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In flexible manufacturing lines with delayed differentiation, the production process may fluctuate sharply when a control action is performed. As a result, the steady-state analysis algorithm is inaccurate for these production lines, and transient behavior studies have become crucial. However, dynamic analysis remains unexplored compared with the well-established theoretical system of steady-state analysis. Therefore, in this study, we propose a fast algorithm for predicting the production process performance in the delayed differentiation-based flexible production line under operation control. We first formulate practical problems existing in the auto, food, and furniture industries into a mathematical formation. Then, we offer closed-form formulae for predicting the production process performance using the built stochastic model in the production line with three machines. We also propose an algorithm to predict the performance of a production line having more than three machines. The proposed methods were verified to be highly accurate through comparison experiments. In terms of theoretical contributions, this study offers a research foundation for other transient-based studies. From a practical perspective, the proposed algorithms can be employed to predict the production process performance of processing lines under production control in advance. [ABSTRACT FROM AUTHOR]

The carpooling service, which is the sharing of journeys heading in similar directions, is expected to be more efficient. In this article, we study the impact of providing a carpooling service on customer waiting time and driver utilization in an on-demand service platform. We build an M / G / N queueing model to approximate the dynamics of the on-demand service platform that provides two services: a standard service and a carpooling service. We find that two factors influence the efficiency of the carpooling service: the source of customers who use the carpooling service, and the length of a normalized detour. If the carpooling service attracts customers who have not used this on-demand service before, i.e., the market size increases, then both the customer waiting time and driver utilization increase when more customers use the carpooling service. If the carpooling service primarily attracts customers who already use the standard on-demand service before, i.e., customers switch from the standard service to the carpooling service, then the impact of the carpooling service depends on the normalized detour. It is possible that customer waiting time and driver utilization first increase and then decrease as customers switch from the standard service to the carpooling service. [ABSTRACT FROM AUTHOR]

We address a dynamic covering location problem of an unmanned aerial vehicle base station (UAV-BS), in which the location sequence of a single UAV-BS in a wireless communication network is determined to satisfy data demand arising from ground users. This problem is especially relevant in the context of smart grid and disaster relief. The vertical movement ability of the UAV-BS and nonconvex covering functions in wireless communication restrict utilizing classical planar covering location approaches. Therefore, we develop new formulations to this emerging problem for a finite time horizon to maximize the total coverage. In particular, we develop a mixed-integer nonlinear programming formulation that is nonconvex in nature and propose a Lagrangean decomposition algorithm (LDA) to solve this formulation. Because of the high complexity of the problem, the LDA is still unable to find good local solutions to large-scale problems. Therefore, we develop a continuum approximation (CA) model and show that CA would be a promising approach in terms of both computational time and solution accuracy. Our numerical study also shows that the CA model can be a remedy to build efficient initial solutions for exact solution algorithms. Summary of Contribution: This paper addresses a facet of mixed integer nonlinear programming formulations. Dynamic facility location problems (DFLPs) arise in a wide range of applications. However, classical DFLPs typically focus on the two-dimensional spaces. Emerging technologies in wireless communication and some other promising application areas, such as smart grids, have brought new location problems that cannot be solved with classical approaches. For practical reasons, many research attempts to solve this new problem, especially by researchers whose primary research area is not OR, have seemed far from analyzing the characteristics of the formulations. Rather, solution-oriented greedy heuristics have been proposed. This paper has two main objectives: (i) to close the gap between practical and theoretical sides of this new problem with the help of current knowledge that OR possesses to solve facility location problems and (ii) to support the findings with an exhaustive computational study to show how these findings can be applied to practice. [ABSTRACT FROM AUTHOR]

• We provide a review of existing operations studies of electric vehicles (EVs). • This review focuses on mathematical modeling-based solutions to EV operations management problems. • The literature is grouped into EV charging infrastructure planning, EV charging operations, and public policy and business models. • New research opportunities for operations management of EVs are suggested. Electric vehicles (EVs) are widely considered to be a solution to the problems of increasing carbon emissions and dependence on fossil fuels. However, the adoption of EVs remains sluggish due to range anxiety, long charging times, and inconvenient and insufficient charging infrastructure. Various problems with EV service operations should be addressed to overcome these challenges. This study reviews the state-of-the-art mathematical modeling-based literature on EV operations management. The literature is classified according to recurring themes, such as EV charging infrastructure planning, EV charging operations, and public policy and business models. In each theme, typical optimization models and algorithms proposed in previous studies are surveyed. The review concludes with a discussion of several possible questions for future research on EV service operations management. [ABSTRACT FROM AUTHOR]

