A review of recent advances in the operations research literature on the green routing problem and its variants

Since early 2010s, the Green Routing Problem (GRP) has dominated the literature of logistics and transportation. The problem itself consists of finding a set of vehicle routes for a set of customers while minimizing the detrimental effects of transportation activities. These negative externalities have been intensively tackled in the last decade. Operations research studies have particularly focused on minimizing the energy consumption and emissions. As a result, the rich literature on GRPs has already reached its peak, and several early literature reviews have been conducted on various aspects of related vehicle routing and scheduling problem variants. The major contribution of this paper is that it represents a comprehensive review of the current reviews on GRP studies. In addition to that, it is an up-to-date review based on a new chronological taxonomy of the literature. The detailed analysis provides a useful framework for understanding the research gaps for the future studies and the potential impacts for the academic community

Integrating passenger and freight transportation : model formulation and insights

Integrating passenger and freight flows creates attractive business opportunities because the same transportation needs can be met with fewer vehicles and emissions. This paper seeks an integrated solution for the transportation of passenger and freight simultaneously, so that fewer vehicles are required. The newly introduced problem concerns scheduling a set of vehicles to serve the requests such that a part of the journey can be carried out on a scheduled passenger transportation service. We propose an arc-based mixed integer programming formulation for the integrated transportation system. Computational results on a set of instances provide a clear understanding on the benefits of integrating passenger and freight transportation in the current networks, considering multi-modality of traditional passenger-oriented transportation modes, such as taxi, bus, train or tram

Understanding freight drivers’ behavior and the impact on vehicles’ fuel consumption and CO2e emissions

Despite the significant impact of driver behavior on fuel consumption and carbon dioxide equivalent (CO2e) emissions, this phenomenon is often overlooked in road freight transportation research. We review the relevant literature and seek to provide a deeper understanding of the relationship between freight drivers’ behavior and fuel consumption. This study utilizes a real-life dataset of over 4000 driving records from the freight logistics sector to examine the effects of specific behaviors on fuel consumption. Analyzed behaviors include harsh acceleration/deceleration/cornering, over-revving, excessive revolutions per minute (RPM), and non-adherence to legal speed limits ranging from 20 to 70 miles per hour (mph). Our findings confirm existing literature by demonstrating the significant impact of certain driving characteristics, particularly harsh acceleration/cornering, on fuel consumption. Moreover, our research contributes new insights into the field, notably highlighting the substantial influence of non-adherence to the legal speed limits of 20 and 30 mph on fuel consumption, an aspect not extensively studied in previous research. We subsequently introduce an advanced fuel consumption model that takes into account these identified driver behaviors. This model not only advances academic understanding of fuel consumption determinants in road freight transportation, but also equips practitioners with practical insights to optimize fuel efficiency and reduce environmental impacts

