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The generation maintenance scheduling deals with a time sequence of preventive maintenance outages for a given set of generation units in an electricity market subject to power system restrictions. Incorporating a leader–follower structure in generation maintenance scheduling models is essential because of the inherent conflict between the interests of an independent system operator (ISO) and generation companies (GENCOs). The present paper proposes a new preventive maintenance scheduling model for generation companies facing the risk of involving generation units' disruption and demand variations while ensuring the reliability of the power system. Each GENCO proposes the maintenance schedule of its generation units to the ISO in a non-cooperative manner intending to maximize its net profit. Then ISO reacts to the aggregated schedule according to the power system's reliability index. Thus, a new formula is developed to consider all the interactions between the power system's stakeholders. In this regard, a stochastic multi-leader one-follower approach is applied. The GENCOs are considered independent leaders at the upper-level and the ISO is considered a follower at the lower-level. Then an equivalent single-level counterpart model is presented for each leader. So, the whole problem is converted into multiple individual stochastic single-level models, and then the Nash Equilibrium concept is used to determine GENCO equilibrium strategies. The proposed methodology is evaluated using some modified IEEE reliability test systems. The numerical analysis confirms that the proposed model is more effective in cases with higher uncertainties. Moreover, the performed analysis demonstrated the importance of applying a bi-level approach to the problem. Finally, the superiority of the proposed approach compared to the existing one is confirmed. [ABSTRACT FROM AUTHOR]

The present article considers a location-arc routing problem (LARP) where the demands are on the edges rather than nodes on an undirected network. A mixed integer programming model is developed for an LARP with vehicle and depot capacity constraints and a fleet of heterogeneous vehicles. To adapt with reality, it is assumed that the demand of each road is an uncertain value that belongs to a bounded uncertainty set. In order to have a less conservative decision, we employ the robust optimization model proposed by Bertsimas and Sim (2003) to handle uncertainty. The proposed robust model determines a subset of potential depots to be opened along with their allocated roads in order to have an efficient location-routing decision which is immune to different realization of uncertainties. The proposed robust model is less sensitive to demand variations and is validated through Monte-Carlo simulation and relative extra cost (REC) measure with promising results. The results of sensitivity analysis showed that by increasing the degrees of conservatism, planners may employ more vehicles. Also, more depots may be opened to service all required roads. [ABSTRACT FROM AUTHOR]

The concern about environmental and social impacts of business activities has led to introducing a new paradigm called, sustainable development. It can help to build a low-carbon high-growth global economy and guarantee the global well-being of people. In this paper, three pillars of sustainable development, i.e., economic, environmental, and social, are considered and discussed to design a supply chain network. The proposed model tries to maximize profit primarily while capturing societal community development by prioritizing the less developed regions. Moreover, the model ensures that the environmentally friendly facilities can operate in the supply chain network while others have to be repaired. Furthermore, quantifying the benefits of transportation decisions in terms of both cost and environmental impact savings to improve the sustainability of logistics systems is considered. In addition, the model is regarded as robust programming for the problem to approximate real situations. The proposed model is implemented in some numerical examples and in a real case study. Numerical results and computational analysis are indicative of the significance of the model and through conducting the case study, it is demonstrated that the proposed model can be implemented successfully in practice and it would be beneficial to all the three pillars of sustainable development. Moreover, the managerial insights for the managers of the supply chain networks are provided to make the most appropriate decisions. • Presenting a sustainable supply chain network design model considering a weighted balanced regional development scheme. • Proposing an innovative methodology for estimating the sustainability dimensions. • Tackling an uncertain parameter and utilizing robust optimization to create a protected solution against uncertainty. • Applying the proposed model in a case study and providing the managerial insights for the managers of the supply chain. [ABSTRACT FROM AUTHOR]

In this study, a new mobile p -hub location problem in a dynamic environment is proposed, where there are mobile facilities inside hub nodes that can be transferred to other nodes in the next period. Mobile facilities have a mobility feature and can be transferred to other nodes in order to meet demand. Using such facilities will save extra hub establishment and closing costs in networks. This approach can be used in some real-world applications with rapidly changing demand, such as mobile post offices or emergency medical service centers, because designing immobile hub networks may be less efficient. In addition, designing dynamic hub networks entails establishing and closing costs in different periods. The model also considers a mobility infrastructure of hub facilities. The numerical examples confirm that a mobile hub network is more efficient than an immobile hub network in a dynamic environment. The effect of different parameters on the model is analyzed to consider its applicability conditions. A genetic algorithm, along with tuned parameters and a simulated annealing algorithm, are proposed to solve the model in large instances. Proposing of a model considering mobility feature in the hub location networks, proving its efficiency and finally proposing a proper solution algorithm are main contributions of this study. The model and solutions algorithms were analyzed by more numerical instances using Australia post (AP) dataset. [ABSTRACT FROM AUTHOR]