A bi-attribute concave shortest path (BC-SP) problem seeks to find an optimal path in a bi-attribute network that minimizes a linear combination of two path costs, one of which is evaluated by a nondecreasing concave function. Due to the nonadditivity of its objective function, Bellman’s principle of optimality does not hold. This paper proposes a parametric search method to solve the BC-SP problem, which only needs to solve a series of shortest path problems, i.e., the parameterized subproblems (PSPs). Several techniques are developed to reduce both the number of PSPs and the computation time for these PSPs. Specifically, we first identify two properties of the BC-SP problem to guide the parametric search using the gradient and concavity of its objective function. Based on the properties, a monotonic descent search (MDS) and an intersection point search (IPS) are proposed. Second, we design a speedup label correcting (LC) algorithm, which uses optimal solutions of previously solved PSPs to reduce the number of labeling operations for subsequent PSPs. The MDS, IPS and speedup LC techniques are embedded into a branch-and-bound based interval search to guarantee optimality. The performance of the proposed method is tested on the mean-standard deviation shortest path problem and the route choice problem with a quadratic disutility function. Experiments on both real transportation networks and grid networks show that the proposed method reduces the computation time of existing algorithms by one to two orders of magnitude. [ABSTRACT FROM AUTHOR]

Traditionally, customers always hail empty-cruising taxis on streets, which may offer low levels of comfort and efficiency especially during rush hours or rainy days. Thanks to the advance of smartphone technology, the e-hailing applications, which enable customers to hail taxis through their smartphones, become popular globally. To provide a systematic account of the impact of e-hailing applications’ wide adoption on the taxi system, we first propose a spatial equilibrium model that not only balances the supply and demand of taxi services but also captures both the taxi drivers’ and customers’ possible adoption of the newly-emerging e-hailing applications in a well-regulated taxi market. We then prove the existence of the proposed equilibrium, and further provide an algorithm to solve it. An extensive equilibrium model with elastic taxi-customer demands is also proposed. Lastly, a numerical example is presented to compare the taxi services with and without the e-hailing application and evaluate two types of e-hailing applications. [ABSTRACT FROM AUTHOR]

When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate congestion during public transit disruptions. Passengers with different origins, destinations, and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation problem as an optimal flow problem with uncertain demand information. To tackle the lack of analytical formulation of travel times due to capacity constraints, we propose a simulation-based first-order approximation to transform the original problem into a linear program. Uncertainties in demand are modeled using robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (−20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce system travel times. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model. The improvement of robust models is more prominent when the actual demand pattern is close to the worst-case demand. • A transit path recommendation model for minimizing system travel times under disruptions. • Using robust optimization to consider demand uncertainty. • Propose a simulation-based first-order approximation for the non-tractable objective function. • A case study in the Chicago public transit system with a real-world disruption. • The proposed model can reduce the incident-line passengers' travel time by 20%. [ABSTRACT FROM AUTHOR]

This article studies a one-period two-party supply chain with a service requirement. At the beginning of a single retail season, the retailer can obtain goods by either ordering from a firm or by purchasing and exercising call options. The retailer’s optimal ordering policy and the supplier’s optimal production policy are derived in the presence of options contracts and a service requirement. In addition, it is shown that options contracts benefit both the retailer and supplier. Furthermore, it is shown that the retailer’s optimal expected profit is non-increasing in the service requirement and the supplier’s optimal expected profit is non-decreasing in the service requirement, either with or without options contracts. A special class of distribution-free contracts that can coordinate the supply chain with options contracts and the service requirement are derived. Furthermore, as opposed to the case of non-coordinating contracts, it is shown that there is always a Pareto contract. Finally, the retailer’s and supply chain’s optimal service level are derived. [ABSTRACT FROM AUTHOR]

