by the numerical algorithm, and the use of buffers as a way to compensate for the shortage in the event of a complete shutdown of the production line which is a very effective and efficient way to deal with customer loss. [ABSTRACT FROM AUTHOR]

Today, in many organizations, the debate about the difference in core capabilities has become an important factor for market competition. Companies, based on the field of activity, decide to strengthen some of their capabilities, capacities, and expertise. Therefore, the focus of an organization on the strengths and efforts to develop its sustainability will lead to a competitive advantage in the marketplace. Due to changes in environmental factors, organizations have focused on carbon emissions in procurement and transportation that have the highest carbon footprint. This paper proposes a multi-objective, eco-sustainability model for a supply chain. The objectives are to minimize overall costs, maximize the efficiency of transportation vehicles and minimize information fraud in the process of information sharing within supply chain elements. Big data is considered in the amount of information exchanged between customers and other elements of the proposed supply chain; since there are frauds in information sharing then using big data 5Vs the model is adapted to control the cost of information loss leading to customer dissatisfaction. Since uncertainty is inevitable in the real environments, in this research hybrid uncertainty is considered. Because two sources of uncertainty are considered in most of the parameters, thus it is necessary to robustify the decision-making process. The model is a mixed integer nonlinear program including big data for an optimal sustainable procurement and transportation decision. A heuristic method is used to solve the big data problem that makes use of a robust fuzzy stochastic programming approach. The proposed model can prevent disturbances by using a scenario-based stochastic programming approach. An effective hybrid robust fuzzy stochastic method is also employed for controlling uncertainty in parameters and risk taking out of outbound decisions. To solve the multi-objective model, augmented ε-constraint method is utilized. The model performance is investigated in a comprehensive computational study. [ABSTRACT FROM AUTHOR]

In this study, we analyze the proficiency of a single-server Markovian queuing system M/M/1/N with unpleasant services and encouraged arrivals to compare the policies for service delivery, which is a context of social planning to improve the social welfare. Then, we examine the economic analysis of the cost in an uncertain environment. In this regard, the entry rate, the service rate, and the processing time are presumed as vague numbers and developed a concept that is an essential condition of vague set that is applied. In addition, taking into account to the expected costs, server maintenance costs, customer dissuasion cost, and financing costs in this investigation can enhance the managers capability in order to adopt the decision in organizations. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a model for assessing the hazard system based on the failure structure of aircraft wing. The model considers reliability maintenance and repair factors. Failure Mode and Effect Analysis (FMEA) is one of the well-known methods of quality management being used for continuous improvement in product or process plans. One serious issue of FMEA is the definition of the risk priorities of failure modes. This paper proposes an analytical method based on failure modes and failure analysis in criticality and suggests a fuzzy evaluation method for evaluating the risk level of the wing of aircraft. The analytical technique includes qualitatively analyzing the operation, failure modes, failure cause, failure rate, and severity through FMEA and quantitatively assessing the safety by using the fuzzy evaluation method. Fuzzy logic and Genetic Algorithms are integrated using a risk-cost model based on FMEA and comparisons with Simulated Annealing algorithms. [ABSTRACT FROM AUTHOR]

Prediction of surface roughness is a key element for an automated machining center. In this regard, it is important to optimize the machining process. In this paper, fuzzy linear regression approach is employed to predict the surface roughness for a turning process in an uncertain condition. The important process parameters such as cutting speed, cutting depth, speed, and tool tip radius are considered as inputs to determine their significance for prediction. To handle uncertainty, fuzzy theory is employed. Thus, fuzzy liner regression is modeled. To optimize the estimated values of prediction errors, a genetic algorithm (GA) is developed. In addition, tabu search is used to facilitate GA for better performance. A numerical example is worked out to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose Due to the fact that the non-standard products, being used by customers, may cause failures in products with sales delays, which naturally affect the warranty policy. Thus, it seems to be necessary to study these two concepts simultaneously. The paper aims to discuss these issues.Design/methodology/approach In this paper, a model is developed for estimating the expected warranty costs under sales delay conditions when two operator costs (failing but not reported and non-failing but reported) are included.Findings The proposed model is validated using a numerical example for a two types of intermittent and fatal failures occur under a non-renewing warranty policy.Originality/value Sales delay is the time interval between the date of production and the date of sale. Most reported literature on warranty claims data analysis related to sales delay have mainly focussed on estimating the probability distribution of the sales delay. [ABSTRACT FROM AUTHOR]

