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When network users are satisficing decision makers, the resulting traffic pattern attains a satisficing user equilibrium, which may deviate from the (perfectly rational) user equilibrium. In a satisficing user equilibrium traffic pattern, the total system travel time can be worse than in the case of the perfectly rational user equilibrium. We show how bad the worst-case satisficing user equilibrium traffic pattern can be compared with the perfectly rational user equilibrium. We call the ratio between the total system travel times of the two traffic patterns the price of satisficing, for which we provide an analytical bound. We compare the analytical bound with numerical bounds for several transportation networks. [ABSTRACT FROM AUTHOR]

We consider a leader-follower game in the form of a bi-level optimization problem that simultaneously optimizes facility locations and network design in hazardous materials transportation. In the upper level, the leader intends to reduce the facility setup cost and the hazmat exposure risk, by choosing facility locations and road segments to close for hazmat transportation. When making such decisions, the leader anticipates the response of the followers who want to minimize the transportation costs. Considering uncertainty in the hazmat exposure and the hazmat transport demand, we consider a robust optimization approach with multiplicative uncertain parameters and polyhedral uncertainty sets. The resulting problem has a min-max problem in the upper level and a shortest-path problem in the lower level. We devise an exact algorithm that combines a cutting plane algorithm with Benders decomposition [ABSTRACT FROM AUTHOR]

We consider a road-ban problem in hazardous material (hazmat) transportation. We formulate the problem as a network design problem to select a set of closed road segments for hazmat traffic and obtain a bilevel optimization problem. While modeling probabilistic route choices of hazmat carriers by the random utility model (RUM) in the lower level, we consider a risk-averse measure called conditional value at risk (CVaR) in the upper level, instead of the widely used expected risk measure. Using the RUM and CVaR, we quantify the risk of having hazmat accidents and large consequences and design the network policy for road bans accordingly. Although CVaR has been used in hazmat routing problems, this paper is the first attempt to apply CVaR in risk averse hazmat network design problems considering stochastic route choices of hazmat carriers. The resulting problem is a mixed integer nonlinear programming problem, for which we devise a line search approach combined with Benders decomposition. We demonstrate the efficiency of the proposed computational method with case studies. The average computation time for a network with 105 nodes and 268 arcs is three hours. By applying CVaR to the route-choice behavior of hazmat carriers, we protect the road network from undesirable route choices that may lead to severe consequences. We define the value of RUM-CVaR solutions (VRCS) over the deterministic model based on shortest-path problems and the expected risk measure. Our case study shows that the VRCS can range from 4.9% to 64.1% depending on the probability threshold used in the CVaR measure. [ABSTRACT FROM AUTHOR]

Despite significant advances in risk management, the routing of hazardous materials (hazmat) has relied on relatively simplistic methods. In this paper, we apply an advanced risk measure, called conditional value-at-risk (CVaR), for routing hazmat trucks. CVaR offers a flexible, risk-averse, and computationally tractable routing method that is appropriate for hazmat accident mitigation strategies. The two important data types in hazmat transportation are accident probabilities and accident consequences, both of which are subject to many ambiguous factors. In addition, historical data are usually insufficient to construct a probability distribution of accident probabilities and consequences. This motivates our development of a new robust optimization approach for considering the worst-case CVaR (WCVaR) under data uncertainty. We study important axioms to ensure that both the CVaR and WCVaR risk measures are coherent and appropriate in the context of hazmat transportation. We also devise a computational method for WCVaR and demonstrate the proposed WCVaR concept with a case study in a realistic road network. [ABSTRACT FROM AUTHOR]

We investigate dual-toll setting as a policy tool to mitigate the risk of hazardous material (hazmat)shipment in road networks. We formulate the dual-toll problem as a bi-level pro-gram wherein the upper level a imsat minimizing the risk, and the lower level explores the user equilibrium decision of the regular vehicles and hazmat carriers given the toll. When the upper level objective is to minimize the risk and all links are tollable, we decompose the for-mulation into first-stage and second-stage, and suggest a computational method to solve each stage. Our two-stage solution methodology guarantees nonnegative valid dual tolls regardless of the solution accuracy of the first-stage problem. We also consider a general dual-toll setting problem where the regulator rather wishes to minimize a combination of risk and the paid decompose the problem into sub problems each being solved by a line search. Case studies based on the Sioux Falls network illustrate the insights on the dual-toll policies. [ABSTRACT FROM AUTHOR]