Recent research has pointed out that the optimal strategies to mitigate supply disruptions and demand uncertainty are often mirror images of each other. In particular, risk diversification is favorable under the threat of disruptions and risk pooling is favorable under demand uncertainty. This article studies how dynamic sourcing in supply chain design provides partial benefits of both strategies. Optimization models are formulated for supply chain network design with dynamic sourcing under the risk of temporally dependent and temporally independent disruptions of facilities. Using computational experiments, it is shown that supply chain networks that allow small to moderate degrees of dynamic sourcing can be very robust against both disruptions and demand uncertainty. Insights are attained on the optimal degree of dynamic sourcing under different conditions. [ABSTRACT FROM PUBLISHER]

The mission of the Institute of Industrial and Systems Engineers (IISE) is to serve those who solve complex and critical problems of the world. Notably, the research–practice gap in Operations Management (OM) marginalizes the value and relevance of the IISE. To maintain and enhance the impact of the IISE, we identify major bottlenecks that limit the industrial installation of OM research outcomes. Ranked by the relative importance, the three bottlenecks are verifying the performance improvement, building trust with practitioners, and balancing model accuracy and simplicity, respectively, in the stages of value verification, implementation and development. We propose potential research opportunities and illustrate the challenges and opportunities using real case studies from three Fortune Global 500 companies. In particular, we emphasize the role of data-driven decision methods in dealing with the three bottlenecks. [ABSTRACT FROM AUTHOR]

This research was motivated by challenges facing inventory managers at a major retail chain in deciding how often to order each product and whether to use a standard batch size of a pallet, a half-pallet, or even less. The retailer offers thousands of different products, but the total demand for a typical product is only a few pallets per year. Manufacturers offer lower per unit prices for larger standard batch sizes, but larger order quantities increase inventory holding costs. The inventory managers are also concerned about how the ordering strategy might affect transportation costs and material handling costs at the warehouse.    We develop a framework and solution strategy to determine the best shipment frequency, standard batch size (from a set of options), and an ordering plan for a set of products procured from a single supply location. To do so, the inventory holding and material handling costs incurred by a single product are derived for a given review interval and standard batch size. We incorporate the individual product costs into an optimization model to find, for a given transportation interval (with a limit on transport capacity for each shipment), the best procurement plan considering variable procurement, inventory, material handling, and excess transportation costs. With this, several transportation intervals can be compared and the best one selected. To the best of our knowledge, this work is the first to consider the effects of transportation capacity and standard batch sizes in a multi-item procurement problem with the goal of minimizing transportation, inventory, and material handling costs. [ABSTRACT FROM PUBLISHER]

We consider a joint location-inventory problem involving a single supplier and multiple retailers. Associated with each retailer is some variable demand. Due to this variability, some amount of safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers (and therefore inventory storage locations) for other retailers. The problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. We formulate this problem as a nonlinear integer-programming model. We then restructure this model into a set-covering integer-programming model. The pricing problem that must be solved as part of the column generation algorithm for the set-covering model involves a nonlinear term in the retailer-distribution-center allocation terms. We show that this pricing problem can (theoretically) be solved efficiently, in general, and we show how to solve it practically in two important cases. We present computational results on several instances of sizes ranging from 33 to 150 retailers. In all cases, the lower bound from the linear-programming relaxation to the set-covering model gives the optimal solution. [ABSTRACT FROM AUTHOR]

To understand non-compliance behaviors in investment goods in pandemic preparedness and resilience, we resort to a form of bounded rationality that people suffer from a lack of self-control due to "present bias", and differ in their sophistication levels (the degree to which they are aware of such compliance barriers). We focus on (i) the manufacturer's pricing strategy under an advance selling framework, and (ii) the subsidy policy to mitigate under-adoption, to generate operational insights. We show that subsidizing the manufacturer is more cost-effective than subsidizing consumers, because the latter subsidy will not fully trickle down to consumers when the manufacturering manipulates prices in response. In particular, when the subsidy program is budget-constrained, the manufacturer subsidy should be provided only in the spot period. In contrast, when the budget constraint is relaxed, subsidies in both periods should be provided. Surprisingly, such a subsidy program has non-monotone impacts on consumers' adoption quantities. Intuitively, this is because the spot-period subsidy induces consumers' non-compliance behaviors in the advance period. In response, the manufacturer/seller will shift to a pricing strategy that may further reduce the advance-selling quantity. This result provides a natural explanation to reconcile the mixed effects of adoption subsidies. [ABSTRACT FROM AUTHOR]