Hub locations are NP-hard problems used in transportation systems. In this paper, we focus on a single-allocation hub covering location problem considering a queue model in which the number of servers is a decision variable. We propose a model enhanced with a queue estimation component to determine the number and location of hubs and the number of servers in each hub, and to allocate non-hub to hub nodes according to network costs, including fixed costs for establishing each hub and server, transportation costs, and waiting costs. Moreover, we consider the capacity for a queuing system in any hub node. In addition, we present a metaheuristic algorithm based on particle swarm optimization as a solution method. To evaluate the quality of the results obtained by the proposed algorithm, we establish a tight lower bound for the proposed model. Genetic programming is used for lower bound calculation in the proposed method. The results showed better performance of the proposed lower bound compared to a lower bound obtained by a relaxed model. Finally, the computational results confirm that the proposed solution algorithm performs well in optimizing the model with a minimum gap from the calculated lower bound. [ABSTRACT FROM AUTHOR]

Optimization of multi-response problems is a popular subject in the literature. However, the problem becomes complicated when the responses are functional due to the existence of signal factors. In this article, we have proposed a combined index to optimize multivariate multiple functional responses by considering functional specification limits and a target. The relation among the responses and controllable factors is characterized by polynomial equations to consider the curvature of the response functions. The validity of the proposed method is checked by a simulation example. To show the applicability of the proposed method, a real case about Tehran air quality is analyzed. Latitude and longitude are considered to be signal factors, and different pollutant values are responses of the experiment. Government policies in each time interval are considered as controllable factors. Finally, an optimization algorithm is used to find the best decisions for government policies. [ABSTRACT FROM AUTHOR]

By considering uncertainty in the attributes common methods cannot be applicable in data clustering. In the recent years, many researches have been done by considering fuzzy concepts to interpolate the uncertainty. But when data elements attributes have probabilistic distributions, the uncertainty cannot be interpreted by fuzzy theory. In this article, a new concept for clustering of elements with predefined probabilistic distributions for their attributes has been proposed, so each observation will be as a member of a cluster with special probability. Two metaheuristic algorithms have been applied to deal with the problem. Squared Euclidean distance type has been considered to calculate the similarity of data elements to cluster centers. The sensitivity analysis shows that the proposed approach will converge to the classic approaches results when the variance of each point tends to be zero. Moreover, numerical analysis confirms that the proposed approach is efficient in clustering of probabilistic data. [ABSTRACT FROM AUTHOR]

In this paper first we apply ranked set sampling (RSS) procedure in joint monitoring of both process mean and variance. Then, we study the effect of measurement error on joint monitoring of process mean and variance when simple random sampling (SRS) as well as RSS procedure is used. The results prove that the measurement error can seriously deteriorate the ability of the control chart in detecting all out-of-control scenarios. The results also represent that using RSS procedure can improve the ability of the monitoring scheme in detecting mean shifts, variance shifts as well as joint shifts either measurement error exists or not. In other words, RSS procedure can reduce the adverse effect of measurement error on detecting ability of joint monitoring scheme. After that, we utilize multiple measurements at each sample point when both SRS and RSS procedures are used. We investigate the effect of parameters in the covariate model thorough a sensitivity analysis. Finally, the applicability of the proposed control charts is illustrated using a real case of the piston rings in an automotive engine manufactured by a forging process. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a vehicle sharing system with multi-transportation modes and allowable shortage is presented. This model aims to minimize the system's total cost by using optimum locations and number of stations, routes, transportation modes, station capacities for different modes and time between stations balancing. Because of the model's complexity, currently available proprietary software is not able to solve the model in a reasonable computational time, so a hybrid algorithm based on a genetic algorithm (GA) and particle swarm optimization is presented. The results confirm its efficiency compared with the classic GA and exact solution methods. Moreover, a sensitivity analysis shows the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The design of relief logistics networks is one of the most important activities for disaster preparedness. In emergency conditions, relief operations can be performed for a node if it is inside of the relief radius; thus, the proposed model contains a covering location problem. Simultaneously, the relief transportation network is designed based on the hub location problem. Moreover, the Lagrangian relaxation method was proposed to find a proper lower bound of the problem for large instances. Some numerical examples were analyzed to evaluate the performance of the proposed approach. The computational results confirmed that the proposed model showed appropriate performance for disaster management compared to classic network designs. [ABSTRACT FROM AUTHOR]