Highlights: [•] We use CVaR as a risk measure in hazmat dynamic routing problems. [•] CVaR model offers a flexible framework to the decision makers. [•] The proposed model offers the desirable risk-averse attitude without adding complexity. [•] An efficient and effective algorithm is proposed and tested in a dynamic network. [•] CVaR is proven to be a proper risk measure for hazmat dynamic route decision making. [ABSTRACT FROM AUTHOR]

Abstract: We study an equivalent optimization problem with an inequality constraint and boundary conditions, whose necessary condition for optimality is the variational inequality presentation of American options. To solve the problem, we use the gradient projection method, with discretizations both in time and space. We tested the algorithm and compared with the projective successive over-relaxation method. [Copyright &y& Elsevier]

• We perform a multi-day scenario analysis on potential uses, feasibility, and charging requirements of Battery Electric Vehicles (BEVs). It is shown that single-day analysis overestimates BEV feasibilities compared to multi-day analysis. • We employ detailed activity-based charging behaviors and assumptions and derive managerial, policy, and planning insights of charging stations deployment. • Four different charging demand management strategies (immediate charging, optimal charging, factional average charging, and fractional optimal charging) are studied, and results indicate that these charging strategies reduces infrastructure requirements. Multi-day activity-travel patterns help create potential vehicle usage profiles that contain vehicle operations and battery status under different scenarios with varying location-based charging opportunities, based on travel needs and charging availability/behaviors. Utilizing a multi-day data sampling method, analyses of scenarios are designed to provide insights on bounds of potential BEV market under different charging opportunities, including level 2 activity charging and level 3 trip charging. Single-day data results tend to overestimate travelers' BEV feasibility assuming that multi-day sample data provides accurate estimations. Facility utilization can be improved without affecting travelers' charging demand under correct pricing scheme for most cost-sensitive users. Smart grid charging strategy can greatly reduce the total number of operating chargers during the same time in a day, and BEV users' charging behaviors have minor impact on this improvement. Our numerical results indicate that an appropriate number of chargers installed in shopping and leisure locations should be more profitable and have higher charger utilization rate since those chargers help cover BEV users' trips. [ABSTRACT FROM AUTHOR]

In conservation planning, the data related to size, growth and diffusion of populations is sparse, hard to collect and unreliable at best. If and when the data is readily available, it is not of sufficient quantity to construct a probability distribution. In such a scenario, applying deterministic or stochastic approaches to the problems in conservation planning either ignores the uncertainty completely or assumes a distribution that does not accurately describe the nature of uncertainty. To overcome these drawbacks, we propose a robust optimization approach to problems in conservation planning that considers the uncertainty in data without making any assumption about its probability distribution. We explore two of the basic formulations in conservation planning related to reserve selection and invasive species control to show the value of the proposed robust optimization. Several novel techniques are developed to compare the results produced by the proposed robust optimization approach and the existing deterministic approach. For the case when the robust optimization approach fails to find a feasible solution, a novel bi-objective optimization technique is developed to handle infeasibility by modifying the level of uncertainty. Some numerical experiments are conducted to demonstrate the efficacy of our proposed approach in finding more applicable conservation planning strategies. [ABSTRACT FROM AUTHOR]