Connected and automated vehicle (CAV) technology is providing urban transportation managers tremendous opportunities for better operation of urban mobility systems. However, there are significant challenges in real-time implementation as the computational time of the corresponding operations optimization model increases exponentially with increasing vehicle numbers. Following the companion paper (Chen et al. 2021), which proposes a novel automated traffic control scheme for isolated intersections, this study proposes a network-level, real-time traffic control framework for CAVs on grid networks. The proposed framework integrates a rhythmic control method with an online routing algorithm to realize collision-free control of all CAVs on a network and achieve superior performance in average vehicle delay, network traffic throughput, and computational scalability. Specifically, we construct a preset network rhythm that all CAVs can follow to move on the network and avoid collisions at all intersections. Based on the network rhythm, we then formulate online routing for the CAVs as a mixed integer linear program, which optimizes the entry times of CAVs at all entrances of the network and their time–space routings in real time. We provide a sufficient condition that the linear programming relaxation of the online routing model yields an optimal integer solution. Extensive numerical tests are conducted to show the performance of the proposed operations management framework under various scenarios. It is illustrated that the framework is capable of achieving negligible delays and increased network throughput. Furthermore, the computational time results are also promising. The CPU time for solving a collision-free control optimization problem with 2,000 vehicles is only 0.3 second on an ordinary personal computer. [ABSTRACT FROM AUTHOR]

A flow picking system in which the existing picking list is updated in real time has been considered as an effective solution for e-commerce warehouses to increase order fulfillment efficiency. The pivotal issues of performance analysis of flow picking systems, and comparison between batch picking systems and flow picking systems are of great concern, both for academics and practitioners of warehouse operation management. In this study, we first develop analytic models to estimate the critical performance indicators of a flow picking system, including picking density and turnover time of an order. Second, we leverage the proposed models and real warehouse data to compare the performance of batch picking and flow picking systems through simulation. Our results show that a flow picking system requires fewer order pickers and shorter walking distances than a batch picking system in most scenarios, especially those with a higher order arrival rate to achieve the same service level. Our study can provide valuable guidelines to warehouse managers and decision-makers for choosing an order fulfillment solution by comparing a batch picking system and a flow picking system. [ABSTRACT FROM AUTHOR]

A robotic mobile fulfillment system (RMFS) performs the order fulfillment process by bringing inventory to workers at pick-pack-and-ship warehouses. In the RMFS, robots lift and carry shelving units, called inventory pods, from storage locations to picking stations where workers pick items off the pods and put them into shipping cartons. The robots then return the pods to the storage area and transport other pods. In this article, we consider an item assignment problem in the RMFS in order to maximize the sum of similarity values of items in each pod. We especially focus on a reoptimization heuristic to address the situation where the similarity values are altered so that a good assignment solution can be obtained quickly with the changed similarity values. A constructive heuristic algorithm for the item assignment problem is developed, and then, a reoptimization heuristic is proposed based on the constructive heuristic algorithm. Then, computational results for several instances of the problem with 10–500 items are presented. We further analyze the case for which an item type can be placed into two pods. Note to Practitioners—This article proposes an efficient heuristic algorithm for assigning items to pods in a robotic mobile fulfillment system (RMFS) so that items ordered together frequently are put into the same pod. Computational results with 10–500 items show that the gaps from upper bounds are very small on average. For cases where the similarity values between items change or their estimation is not accurate due to the fluctuations in demand, a reoptimization heuristic algorithm that alters the original assignment is developed. The experimental results show that the reoptimization algorithm is robust when perturbation levels are approximately 40%–50% of the original similarity values with much less computation times. We believe that this research work can be very helpful for operating the RMFS efficiently. [ABSTRACT FROM AUTHOR]