The location routing problem (LRP) considers locating depots and vehicle routing decisions simultaneously. In classic LRP the number of customers in each route depends on the capacity of the vehicle. In this paper a capacitated LRP model with auxiliary vehicle assignment is presented in which the length of each route is not restricted by main vehicle capacity. Two kinds of vehicles are considered: main vehicles with higher capacity and fixed cost and auxiliary vehicles with lower capacity and fixed cost. The auxiliary vehicles can be added to the transportation system as an alternative strategy to cover the capacity limitations and they are just used to transfer goods from depots to vehicles and cannot serve the customers by themselves. To show the applicability of the proposed model, some numerical examples derived from the well-known instances are used. Moreover the model has been solved by some meta-heuristics for large sized instances. The results show the efficiency of the proposed model and the solution approach, considering the classic model and the exact solution approach, respectively. [ABSTRACT FROM PUBLISHER]

Design of process parameters to meet the required specification of quality characteristics has become a crucial issue for many industries. The underlying relationships between process inputs and outputs are one of the main tasks in quality engineering. Most of researches assume independency and consider same relative importance of quality characteristics with constant variances over experimental space. This study represents a novel robust approach based on desirability function and global criterion methods that not only obtains the parameter design but also considers different variance, correlation, and relative importance level of outputs. The suggested method enforces all quality measurements to fall within specification limits. To illustrate computational aspects of the proposed method, two realistic examples have been conducted. The obtained results demonstrate the superiority of the proposed approach with respect to the existing approaches. [ABSTRACT FROM AUTHOR]

Response surfaces express the behavior of responses and can be used for both single and multi-response problems. A common approach to estimate a response surface using experimental results is the ordinary least squares (OLS) method. Since OLS is very sensitive to outliers, some robust approaches have been discussed in the literature. Although there are many methods available in the literature for multiple response optimizations, there are a few studies in model building especially robust models. Assuming correlated responses, in this paper, a robust coefficient estimation method is proposed for multi response problem based on M-estimators. In order to illustrate the performance of the proposed procedure, a contaminated experimental design using a numerical example available in the literature with some modifications is used. Both the classical multivariate least squares method and the proposed robust multivariate approach are used to estimate regression coefficients of multi-response surfaces based on this example. Moreover, a comparison of the proposed robust multi response surface (RMRS) approach with separate robust estimation of single response show that the proposed approach is more efficient. [ABSTRACT FROM AUTHOR]

In the real problems, there are many cases which have correlated quality characteristics so multiple response optimization can be more realistic if we can consider correlation structure of responses. In this study we propose a new method which uses multivariate normal probability to find the optimal treatment in an experimental design. Moreover, a heuristic method is used to find better factors’ level in all possible combinations in the designs with large number of controllable factors and their levels. Some simulated numerical examples and a real case were studied by the proposed approach and the comparison of the results with previous methods show efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

Hub location problem is further used in transportation and telecommunication networks (airlines, post delivery services, etc.) so origin-destination pairs, receive or send commodities via special facilities called Hub. Hub median problem with multiple allocation is an NP-hard problem which includes both locating hub facilities and allocating non-hub nodes to hubs as minimizes total transportation and location costs. In this paper, the hub median problem is considered in an environment which network flow varies during the time periods and the capacities of hubs and arcs are unlimited. Also opening and closing hubs in different periods of planning horizon are permitted. The model and the proposed algorithm for this problem were considered to Iran airlines network using real passenger flows data. Computational results state that the dynamic network compared with static model has lower cost and whatever the number of time periods in dynamic case increases, the cost will be reduced as well. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of deciding the locations of dry ports by providing an intermodal rail-road p-hub median model, adopting a bi-level programming approach. In the proposed model, direct transportation and shipment between nodes are allowed instead of transportation merely through the hubs. In the bi-level programming approach, at the top level, the government/authority will decide the locations of the dry ports to increase the utilization of railways and minimize the construction and maintenance costs of dry ports as one of the important transportation infrastructures. Freight forwarders who are considered at the lower level aim to minimize the shipping costs by deciding the optimal shipping routes. A matheuristic approach based on the Genetic Algorithm (GA) is proposed to solve the given problem. Numerical analysis confirms that the proposed algorithm can provide satisfactory solutions for large instances where commercial solvers are not capable of finding the near optimal solutions in a reasonable computational time. Finally, the experimental results show that using the proposed transportation network model can decrease the total transportation costs along with significantly reduction of air pollution. [ABSTRACT FROM AUTHOR]

