No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Bus Fleet Management Optimization Using the Augmented Weighted Tchebycheff Method
... Thus, understanding that BFMS is getting more sophisticated, Vaughan et al. [12,27,53] developed an interactive expert computer-based tool dedicated to fleet managers in aiding decision-making, taking into account four goals, namely: technological, economic [54], transportation [55], and environmental [56]. Besides, the development of ITS (Intelligent Transport System) architecture, digitalisation, and IoT (Internet-of-Things) facilitate the decision-making process considering these interrelated issues [46]. However, looking over the concepts it seems as if BFM is intertwined between mobility issues and asset issues, which is one of the drivers for the research. ...
... Additionally, Southworth et al. [38] state that BFM belongs to a wide range of operation and maintenance (O&M) activities [38,39] aimed at reducing fuel consumption. In contrast, some [40] classify BFM into fleet maintenance and fleet replacement problems [41], where BFM uses a variety of strategies for early replacement [39,[42][43][44] concerning pollution reduction and energy optimisation [45][46][47][48]. Even though all concepts aim to improve bus availability, there is a difference between mobility and asset issues. ...
The research on Bus Fleet Management (BFM) has undergone significant changes. It is unclear whether these changes are accepted as technological change or as a paradigm shift. Perhaps unintentionally, BFM is still perceived as routing and scheduling by some, and by others as maintenance and replacement strategy. Therefore, the authors conducted a Systematic Literature Review (SLR) to overview the existing concepts and school of thoughts about how stakeholders perceive the BFM. The SLR post-study exposed that BFM should be acknowledged as a multi-realm system rather than a uniform dimension of fulfilling timely service. Nonetheless, the work encapsulates BFM evolution which shows the need for the multi-realm research abstracted as "Bus Fleet Mobility Management" and "Bus Fleet Asset Management". The difficulties of transport agencies and their ability to switch from conventional to Zero-Emission Buses (ZEBs) illustrates why we propose such an agenda, by which the research is validated through needs both in academia and in practice.
State and local transit agencies require government support to preserve their aging transit fleets. With the passage of time, transit fleets get older and require maintenance costs to keep them operational. To provide services at a desired level, transit agencies must maintain a minimum fleet size. Two imperative considerations from the transit planning viewpoint are (a) the remaining life of the total fleet and (b) the cost required to maintain the fleet size. While the former is a quality measure indicating the health of the fleet, the latter is an economic measure requiring minimum expenditure levels. Ideally, agencies would like to maximize the total remaining life of the fleet and minimize the total cost required to maintain the fleet size. In this paper, a multiobjective optimization (MO) model is proposed to incorporate simultaneously the two objectives when subjected to budget and various operational constraints. The MO problem is solved with a classical weight sum approach by using the branch and bound algorithm, which has proved to be better than other solution methodologies. The MO results in Pareto-optimal solutions with possible trade-offs between the two objectives. The model is applied to a large-scale transit fleet system in the state of Michigan. The case study results demonstrate that the proposed model is compact, efficient, robust, and suitable for long-range planning with multiple solutions to choose from a Pareto-optimal frontier. The correlation between decision variables and objective functions has been investigated in-depth, providing important insights. The proposed model can act as a tool for resource allocation for state and local agency transit fleets.
This research presents a bus fleet replacement optimization model to analyze vehicle replacement decisions when there are competing technologies. The focus of the paper is on sensitivity analysis. Model properties that are useful for sensitive analysis are derived and applied utilizing real-world data from King County (Seattle) transit agency. Two distinct technologies, diesel hybrid and conventional diesel vehicles, are studied. Key variables affecting optimal bus type and replacement age are analyzed. Breakeven values and elasticity values are estimated. Results indicate that a government purchase cost subsidy has the highest impact on optimal replacement periods and total net cost. Maintenance costs affect the optimal replacement age but are unlikely to change the optimal vehicle type. Greenhouse gas emissions costs are not significant and affect neither bus type nor replacement age.