Rice is the staple food of nearly half of the population of the world, most of whom live in developing countries. Ensuring a domestic supply of rice from outside sources is difficult for developing countries as less than 5% of the total world's production is available for international trade. Hence, in order to ensure domestic food security, e.g., food availability and access, governments provide subsidies in agriculture. In many occasions, public money used for the subsidy goes toward promoting undesirable crops like tobacco. Although the strategic interaction between governments and manufacturers is critical, it has not been studied in the literature. This study fills this gap by considering a game between a government (of a developing country) and a tobacco manufacturer in which the government decides on a mix of subsidies and the tobacco manufacturer decides on declaring a purchasing price of tobacco. We provide a numerical study to show that controlling the output harvest price is more effective in reaching the desired end result for both the government and the tobacco manufacturer. A subsidy in fertilizer results in the measurable increase in the government spending but does not have significant effect in reaching the production target. The fertilizer subsidy should be provided only when the output price is too high to be affordable for the population. [ABSTRACT FROM AUTHOR]

We develop a multi-period capacitated flow refueling location problem for electric vehicles (EVs) as the EV market responds to the charging infrastructure. The optimization model will help us determine the optimal location of level 3 chargers as well as the number of charging modules at each station over multiple time periods. Our model can also be applied to fast-filling gaseous alternative fuel vehicles under similar assumptions. We define a number of demand dynamics, including flow demand growth as a function of charging opportunities on path as well as natural demand growth independent of charging infrastructure. We also present an alternative objective function of maximizing electric vehicle demand in addition to maximizing flow coverage. A case study based on a road network around Washington, D.C., New York City, and Boston is presented to provide numerical experiments related to demand dynamics, showing the potential problems in multi-period planning. [ABSTRACT FROM AUTHOR]

The focus of this research is on supplying gasoline after a natural disaster. There are two aspects for this work: determination of which gas stations should be provided with generators (among those that do not have electric power) and determination of a delivery scheme that accounts for increased demand due to lack of public transportation and considerations such as equity. We develop a mixed integer program for this situation. Two case studies based on Hurricane Sandy in New Jersey are developed and solved in CPLEX. As expected, increasing equity increases cost and also tends to place generators to stations with large initial inventories. It is further observed that CPLEX can solve the largest instances of the problem for a 5% tolerance gap, indicating that the model is efficient. [ABSTRACT FROM AUTHOR]

We study robust network design problems for hazardous materials transportation considering risk uncertainty. Risk uncertainty is considered in two ways: (1) uncertainty on each link for each shipment, and (2) uncertainty on each link across all shipments. We extend an existing heuristic framework to solve these two robust network design problems and propose a Lagrangian relaxation heuristic to solve subproblems within the framework. We present our computational experiences and illustrate general insights based on real networks. [ABSTRACT FROM AUTHOR]

This paper presents a methodological framework to identify population-wide traveler type distribution and simultaneously infer individual travelers’ Origin-Destination (OD) pairs, based on the individual records of a shared mobility (bike) system use in a multimodal travel environment. Given the information about the travelers’ outbound and inbound bike stations under varied price settings, the developed Selective Set Expectation Maximization (SSEM) algorithm infers an underlying distribution of travelers over the given traveler “types,” or “classes,” treating each traveler’s OD pair as a latent variable; the inferred most likely traveler type for each traveler then informs their most likely OD pair. The experimental results based on simulated data demonstrate high SSEM learning accuracy both on the aggregate and dissagregate levels. [ABSTRACT FROM AUTHOR]

In this article, we proposed a bidirectional dual active bridge (DAB) converter adopting a boost-up function. The proposed converter can be implemented by adding only one capacitor to the high voltage side without additional active components. By utilizing the boost-up function, the proposed converter can achieve higher output power than the conventional DAB converter under the same leakage inductance condition. In comparison with the resonant converter in addressing the output power limitation, the proposed converter can reduce the switching and conduction losses due to the low turn-off and circulating currents, and complex control is not required to drive the synchronous rectifier. Furthermore, the direction of the output power can be easily changed with a simple control circuit. Experimental results from the proposed converter with a 3 kW rated power, 28-V low-side voltage, and 600-V high-side voltage are demonstrated to verify the proposed works. [ABSTRACT FROM AUTHOR]

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research. [ABSTRACT FROM PUBLISHER]

