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Industrial and Systems Engineering - Volume:10 Issue: 4, Autumn 2017

Journal of Industrial and Systems Engineering
Volume:10 Issue: 4, Autumn 2017

  • تاریخ انتشار: 1396/08/24
  • تعداد عناوین: 10
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  • Kaveh Fahimi, Seyed Mohammad Seyedhosseini, Ahmad Makui Pages 1-27
    This paper develops a decentralized leader-follower game for network design of a competitive supply chain problem in which a new chain as the leader enters a market with one existing supply chain as a follower. Both chains produce an identical product, customer demand is inelastic and customer utility function is based on Huff gravity-based model. The leader wants to shape his network and set assignments where the follower will show reactions by changing her networks in a sequential manner.
    Multi-level mixed integer nonlinear programming model is used to model the problem. Each chain can enter the market in centralized, decentralized or cooperative modes. Enumeration method is applied to solve the problem by the help of Stackelberg equilibrium concept. Finally, some numerical examples are used to explore the algorithm and different mode structures affect the equilibrium solution.
    Keywords: Competitive supply chain network design_leader - follower game_bi-level programming model_multi-level programming model
  • Mohammad Saeedi Mehrabad, Armin Jabarzadeh, Mahyar Alimian Pages 28-42
    Production planning and maintenance are two important problems in manufacturing systems. Despite the relationship exists between these two problems due to sudden failures and production capacity occupied by maintenance activities, each of these problems planned separately and as a result program and model efficiencies reduce in the real world. The aim of integrated production and maintenance planning models is to plan production and maintenance simultaneously due to the interaction of these two programs on one another. In this study, an integrated model is provided for planning production and preventive maintenance in a multi-state system. This model can be used to determine the production planning variables such as time to setup, the levels of production, inventory and shortages simultaneously with maintenance planning variables such as time and type of preventive maintenance. This research considers the concept of imperfect maintenance in modeling the preventive maintenance variable. At the end, a numerical example is used to assess the performance of the model. It is shown that using proposed model with two type of preventive maintenance will reduce the total cost of the integrated problem.
    Keywords: Production Planning, Preventive Maintenance, Multi-State System, Integrated Model
  • Mojtaba Salehi, Mohammad Mahdi Goorkani Pages 43-68
    This research presents a model for optimal allocation of Iranian oil and gas resources in sanction condition based on stochastic linear multi-objective programming. The general policies of the resistive economy include expanding exports of gas, electricity, petrochemical and petroleum products, expanding the strategic oil and gas reserves, increasing added value through completing the petroleum value chain and decreasing crude oil and gas sale. The proposed mathematical model includes three objective functions: minimizing imports of petrochemical products and crude oil sale, maximizing economic benefits, and minimizing the environmental pollutions. The model includes constraints of gas, oil, and electricity flow balance and also supply and demand capacity constraints. A Pareto-archive-based particle swarm algorithm was used to solve the model. The results of proposed algorithm were compared with NSGA-IIresults. The comparison showed the proposed algorithm is more accurate in solving of the energy resource allocation model in 2016-2031 timespan. The results of this study can present helpful solutions to oil and gas resource allocation planning in Iran. The main contribution of this paper is proposing a new stochastic linear multi-objective programming with considering the general policies of resistive economy and solving the model with a new Pareto-archive-based particle swarm algorithm.
    Keywords: Oil, Gas Resource Allocation, Stochastic Linear Multi-Objective Programming, Economic Sanction, Resistive Economy, Particle swarm optimization
  • Yahya Zare Mehrjerdi, Amin Yazdekhasti Pages 69-95
    Nowadays, offering extended warranty is considered as a lucrative source of income from the perspective of the after-sale service providers. Meanwhile, the main concern is presence or absence of base warranty and strategies adopted by the manufacturer during this period. Moreover, extended warranty structure must be responsive and customer oriented, which not only control the services cost but also to handle the customers’ requirement in a timely manner. In this paper, an extended warranty distribution network is designed from the perspective of a third party (3P) for supporting multi-indenture products in conjunction with base warranty. The proposed network is two-echelon; in this regard, a depot repair center is considered as the first echelon and a number of operational repair centers are selected at the second echelon. In order to decrease the cost of maintenance and spare parts logistics, a novel imperfect preventive maintenance approach is established based on the concept of virtual age. The third party aims to determine the optimum level of spare parts for each component of products at each repair centers in a way that: (1) total expected backorders is minimized (2) total maintenance and retrieval costs of product components are controlled. For optimizing the proposed model, an exact hybrid solution approach regarding Branch-and-Bound algorithm and Variable Neighborhood Search is presented. The obtained results showed the presence of a base warranty on a product has more advantages for third parties even without preventive maintenance.
    Keywords: Extended warranty, Warranty distribution network, Imperfect preventive maintenance, Branch, Bound Algorithm, Variable neighborhood search algorithm, Monte-Carlo simulation
  • Kazem Noghondarian, Emran Mohammadi, Ali Shahrabi Farahani Pages 96-109
    Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedure of these methods. Furthermore, we analyzed the performance of these methods in prediction the global gold price. For this purpose, 200 gold price data from February 2015 to October 2015 were gathered. We used both methods for determination of model parameters then we predicted the test data. With respect to reliable standards of evaluation prediction as root mean square of errors, it was seen that in time series data, prediction of adaptive neuro-fuzzy inference system model is more accurate than the auto-regressive integrated moving average model. So we can conclude that at least in some cases where time series have a non-linear trend, it is better to use adaptive neuro-fuzzy inference system model for prediction. In this manner, we can reach our goals in future with higher accuracy in our decisions, in future.