Time Dependent Green Vehicle Routing Problem

Green Vehicle Routing problem (GVRP) is a variant of standard Vehicle Routing Problem in which the environmental externalities of routing operations are minimized as a part of the routing cost. Early studies on GVRP were focused on minimizing energy consumption and pollution of internal combustion engine commercial vehicles. With the introduction of electric commercial vehicles and the increasing trend in their adoption in green logistics and last mile delivery operations a new strand of GVRP is introduced called “Alternative Fuel Vehicle Routing Problem (AFVRP).” The objective in AFVRP is to find optimal routes with minimum energy, time or money requirements for a fleet of alternative fuel vehicles while accounting for their operation limitations such as limited driving autonomy. The goal of this dissertation is to develop a model for a Time-Dependent GVRP (TDGVRP) with a mixed fleet of electric and internal combustion engine commercial vehicles that finds the optimal fleet design for last mile delivery operations of a company and allocates minimum cost routes to each of these vehicles in order to satisfy the customer demands in a typical operation day. The routing cost includes vehicle purchase cost, energy consumption cost, early or late service penalty cost, labor cost, emission trading cost and Electric Commercial Vehicle (ECV) battery degradation cost. An extensive model is used to estimate the energy consumption of vehicles that accounts for not only the travel distance, but also speed, acceleration, and cargo load as contributing factors to energy consumption of vehicles. Moreover, by considering the time dependency of travel times along the network, the effect of congestion on the vehicle energy requirements is accounted for. This is very important in the context of ECVs where the energy consumption of the vehicle determines the remaining battery and driving range of the vehicle. While previous studies on GVRP focus only on the limitations of ECVs, the GVRP model proposed in this dissertation takes into account the limitations of both ECVs and Internal Combustion Commercial Vehicles (ICCVs). These limitations are in terms of limited range and higher purchase cost for electric commercial vehicles, and carbon emission limitations imposed by government regulations (e.g., Cap and Trade project) and Low Emission Zone (LEZ) penalties for ICCVs. Emission trading or LEZs are government-mandated regulations to control pollution by providing economic incentives for reducing emission of pollutants and electrifying distribution operations. This is a unique and complex model and no study in the literature has addressed this problem sufficiently. The results of the proposed model in this study can be used to illustrate the changes in the fleet design and routing of a delivery company as a result of these regulations. A mathematical formulation is developed for the proposed Time-Dependent GVRP and numerical experiments are designed to demonstrate its capabilities. Commercial solvers like Xpress can be used to solve the proposed model on small-size problems but for large-size and real-world problems an appropriate heuristic is needed. A heuristic method that can find good solutions in reasonable time for this problem is developed and tested on several cases. Also, the model is applied to a large size case study to test its performance. At last a set of sensitivity analysis is performed on the problem characteristics to evaluate the heuristic’s potential outcome in different situations. The results show that the proposed heuristic is performing very well and efficient and it can be used to identify the changes in fleet size and routing of last mile delivery operations as a result of green logistics policies