Abstract: The multiresponse surface problem is modelled as a multiobjective stochastic optimisation, and diverse solutions are proposed. There are several crucial differences highlighted between this approach and the other proposed solutions. Finally, some particular solutions are applied and described in detail in a numerical example. [Copyright &y& Elsevier]

Abstract: This paper proposes a new mathematical model to solve the cell formation, operator assignment and inter-cell layout problems, simultaneously. The objectives of proposed model are minimization of inter–intra cell part trips, machine relocation cost and operator related issues. Since the objective function of the proposed model consists of none commensurable statements, the preferred solution is obtained by the LP-metric approach. In order to validate the proposed model, some numerical examples are generated randomly and solved by branch and bound technique. Moreover; a real case study is illustrated in order to verify its applicability in an automobile producer company. Moreover the sensitivity analysis of proposed model shows that considering the operator assignment problem has significant impact on the overall system efficiency. [Copyright &y& Elsevier]

Abstract: This study considers supply chain network configuration in an innovative environment while the new product development (NPD) will affect the supply chain configuration (SCC). The time of new product introduction has a significant effect on the market performance while it has an effect on the supply chain configuration. Supplier integration into the new product introduction is the key parameter for successfully new product introduction, which may contribute to supply chain reconfiguration. Consequently By considering the new product development concept, we may face with dynamic supply chain configuration during a planning horizontal time. In this study, a new model is presented to consider the dynamic configuration of a supply chain by developing new products. In the proposed model, the dynamic configuration of a supply chain and the new product launching time is optimized simultaneously. The proposed model considers production, sales and transportation planning for the entire supply chain in order to achieve an integrative and efficient supply as well. Then some numerical analyses have been done to show the applicability of the proposed model. The results show that the new product development has a significant effect on the configuration of supply chain. [Copyright &y& Elsevier]

Setting of process variables to meet the required specification of quality characteristics is a common problem in the process quality control. To obtain the most satisfactory solution, a decision maker's (DM) preference information on the trade-offs among the quality characteristics should be incorporated into the optimization procedure. In this regard, several multiple response surface optimization (MRO) techniques have been proposed in recent years. Most of these techniques require that all the preference information is specified in advance which is very difficult in practice. Furthermore, most of them assume independency of quality characteristics where their variances are constant over the experimental space. An interactive approach to optimize multiple responses is presented that does not require any information about DM's preference before solving process. This method aims to identify process variables to consider correlation among quality characteristics and minimize the variation in deviation of responses from their targets. It also accommodates dispersion effects and specification limits as well as location effects in a unified framework based on desirability functions. The features of the proposed method are investigated and the results are compared with some existing techniques through a real numerical example. Obtained results indicate the superiority of proposed methodology with respect to the major existing MRO techniques. [ABSTRACT FROM AUTHOR]

This paper presents a robust design for a multi-product, multi-echelon, closed-loop logistic network model in an uncertain environment. The model includes a general network structure considering both forward and reverse processes that can be used in various industries, such as electronics, digital equipment, and vehicles. Because logistic network design is a time consuming and costly project as well as a strategic and sensitive decision (i.e. the change of such decision is difficult in the future), a robust optimisation approach is adopted to cope with the uncertainty of demand and the return rate described by a finite set of possible scenarios. Hence, to obtain robust solutions with better time, the scenario relaxation algorithm is employed for the proposed model. Numerical examples and a sensitivity analysis are presented to demonstrate the significance and applicability of the presented model. It is shown that solutions resulted from the suggested approach insure more situations, especially in worst case ones. The results show that although the profit values of the robust configuration are less than the deterministic configuration, the robust configuration is more reliable than the deterministic one because the deterministic configuration is infeasible under some demand and return rates (i.e. in the worst cases). Moreover, the results show the computing time superiority of the algorithm compared to the extensive form model as well as optimality of the resulted solutions. [ABSTRACT FROM AUTHOR]