• Large-scale charging station planning for electric buses. • Optimize the planning jointly under the transportation system and power grid. • A spatial-temporal model decides the sites of facilities for multiple stages. • Equivalently transform the model into a mixed-integer second-order cone programming. • Implement a case study of the real-world transportation network in Shenzhen, China. With the applications of electric buses (e-buses), potential solutions to problems related to infrastructures for charging e-buses are emerging. This study particularly focused on large-scale fast charging-station planning for e-buses in the public transportation electrification process, according to the characteristics of e-bus operation and plug-in fast charging mode. We conducted an interdisciplinary study to optimize planning jointly under the transportation system and power grid. In addition to capturing the spatiality of the e-bus charging service network, we further considered temporality in order to conduct long-term planning in view of the continuously growing e-bus charging demand. A spatial-temporal model, which determines the sites and sizes of e-bus charging stations, was proposed and the strategies for multistage infrastructure planning were put forward. The model was equivalently transformed into a mixed-integer second-order cone programming with high computational efficiency. The model and the multistage planning strategies were justified through a series of numerical experiments. A case study of Shenzhen, China was implemented and the robustness of the model to plan changes was studied. [ABSTRACT FROM AUTHOR]

Rolling stock needs regular maintenance in a maintenance facility. Rolling stock from different fleets are routed to maintenance facilities by interchanging the destinations of trains at common stations and by using empty drives. We consider the problem of locating maintenance facilities in a railway network under uncertain or changing line planning, fleet planning, and other uncertain factors. These uncertainties and changes are modeled by a discrete set of scenarios. We show that this new problem is NP-hard and provide a two-stage stochastic programming and a two-stage robust optimization formulation. The second-stage decision is a maintenance routing problem with similarity to a minimum cost-flow problem. We prove that the facility location decisions remain unchanged under a simplified routing problem, and this gives rise to an efficient mixed-integer programming (MIP) formulation. This result also allows us to find an efficient decomposition algorithm for the robust formulation based on scenario addition (SA). Computational work shows that our improved MIP formulation can efficiently solve instances of industrial size. SA improves the computational time for the robust formulation even further and can handle larger instances due to more efficient memory usage. Finally, we apply our algorithms on practical instances of the Netherlands Railways and give managerial insights. [ABSTRACT FROM AUTHOR]

We conduct a multi-round experiment on a punishment game using both the strategy method and the direct-response method. In the game, the sender may make decisions that are either favorable or unfavorable to the receiver, whereas the receiver may reward the favorable decision and/or punish the unfavorable decision. The hot-versus-cold effect is observed in both sender behaviors and receiver behaviors. Under the strategy method, the senders more often adopt decisions that are favorable to the receivers, whereas the receivers impose punishment more frequently than under the direct-response method. We suggest that in the multi-round experiment, the strategic value of punishment dominates the taste for punishment from the perspective of the receiver. According to our explanation based on mental accounting, the utility of imposing punishment is higher under the strategy method than under the direct-response method. Hence, the receiver is more likely to make punishment decisions. This difference in receiver behaviors prompts the sender to make receiver-favorable decisions more frequently under the strategy method. [ABSTRACT FROM AUTHOR]

This study introduces a challenging service network design problem with stochastic demands and fixed routes. In this problem, the routes of some service vehicles are fixed during the whole planning horizon, while the remaining vehicles are flexible in that their routes can adapt to different realizations of customer demands each day. The problem is to determine which vehicles should be set as fixed and how their fixed routes should be designed. To solve this problem, we formulate a two-stage stochastic mixed-integer linear program. The fixed routes are designed in the first stage before customer demands are realized, and the routes for flexible vehicles are designed in the second stage after customer demands are observed. A learning-based multiple scenario approach is developed as the solution method. Numerical experiments on real-world operational data from a logistics company show that fixing the routes of some service vehicles may reduce the operational cost by an average of approximately 6.37% compared with that in the case of no fixed routes. • The service network design problem with stochastic demands and fixed routes is introduced. • A two-stage stochastic mixed-integer linear programming model is formulated. • A learning-based multiple scenario approach is developed. • The model and solution algorithm are demonstrated on real-world operational data. [ABSTRACT FROM AUTHOR]

