A New School Bus Routing Problem Considering Gender Separation, Special Students and Mix Loading: A Genetic Algorithm Approach
محورهای موضوعی : Cultural and Language StudiesAlireza Rashidi Komijan 1 , Peiman Ghasemi 2 , Kaveh Khalili-Damghani 3 , Fakhrosadat HashemiYazdi 4
1 - Department of Industrial Engineering
Firoozkooh Branch
Islamic Azad University
Firoozkooh, Iran
2 - Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Department of Industrial Management,Faculty of Management and Accounting,
Allame Tabatabai University, Tehran, Iran
کلید واژه: Genetic Algorithm, School bus routing problem, mixed integer mathematical programming, Gender separation, Mix loading,
چکیده مقاله :
In developing countries, whereas the urban bus network is a major part of public transportation system, it is necessary to try to find the best design and routing for bus network. Optimum design of school bus routes is very important. Non-optimal solutions for this problem may increase traveling time, fuel consumption, and depreciation rate of the fleet. A new bus routing problem is presented in this study. A multi-objective mixed integer model is proposed to handle the associated problem. Minimization of transportation cost as well as traveling time is the main objectives. The main contributions of this paper are considering gender separation as well as mixed-loading properties in the school bus routing problem. Moreover, special and handicapped students are considered in this problem. The proposed model is applied in a real case study including 4 schools in Tehran. The results indicate the efficiency of the proposed model in comparison with the existing system. This comparison shows that the students’ travelling time is reduced by 28% for Peyvand middle smart school, 24% for Tehran international school, 13% for Hemmat School and 21% for Nikan High school. A customized Genetic Algorithm (GA) is proposed to solve the model. Penalty functions are used to handle the several constraints of the problem in Genetic Algorithm. The results justify the applicability and efficacy of the both proposed model and solution approach.
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