Simulation of a multi-layered facility location model by cosidering queueing theory | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 3، دوره 7، شماره 1 - شماره پیاپی 12، مهر 2021، صفحه 51-79 اصل مقاله (905.44 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2015.582 | ||
| نویسندگان | ||
| Mahdi Yousefi Nejad Attari1؛ Saeed Kolahi-Randji* 2؛ Aniseh Neishabouri Jami1 | ||
| 1Assistant Prof. Department of Industrial Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran | ||
| 2Young Researchers and Elite Club, Ilkhichi Branch, Islamic Azad University, Ilkhichi, Iran | ||
| چکیده | ||
| In multi-layered facility location models, customers receive different services at different layers. When the customer enters the system, he must receive all services at different layers; in fact, the customer will not leave the system in the middle layers. In this study, we are seeking to provide a facility location model with multiple service layers respect to the density of the system. The proposed model is a nonlinear integer programming model and it is in the field of highly complex problems. In order to solve the mathematical model, discrete event simulation approach has been used to increase efficiency. Interactions and complexities of the system, makes it difficult or impossible to predict the performance. Simulation models are able to show variability, interactions and complexities of the system. In this regard, the demand has considered as random and objective functions consist of minimization of customer’s travel time to desired facility, customer’s waiting time in queue and the possibility of unemployment of a facility which has the highest rate of unemployment. According to the results of simulation and testing 4 different scenarios, it can be stated that in scenario (4), only by adding 1 source to each available facility in the fourth layer, which is totally increasing 4 source, costumers wait time in queue will be improved about 46%. | ||
| کلیدواژهها | ||
| Facilities locating؛ Queueing theory؛ Multi-objective decision making؛ Simulation | ||
| عنوان مقاله [English] | ||
| -شبیه سازی یک مدل مکان یابی چند لایه ای تسهیلات با در نظر گرفتن تئوری صف | ||
| نویسندگان [English] | ||
| مهدی یوسفی نژاد عطاری1؛ سعید کلاهی رنجی2؛ انیسه نیشابوری جامی1 | ||
| 1استادیار گروه مهندسی صنایع دانشگاه آزاد اسلامی واحد بناب | ||
| 2مربی دانشگاه آزاد اسلامی واحد ایلخچی | ||
| چکیده [English] | ||
| در مدلهای مکانیابی تسهیلات چند لایهای، مشتریان در لایههای مختلف خدمات مختلفی را دریافت میکنند. زمانی که مشتری وارد سیستم میشود باید تمامی خدمات را در لایههای مختلف دریافت کند؛ در واقع مشتری در لایههای میانی سیستم را ترک نخواهد کرد. در این تحقیق به دنبال ارائه یک مدل مکانیابی تسهیلات با چندین لایه خدمتدهی و با در نظر گرفتن تراکم در سیستم هستیم. مدل ارائه شده بصورت یک مدل برنامهریزی غیرخطی عدد صحیح بوده و در رسته مسائل با پیچیدگی بالا قرار داد. بمنظور حل مدل ریاضی ارائه شده، از رویکردهای شبیهسازی گسسته پیشامد با هدف افزایش بهرهوری، بهره جستهایم. تعاملات و پیچیدگیهای سیستم، پیشبینی عملکرد آن را دشوار یا ناممکن میسازد. مدلها شبیهسازی قادرند تغییرپذیری، تعاملات و پیچیدگیهای یک سیستم را نشان دهند. در این راستا، تقاضا بصورت تصادفی در نظر گرفته شده است. توابع هدف شامل کمینهسازی مدت زمان سفر متقاضی به تسهیل مورد نظر، مدت زمان انتظار متقاضی درون صف و احتمال بیکاری تسهیلی است که با بیشترین احتمال بیکاری مواجه است. با توجه به نتایج بدست آمده از اجرای شبیهسازی و آزمایش 4 سناریوی مختلف، میتوان اظهار داشت که سناریوی شماره 4 تنها با افزایش 1 منبع به هر یک از تسهیلات موجود در لایه چهارم، که مجموعاً افزایش 4 منبع است، زمان انتظار متقاضیان درون صف در حدود 46٪ بهبود میدهد. | ||
| کلیدواژهها [English] | ||
| تصمیم گیری چندهدفه, تئوری صف, شبیه سازی, مکانیابی تسهیلات | ||
| مراجع | ||
|
