فهرست مطالب

Scientia Iranica - Volume:26 Issue:5, 2019
  • Volume:26 Issue:5, 2019
  • Transactions on Industrial Engineering (E)
  • تاریخ انتشار: 1398/07/08
  • تعداد عناوین: 9
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  • Parvaneh Keyvani, M. M. Lotfi * Pages 2885-2903

    In make-to-order systems, customers expect to have more freedom to choose the accessories. But demand variations and internal disorders induce some uncertainties. Hence, a different inventory system is needed for such items that dynamically manage those variations. In this paper, a dynamic approach based on the theory of constraints is proposed for inventory planning and control of accessories. First, the risk of processing time variation is balanced while keeping cycle time balancing. Second, the ribbons of buffer control charts are determined by a buffer planning model in which a multi-criteria ABC analysis is used to apply different customer service levels. To detect demand variations and monitor the buffer, trend of consumption in each monitoring window is carefully traced. Also, simulation-based procedures are recommended to update control ribbons. Comparing the performance of proposed approach to common methods using the data of an automobile company as well as several random test problems confirms that it can significantly reduce the costs and improve the efficiency of inventory system.

    Keywords: Accessory, Inventory management, Theory of constraints, Line Balancing, ABC analysis, Buffer Management
  • Mojtaba Soleimani Sedehi, Ahmad Makui *, Ehsan Bolandifar Pages 2904-2918
    Group purchasing organizations (GPOs) are well-known intermediary firms that play an important role in some supply chains. An important question that arises regarding the GPOs, is whether a GPO that benefits from group buying discounts, always benefit the OEMs in the presence of market competition. In other words, does a GPO always lead to a win-win outcome for OEMs and the GPO? To answer this question, a bargaining framework has been used to investigate competing OEMs' procurement's strategies. The entrance of a GPO in a two tier supply chain that consists of two competing OEMs with a common supplier that has a quantity discount menu is analyzed. The result shows that low purchasing cost GPO may harm OEMs in a cost-benefit perspective. This unintuitive result can be explained by different impacts that a GPO has in purchasing process. Although, it can enlarge the size of trade surplus; but, it has an important influence on the size of the slice of the pie (profit sharing). Moreover, an OEM's procurement strategy in equilibrium not just only depends on his bargaining power; but also depends on his competitor OEM. Interestingly, a strong OEM may not prefer procuring through GPO, as well as a weak OEM does.
    Keywords: Purchasing Strategy, Multiunit bilateral bargaining, market competition, quantity discount, wholesale price contract
  • Hamed Habibnejad Ledari, Masoud Rabbani *, Nastaran Ghorbani Kutenaie Pages 2919-2935

    Home care (HC) staff assignment problem is defined as deciding which staff to assign to each patient. In this study, a multi-objective non-linear mathematical programming model is presented to address staff assignment problem considering cross-training of caregivers for HC services. The first objective of the model minimizes costs of workload balancing, cross-training and maintenance. The second objective minimizes the number of employees for each service while the satisfaction level of caregivers is maximized through the third objective function. Several constraints including skill matching, staff preferences, regularity, synchronization, staff absenteeism and multi-functionality are considered to build a service plan. Due to NP-hardness of the problem, a non-dominated sorting genetic algorithm (NSGA-II) with a proposed who-rule heuristic initialization procedure is applied. Due to absence of benchmark available in the literature, a non-dominated ranking genetic algorithm (NRGA) is employed to validate the obtained results. The data required to run the model are gathered from a real-world HC provider. The results indicate that the proposed NSGA-II is superior to the NRGA with regard to comparison indexes. Based on the results obtained, it can be determined which staff should be cross-trained for each service and how the staff are assigned to services.

    Keywords: Home care, Staff assignment, Cross-training, optimization, NSGA-II, NRGA
  • B.C. Giri *, M. Masanta Pages 2936-2951

    This paper considers a closed-loop supply chain with one manufacturer and one retailer for trading a single product. On behalf of the manufacturer, the retailer collects the used items from the end customers for possible remanufacturing. The production of the finished product (manufactured and remanufactured) is subject to learning. The lead time at the retailer is assumed to be stochastic. The manufacturer delivers the retailer’s order quantity in a number of equal-sized shipments. The objective is to determine the optimalnumber of shipments and shipment size by minimizing the average expected total cost of the closed-loop supply chain. A solution method for the model is presented and important results are obtained for numerical examples. From the numerical study, an impressive cost reduction due to consideration of learning in production and remanufacturing is observed. To investigate the impact of key model-parameters on the optimal results, a sensitivity analysis is also carried out. The proposed model is applicable to those business firms where production process is executed by the human beings.

