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Mousavi S.M.,Shahed University | Tavakkoli-Moghaddam R.,University of Tehran | Tavakkoli-Moghaddam R.,Research Center for Organizational Processes Improvement
Engineering Applications of Artificial Intelligence | Year: 2015

Most of complex selection problems in real-life applications are considered under multiple conflicting attributes for manufacturing firms. The appropriate selection plays an important role in the firms performance from the tactical and operational viewpoints. The classical methods for the selection problems in manufacturing firms are inadequate to deal with uncertainties, including insufficiency in information availability and the imprecise or vague nature in experts judgments and preferences. To overcome these difficulties, this paper introduces a novel distance-based decision model for the multi-attributes analysis by considering the concepts of intuitionistic fuzzy sets (IFSs), grey relations and compromise ratio approaches. A weighting method for the attributes is first developed based on a generalized version of the entropy and IFSs along with experts judgments. Then, a new grey relational analysis is introduced to analyze the extent of connections between two potential scenarios by an intuitionistic fuzzy distance measurement. Finally, a new intuitionistic fuzzy compromise ratio index to prioritize the scenarios is proposed by considering the weight of the strategy for the maximum group utility in intuitionistic fuzzy grey environment. The feasibility and practicability of the proposed distance-based decision model is illustrated in detail, and it is implemented in a real case study to the inspection planning for the oil pump housing from Renault automobile manufacturing. © 2014 Elsevier Ltd.


Nekooghadirli N.,Islamic Azad University at Tehran | Tavakkoli-Moghaddam R.,University of Tehran | Tavakkoli-Moghaddam R.,Research Center for Organizational Processes Improvement | Ghezavati V.R.,Islamic Azad University at Tehran | Javanmard S.,Islamic Azad University at Tehran
Computers and Industrial Engineering | Year: 2014

This paper presents a novel bi-objective location-routing-inventory (LRI) model that considers a multi-period and multi-product system. The model considers the probabilistic travelling time among customers. This model also considers stochastic demands representing the customers' requirement. Location and inventory-routing decisions are made in strategic and tactical levels, respectively. The customers' uncertain demand follows a normal distribution. Each vehicle can carry all kind of products to meet the customer's demand, and each distribution center holds a certain amount of safety stock. In addition, shortage is not allowed. The considered two objectives aim to minimize the total cost and the maximum mean time for delivering commodities to customers. Because of NP-hardness of the given problem, we apply four multi-objective meta-heuristic algorithms, namely multi-objective imperialist competitive algorithm (MOICA), multi-objective parallel simulated annealing (MOPSA), non-dominated sorting genetic algorithm II (NSGA-II) and Pareto archived evolution strategy (PAES). A comparative study of the forgoing algorithms demonstrates the effectiveness of the proposed MOICA with respect to four existing performance measures for numerous test problems. © 2014 Elsevier Ltd. All rights reserved.


Aghajani-Delavar N.,Islamic Azad University at Qazvin | Mehdizadeh E.,Islamic Azad University at Qazvin | Torabi S.A.,University of Tehran | Tavakkoli-Moghaddam R.,University of Tehran | Tavakkoli-Moghaddam R.,Research Center for Organizational Processes Improvement
International Journal of Engineering, Transactions B: Applications | Year: 2015

This paper presents a new mathematical model for integrated dynamic cellular manufacturing systems and production planning that minimizes machine purchasing, intra-cell material handling, cell reconfiguration and setup costs. The proposed model forms the manufacturing cells and determines the quantity of machine and movements during each period of time. This problem is NP-hard, so a meta-heuristic algorithm based on genetic algorithm (GA) is developed to solve it. Experimental results confirm the efficiency and the effectiveness of the proposed GA to provide good solutions, especially for medium and large-sized problems. © 2015, Materials and Energy Research Center. All rights reserved.