• A data-driven framework is developed to quantify EV performance degradation. • An iterative learning process is proposed for impact decoupling. • EV big data are used for range and energy consumption assessments. • Multiple data sources are coupled by using spatiotemporal data. Electric vehicle (EV) performance in terms of the available driving range per charge and the energy consumption rate continuously degrades during its service life. Quantitative assessments of EV performance degradation play an important role in EV residual value analysis, battery management, and battery recycling. However, EV performance degradation is highly sensitive to both ambient temperature and battery aging states; coupled factors make its quantification challenging. Here, a novel big data-driven decoupling framework is proposed to investigate the partial relationships between EV performance degradation and each individual variable (e.g., temperature and total driving distances). The core innovation involves the decoupling process that can enable real-world and large-scale degradation assessments. The basic functionality of the decoupling is achieved by an iterative learning framework where different machine learning-based models can communicate with each other. It achieves the advantages of unsupervised training and high performance; the mean absolute error can be controlled less than 0.1 in the model validation of EV ranges. Its effectiveness is verified using different real-world EV datasets. By utilizing the framework, the changes in the range and energy consumption of EVs across 10 urban areas in China are assessed. The results show that the range and energy consumption rate of EVs are more greatly influenced by ambient temperature than by battery aging. Less consideration of variable decoupling may yield misleading results in EV performance analysis. Our proposed framework opens avenues for quantifying EV performance degradation via real-world EV data, which is critical to onboard and cloud-based EV research. [ABSTRACT FROM AUTHOR]

The increasing concern over global warming has led to the rapid development of the electric vehicle industry. Electric vehicles (EVs) have the potential to reduce the greenhouse effect and facilitate more efficient use of energy resources. In this paper, we study several EV route planning problems that take into consideration possible battery charging or swapping operations. Given a road network, the objective is to determine the shortest (travel time) route that a vehicle with a given battery capacity can take to travel between a pair of vertices or to visit a set of vertices with several stops, if necessary, at battery switch stations. We present polynomial time algorithms for the EV shortest travel time path problem and the fixed tour EV touring problem , where the fixed tour problem requires visiting a set of vertices in a given order. Based on the result, we also propose constant factor approximation algorithms for the EV touring problem , which is a generalization of the traveling salesman problem. [ABSTRACT FROM AUTHOR]

This study reviews the literature on air cargo operations and compares theoretical studies with the practical problems of airlines, freight forwarders, and terminal service providers. In particular, we review studies in which mathematical models were used to identify the essential characteristics of air cargo operations, such as the intrinsic differences from passenger operations, and to explore the service processes in air cargo operations. The typical models used in previous studies are summarized. We then highlight the insightful findings from an industrial interview and present the gaps between previous research and practical realities. We finally discuss the new research opportunities of air cargo operations according to the gaps. [ABSTRACT FROM AUTHOR]

Abstract: Product substitution can mitigate supply chain disruptions. However, it may not be very effective without multiple sourcing. In this paper, we consider a supply chain with two downward substitutable products. The products can be ordered from an unreliable supplier or a reliable but more expensive supplier. It is found that in an optimal sourcing policy the higher-grade product should be preferred over the lower-grade product. A sufficient condition is given for an optimal policy where only the higher-grade product is dual-sourced. The effect of substitution is contrasted with the non-substitution case. Numerical study shows the impact of demand variability and correlation on the effect of product substitution and the corresponding optimal sourcing policy. [Copyright &y& Elsevier]

• A novel framework for large-scale EV charging energy predictions is introduced. • The MAPE retains at 2.5–3.8% with a testing/training ratio varying from 0.1 to 1000. • MICs and PCCs are combined for feature analyses of charging energy predictions. • Multiple data sources are coupled by linking the timestamps and location data. Large-scale and high-precision predictions of the charging energy required for electric vehicles (EVs) are essential to ensure the safety of EVs and provide reliable inputs for grid-load calculations. However, the complex and dynamic operating conditions of EVs make it challenging to accurately predict the charging energy under real-world conditions, especially for large-scale EV utilization. In this study, a novel data-driven framework for large-scale charging energy predictions is developed by individually controlling the strongly linear and weakly nonlinear contributions. The proposed framework concurrently addresses the overfitting of nonlinear networks using a low proportion of training data as well as the poorly descriptive ability of linear networks under complex environments. For each charging session, the charging energy predictions appropriately account for important factors such as the variations in the state of charge (SOC) of the battery, ambient temperatures, charging rates, and total driving distances. The results suggest that, compared with existing prediction models (such as the random forest, xgboost, and neural network), the proposed framework persists with evidently higher accuracy and stability over a wide range of the ratio between the number of EVs used for testing and training; its mean absolute percentage error (MAPE) is maintained at 2.5–3.8% when the ratio ranges from 0.1 to 1000. The proposed models can be further utilized for cloud-based battery diagnoses and large-scale forecasting of the energy demands of EVs. [ABSTRACT FROM AUTHOR]

