Optimization of route and movement schedule of ready-mixed concrete vehicles in probabilistic networks

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Optimization of route and movement schedule of ready-mixed concrete vehicles in probabilistic networks

ارائه دهنده: Provider: Mehrzad Abdolvand

اساتید راهنما: Supervisors: Dr. Mohsen Babaei

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr. Javad Taherinezhad and Dr. Ebrahim Ghiasvand

زمان و تاریخ ارائه: Time and date of presentation: 1401/04/22 10:00 at

مکان ارائه: Place of presentation: Faculty of Engineering

چکیده: Abstract: Due to the significant increase in concrete consumption in the country in the construction industry, dams and other concrete components, as well as the expansion of cities and communications and population growth and, consequently, increased traffic, timely delivery of concrete to demand points has become very important.Restrictions on natural resources and preventing the decay of concrete by sending it on time on the one hand and increasing the demand for concrete and heavy urban traffic and trying to choose the best and fastest route that increases factory efficiency on the other hand, is the most important necessities of this study. Another necessity of this research is to increase the satisfaction of buyers of ready-mixed concrete from the factory. In such a way that the interruption of concrete delivery is reduced to its standard level so as not to cause cold joint in the concrete. All of the above are limitations that have not been addressed in recent decades due to low demand, short distances and lack of urban traffic, while having a significant impact on a country's economy, applicant satisfaction and the environment. This research seeks to provide a suitable method for optimizing the route and delivery time of concrete so that in the case of probabilistic travel time, the reliability of concrete can be provided at the right time to the points of demand. In this research, a new mathematical planning model is presented that presents the number of machines required, the time of sending the machines and how to allocate trips to the machines. The proposed model is a linear model and therefore has the ability to be solved accurately by optimization software.The proposed model for probabilistic conditions has also been rewritten and solved. In the first method, the model is solved definitively by the scenario production method. In this method, using random number extraction techniques, different scenarios are created with different trip times, and the scenario with the highest reliability coefficient is presented as the optimal scheduling program. Reliability refers to the timely delivery of concrete to construction sites without delay and the formation of cold joints in all scenarios that may occur in a working day. In the second method, which is a probabilistic method, the parameter that has uncertainty, which is the travel time here, is considered as a trapezoidal fuzzy with a specific membership function, and the deterministic model is written probabilistically and solved in GAMS optimization software. The two case problems that have been solved in this research have real data from Omran Beton Hamedan located at the beginning of Malayer Road. After solving the problem in the two methods, the model with 4 mixers in the first problem and 5 mixers in the second problem with the least service delay time, while the factory operator has used 6 mixers for service in both of these problems.In other way, in complex working days when the number of demand points is high and the reliability of the decision maker, who is normally the factory operator, is reduced; It ispossible to plan one-day trips to the concrete plant with high reliability and to help the economic and environmental conditions by maximizing production and minimizing the use of construction machinery. It is important to note that the solution time of the model is less than 2 seconds for each scenario in either definite or probabilistic and has a very low solution time compared to similar models

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