This paper introduces a Value-at-Risk (VaR) model to generate route choices for a hazmat shipment based on a specified risk confidence level. VaR is a threshold value such that the probability of the loss exceeding the VaR value is less than a given probability level. The objective is to determine a route which minimizes the likelihood that the risk will be greater than a set threshold. Several properties of the VaR model are established. An exact solution procedure is proposed and tested to solve the single-trip problem. To test the applicability of the approach, routes obtained from the VaR model are compared with those obtained from other hazmat objectives, on a numerical example as well as a hazmat routing scenario derived from the Albany district of New York State. Depending on the choice of the confidence level, the VaR model gives different paths from which we conclude that the route choice is a function of the level of risk tolerance of the decision-maker. Further refinements of the VaR model are also discussed. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a dual-time-scale formulation of dynamic user equilibrium (DUE) with demand evolution. Our formulation belongs to the problem class that refer to as differential variational inequalities. It combines the within-day time scale for which route and departure time choices fluctuate in continuous time with the day-to-day time scale for which demand evolves in discrete time steps. Our formulation is consistent with the often told story that drivers adjust their travel demands at the end of every day based on their congestion experience during one or more previous days. We show that analysis of the within-day assignment model is tremendously simplified by expressing dynamic user equilibrium as a differential variational inequality. We also show there is a class of day-to-day demand growth models that allow the dual-time-scale formulation to be decomposed by time-stepping to yield a sequence of continuous time, single-day, dynamic user equilibrium problems. To solve the single-day DUE problems arising during time-stepping, it is necessary to repeatedly solve a dynamic network loading problem. We observe that the network loading phase of DUE computation generally constitutes a differential algebraic equation (DAE) system, and we show that the DAE system for network loading based on the link delay model (LDM) of may be approximated by a system of ordinary differential equations (ODEs). That system of ODEs, as we demonstrate, may be efficiently solved using traditional numerical methods for such problems. To compute an actual dynamic user equilibrium, we introduce a continuous time fixed-point algorithm and prove its convergence for effective path delay operators that allow a limited type of nonmonotone path delay. We show that our DUE algorithm is compatible with network loading based on the LDM and the cell transmission model (CTM) due to . We provide a numerical example based on the much studied Sioux Falls network. [Copyright &y& Elsevier]

Shared autonomous electric vehicles (SAEVs) are expected to serve a significant fraction of the passenger transportation needs in cities and surrounding urban areas. In this paper, we consider optimal operation of a cyber-physical system (CPS) comprising a large fleet of SAEVs and a set of charging hubs located across the transportation network and supported by the power grid. The hubs are considered to have a number of charging stations, a stand-alone battery bank for energy storage, and limited rooftop photo-voltaic (PV) generation capacity. We developed a robust mixed integer linear programming model. It considers a number of practical features of both the power and transportation systems, including day-ahead load commitment for electricity via an alternative current power flow model, real time price spikes of electricity, energy arbitrage, uncertainty in passenger demand, and balking of passengers while waiting for a ride. We demonstrated our methodology by implementing it on a sample CPS with 500 SAEVs and five hubs with fifty charging stations in each. Our methodology yields operational decisions for day ahead commitment of power and real time control of the SAEVs and the hubs. The sample CPS is used to examine impact of hub capacity and fleet size on various system performance measures. We discuss the computational challenges of our methodology and propose a simplified myopic approach that is capable of dealing with much larger fleet sizes and a variety of hub capacities. Reduction in computation time and the optimality gap for the myopic approach are examined. • Shared autonomous electric vehicles will serve transportation needs in urban areas. • This will require design and development of new cyber–physical systems. • A comprehensive planning and operational methodology is presented. • Power market considerations can yield nearly a million dollars in savings per year. [ABSTRACT FROM AUTHOR]

