A Integer Linear Programming Agricultural Machinery Fleet Selection Model for Multicropping Systems

Document Type : Research Paper

Authors

Abstract

          Each enterprise considering its structure, requires different types of management to achieve the goals. Management of farmlands mechanized agricultural systems is one of the important factors to succeed farmers. In this research, a model was developed to select agricultural machinery fleet, particularly in multicropping systems. The Model was a linear integer optimization model for minimization of costs and performed under LP-ILP (QSB) program. Raw data for three crops of wheat, barley and potato were obtained from the Ahyai Agriculture Complex located in Hamedan city, and related analyses were performed. Finally, according to the constraints of working periods, optimal required machinery set was calculated and proposed. This optimal machinery set supplied machinery requirements of farms and also minimized machinery costs.

Keywords


الماسی مرتضی، کیانی شهرام و لویمی نعیم، 1384. مبانی مکانیزاسیون کشاورزی. مؤسسه انتشارات حضرت معصومه (س). چاپ سوم.
بی­نام، 1388. اطلاعات پایه شرکت تعاونی تولید روستای قرخلر.
فلاح شمسی سیدرشید، سبحانی هوشنگ، ارسطو سعید، درویش صفت علی اصغر و فرجی دانا احمد، 1384. مدل برنامه ریزی خطی در تخصیص زمین به کاربری های مختلف در حوزه آبخیز کلیبر چای وسطی. مجله منابع طبیعی ایران، جلد 85، شماره 3. صفحه­های 579 تا 589.
کوپاهی مجید و کیانی غلامحسین، 1385. تعیین برنامه بهینه حمل و نقل گندم در ایران با استفاده از روش برنامه ریزی خطی. مجله علوم کشاورزی ایران، جلد 2-37، شماره 1. صفحه­های 127 تا 135.
گس سل آی، 1385. برنامه ریزی خطی روشها و کاربردها. ترجمه فائزه توتونیان. انتشارات دانشگاه فردوسی مشهد. ویرایش پنجم.
مدرس رضوی مجتبی، 1387. مدیریت ماشین های کشاورزی. انتشارات دانشگاه فردوسی مشهد.
 
ASAE Standards, 2009. EP497.6 . Agriculture machinery management data. St. Joseph, Mich.
Audsley E, 1981. An arable farm model to evaluate the commercial viability of new machines or techniques. Journal of agriculture engineering research 26: 135-149.
Butani KM and Singh G, 1994. Decision support system for the selection of agriculture machinery with a case study in India. Computers and electronics in agriculture 10: 91-104.
Camarena EA, Gracia C and Cabrerra sixto JM, 2004. A mixed integer linear programming machinery selection model for multifarm systems. Biosystems engineering 87: 145-154.
Grisso RD, Kocher MF and Vaughan DH, 2004. Predicting tractor fuel consumption. American society of agriculture engineers. ISSN 0883-8542.
Haffar I and Khoury R, 1992. A computer model for field machinery selection under multiple croppong. Computers and electronics in agriculture 7: 219-229.
Jannot Ph and Cairol D, 1994. Linear programming as an aid to decision making for investments in farm equipment for arable farms. Agriculture engineering research 59: 173-179.
Kay RD, Edwards WM and Duffy PA, 2008. Farm management. McGraw-Hill Inc., sixth edition, ISBN 9780071259538.
Kline DE, Bender DA, McCarl BA and Van Donge CE, 1988. Machinery selection using expert systems and linear programming. Computers and electronics in agriculture 3: 45-61.
Kotzabassis and Stout BA, 1990. Farm machinery management another approach. ASAE paper. No. 90-1106.
Lazzari M and Mazzetto F, 1996. A PC model for selecting multicropping farm machinery systems. Computers and electronics in agriculture 14: 43-59.
Mehta CR, Singh K and Selvan MM, 2011. A decision support system for selection of tractor-implement system used on Indian farms. Journal of terramechanics 48: 65-73.
Parmar RS, McClendon RW and Potter WD, 1996. Fram machinery selection using simulation and genetic algorithms. American society of agriculture engineers 39: 1905-1909.
Sogaard HT and Sorensen CG, 2004. A model for optimal selection of machinery sizes within the farm machinery system. Biosystems engineering 89: 13-28.
Witney B, 1988. Choosing and using farm machines. John Wiley & sons Inc., New York.
Yoo KH, 1985. Planning of irrigation distribution and application systems by mixed integer linear programming. Agriculture water management 10: 265-282.