This work proposes a memory-efficient multi-objective optimization algorithm to perform optimal path selection (OPS) in electric vehicles (EVs). The proposed algorithm requires less computational time and executes efficiently on fast-processor-based embedded systems. It is a population-based simulated evolution algorithm that incorporates innovative functions for calculating the goodness of particles and performing the allocation operation. The goodness and allocation operations ensure the exploration of new paths and the preservation of Pareto-optimal solutions. We executed our algorithm on an Intel Celeron processor, which is also used in embedded systems and compared its performance with that of the non-dominated sorting genetic algorithm-II (NSGA-II). Our experiments used real road networks. The comparison shows that on an average, our algorithm found 5.5 % more Pareto-optimal solutions than NSGA-II. Therefore, our proposed algorithm is suitable for performing OPS in EVs.
The Life-Cycle Cost method (LCC) is a tool that allows the comparison of alternative solutions considering all costs incurred during their lifetime. This method has been used to study fleet replacement problems and to analyze the impact of environmentally friendly technological changes on transportation. Furthermore, electric buses have been tested in several countries mainly due to favorable business environment , environmental laws, expected reduction in operating costs and positive effects of some externalities. Traditional LCC models reported in the literature are merely focused on costs, disregarding the impacts of income losses that are implicit to alternative solutions. This paper presents a LCC model that incorporates such impacts , by weighting income properly. The model has been applied in the analysis of three bus lines in Joinville, Brazil. The paper shows that the income generated or lost by the various alternatives may be critical to properly determine the best solution in certain situations.
Increasing levels of industrialization of developed nations associated with globalization trends have been creating new challenges to supply chain management (SCM). For decades, the main focus of SCM has been on efficient ways of managing the flows through complex networks of supplier, producers and customers. More recently, and as a result of the exponential increase of energy and materials consumption rates of energy and materials, sustainable development arise as an urgent issue and new approaches to SCM are required to incorporate environmental and economic concerns in the design of supply chains. In this paper, a new green supply chain (GSC) design approach has been proposed to deal with the trade-offs between environmental and financial issues in order to reduce negative impacts on the environment caused by the increasing levels of industrialization. The new approach incorporates a closed loop network to accommodate the reprocessing paradigm of disposal products and a multi-objective optimization mathematical model to minimize overall costs and carbon dioxide emissions when setting the supply chain. Optimization process is performed using three scalarization approaches, namely weighted sum method, weighted Tchebycheff and augmented weighted Tchebycheff. Computational results are analyzed to identify the advantages and drawbacks of each approach. The model was tested in a case study and results allowed to identify the capability of the model to deal with the trade-offs between the costs and environmental issues as well as to identify its main limitation when addressing real size problems.
Abstract The electrification of road vehicles was introduced as a way to significantly reduce oil dependence, increase efficiency, and reduce pollutant emissions, especially in urban areas. The goal of this paper is to find the best alternative vehicle to replace a conventional diesel bus operating in urban environments, aiming to reduce the carbon footprint and still being financially advantageous. The multi-objective nondominated sorting genetic algorithm is used to perform the vehicle optimization, covering pure electric and fuel cell hybrid possibilities (with and without plug-in capability). The used multi-objective genetic algorithm optimizes the powertrain components (type and size) and the energy management strategy. Although multiple optimal solutions were successfully achieved, a decision method is implemented to select one unique solution. A global criterion approach, a pseudo-weight vector approach, and a new multiple criteria score approach are considered to choose a preferred optimal vehicle. Real and synthetic driving cycles are used to compare the optimized buses concerning their powertrain components, efficiency and life cycle of fuel and vehicle materials. The conflict between objectives and the importance of the decision considerations in the final solutions are discussed. Passengers load and air conditioning system influence in the solutions and its life cycle is addressed.