Sustainable reverse logistics for household plastic waste

Summary of the thesis titled “Sustainable Reverse Logistics for Household Plastic Waste” PhD Candidate: Xiaoyun Bing Recycled plastic can be used in the manufacturing of plastic products to reduce the use of virgin plastics material. The cost of recycled plastics is usually lower than that of virgin plastics. Therefore, it is environmentally and economically beneficial to improve the plastic recycling system to ensure more plastic waste from households is properly collected and processed for recycling. Plastic waste has a complex composition and is polluted, thus requires a substantial technical effort to separate the plastics from the waste and to sort these into recyclable materials. There are several alternatives in the existing collection methods (curb-side and drop-off) and separation methods (source separation and post-separation). It is challenging to select a suitable combination of these methods and to design a network that is efficient and sustainable. It is necessary to build a suitable, efficient and sustainable recycling network from collection to the final processor in order to provide solutions for different future scenarios of plastics household waste recycling. Decision support is needed in order to redesign the plastic waste reverse logistics so that the plastic waste recycling supply chain can be improved towards a more sustainable direction. To improve the efficiency in the recycling of plastic packaging waste, insights are required into this complex system. Insights solely on a municipal level are not sufficient, as the processing and end market are important for a complete network configuration. Therefore, we have investigated the problem at three levels: municipal, regional, and global. Decision support systems are developed based on optimization techniques to explore the power of mathematical modelling to assist in the decision-making process. This thesis investigates plastic waste recycling from a sustainable reverse logistics angle. The aim is to analyse the collection, separation and treatments systems of plastic waste and to propose redesigns for the recycling system using quantitative decision support models. We started this research project by identifying research opportunities. This was done through a practical approach that aimed to find future research opportunities to solve existing problems (Chapter 2). We started from a review of current municipal solid waste recycling practices in various EU countries and identified the characteristics and key issues of waste recycling from waste management and reverse logistics point of view. This is followed by a literature review regarding the applications of operations research. We conclude that waste recycling is a multi-disciplinary problem and that research opportunities can be found by considering different decision levels simultaneously. While analyzing a reverse supply chain for Municipal Solid Waste (MSW) recycling, a holistic view and considering characteristics of different waste types are necessary. Municipal Level In Chapter 3, we aim to redesign the collection routes of household plastic waste and compare the collection options at the municipal level using eco-efficiency as a performance indicator. The collection problem is modeled as a vehicle routing problem. A tabu search heuristic is used to improve the routes. Scenarios are designed according to the collection alternatives with different assumptions in collection method, vehicle type, collection frequency, and collection points, etc. The results show that the source-separation drop-off collection scenario has the best performance for plastic collection, assuming householders take the waste to the drop-off points in a sustainable manner. In Chapter 4, we develop a comprehensive cost estimation model to further analyze the impacts of various taxation alternatives on the collection cost and environmental impact. This model is based on such variables as fixed and variable costs per vehicle, personnel cost, container or bag costs, as well as emission costs (using imaginary carbon taxes). The model can be used for decision support when strategic changes to the collection scheme of municipalities are considered. The model, which considers the characteristics of municipalities, including degree of urbanization and taxation schemes for household waste management, was applied to the Dutch case of post-consumer plastic packaging waste. The results showed that post-separation collection generally has the lowest costs. Curb-side collection in urban municipalities without residual waste collection taxing schemes has the highest cost. These results were supported by the conducted sensitivity analysis, which showed that higher source-separation responses are negatively related to curb-side collection costs. Regional Level Chapter 5 provides decision support for choosing the most suitable combination of separation methods in the Netherlands. Decision support is provided through an optimized reverse logistics network design that makes the overall recycling system more efficient and sustainable, while taking into account the interests of various stakeholders (municipalities, households, etc.). A mixed integer linear programming (MILP) model, which minimizes both transportation cost and environmental impact, is used to design this network. The research follows the approach of a scenario study; the baseline scenario is the current situation and other scenarios are designed with various strategic alternatives. Comparing these scenarios, the results show that the current network settings of the baseline situation is efficient in terms of logistics, but has the potential to adapt to strategic changes, depending on the assumptions regarding availability of the required processing facilities to treat plastic waste. In some of the tested scenarios, a separate collection channel for polyethylene terephthalate (PET) bottles is cost-efficient and saves carbon emission. Although the figures differ depending on the choices in separation method made by municipalities, our modeling results of all the tested scenarios show a reduction in carbon emissions of more than 25 percent compared to the current network. Chapter 6 studies a plastic recycling system from a reverse logistics angle and investigates the potential benefits of a multimodality strategy to the network design of plastic recycling. The aim was to quantify the impact of multimodality in the network in order to provide decision support for the design of more sustainable plastic recycling networks in the future. A MILP model is developed in order to assess different plastic waste collection, treatment, and transportation scenarios. A baseline scenario represents the optimized current situation, while other scenarios allow multimodality options (barge and train) to be applied. With our input parameter settings, results show that transportation costs contribute to approximately 7 percent of the total costs, and multimodality can help reduce transportation costs by almost 20 percent (CO_2-eq emissions included). In our illustrative case with two plastic separation methods, the post-separation channel benefits more from a multimodality strategy than the source-separation channel. This relates to the locations and availability of intermediate facilities and the quantity of waste transported on each route. Global Level After the regional network redesign, Chapter 7 shows a global network redesign. The aim of this chapter was to redesign a reverse supply chain from a global angle based on a case study conducted on household plastic waste distributed from Europe to China. Emissions trading restrictions are set on processing plants in both Europe and China. We used a mixed-integer programming model in the network optimization to decide on location reallocation of intermediate processing plants under such restrictions, with the objective of maximizing total profit under Emission Trading Schemes (ETS). Re-locating facilities globally can help reduce the total cost. Once carefully set, ETS can function well as incentive to control emissions in re-processors. Optimization results show that relocating re-processing centers to China reduces total costs and total transportation emissions. ETS applied to re-processors further helps to reduce emissions from both re-processors and the transportation sector. Carbon caps should be set carefully in order to be effective. These results give an insight in the feasibility of building a global reverse supply chain for household plastic waste recycling and demonstrate the impact of ETS on network design. The results also provide decision support for increasing the synergy between the policy for global shipping of waste material and the demand of recycled material. Conclusions Chapter 8 summarizes the findings from chapters 2 to 7 and provides brief answers to the research questions. Beyond that, the integrated findings combine the results from different decision levels and elaborate the impacts of various system characteristics and external factors on the decision making in order to achieve an improved sustainable performance. Main findings are: Regarding the impact of carbon cost, the results from different chapters are consistent in terms that emission cost is only a small part of the total cost, even when carbon cost is set at its historically highest figure. When carbon price is set to a different value, impact of carbon cost on the change of optimization results is higher on the upstream of the reverse supply chain for plastic waste than the downstream.In Emission Trading scheme (ETS), carbon cap has a larger impact on eco-efficiency performance of the global network than carbon price.On one decision level, models can help to find the ``best option". For example, in the collection phase, the average total collection costs per ton of plastic waste collected for source-separation municipalities are more than twice of the post-separation municipalities' collection costs due to the frequent stops made and idling time at each stop. From the regional network perspective, post-separation scenarios have higher costs and environmental impact than source separation due to the limited number of separation centers compared to the numerous cross-docking sites for source-separation. When combining decision levels, however, it is difficult to find one ``best option" that fits all, as there are contradictory results when looking at the same factor from different decision levels. Through decision support models, we provided clear insights into the trade-offs and helped to quantify the differences and identify key factors to determine the differences.Population density differences in various municipalities influence the performance of curbside collection more than drop-off collection. This information is valuable for decision makers to consider in the decision making process. Finally, managerial insights derived from sustainable reverse logistics for household plastic waste are summarized in conclusion section.</p