Ghodratnama A.,Kharazmi University | Tavakkoli-Moghaddam R.,University of Tehran | Tavakkoli-Moghaddam R.,Research Center for Organizational Processes Improvement | Kalami-Heris S.M.,K. N. Toosi University of Technology | Nagy G.,University of Kent
Journal of Intelligent and Fuzzy Systems | Year: 2015

This paper deals with three characteristics of transportation costs, crowding and traffic costs, and the costs of hub installation. The main aim of this paper is to define the independent cost function in order to connect to the crowding rate and incurred cost in an exponential way not considered in the literature directly. In this function, the independent variable is the crowding and traffic rate input, and the output is the cost incurred. However, involving three separate objective functions namely total cost, congestion and hub installation costs are not considered up to now. Also, considering the contrast among three foregoing costs, each function is considered independently. Due to the NP-hardness of this kind of problem to solve this multi-objective mathematical model, at first we devised an efficient approach to navigate through the feasible solution space iteratively without using penalty function. To solve our developed multi-objective mathematical model we propose five multi-objective meta-heuristic algorithms, namely 1) NSGA-II with an elitism solution, 2) NSGA-II without an elitism solution, 3)NRGAwith an elitism solution, 4) NRGA without an elitism solution, and 5) MOPSO. Finally, three criteria are used to compare the related results obtained by these five algorithms. © 2015-IOS Press and the authors.


Poursafary S.,Mazandaran University of Science and Technology | Javadian N.,Mazandaran University of Science and Technology | Tavakkoli-Moghaddam R.,University of Tehran | Tavakkoli-Moghaddam R.,Research Center for Organizational Processes Improvement
International Journal of Engineering, Transactions A: Basics | Year: 2014

Nowadays, in majority of academic contexts, it has been tried to consider the highest possible level of similarities to the real world. Hence, most of the problems have complicated structures. Traditional methods for solving almost all of the mathematical and optimization problems are inefficient. As a result, meta-heuristic algorithms have been employed increasingly during recent years. In this study, a new algorithm, namely Seeker Evolutionary Algorithm (SEA), is introduced for solving continuous mathematical problems, which is based on a group seeking logic. In this logic, the seeking region and the seekers located inside are divided into several sections and they seek in that special area. In order to assess the performance of this algorithm, from the available samples in papers, the most visited algorithms have been employed. The obtained results show the advantage of the proposed SEA in comparison to these algorithms. At the end, a mathematical problem is designed, which is unlike the structure of meta-heuristic algorithms. All the prominent algorithms are applied to solve this problem, and none of them is able to solve.


Tavakkoli-Moghaddam R.,University of Tehran | Tavakkoli-Moghaddam R.,Research Center for Organizational Processes Improvement | Sadri S.,University of Tehran | Pourmohammad-Zia N.,University of Tehran | Mohammadi M.,University of Tehran
Journal of Intelligent and Fuzzy Systems | Year: 2015

A closed-loop supply chain (CLSC) network consists of both forward and reverse supply chains. In this paper a CLSC network is investigated that involves four echelons in a forward direction including suppliers, manufacturer, distribution center and demand market, and three echelons in a backward direction including disposal, rework and collection centers. This paper presents a bi-objective model in order to design a network of bi-directional facilities in logistics network under uncertainties. Its objectives are to minimize the total costs as well as the total defective rate, disposal rate and pollution production rate. To solve the model, a hybrid solution approach is applied that combines fuzzy possibilistic programming and fuzzy multi-objective programming. Furthermore, in order to illustrate the validity of the model and applicability of the proposed solution approach, numerical experiments and the related sensitivity analysis are provided. Finally, the conclusion is provided. © 2015-IOS Press and the authors.


Ghezavati V.R.,Islamic Azad University at South Tehran | Hooshyar S.,Islamic Azad University at South Tehran | Tavakkoli-Moghaddam R.,University of Tehran | Tavakkoli-Moghaddam R.,Research Center for Organizational Processes Improvement
Central European Journal of Operations Research | Year: 2015

This paper presents a periodical planning mathematical model for distribution of fresh agri-food (a case study of tomato) after qualitative segregating. The main objective of the model is to maximize the profit of a distributor that has relative control on logistics decisions associated with distribution of fresh products in a agri-food supply chain. In a real world, there are some differences between suitable qualities of each customer and thus, fair pricing is determined by their level of satisfaction. Simultaneously, this model takes into account freshness and ripeness as for the food grade. For estimation of the ripeness, a formulation is used that is related to postharvest biological behavior of the fresh crops. In turn, quality loss functions for quantification of degrading are designed to accommodate fair pricing. In addition, potential warehouses are considered in this model to achieve suitable maturity and service level. This paper presents a mixed integer programming model according to the problem descriptions. Since the model is hard to be solved for large scale problems, a primal decomposition solution procedure is proposed based on Benders’ decomposition method. Meanwhile, performance of the proposed solution method will be evaluated through some test problems. Finally, the model is validated through decision making for a domestic distributor of fresh tomato in Iran. © 2015 Springer-Verlag Berlin Heidelberg

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