Over the past decade, renewable energy supply chains have been widely studied to help reduce environmental problems. The biomass has become a vital renewable energy source, but due to the cost and complexity of logistics operations, all stakeholders collaborated in the management and design of biomass-to-energy supply chains (BESC) are interested in assessing the risks of this promising investment. The main purpose of this study is to identify and evaluate the investment risk linked to the development of biomass renewable supply chain. The risks of generic system components are initially presented and then the unique risks exclusively associated to BESCs. We discuss the main associated risks, namely market risk, financial risk, policy risk, and investment environment risk. The uncertainty in quantification of effective factors is then considered. By using the system dynamics methodology, several stock-flow charts and risk assessment models are evaluated for an effective BESC investment plan. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • This study deals with the prediction and optimisation of an electrical discharge machine's parameters. • According to the uncertainty conditions, a scenario-based fuzzy RDEA model is developed. • We propose a hybrid ANFIS approach and scenario-based robust optimisation. • The fuzzy possibility regression with fuzzy hypothesis testing is contributed for our experiments. An electrical discharge machine (EDM) has a high impact on production management, with its process having many advantages over conventional machining processes, including the ability of the machine to create very high-quality material that is intricate to inner industrial sections. This study investigates the impact of EDM machining parameters on the volumetric flow rate, electrode corrosion percentage, and surface roughness. These machining parameters are increasingly important for the quality of the final product, leading to higher customer satisfaction and greater market share of the company. Due to dynamic changes in the machine's parameters and production environmental changes, using an uncertain model is inevitable. To investigate the machining data under uncertainty, a mathematical modelling approach based on the fuzzy possibility regression integrated (FPRI) model is developed. One advantage of the proposed model is that it is able to predict the surface roughness, volumetric flow rate, and corrosion percentage of the electrode. An adaptive-network-based fuzzy inference system (ANFIS) is applied to achieve the optimal levels of each output. Since the results and numbers obtained from the neural network are uncertain and their distribution is not clear, a robust data envelopment analysis approach (RDEA) is employed to select the best tuned-level of the parameters. The findings confirm the accuracy and reliability of the proposed method for prediction and optimisation of the EDM's parameters and encourage further tests for other production and supply chain applications. [ABSTRACT FROM AUTHOR]

Industrial companies would attempt to keep themselves agile in the dynamic market. Given the competition among industries, lean methods are employed to reduce costs in the system. Among them, maintenance is significant to have a system available for manufacturing tasks. Maintenance is identified as the largest cost control tools in the equipment-driven industry. Implementing an effectual plan for preventive maintenance helps to be much more flexible and create an innovative solution for planning in the production. In this vein, this paper introduces an optimization model related to flexible flow-shop system scheduling in a series-parallel production system of disposable appliances by considering the preventive maintenance (PM) policy. By planning preventive maintenance, extra operation time is incurred to the system which may influence the cycle time and lead to lost sales and back orders. Therefore, the paper proposes a mathematical model to consider both operations times and availability of the whole production system to minimize the delays to reach an optimal sequence of processing. Since uncertainty exists in real industrial systems, the processing times are uncertain here. To handle uncertainty, robust optimization has been applied to solve the problem. In addition, a scenario-based genetic algorithm (SBGA) and Particle Swarm Optimization (PSO) algorithm have been developed to solve the proposed model. The results indicate the appropriate performance of the proposed approach in terms of time-saving leads to saving the cost of PM., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)