In urban rail transit, the train plan directly influences passenger's travel cost, thus it affects the passenger travel demand. According to current planning and operation for urban rail transit, the train plan consists of train formation, train operation period, and train numbers in each train routing. On the basis of analyzing passengers' general travel cost, the passenger elastic demand function is established, then passenger's travel behaviors come down to the distribution of user equilibrium with elastic demands and capacity constraints. According to the relationship between passengers and the operator and on the basis of analyzing related cost, benefits, and constraints of the train plan, a multi-objective bi-level programming model is put forward, and the optimal algorithm based on the simulated annealing algorithm is designed. The instance of Changsha metro line 2 shows that the method can provide results with high efficiency.
The augmented weighted Tchebycheff norm was introduced in the context of multicriteria optimization by Steuer and Choo [21] in order to avoid the generation of weakly nondominated points. It augments a weighted l"~-norm with an l"1-term, multiplied by a ''small'' parameter @r>0. However, the appropriate selection of the parameter @r remained an open question: A too small value of @r may cause numerical difficulties, while a too large value of @r may lead to the oversight of some nondominated points. For discrete bicriteria optimization problems we derive a method for a problem dependent determination of all parameters of the augmented weighted Tchebycheff norm such that all nondominated points can be found and @r is as large as possible. In a computational study based on randomly generated instances of a bicriteria knapsack problem, the resulting adaptive augmented weighted Tchebycheff method is compared with the lexicographic weighted Tchebycheff method and with the augmented weighted Tchebycheff method with preset parameter values as well as with augmented @e-constraint scalarizations.
Hyundai Heavy Industries Co., Ltd and Hankuk Fiber Co., Ltd completed joint development of ultra low floor electric bus with light-weight body using fiber reinforced plastic and carbon material in 2009, beginning commercial operation of buses circling Seoul downtown and Namsan in December 2010. High-speed charge infrastructure for commercial operation of electric buses was established at the last stop of the routes and the shed of the bus transportation company. In this paper, vehicle specifications and system composition of the developed electric buses are introduced. Performance simulation results were compared with the test results of commercial operation routes. Finally, electric bus operation expenses and effects of greenhouse gas reduction from the economic and environmental aspects were analyzed based on the results of the commercial operation for 17 months when compared to the existing Compressed Natural Gas bus.
Literature on Multiple Objective Decision Making (MODM) methods and their applications have been reviewed and classified systematically.
This survey provides readers with a capsule look into the existing methods, their characteristics, and applicability to analysis of MODM problems.
The basic MODM concepts are defined and a standard notation is introduced in Part II to facilitate the review.
A system of classifying about two dozen major MODM methods is presented. of these methods have been proposed by various researchers
in the last few years, but here for the first time they are presented together. The basic concept, the computational procedures, and the characteristics of each of these methods are presented concisely in Part III. The computational procedure of each method is illustrated by solving a simple numerical example.
Part IV of the survey deals with the actual or proposed applications of these MODM methods. The literature has been classified into 12 major topics based on the area of applications. Summary of each reference on applications is given.
An updated bibliographical listing of 24 books, monographs or conference proceedings, and 424 papers, reports or theses is presented.
Plug-in Hybrid Electric Vehicles (PHEVs) can significantly reduce petroleum consumption and the only difference from hybrid electric vehicles (HEVs) is the ability of PHEVs to use off-board electricity generation to recharge their energy storage system. The fuel economy of PHEV is highly dependent on All-Electric-Range (AER), drivetrain component size and control strategy parameter. In this study we consider PHEV version of parallel hybrid NOVA transit bus model developed with the Powertrain System Analysis Toolkit (PSAT). A genetic based derivative free algorithm called Multi-Objective Genetic Algorithm (MOGA) is used to optimize conflicting drivetrain and control strategy parameters. The AER, fuel economy, emissions and main performance constraints of the PHEVs will be compared for the initial design and final optimal design.
- L Eudy
- R Prohaska
- K Kelly
- M Post
Electric bus analysis for new york city transit. Master’s thesis
- J Aber