    Keywords: Closed-loop supply chain, stochastic lead time, Learning, remanufacturing
  • Hossein Gitinavard, Mohsen Akbarpour Shirazi *, Seyed Hassan Ghodsypour Pages 2952-2970
    Selecting the most suitable optimal point among the Pareto optimal points could help the experts to make an appropriate decision in an uncertain and complex situation. In this paper, an evaluating and ranking approach is proposed based on hesitant fuzzy set environment to assess the obtained Pareto optimal points from the proposed bi-objective multi-echelon supply chain model with locating distribution centers. In this respect, the proposed model has elaborated for perishable products based on fuzzy customers' demand. To address the issue, the possibilistic chance constraint programming approach has manipulated based on the trapezoidal fuzzy membership function. Moreover, the proposed hesitant fuzzy ranking approach is constructed based on group decision analysis and the last aggregation approach. Thereby, the last aggregation approach by aggregating the experts' opinions in last step could prevent the data loss. However, a case study about the perishable dairy products is considered to indicate the applicability of the proposed bi-objective multi-echelon supply chain model with locating distribution centers. Finally, a comparative analysis is provided between the obtained results and the current practice to show the feasibility and efficiency of the proposed model.
    Keywords: Multi-echelon supply chain, Pareto optimal solution, Perishable products, Group decision analysis, Possibilistic approach
  • Lina Tang, Yizhong Ma, Jianjun Wang *, Linhan Ouyang, Jai, Hyun Byun Pages 2971-2987
    The uncertainty of demand and lead time in inventory management has posed challenges for the supply chain management. The purpose of this paper is to optimize the total profit and customer service level of supply chain by robust parameter design of inventory policies. This paper proposes to use system dynamics simulation, Taguchi method and Response Surface Methodology (RSM) to model a multi-echelon supply chain. Based on the sequential experiment principle, Taguchi method combining location and dispersion modeling method is adopted to locate the optimum area quickly, which is very efficient to optimize the responses in discrete levels of parameters. Then, fractional factorial design and full factorial design are used to recognize significant factors. Finally, RSM is used to find the optimal combinations of factors for profit maximization and customer service level maximization in continuous levels of parameters. Furthermore, a discussion of multi-response optimization is addressed with different weight of each response. Confirmation experiment results have shown the effectiveness of the proposed method.
    Keywords: Supply chain, inventory policy, Simulation, Taguchi method, Response Surface Methodology
  • A. Hadi, Vencheh *, A. Mohammadghasemi, Farhad Hosseinzadeh Lotfi, M. Khalilzadeh Pages 2988-3006
    The traditional ABC inventory classification is one of such approaches in which items are classified into three classes: A, very important; B, moderately important; and C, relatively unimportant based on annual dollar usage. However, other qualitative or quantitative criteria in real world may affect grouping items in the ABC inventory classification. Hence, multiple criteria ABC inventory classification (MCABCIC) is applied to classify items, instead of using the traditional ABC inventory classification. Hence, it can be taken into account as a multiple-criteria decision making (MCDM) problem due to using different criteria. The aim of this paper is to the extent TOPSIS approach with Gaussian interval type-2 fuzzy sets (GIT2FSs) as an alternative to the traditional triangular membership functions (MFs) for the MCABCIC in which GIT2FSs are more suitable for stating curved MFs. For this purpose, a new limit distance is presented for prioritizing GIT2FSs which is based on alpha cuts. The proposed method determines the positive and negative ideal solutions as left and right reference limits and then calculates distances between assessments and these limits. In our approach, the weights of the quantitative and qualitative criteria are attained via two linear programming, respectively. The model is illustrated using a real case.
    Keywords: Multiple criteria ABC inventory classification, Gaussian interval type-2 fuzzy sets, TOPSIS
  • Adel Aazami, Mohammad Saidi Mehrabad * Pages 3007-3031

    In operations research, bi-level programming is a mathematical modeling which has another optimization problem as a constraint. In the present research, regarding the current intense competition among large manufacturing companies for achieving a greater market share, a bi-level robust optimization model is developed as a leader-follower problem using Stackelberg game in the field of aggregate production planning (APP). The leader company with higher influence intends to produce new products, which can replace the existing products. The follower companies, as rivals, are also seeking more sales, but they do not have the intention and ability to produce such new products. The price of the new products is determined by the presented elasticity relations between the uncertain demand and price. After linearization, using the KKT conditions, the bi-level robust model is transformed into an ordinary uni-level model. Due to the NP-hard nature of the problem, Benders decomposition algorithm (BDA) is proposed for overcoming the computational complexities in large scale. Finally, using the real data of Sarvestan Sepahan Co as a leader company, the validity of the developed model as well as efficiency and convergence of the BDA are investigated. The computational results clearly show the efficiency and effectiveness of the proposed BDA.

    Keywords: Bi-level Aggregate Production Planning, robust optimization, Competitive Condition, Pricing, Benders Decomposition
  • B. Abbasi, Aboulfazl Mirzazadeh *, Mohammad Mohammadi Pages 3032-3050
    In the supply chain of Fast-moving consumer goods, logistics costs are a main part of the expenses. In low levels of these chains, we usually face with a Vehicle Routing Problem. In practice, due to the high cost of service in many cases, some customers are not chosen for serving. Restrictions associated with the investment in many cases makes impossible serve to some potential clients. In this problem, design a supply chain network, including a location-allocation problem in the warehouse, Multiple Depot Vehicle Routing Problem at the level of distribution and customer selection at the retail level in several periods of time is considered. In this issue, in addition to certain methods that can be used in small sizes, Meta-heuristic algorithms to solve large-scale model have been used. With the aim of improving performance, if not improve in a few diversifications, algorithms are temporarily enhanced. In GA this change leads to improve 1.2 percent and 1.6 percent about the mean of solutions and the best solution and in SA algorithm, this change is led to improving by 2.1% and 2.4% in average solutions and the best solutions. Finally, this approach about a real problem in Sepahan Oil Company is employed.
    Keywords: Supply chain, optimization, Meta-Heuristic, Reinvestment, Multi Stage Investment Planning