In this paper, we define and investigate a novel model-free deep reinforcement learning framework to solve the taxi dispatch problem. The framework can be used to redistribute vehicles when the travel demand and taxi supply is either spatially or temporally imbalanced in a transportation network. While previous works mostly focus on using model-based methods, the goal of this paper is to explore the policy-based deep reinforcement learning algorithm as a model-free method to optimize the rebalancing strategy. In particular, we propose an actor-critic algorithm with feed-forward neural networks as approximations of both policy and value functions, where the policy function provides the optimal dispatch strategy and the value function estimates the expected costs at each time stamp. Our numerical studies show that the algorithm converges to the theoretical upper bound with less than 4% optimality gap, whether the system dynamics are deterministic or stochastic. We also investigate the scenario where we consider user priority and fairness, and the results indicate that our learned policy is capable of producing a superior strategy that balances equity, cancellation, and level of service when user priority is considered. [ABSTRACT FROM AUTHOR]

We study the energy consumption minimization problems of natural gas transmission in gunbarrel structured networks. In particular, we consider the transient-state dynamics of natural gas and the compressor's nonlinear working domain and min-up-and-down constraints. We formulate the problem as a two-level dynamic program (DP), where the upper-level DP problem models each compressor station as a decision stage and each station's optimization problem is further formulated as a lower-level DP by setting each time period as a stage. The upper-level DP faces the curse of high dimensionality. We propose an approximate dynamic programming (ADP) approach for the upper-level DP using appropriate basis functions and an exact approach for the lower-level DP by exploiting the structure of the problem. We validate the superior performance of the proposed ADP approach on both synthetic and real networks compared with the benchmark simulated annealing (SA) heuristic and the commonly used myopic policy and steady-state policy. On the synthetic networks (SNs), the ADP reduces the energy consumption by 5.8%–6.7% from the SA and 12% from the myopic policy. On the test gunbarrel network with 21 compressor stations and 28 pipes calibrated from China National Petroleum Corporation, the ADP saves 4.8%–5.1% (with an average of 5.0%) energy consumption compared with the SA and the currently deployed steady-state policy, which translates to cost savings of millions of dollars a year. Moreover, the proposed ADP algorithm requires 18.4%–61.0% less computation time than the SA. The advantages in both solution quality and computation time strongly support the proposed ADP algorithm in practice. [ABSTRACT FROM AUTHOR]

• We propose a method of producing synthetic connected populations required to advance agent-based exploration of phenomena involving social influence on travel behaviours. • The method reproduces socio-economic characteristics for the synthetic households using a Bayesian network model estimated from household survey data. • The method detects and assigns the population to the observed communities, using an integer programming approach. • The method generates social networks following the Exponential Random Graph Models estimated from the available social network data. • A scalable implementation of the framework is developed (with the source code made available), and illustrated experimentally for a region of the San Francisco Bay Area. Agent-based modeling in transportation problems requires detailed information on each of the agents that represent the population in the region of a study. To extend the agent-based transportation modeling with social influence, a connected synthetic population with both synthetic features and its social networks need to be simulated. However, either the traditional manually-collected household survey data (ACS) or the recent large-scale passively-collected Call Detail Records (CDR) alone lacks features. This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behavior to generate connected synthetic populations. The generated populations coupled with recent advances in graph (social networks) algorithms can be used for testing transportation simulation scenarios with different social factors. [ABSTRACT FROM AUTHOR]

Highlights • Joint location and capacity design problem for charging stations. • A novel expanded network structure. • Queuing time approximation formula. • Customized neighborhood search heuristics. Abstract This study is devoted to designing locations and capacities of charging stations for supporting long-distance travel by electric vehicles (EVs). We first establish an expanded network structure to model the set of valid charging strategies for EV drivers, and then a variational inequality (VI) is formulated to capture the equilibrated route-choice and charging behaviors of EVs by incorporating an approximated queuing time function for a capacitated charging facility. Next, we formulate the problem of designing the locations and capacities of charging facilities under a fixed budget constraint and solve the optimization problem with a customized neighborhood search strategy. A lower bound for the system cost is also developed to evaluate the qualities of solutions acquired using our proposed heuristic. Numerical examples with a toy network and a highway network extracted from the Yangtze River Delta are used to show the effectiveness of the proposed methodology, and we observe that our strategy can solve a large-scale problem within an optimality gap of less than 5%. [ABSTRACT FROM AUTHOR]