Aboolian R. & Berman O. & Drezner Z. (2009). The multiple server center location problem. Annals of Operations Research, 167: 337–352. Abouee-Mehrizi H. & Babri S. & Berman O. & Shavandi H. (2011). Optimizing capacity, pricing and location decisions on a congested network with balking. Mathematical Methods of Operations Research, 74 (2): 233-255. Berman O. & Krass D. & Wang J. (2006). Locating service facilities to reduce lost demand. IIE Transactions, 38 (11): 933 – 946. Berman O. & Larson R.C. & Chiu S.S. (1985). Optimal Server Location on A Network Operating as an M/G/1 Queue. Operations Research, 33 (4): 746-771. Berman O. & Tong D. & Krass D. Pricing, (2010a). location and capacity planning with elastic demand and congestion. Working paper. University of Toronto. Berman O. & Tong D. & Krass D. Pricing, (2010b). location and capacity planning with equilibrium driven demand and congestion working paper. University of Toronto. Berman O. (2007). Locating capacitated facilities to maximize captured demand. IIE Transactions, 39 (11): 1015-1029. Boffey B. & Galvao R. & Espejo L. (2007). A review of congestion models in the location of facilities with immobile servers. European Journal of Operational Research, 178 (3): 643–662. Brandeau M.L. & Chiu S.S. (1990). A unified family of single-server queuing location models. Operations Research, 38 (6): 1034–1044. Chambari A.H. & Rahmaty S.H. & Hajipour V. & Karimi A. (2011). A bi-objective model for location-allocation problem within queuing framework. World Academy of Science. Engineering and Technology, 5 (6): 138-145. Current J. & Daskin M. & Schilling D. (2002). Discrete network location models. facility location: applications and theory. Springer, Heidelberg, 80-118. Dong M. & Wua C. & Hou F. (2009). Shortest path based simulated annealing algorithm for dynamic facility layout problem under dynamic business environment. Expert Systems with Applications, 36 (8): 11221–11232. Farahani R.Z. & Steadie Seifi M. & Asgari N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34 (7): 1689-1709. Hakimi S.L. (1964). Optimum locations of switching centers and the absolute centers andmedians of a graph. Operation Research, 12 (3): 450–459. Hodgson M.J. & Berman O. (1997). A billboard location model, Geographical and Environmental Modeling 1, 25–43 Love R.L. & Morris J.G. & Wesolowsky G.O. (1988). Facility Location: Models and Methods. North-Holland Publishing Company, New York, (Cited by Francis). Marianov V. & Revelle C. (1995). Siting emergency services in Facility Location: A Survey of Applications and Methods. Springer Series in Operations Research. Marianov V. & Rios M. (2000). A probabilistic quality of service constraint for a location model of switches in ATM Communications networks. Annals of Operations Research, 96: 237–243. Mehdizadeh E. & Tavarroth M.R. & Hajipour V. (2011). A New hybrid Algorithm to Optimize Stochastic-Fuzzy Capacitated Multi-Facility Location-Allocation Problem. Journal of Industrial Engineering Islamic Azad University of Qazvin, 4 (7): 71–80. Melo M.T. & Nickel S. & Saldanha-da-Gama F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196 (2): 401–412. Pasandideh S.H.R. & Niaki S.D.A. (2010). Genetic application in a facility location problem with random demand within queuing framework. Journal of Intelligent Manufacturing, 23 (3): 234-546. Pasandideh SHR. & Niaki STA. & Hajipour V. (2011). A multi-objective facility location model with batch arrivals: two parameter-tuned meta-heuristic algorithms. Journal of Intelligent Manufacturing, 24 (2): 331-348. Shanthikumar J.G. & Yao D.D. (1987). Optimal Server Allocation in a System of Multi- Server Stations. Management Science, 33 (9): 1173-1180. Shavandi H. & Mahlooji H. (2006). A Fuzzy Queuing Location Model with A Genetic Algorithm Congested Systems. Applied Mathematics and Computation, 181 (1): 440 -456. Syam S.S. (2008). A multiple server location–allocation model for service system design. Computers and Operations Research, 35 (7): 2248–2265. Wang D. & Fung R. & Ip W. (2009). An immune-genetic algorithm for introduction planning of new products. Computers & Industrial Engineering, 56 (3): 902–917. Wang Q. & Batta R. & Rump C. (2002). Algorithms for a facility location problem with stochastic customer demand and immobile servers, Annals of Operations Research, 111: 17–34. Wang Q. & Batta R. & Rump C. (2004). Facility Location Models for Immobile Servers with Stochastic Demand. Naval Research Logistic, 51(1):137 - 152. Zarrinpoor N. & Seifbarghy M. (2011). A competitive location model to obtain a specific market share while ranking facilities by shorter travel time. The International Journal of Advanced Manufacturing Technology, 55 (5): 807-816. | ||
|
آمار تعداد مشاهده مقاله: 2,405 تعداد دریافت فایل اصل مقاله: 1,110 |
||