• Peer to peer energy trading will expand beyond prosumers to regular consumers. • IoT and AMI is empowering end-use consumers to from aggregations. • Methodology allows aggregations of consumers to schedule load and trade electivity. • A generalized Nash bargaining solution model was proposed to assess optimal trading mechanisms. • Offers a novel cost-sharing strategy guided by cost minimization and Nash bargaining. In the last several years, the growth in household solar generation and the lack of success of the feed-in-tariff programs have led to the rise of peer-to-peer (P2P) energy trading schemes among prosumers. However, a change that has started more recently is the growth of smart homes and businesses, of which loads are IoT controlled and are supported by advanced metering infrastructure (AMI). This has created a new opportunity for smart homes and businesses to form aggregations (coalitions) and participate in cooperative load management and energy trading. Unlike energy trading among individual prosumers in most P2P networks, a new trading opportunity that is emerging is between aggregations of peers of smart homes and businesses and electric vehicles (EVs). In this paper, we consider one such trading scenario between two aggregations, of which one has smart homes and businesses with load consuming entities (not prosumers), and the other has EVs only. The aggregation with smart homes and businesses derive cost reduction through optimal load scheduling based on load preferences, market-based pricing of electricity, and opportunity to trade (buy) energy from the aggregation with EVs. Whereas the aggregation of EVs optimally schedules charging to meet EV needs and uses stored energy to trade (sell). A generalized Nash bargaining model is developed for obtaining optimal trading strategies in the form of plain or swing option contracts. A sample numerical problem scenario is used to show that suitable contracts can be derived that allow aggregations of peers to mutually benefit from energy trading.It is shown that there exist numerous alternative optimal solutions to the Nash bargaining problem. The solutions comprise different combinations of strike price and option value, for all of which savings to the parties remain constant. For plain option, with a contract quantity of 1 MWh, the total savings generated is equivalent to the average price of 1.62 MWh of electricity. Interactions among contract parameters (such as strike price, option value, and option quantity) and the relative market power of the aggregations are also examined. [ABSTRACT FROM AUTHOR]

• Aggregation and cooperation among peers will expand beyond prosumers in microgrids. • Growth in IoT and metering infrastructure is empowering end-use consumers. • Methodology allows aggregated consumers to schedule load and share energy. • Application demonstrates a significant cost reduction while assuring fairness. • Offers a novel cost-sharing strategy guided by cost minimization and Nash bargaining. End-use consumers (peers) are being empowered to aggregate for direct demand-side participation through load scheduling and energy sharing. This is the result of the growth of Internet of Things (IoT) enabled loads, availability of advanced metering infrastructure, and the move towards real-time (RT) pricing of electricity. Peer-to-peer (P2P) cooperation has received significant interest in recent years, though the focus of this growing body of research is on modeling prosumer behavior in microgrids. Hence, there is a need for new methodologies to examine empowerment of all end-use consumers (not limited to prosumers) to form aggregations and develop fair rules of cooperation to reduce cost. This paper offers an optimization based methodology to address the above need for power systems. It minimizes the total cost and considers fairness using a Nash bargaining approach. Since cost and fairness are often in conflict, trade-off strategies are also presented. The model to asses fairness is nonlinear. Hence, it is transformed into a second order cone program (SOCP) and solved using GUROBI software version 7.5.2. The methodology is implemented on a sample 5-bus network, built using price and demand data from one of the load zones of Pennsylvania, New Jersey, and Maryland (PJM) power network in the United States. It is shown that two aggregations of peers participating in the sample network can reduce their total cost by 14.17% and 22.7%, while maintaining fairness. Concluding remarks highlight some of the limitations of the methodology. [ABSTRACT FROM AUTHOR]

Due to catastrophic consequences of potential accidents in hazardous materials (hazmat) transportation, a risk-averse approach for routing is necessary. In this article, we consider spectral risk measures, for risk-averse hazmat routing, which overcome challenges posed in the existing approaches such as conditional value-at-risk. In spectral risk measures, one can define the spectrum function precisely to reflect the decision maker's risk preference. We show that spectral risk measures can provide a unified routing framework for popular existing hazmat routing methods based on expected risk, maximum risk, and conditional value-at-risk. We first consider a special class of spectral risk measures, for which the spectrum function is represented as a step function. We develop a mixed-integer linear programming model in hazmat routing to minimize these special spectral risk measures and propose an efficient search algorithm to solve the problem. For general classes of spectral risk measures, we suggest approximation methods and path-based approaches. We propose an optimization procedure to approximate general spectrum functions using a step function. We illustrate the usage of spectral risk measures and the proposed computational approaches using data from real road networks. [ABSTRACT FROM AUTHOR]