Motivated by the recent boom of the sharing economy, this paper presents a scheme of sharing demand-side energy resources (DERs) among multiple prosumers. Successful sharing must achieve enhanced utilization efficiency of DERs and, in the mean time, ensure voluntary participation of prosumers and a sharing-enabling aggregator. It is also desirable to incentivize the adoption of DERs. To fulfill these goals, we formulate a mathematical program with equilibrium constraints (MPEC) for DER valuation within a sharing community. The aggregator coordinates DER operations in real-time; then it solve this MPEC problem after each billing period. In doing so, the aggregator evaluates two operating costs for each prosumer: the actual cost under coordination and the counter-factual cost if the prosumer independently traded power with the aggregator. We define the difference in these two costs as the coordination surplus, which the aggregator and the prosumer split. Simulation results demonstrate that this sharing procedure effectively achieves the aforementioned goals. [ABSTRACT FROM AUTHOR]

High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agent-operated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a two-tier classifier to infer interchange states during a metro trip, and further distinguishes 10 fine-grained cases during interchanges. Experimental results within 6 months across over 14 subway lines in 3 major cities demonstrate that our approach yields an overall interchange state inference F1-measurement of 91.0% and an average time error of less than 2 min at an inference interval of 20 s, and an average accuracy of 89.3% to distinguish the 10 fine-grained interchange cases. We also conducted a series of case studies using measurements collected from crowdsourced users during 3 months, which reveals findings previously unattainable without fine-grained interchange time measurements, such as portions of waiting time during interchange, interchange directions, usage of facilities (stairs/escalators/lifts), and the root causes of long interchange times. [ABSTRACT FROM AUTHOR]

We introduce and study a challenging service network design problem arising within a city logistics company based in Beijing, China. To provide cost-effective same-day delivery services, the company divides the whole urban area into regions. Within each region, a fleet of small-capacity vehicles picks up and delivers packages. A fleet of line-haul vehicles (large-capacity vehicles) executes consistent multiple trips over the regions to transport consolidated packages, during which transshipment is allowed. In this study, we focus on designing the trips of line-haul vehicles, i.e., their region visiting sequences, as the performance of the logistics system depends significantly on the coordination of line-haul vehicles. We model the trip design problem as a service network design problem with consistent multiple trips. We develop a continuous-time MIP model to formulate the problem and optimally solve small-size instances. In addition, we propose an adaptive large neighborhood search heuristic to solve larger instances. Numerical experiments on randomly generated instances as well as real-world operational data demonstrate the effectiveness and efficiency of our solution approach. • The service network design problem with consistent multiple trips is introduced. • A continuous-time mixed integer programming model is formulated. • An adaptive large neighborhood search solution algorithm is developed. • The model and solution algorithm are demonstrated on real-world operational data. [ABSTRACT FROM AUTHOR]

Increasing wildfire activity globally has become an urgent issue with enormous ecological and social impacts. In this work, we focus on analyzing and quantifying the influence of landscape topology, understood as the spatial structure and interaction of multiple land-covers in an area, on fire ignition. We propose a deep learning framework, Deep Fire Topology, to estimate and predict wildfire ignition risk. We focus on understanding the impact of these topological attributes and the rationale behind the results to provide interpretable knowledge for territorial planning considering wildfire ignition uncertainty. We demonstrate the high performance and interpretability of the framework in a case study, accurately detecting risky areas by exploiting spatial patterns. This work reveals the strong potential of landscape topology in wildfire occurrence prediction and its implications to develop robust landscape management plans. We discuss potential extensions and applications of the proposed method, available as an open-source software. • We show the impact of different landscape topologies on wildfire ignitions. • We develop a novel and interpretable deep learning framework. • The model detects and highlights low/high-risk land-cover topological patterns. • We obtain accurate fire occurrence predictions only using land-cover data. • Our framework can be applied in any field exploiting computer vision. [ABSTRACT FROM AUTHOR]