Abstract: Hub and spoke networks are used to switch and transfer commodities between terminal nodes in distribution systems at minimum cost and/or time. The p-hub center allocation problem is to minimize maximum travel time in networks by locating p hubs from a set of candidate hub locations and allocating demand and supply nodes to hubs. The capacities of the hubs are given. In previous studies, authors usually considered only quantitative parameters such as cost and time to find the optimum location. But it seems not to be sufficient and often the critical role of qualitative parameters like quality of service, zone traffic, environmental issues, capability for development in the future and etc. that are critical for decision makers (DMs), have not been incorporated into models. In many real world situations qualitative parameters are as much important as quantitative ones. We present a hybrid approach to the p-hub center problem in which the location of hub facilities is determined by both parameters simultaneously. Dealing with qualitative and uncertain data, Fuzzy systems are used to cope with these conditions and they are used as the basis of this work. We use fuzzy VIKOR to model a hybrid solution to the hub location problem. Results are used by a genetic algorithm solution to successfully solve a number of problem instances. Furthermore, this method can be used to take into account more desired quantitative variables other than cost and time, like future market and potential customers easily. [Copyright &y& Elsevier]

Abstract: Logistic network design has an important and strategic platform in an efficient and effective supply chain management, and usually involves multiple and conflicting goals, such as cost/profit, resource balance, customer responsiveness, quality, and the like. Besides, due to the implementation of government legislation, environmental concern, social responsibility and customer awareness, companies have been forced by customers not only to supply environmentally amicable products but also to be responsible for the returned products. Hence, this paper presents a stochastic multi-objective model for forward/reverse logistic network design under a uncertain environment including three echelons in forward direction (i.e., suppliers, plants, and distribution centers) and two echelons in backward direction (i.e., collection centers and disposal centers). We demonstrate a method to evaluate the systematic supply chain configuration maximizing the profit, customer responsiveness, and quality as objectives of the logistic network. The set of Pareto optimal solutions is obtained and also financial risk relevant to them is computed in order to show the tradeoff between objectives. The results give important insight for fostering the decision making process. [Copyright &y& Elsevier]

Process capability indices (PCIs) are used in statistical process control to evaluate the capability of the processes in satisfying the customer's needs. In the past two decades varieties of PCI are introduced by researchers to analyze the process capability with univariate or multivariate quality characteristics. To the best of our knowledge, most famous multivariate capability indices are proposed when the quality characteristics have both upper and lower specification limits. These indices are incapable to assess the multivariate processes capability with unilateral specification. In this article, we propose a new multivariate PCI to analyze the processes with one or more unilateral specification limits. This new index also accounts for all problems in the best PCIs of the literature. The performance of the proposed index is evaluated by real cases under different situations. The results show that the proposed index performs satisfactorily in all cases considered. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a developed approach to Multiple Response Optimization (MRO) in two categories; responses without replicates and with some replicates based on fuzzy concepts. At first, the problem without any replicate in responses is investigated, and a fuzzy Decision Support System (DSS) is proposed based on Fuzzy Inference System (FIS) for MRO. The proposed methodology provides a fuzzy approach considering uncertainty in decision making environment. After calculating desirability of each response, total desirability of each experiment is measured by using values of each response desirability, applying membership function and fuzzy rules expressed by experts. Then Response Surface Methodology (RSM) is applied to fit a regression model between total desirability and controllable factors and optimize them. Next, a methodology is proposed for MRO with some replicates in responses which optimizes mean and variance simultaneously by applying fuzzy concepts. After introducing Deviation function based on robustness concept and using desirability function, a two objective problem is constituted. At last, a fuzzy programming is expressed to solve the problem applying degree of satisfaction from each objective. Then the problem is converted to a single objective model with the goals of increasing desirability and robustness simultaneously. The obtained optimum factor levels are fuzzy numbers so that a bigger satisfactory region could be provided. Finally, two numerical examples are expressed to illustrate the proposed methodologies for multiple responses without replicates and with some replicates. [ABSTRACT FROM AUTHOR]

Abstract: Today the most important concern of the managers is to make their firms viable in the competitive trade world. Managers are looking for effective tools for decision making in the complex business world. This paper presents a new mathematical model for strategic and tactical planning in a multiple-echelon, multiple-commodity production–distribution network. In the proposed model, different time resolutions are considered for strategic and tactical decisions. Also expansion of the network is planned based on cumulative net incomes. To illustrate applications of the proposed model as well as its performance based on the solution times, some hypothetical numerical examples have been generated and solved by CPLEX. Results show that in small and medium scale of instances, high quality solutions can be obtained using this solver, but for larger instances, some heuristics has to be designed to reduce solution time. [Copyright &y& Elsevier]