    Keywords: Adaptive Neuro-Fuzzy Inference System, auto-regressive integrated moving average, global gold price
  • Fatemeh Bayatloo, Ali Bozorgi-Amiri, Fatemeh Sabouhi Pages 110-122
    Nowadays, utilization of renewable energies for satisfying electricity demand has received more interest, and renewable electricity generation has been growing in the world. This study addresses the operational planning of a renewable electricity supply chain over a multi-period planning horizon. The purpose of this study is to maximize total profit and to optimize the operational decisions related to power transmission and storage in a wind-based electricity supply chain. The applicability of the developed model is demonstrated by a case study. Due to the wind intermittency and demand variations, some probable scenarios are considered. Sensitivity analysis provides several managerial insights. Numerical results indicate that line capacity expansion can make a good promotion in each scenario by reducing unmet demand and making more profit. Moreover, incorporating an electricity storage system in wind farms improves demand covering in peak load hours.
    Keywords: Renewable energies, electricity supply chain, operational planning, electricity storage
  • Ghorbanali Moslemipour Pages 123-140
    In this paper, a novel quadratic assignment-based mathematical model is developed for concurrent design of robust inter and intra-cell layouts in dynamic stochastic environments of manufacturing systems. In the proposed model, in addition to considering time value of money, the product demands are presumed to be dependent normally distributed random variables with known expectation, variance, and covariance that change from period to period at random. This model is verified and validated by solving a number of different-sized test problems and a real world problemas well as doing sensitivity analysisby using the analysis of variance (ANOVA) technique.The validation process will be ended by investigating the effect of considering dependent product demands and time value of money (interest rate) on the total cost. Dynamic programming andsimulated annealing algorithms programmed in Matlab are used to solve the problems.Some conclusions can be summarised as follows: (i) the simulated annealing algorithm has a performance as good as the dynamic programming algorithm from solution quality point of view; (ii) the simulated annealingis a robust algorithm; (iii) different values of the input parameters lead to design of different facility layouts; (iv) total cost of inter and intra-cell layouts is affected by the interest rate and the percentile level; and (v) the proposed model can be used in both of the stochastic and deterministic environments.
    Keywords: simulated annealing, Stochastic dynamic programming, robust cell layout
  • Hamid Rastegar, Dr. Morteza Rasti-Barzoki Pages 141-157
    Project portfolio selection is very important subject of decision-makers in project-based organizations. The best assignment of resources to the most appropriate projects is necessary as financing projects with low benefit is just waste of organization's resources. However, existing project selection models pay not much attention the structure and special features of projects as a selection criterion, while their hardness may prolong the project duration and even result in stopping the project. Furthermore, the models cannot consider correlations between projects which may affect the results of projects. In this paper, a model is proposed to measure the structural hardness of projects. Then, a project portfolio selection model is proposed considering hardness and correlations between projects. A case study is presented to test the performance of the model in real world problems. At the end, some large-sized numerical example has been solved. The results show the capability of the model to solve large-sized problems in a suitable time. The important role of structural hardness in project selection was discussed and sensitivity analysis has been done.
    Keywords: Project portfolio, structural hardness, Correlation, project-based organization, decision-makers
  • Seyed Mahdi Aghazadeh, Mohammad Mohammadi*, Bahman Naderi Pages 158-176
    Nowadays, working alone on a context is not sufficient and reaching good and worthy results demands cooperation of multi sciences. Healthcare supply chain is one of these sciences that bridges engineering and healthcare sciences. This paper proposes a new multi–objective model for organ transplant supply chain, which is one of consequential fields in Healthcare supply chain, by aiming at having a more effective system. First objective function tries to minimize costs of opened centers, shipping organs, information, and allocations. In this regard, to increase number of transplantations and decrease shortage of demands, a penalty figure is also considered for remained inventory at the end of each period. The second objective function considers three important aspects of location in organ transplant supply chain which have not been studied yet, including; expected number of donors, coverage of other locations by taking into consideration the maximum remaining time for each organ out of body, and safety index. The last objective function tries to find routs with final total minimum time. At the end, some numerical experiments are done with using GAMS optimization software.
    Keywords: healthcare management, organ transplant supply chain, location efficiency, bi-objective MIP optimization
  • Mohammad Saeed Jabalameli*, Monireh Shoaraye Nejati, Mir Saman Pishvaee Pages 177-195
    In this paper, a novel resilient multi-echelon closed-loop location-allocation-inventory problem (RMCLIP) is addressed that optimizes strategic and tactical decisions simultaneously. In order to represent the purchasing cost of raw material from the supplier, a pricing model under quantity discounts is employed in the closed-loop supply chain (CLSC). Considering the capability of returning the reworked products to the forward logistics that can affect the ordering patterns of distribution centers (DCs) is another significant difference between this study and similar related researches. Furthermore, resilient capacity approach is used to provide a flexible SC toward the uncertainty of reworking centers (RCs) and suppliers' capacity. As this point, based on some facilities' capacity uncertainty, the robust model is formulated. The computational results and sensitivity analyses are presented using GAMS software to reveal the applicability of the proposed model. The results are analyzed in depth to provide some managerial insights.
    Keywords: Closed-loop supply chain, resilient capacity, robust, pricing, quantity discount, returned products