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented. [ABSTRACT FROM AUTHOR]

Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems. In particular, the attention-based encoder-decoder models show high effectiveness on various routing problems, including the Traveling Salesman Problem (TSP). Unfortunately, they perform poorly for the TSP with Drone (TSP-D), requiring routing a heterogeneous fleet of vehicles in coordination—a truck and a drone. In TSP-D, the two vehicles are moving in tandem and may need to wait at a node for the other vehicle to join. State-less attention-based decoder fails to make such coordination between vehicles. We propose a hybrid model that uses an attention encoder and a Long Short-Term Memory (LSTM) network decoder, in which the decoder's hidden state can represent the sequence of actions made. We empirically demonstrate that such a hybrid model improves upon a purely attention-based model for both solution quality and computational efficiency. Our experiments on the min-max Capacitated Vehicle Routing Problem (mmCVRP) also confirm that the hybrid model is more suitable for the coordinated routing of multiple vehicles than the attention-based model. The proposed model demonstrates comparable results as the operations research baseline methods. • We solve the Traveling Salesman Problem with Drone using reinforcement learning. • We present the problem as a Markov Decision Process. • We propose an attention encoder-LSTM decoder hybrid model. • We devise the Distribute-and-Follow Policy Gradient training algorithm. • The proposed model demonstrates comparable results as the operations research baseline methods. [ABSTRACT FROM AUTHOR]

For many socioeconomically disadvantaged customers living in food deserts, the high costs and minimum order size requirements make attended grocery deliveries financially non-viable, although it has a potential to provide healthy foods to the food insecure population. This paper proposes consolidating customer orders and delivering to a neighborhood convenience store instead of home delivery. We employ an optimization framework involving the minimum cost set covering and the capacitated vehicle routing problems. Our experimental studies in three counties in the U.S. suggest that by spatial and temporal consolidation of orders, the deliverer can remove minimum order-size requirements and reduce the delivery costs, depending on various factors, compared to attended home-delivery. We find the number and size of time windows for home delivery to be the most important factor in achieving temporal consolidation benefits. Other significant factors in achieving spatial consolidation include the capacity of delivery vehicles, the number of depots, and the number of customer orders. We also find that the number of partner convenience stores and the walkable distance parameter of the model have a significant impact on the number of accepted orders, i.e., the service level provided by the deliverer. The findings of this study imply consolidated grocery delivery as a viable solution to improve fresh food access in food deserts. In light of the recent global pandemic and its exacerbating effects on food insecurity, the innovative solution proposed in this paper is even more relevant and timely. • Consolidating grocery delivery to neighborhood stores can address food insecurity. • We quantify the extent and benefits of spatial and temporal consolidation of orders. • The number and size of time windows for home delivery affect temporal consolidation. • The vehicle capacity, the number of depots, and number of customers impact spatial consolidation. • The number of stores and the walkable distance parameter impact the service level. [ABSTRACT FROM AUTHOR]

In this paper, we propose a revenue optimization framework integrating demand learning and dynamic pricing for firms in monopoly or oligopoly markets. We introduce a state-space model for this revenue management problem, which incorporates game-theoretic demand dynamics and nonparametric techniques for estimating the evolution of underlying state variables. Under this framework, stringent model assumptions are removed. We develop a new demand learning algorithm using Markov chain Monte Carlo methods to estimate model parameters, unobserved state variables, and functional coefficients in the nonparametric part. Based on these estimates, future price sensitivities can be predicted, and the optimal pricing policy for the next planning period is obtained. To test the performance of demand learning strategies, we solve a monopoly firm's revenue maximizing problem in simulation studies. We then extend this paradigm to dynamic competition, where the problem is formulated as a differential variational inequality. Numerical examples show that our demand learning algorithm is efficient and robust. [ABSTRACT FROM PUBLISHER]