Abstract: Response surface methodology (RSM) is a statistical–mathematical method used for analyzing and optimizing the experiments. In analysis process, experts usually face several input variables having effect on several outputs called response variables. Simultaneous optimization of the correlated response variables has become more important in complex systems. In this paper multi-response surfaces and their related stochastic nature have been modeled and optimized by Goal Programming (GP) in which the weights of response variables have been obtained through a Group Decision Making (GDM) process. Because of existing uncertainty in the stochastic model, some stochastic optimization methods have been applied to find robust optimum results. At the end, the proposed method is described numerically and analytically. [Copyright &y& Elsevier]

The transfer point location problem has been introduced recently and for the case of minimax objective and planar topology, has only been studied for situations in which demand points are not weighted and have known coordinates. In this paper, we consider the case in which demand points are weighted and their coordinates have bivariate uniform distribution. Also, the problem is developed from a conceptual view and different distance measures are used to make models more applicable in real world situations. The problem is to find the best location for the transfer point such that the maximum expected weighted distance to all demand points through the transfer point is minimized. Depending on assumptions for uniform distributions, two models are considered, convexity conditions are discussed, properties of the optimal solution are obtained and methods to solve the problems are proposed. Finally, numerical examples are given. [ABSTRACT FROM AUTHOR]

Abstract: Multiple response problems include three stages: data gathering, modeling and optimization. Most approaches to multiple response optimization ignore the effects of the modeling stage; the model is taken as given and subjected to multi-objective optimization. Moreover, these approaches use subjective methods for the trade off between responses to obtain one or more solutions. In contradistinction, in this paper we use the Prediction Intervals (PIs) from the model building stage to trade off between responses in an objective manner. Our new method combines concepts from the goal programming approach with normalization based on negative and positive ideal solutions as well as the use of prediction intervals for obtaining a set of non-dominated and efficient solutions. Then, the non-dominated solutions (alternatives) are ranked by the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) approach. Since some suggested settings of the input variables may not be possible in practice or may lead to unstable operating conditions, this ranking can be extremely helpful to Decision Makers (DMs). The consideration of statistical results together with the selection of the preferred solution among the efficient solutions by Multiple Attribute Decision Making (MADM) distinguishes our approach from others in the literature. We also show, through a numerical example, how the solutions of other methods can be obtained by modifying the relevant approach according to the DM’s requirements. [Copyright &y& Elsevier]

Abstract: Managers in the current business environment face with many decision making problems that affect viability of their organization. Decision making tools and evaluation instruments can help the managers to make more accurate decisions. This paper presents a new decision making tool when decision data are not crisp and the decision maker(s) want(s) to rank the alternatives during an interactive process. In this paper we propose an interactive method which uses qualitative data to calculate weights of criteria and rank the selected alternatives. Because of existence of linguistic terms in the decision matrix and the weight of each criterion which can be expressed in trapezoidal fuzzy numbers, an interactive method is proposed for ranking of each alternative with the best weight for each criterion. Using this method, decision makers can provide and modify their preference information gradually within the process of decision making so as to make the decision result more reasonable. A numerical hypothetical example has been illustrated the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: In the current competitive environment managers of manufacturing and service organizations try to make their organizations competitive by providing timely delivery of high quality products. Maintenance, as a system, plays a key role in reducing cost, minimizing equipment downtime, improving quality, increasing productivity and providing reliable equipment and as a result achieving organizational goals and objectives. This paper presents a new approach for selecting optimum maintenance strategy using qualitative and quantitative data through interaction with the maintenance experts. This approach has been based on linear assignment method (LAM) with some modifications to develop interactive fuzzy linear assignment method (IFLAM). The proposed approach is an interactive method which uses qualitative and quantitative data to rank the maintenance strategies. This method helps managers to find the best maintenance strategy based on the determined criteria. Maintenance experts also can provide and modify their preference information gradually within the interaction process so as to make the result more reasonable. The proposed method has been illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Abstract: Local considerations are one of the major pitfalls in designing, operating and managing systems. As such are the “Supply Chain design decisions”, hence we have tried to incorporate systems thinking approach, and in particular the “Theory of Constraints”, into the problem of “Locating a new Distribution Center” among a producer and a set of existing retailers. While customers face known random demand, we look for the least cost location candidate in a plane with direct shipping. Usefulness of an equity function to balance the inventory levels at retail shops is also discussed. The model is finally reduced to a single nonlinear objective and for a hypothetical numerical example it is solved by Particle Swarm Optimization method. [Copyright &y& Elsevier]

