Search for optimal routes on roads applying metaheuristic algorithms

The design of efficient routes for vehicles visiting a significant number of destinations is a critical factor for the competitiveness of many companies. The design of such routes is known as the vehicle routing problem. Indeed, efficient vehicle routing is one of the most studied problems in the areas of logistics and combinatorial optimization. The present study presents a memetic algorithm that evolves using a mechanism inspired by virus mutations. Additionally, the algorithm uses Taboo Search as an intensification mechanism

Considering emissions in the transit network design and frequency setting problem with a heterogeneous fleet

Urban transportation contributes significantly to CO2 emissions. Public transport systems are a good strategy to reduce these, but the emissions generated by public transport vehicles should not be neglected during the design of the service. The Transit Network Design and Frequency Setting Problem (TNDFSP) has usually been addressed considering only the passengers’ and the operator’s point of view. However, we show it is worthwhile to consider also the emissions already during this planning phase. This paper proposes a memetic algorithm to address the bi-objective TNDFSP where both the total travel time and the CO2 emissions are minimized. The analysis considers a heterogeneous fleet, meaning that buses of different sizes and technologies can be assigned under a budget constraint. The results on benchmark instances show that the proposed memetic algorithm performs as well as state-of-the-art algorithms where CO2 emissions are not considered. In addition, several experiments are carried out to observe the effect of incorporating emissions and heterogeneous fleet into the model. The heterogeneous fleet allows reducing travel times and emissions at the same time, compared to solutions without a heterogeneous fleet. Moreover, the explicit minimization of CO2 emissions within a bi-objective framework allows illustrating the trade-off between both objectives. Reductions of about 30% in the emissions can be achieved by increasing the travel time only 1%, while the costs for the operator remain the same. This clearly demonstrates the benefits of considering both the CO2 emissions and a heterogeneous fleet during the design stage of public transport systems.status: publishe

파레토최적해를 이용한 버스네트워크 설계 다목적 최적화

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 건설환경공학부, 2020. 8. 고승영.Public transportation is a service that provides access to various opportunities and can reduce the mobility gap through efficient network design. However, services are concentrated in a specific area considering economic efficiency, resulting in spatial imbalances in services and inefficiency to users. In this study, bus network design algorithms were presented, including operators, users, and public aspects. An efficiency of operators and users and the competitiveness of public transportation between modes and areas were considered. Toy network was organized according to the urban network topology and demand pattern, and the analysis was performed by applying the algorithm of this study. The applicability of the algorithm was confirmed through the actual network. An improved network could be derived from both operators and the public compared to previous research focused on operational efficiency. Suggested a method to select and apply Pareto optimal according to the planner's judgment. The bus network design algorithm in this study can be used as a means of decision criteria and it can be applied to cities that require a balanced network supply with limited resources.Chapter 1. Introduction ···········································1 1.1 Background ·····································································1 1.2 Research Scope ·····························································4 Chapter 2. Literature Review ·······························7 2.1 Transit Network Design ···········································7 2.2 Unmet Demand ······························································12 2.3 Equity ················································································17 2.4 Algorithm ·········································································26 2.4.1 Multi-objective Optimization ········································26 2.4.2 Local Search ·····································································31 2.5 Summary and Research Direction ·································34 Chapter 3. Methodology ··········································37 3.1 NSGA-II ···········································································37 3.2 Algorithm ·········································································40 3.2.1 Procedure and Network Encoding ······························40 3.2.2 Cross-over and Mutation ··············································43 3.2.3 Local search ······································································44 Chapter 4. Model Formulation ·····························47 4.1 Summary ··········································································47 4.2 Assumption and Variables ·······································48 4.3 Model Formulation ·······················································52 4.3.1 Objective Function ··························································52 4.3.2 Logit Model ······································································54 4.3.3 Traffic Assignment ·························································56 4.3.4 Transit Assignment ······················································58 Chapter 5. Numerical Example ····························61 5.1 Toy Network ································································61 5.1.1 Network Explanation ····················································61 5.1.2 Result Analysis ································································65 5.1.3 Marginal Effect ································································75 5.1.4 Comparison with Previous Research ·························80 5.2 Large Network ······························································83 5.2.1 Network Explanation ······················································83 5.2.2 Result Analysis ································································86 5.2.3 Marginal Effect ································································91 5.2.4 Comparison with Previous Study ·······························92 5.3 Discussion ·······································································94 Chapter 6. Result and Future Research ·········· 97 Reference ·······································································100 Appendix ·······································································110Docto

Multicriteria evaluation as a tool for decision support in the design of sustainable routes

Public transport negative externalities have been increasing over the past years and, consequently, a bigger concern for eliminating and minimizing them has been growing. One way to contribute for this global objective is to design public transport networks towards sustainability, which requires an assessment of its sustainability performance. Thus, the main goal of this dissertation is to develop a tool able to accomplish a multi-objective evaluation of public transport routes regarding the three main sustainability domains - economic, social, and environmental. The considered structure is based on a hierarchical relation between domains, themes, and indicators. Accordingly, for each domain, a selection of indicators within different chosen themes was performed. The quantification of each indicator was estimated prior to the assignment of their themes and domains' importance. This assignment was mainly based on the literature, and given the different sources and opinions, sensibility analyses were performed. The tool was applied to a simulation case constituted by three routes, based on STCP's network data that provides a public transport service in Porto's metropolitan area.Public transport negative externalities have been increasing over the past years and, consequently, a bigger concern for eliminating and minimizing them has been growing. One way to contribute for this global objective is to design public transport networks towards sustainability, which requires an assessment of its sustainability performance. Thus, the main goal of this dissertation is to develop a tool able to accomplish a multi-objective evaluation of public transport routes regarding the three main sustainability domains - economic, social, and environmental. The considered structure is based on a hierarchical relation between domains, themes, and indicators. Accordingly, for each domain, a selection of indicators within different chosen themes was performed. The quantification of each indicator was estimated prior to the assignment of their themes and domains' importance. This assignment was mainly based on the literature, and given the different sources and opinions, sensibility analyses were performed. The tool was applied to a simulation case constituted by three routes, based on STCP's network data that provides a public transport service in Porto's metropolitan area