Iranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82725120110401A Flow shop Production Planning Problem with basic period policy and Sequence Dependent set up times1194038ENMohammadAliabadiDepartment of Industrial Engineering, Islamic Azad University of Qazvin, Qazvin, IranFariborzJolaiDepartment of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranEsmaeilMehdizadehDepartment of Industrial Engineering, Islamic Azad University of Qazvin, Qazvin, IranMasoudJenabiDepartment of Industrial Engineering, Amirkabir University of Technology, Tehran, IranJournal Article20100306Many authors have examined lot sizing, scheduling and sequence of multi-product flow shops, but most of them have assumed that set up times are independent of sequence. Whereas dependence of set up times to sequence is more common in practice. Hence, in this paper, we examine the discussed problem with hypothesis of dependence of set up times to sequence and cyclic schedule policy in basic period form. To do so, a mixed integer non-linear programming (NLP) model is developed for this problem. To solve the model these techniques are applied: Heuristic G-group for determining the frequency of item production and assigning product to periods and three meta heuristic methods including hybrid Particle swarm optimization , hybrid Vibration damping optimization hybrid genetic algorithm are used to determine the sequence and economic lot sizes of each item. In addition, to compare these methods, some random problems are produced and computation of them shows the substantial superiority of hybrid Particle swarm optimization.https://www.jise.ir/article_4038_56577889b3c1cd083b6d7b32d32f99d5.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82725120110401A method of identifying suitable manufacturing system (Cellular) for automotive sector using Analytical Hierarchy Process20344039ENNatesanArunkumarDepartment of Mechanical Engineering, St. Joseph’s College of Engineering, Rajiv Gandhi Salai,
Chennai-600 119, IndiaLoganathanKarunamoorthyCentral Workshop Division, Department of Mechanical Engineering, College of Engineering Guindy,Anna University, Chennai-600 025, IndiaSambandamMuthukumarCentral Workshop Division, Department of Mechanical Engineering, College of Engineering Guindy,Anna University, Chennai-600 025, IndiaJournal Article20100504Manufacturing produces real wealth for any country and constitutes the back bone for the service sector. The objective of any organization is to earn profit. Usually the market fixes the selling price of the manufactured components. Unless there is focus on the manufacturing strategy of reducing manufacturing cost, it is very difficult to sustain in this ever competitive world. A suitable manufacturing system will help in minimizing the cost of production. The suitable manufacturing system should focus on customer satisfaction by finding out the customer’s requirement in terms of quantity, quality, and schedule. A survey of existing literature on evaluation of advanced manufacturing systems indicates that the traditional manufacturing approaches are inadequate for the purpose. Typically new technologies require very high investments, so it is important to identify and justify the manufacturing system suitable for the particular manufacturing industry. In this paper an attempt has been made to overcome the deficiencies of traditional manufacturing system by presenting an approach to determine and account for the justification of the cellular manufacturing system using Analytical Hierarchy Process (AHP).https://www.jise.ir/article_4039_1cd73be1e256a7405516501e94e892ac.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82725120110401Development of a Generic Risk Matrix to Manage Project Risks35514040ENSusan L.MurrayEngineering Management and Systems Engineering (EMSE) Department, Missouri University of Science & Technology, U.S.AKatieGranthamEngineering Management and Systems Engineering (EMSE) Department, Missouri University of Science & Technology, U.S.ASiddharth B.DamleEngineering Management and Systems Engineering (EMSE) Department, Missouri University of Science & Technology, U.S.AJournal Article20100205A generic risk matrix is presented for use identifying and assessing project risks quickly and cost effectively. It assists project managers with few resources to perform project risk analysis. The generic risk matrix (GRM) contains a broad set of risks that are categorized and ranked according to their potential impact and probability of occurrence. The matrix assists PMs in quickly identifying risks and can serve as a basis for contingency planning to minimize cost and schedule overruns. It is suitable for a wide variety of projects and can be modified for specific types of projects using historical data or expert opinion. An R&D project case study is included to demonstrate how the GRM is applied for a specific project.https://www.jise.ir/article_4040_ca4b33532855080dfa79cf8a925d146d.pdfIranian Institute of Industrial EngineeringJournal of Industrial and Systems Engineering1735-82725120110401Queueing Inventory System in a Two-level Supply Chain with One-for-One Ordering Policy52624041ENRasoulHajiIndustrial Engineering Department, Sharif University of Technology, Tehran, IranAlirezaHajiIndustrial Engineering Department, Sharif University of Technology, Tehran, IranMohammadSaffariIndustrial Engineering Department, Sharif University of Technology, Tehran, IranJournal Article20100415Consider a two-level inventory system consisting of one supplier and one retailer. The retailer faces a Poisson demand with a known rate and applies base stock (one-for-one ordering) policy. That is, his inventory position is set to a pre-determined level, so the demand pattern is transferred exactly to the supplier. The supplier has an inventory system and a service unit with exponentially distributed service time to process the orders received from the retailer. Supplier also follows a base stock policy, and its lead time is exponentially distributed. When the supplier has some on-hand inventory, an arriving order from retailer joins the queue. But when the supplier has no on-hand inventory, the retailer does not accept any demand, i.e. the demand is lost. When the retailer has no on-hand inventory but the supplier has on-hand inventory, the arriving demand to the retailer will be backordered. For this system, we derive the steady state joint distributions of the ‘number of retailers order in service unit and the ‘on-hand inventory of the supplier’ and show that it has a product form. Furthermore, we derive the total expected system cost per unit time. After convexity analysis of the cost function, we derive the optimal inventory policy of supplier and retailer. Finally a numerical example is provided.https://www.jise.ir/article_4041_da0b566359c3862f20b5072c3d49bd0f.pdf