Multifacility location problems are considered to find optimal location of new facilities with respect to a set of existing ones. Generally, the objective of the problem is minimizing the total distance traveled between facilities. In this paper, a methodology is proposed for multifacility location problem, investigating decision makers’ opinion as a group decision-making problem. Since uncertainty of information in decision-making environment exists, we consider it by applying a fuzzy set theory. At first, the problem is formulated as a fuzzy multiobjective model. After introducing a decision support system based on fuzzy inference system and using fuzzy analytic hierarchy process approach for obtaining weights between facilities, at last, a two-phase fuzzy linear programming considering the concept of degree of satisfaction of each objective is applied to solve the multiobjective problem. Finally, a numerical example is given to illustrate the proposed method and a sensitivity analysis is expressed to show capability of the method for generating different optimal solutions based on the importance of each decision maker’s opinion. [ABSTRACT FROM AUTHOR]

• Introducing a hierarchical operational hub location problem (HLP) to manage a cold chain of perishable products. • Tackling a p-hub median problem with refreshing and freezing operations for hubs. • Developing a genetic algorithm to solve the problem efficiently. • Applying a modified Feasibility Pump Algorithm for the underlying problem. This paper proposes a multi-objective mixed-integer linear programming to model a cold chain with complementary operations on a hierarchical hub network. Central hubs are linked to each other in the first level of the network and to the star network of the lower-level hubs. As for a case study, different hub levels provide various refreshing or freezing operations to keep the perishable goods fresh along the network. Disruption is formulated by the consideration of stochastic demand and multi-level freshness time windows. Regarding the solution, a genetic algorithm is also developed and compared for competing the large-sized networks. [ABSTRACT FROM AUTHOR]

• Proposing a stochastic model for two-layers time window assignment VRP. • Considering of time window violations penalty cost in the proposed model. • Using the Progressive Hedging algorithm to solve the TL-TWAVRP. • Sensitivity analysis to check the validity & efficiency of the model and algorithm. This paper presents an effective Progressive Hedging algorithm for vehicle routing problem with two-layers time window assignment and stochastic service times (2L-TWAVRPSST). Based on a predefined exogenous time window determined by the customers, an endogenous time window is assigned to each customer. Endogenous time windows have flexible width and composed of two-layers. The outer layer is wider than inner layer and is determined by violation variable. This approach aims to giving more flexibility to career companies for serving more customers using less vehicles. Customers could be visited even after the end of their assigned time windows by paying proportional penalty, while extra violation from the outer layer is not permitted. This problem is formulated as a two-stage stochastic model with the first-stage decisions of assigning inner and outer layers time window. Then in the second stage, routes are planned for each scenario combination of stochastic demand and service time. The validity and effectiveness of the proposed model was examined by various numerical examples. The problem was solved by a Progressive Hedging (PH) algorithm for large-scale instances. The results confirm efficiency of the considered solution approach in different instances. [ABSTRACT FROM AUTHOR]

• A model for supply chain network design with sustainable development paradigm. • Proper carbon regulatory mechanisms to improve three dimensions of sustainability. • To achieve balanced regional development by prioritizing less developed regions. • Utilizing the shipment consolidation decisions to achieve sustainability goals. • Tackling the carbon emission uncertainty using the robust optimization. Nowadays, sustainability issues have received considerable attention in supply chain management because of the governmental requirements as well as expectations of the people. This paper introduces a novel supply chain network design problem to cover three dimensions of sustainability, namely economic, environmental, and social. The advantage of the presented model stems from considering the booming development aligned with reduction in environmental impact. In this paper, to achieve the mentioned benefits and to derive a more sustainable supply chain, a novel model in the presence of the most commonly used carbon policies is proposed. This paper, addresses sustainable development through imposing proper carbon regulatory mechanisms. Main contribution of this study is to consider the effect of imposing carbon policies on environmental advantages as well as improving the regional development level in a supply chain network design problem. Moreover, the shipment consolidation decisions are utilized to reduce cost as well as environmental impact. In addition, a novel mixed uncertainty approach is proposed to capture the uncertain emission parameters. The numerical examples and a case study are analyzed to evaluate the performance of the proposed models. It is concluded that, a high-growth economy with low-carbon can be made and also almost global well-being of people is ensured by applying the proposed model. Some managerial insights are provided for the enterprises of supply chains to make the most appropriate sustainable decisions. Finally, proper carbon emission policies are suggested based on the region sustainability characteristics. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s10479-022-05117-0.Correction To: Annals of Operations Researchhttps://doi.org/10.1007/s10479-022-05117-0Typesetter overlooked affiliation of author Benny Tjahjono and should be read as "Centre for Business in Society, Coventry University, Coventry, UK."Original article has been updated.Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.By Elham Ziar; Mehdi Seifbarghy; Mahdi Bashiri and Benny TjahjonoReported by Author; Author; Author; Author [Extracted from the article]