TOOLS TO SUPPORT TRANSPORTATION EMISSIONS REDUCTION EFFORTS: A MULTIFACETED APPROACH

The transportation sector is a significant contributor to current global climatic problems, one of the most prominent problems that today's society faces. In this dissertation, three complementary problems are addressed to support emissions reduction efforts by providing tools to help reduce demand for fossil fuels. The first problem addresses alternative fuel vehicle (AFV) fleet operations considering limited infrastructure availability and vehicle characteristics that contribute to emission reduction efforts by: supporting alternative fuel use and reducing carbon-intensive freight activity. A Green Vehicle Routing Problem (G-VRP) is formulated and techniques are proposed for its solution. These techniques will aid organizations with AFV fleets in overcoming difficulties that exist as a result of limited refueling infrastructure and will allow companies considering conversion to a fleet of AFVs to understand the potential impact of their decision on daily operations and costs. The second problem is aimed at supporting SOV commute trip reduction efforts through alternative transportation options. This problem contributes to emission reduction efforts by supporting reduction of carbon-intensive travel activity. Following a descriptive analysis of commuter survey data obtained from the University of Maryland, College Park campus, ordered-response models were developed to investigate the market for vanpooling. The model results show that demand for vanpooling in the role of passenger and driver have differences and the factors affecting these demands are not necessarily the same. Factors considered include: status, willingness-to-pay, distance, residential location, commuting habits, demographics and service characteristics. The third problem focuses on providing essential input data, origin-destination (OD) demand, for analysis of various strategies, to address emission reduction by helping to improve system efficiency and reducing carbon-intensive travel activity. A two-stage subarea OD demand estimation procedure is proposed to construct and update important time-dependent OD demand input for subarea analysis in an effort to overcome the computational limits of Dynamic Traffic Assignment (DTA) methodologies. The proposed method in conjunction with path-based simulation-assignment systems can provide an evolving platform for integrating operational considerations in planning models for effective decision support for agencies that are considering strategies for transportation emissions reduction