Abstract This paper presents a bi-objective hierarchical hub location problem with hub facilities as servicing centers. The objectives are to minimize the total cost (i.e., fixed cost of establishing hub facilities and transportation cost) and the maximum route length, simultaneously. Hub facilities are categorized as central and local ones. The queuing frameworks for these types of facilities are considered as M ∕ M ∕ c and M ∕ M ∕ 1 , respectively. Moreover, density functions for the traveling time and number of entities are assumed to be Exponential and Poisson functions. The presented mathematical model is solved by a new game theory variable neighborhood fuzzy invasive weed optimization (GVIWO) as introduced in this paper. To evaluate the efficiency of this proposed algorithm, some experiments are conducted and the related results are compared with the non-dominated sorting genetic algorithm (NSGA-II) and hybrid simulated annealing (HSA) algorithm with respect to some comparison metrics. The results show that the proposed GVIWO algorithm outperforms the NSGA-II and HSA. Finally, the conclusion is provided. Graphical abstract Highlights • Presenting a new bi-objective hierarchical hub location problem that minimizes the total cost and the maximum route length. • Considering the M ∕ M ∕ c and M ∕ M ∕ 1 queuing frameworks for central and local hub facilities, respectively. • Applying an efficient method based on the ME measure to cope with the uncertainty. • Proposing a new Game theory Variable neighborhood fuzzy Invasive Weed Optimization (GVIWO) method. • Comparing the GVIWO results with two well-known meta-heuristic algorithms by some comparison metrics. [ABSTRACT FROM AUTHOR]

Summary: Electric power systems try to maximize resilience by various enhancement strategies as preventive and corrective actions against extreme weather events. This paper presents an operational network reconfiguration strategy during a high wind event to strengthen the resiliency of distribution networks. In the proposed resilience enhancement strategy, a bi‐level optimization problem is formulated with two conflicting objectives (i) maximizing grid resilience and (ii) realize the primary objective by a minimum number of out‐of‐service lines (OSLs) switching operations. Furthermore, the algorithm considers the priority of loads, which is an important characteristic of modern distribution grids. The optimization problem is solved by the bi‐level genetic algorithm (BiGA). The IEEE 33‐bus network is utilized to demonstrate the application of the developed algorithm. The simulation results show a significant improvement in the resiliency of the tested network and reveal that switching operations can be used effectively to increase the resiliency of distribution networks against natural disasters. Furthermore, sensitivity analysis is performed to show the effectiveness of the proposed approach for finding the global optimal point. The proposed network reconfiguration strategy helps to prepare for severe weather conditions. [ABSTRACT FROM AUTHOR]

Background: Compared to males, female adolescents show greater concerns about their appearance, concerns related to their self-esteem. We explored the associations between self-esteem, body image and BMI as proxies for appearance, and eating-disordered behavior among adolescent females. Methods: A total of 263 females (mean age:15.78 years) took part in this study. They completed questionnaires covering anthropometric characteristics, self-esteem, eating-disordered behavior, subjective physical activity levels, and body image. Results: Higher scores for self-esteem were associated with higher scores for eating-disordered behavior, indices of physical activity, and slimmer body image. Body image was not associated with eating-disordered behavior. Multiple regression analyses showed that self-esteem, but not physical activity, or body image predicted eating-disordered behavior. Conclusions: Among a non-clinical sample of female adolescents, self-esteem and eating-disordered behavior were positively associated. Body image was associated in a complex and contradictory fashion. It is possible that cognitive-emotional mastering of the vital impulse to eat may enhance self-esteem. [ABSTRACT FROM AUTHOR]

With the capacity increase of large‐scale DC renewable energy and DC loads, the reliability issues of hybrid AC/DC networks become increasingly prominent. On the other hand, a modern distribution network should also deliver power to individual customers based on their priority levels and the traditional reliability indices are not suitable for modern networks. Flexible reliability (FR) is a new concept that considers the loads' importance in the reliability evaluation of distribution grids. In this study, a novel comprehensive FR index is introduced for hybrid AC/DC networks. To determine the proposed index, power networks are modelled as a topological combination of edges and nodes. An axiomatic design concept which is a systematic binary matrix‐based approach is then employed for topology analysis. The proposed index is based on the systematic counting of feasible paths from generators to loads, considering loads' importance. The effectiveness of the proposed index is demonstrated through an illustrative example and IEEE 33‐bus distribution system. [ABSTRACT FROM AUTHOR]