Rolling Horizon Research Articles

Logistics for diagnostic testing: An adaptive decision-support framework

Diagnostic testing is a fundamental component in effective outbreak containment during every phase of a pandemic. Test samples are collected at testing facilities and subsequently analyzed at specialized laboratories. In high-income countries where health care providers are often privately owned, the assignments of samples from testing facilities to laboratories are determined by individual stakeholders. While this decentralized system effectively matches supply and demand during normal times, dispersed outbreaks, e.g., as encountered during the COVID-19 pandemic, lead to imbalanced requests for diagnostic capacity. With no coordinating entity in place to match demands at testing facilities to laboratory capacities, local backlogs build up rapidly thus increasing waiting times for test results and thus impeding subsequent containment efforts. To ease the impact of erratic regional outbreaks through improved logistics activities, we develop a rolling horizon framework which repeatedly solves a mathematical programming snapshot problem based on the current number of test samples. The procedure dynamically adapts to requirements resulting from the pandemic activity and supports rather than replaces decentralized operations in order to match testing requests with available laboratory capacities. We present problem-specific performance indicators and assess the quality of our procedure in a case study based on the COVID-19 outbreak in 2020 in Germany. Experimental results demonstrate the potential of coordinating mechanisms to support the logistics related to diagnostic testing and hence to reduce waiting times for PCR test results. Significant improvements are achieved even when interventions in the decentralized assignment process only occur in response to increased pandemic activity.

Optimising Electric Vehicle Charging Station Placement Using Advanced Discrete Choice Models

We present a new model for finding the optimal placement of electric vehicle charging stations across a multiperiod time frame so as to maximise electric vehicle adoption. Via the use of stochastic discrete choice models and user classes, this work allows for a granular modelling of user attributes and their preferences in regard to charging station characteristics. We adopt a simulation approach and precompute error terms for each option available to users for a given number of scenarios. This results in a bilevel optimisation model that is, however, intractable for all but the simplest instances. Our major contribution is a reformulation into a maximum covering model, which uses the precomputed error terms to calculate the users covered by each charging station. This allows solutions to be found more efficiently than for the bilevel formulation. The maximum covering formulation remains intractable in some instances, so we propose rolling horizon, greedy, and greedy randomised adaptive search procedure heuristics to obtain good-quality solutions more efficiently. Extensive computational results are provided, and they compare the maximum covering formulation with the current state of the art for both exact solutions and the heuristic methods. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Hydro-Québec and the Natural Sciences and Engineering Research Council of Canada [Discovery Grant 2017-06054; Collaborative Research and Development Grant CRDPJ 536757–19]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0185 .

Data for paper "Influence of periodic forecast updates on a rolling horizon MRP planned production system"

Data for paper "Influence of periodic forecast updates on a rolling horizon MRP planned production system"

Efficient and effective large-scale vaccine distribution

The goal of pandemic response is to provide the greatest protection, for the most people, in the least amount of time. Short response times minimize both current and future health impacts for evolving pathogens that pose global threats. To achieve this goal, efficient and effective systems are needed for distributing and administering vaccines, a cornerstone of pandemic response. COVID-19 vaccines were developed in record time in the U.S. and abroad, but U.S. data shows that they were not distributed efficiently and effectively once available. In an effort to “put vaccines on every corner”, pharmacies and other small venues were a primary means for vaccinating individuals, but daily throughput rates at these locations were very low. This contributed to extended times from manufacture to administration. An important contributing factor to slow administration rates for COVID-19 was vaccine transport and storage box size. In this paper, we establish a general system objective and provide a computationally tractable approach for allocating vaccines in a rolling horizon manner optimally. We illustrate the consequences of both box size and the number and capacity of dispensing locations on achieving system objectives. Using U.S. CDC data, we demonstrate that if vaccines are allocated and distributed according to our proposed strategy, more people would have been vaccinated sooner in the U.S. Many additional days of protection would have occurred, meaning there would have been fewer infections, less demand for healthcare resources, lower overall mortality, and fewer opportunities for the evolution of vaccine-evading strains of the disease.

Multiobjective optimization and analysis of low-temperature district heating systems coupled with distributed heat pumps

Developing district heating can help decarbonizing energy systems. Modern concepts of district heating revolve around low operational temperature to reduce heat losses and enhance the efficiency of the supply plants with technologies such as cogeneration, condensing boilers or geothermal heat pumps. However, temperature reduction in district heating networks may be limited by technical requirements on the building side. Booster heat pumps on the end-users’ side can be a solution to bridge the network-building temperature gap. However, the current state-of-the-art on such systems does not provide ways for optimizing in real-time the operating temperature, which is needed to fully exploit its benefits, and proposed control strategies focus solely on reducing costs without considering environmental aspects. This article proposes a new multi-objective optimization framework to determine the real-time supply temperature for low-temperature district heating with booster heat pumps. The optimization approach uses rolling horizon and both operating costs and GHG emissions are minimized simultaneously. The model on which the optimization relies includes efficiency curves for equipment and water transport delays in the network. Many scenarios are simulated to understand how key factors affect the optimal supply temperature, such as the fuel type (i.e., wood chips or natural gas), price of electricity, emission factor of electricity, and relative weight of costs and emissions in the objective function. Results show that the proposed framework can indeed reduce costs and emissions, but the viability of the system depends strongly on the above-mentioned factors. In the case-study with typical electricity costs and emissions for Quebec (Canada), the average operating temperature could be reduced to 42 and 66 °C depending on the type of boilers in the system. While emissions were always smaller than with a conventional network, operating costs could be higher (with wood chip boiler) or lower (with natural gas boiler). This work can contribute to the deployment of efficient low temperature networks.

Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness

The incorporation of decentralized energy solutions, including solar photovoltaic (PV) installations and energy storage systems (ESSs), into residential communities has received substantial interest in recent times. This is driven by the objective of enhancing the self-reliance of residential zones while decreasing their reliance on centralized power grids. Effective management of these systems can yield advantages such as load equalization, cost reduction, and diminished emissions. The process of optimizing energy management in residential communities entails addressing multiple factors, including uncertainty, equity, and efficiency.This study introduces an innovative methodology for real-time energy management in a residential community, taking into account both the equitable utilization of solar PV and ESS and the minimization of costs. The suggested technique employs rolling horizon optimization and mixed-integer linear programming (MILP) to tackle uncertainties in PV production and guarantee a fair allocation of shared resources. The outcomes of this novel approach showcase its capability to sustain cost-effectiveness while fostering equitable energy consumption.

Dynamic ride-sourcing systems for city-scale networks, Part II: Proactive vehicle repositioning

The emergence of ride-sourcing systems has unprecedentedly changed the market for on-demand mobility services. The service quality of the ride-sourcing systems and their effects on transportation networks rely on effective matching and redistributing of idle vehicles. In Part I, we introduced a vehicle–passenger matching method that jointly determines the maximum matching distance and matching intervals to minimize passengers waiting time. Moreover, a non-equilibrium macroscopic model is developed to predict the evolution of the ride-sourcing system states. In this paper, we propose a control method to transfer idle vehicles to balance the passengers’ demand and supply of ride-sourcing vehicles by repositioning the idle vehicles in locations where there is a higher possibility of faster pickups of new passengers. This paper develops a Nonlinear Model Predictive Controller (NMPC) that proactively transfers idle vehicles based on the predicted future state of the ride-sourcing system using the non-equilibrium model developed in Part I paper. The proposed NMPC implements an optimum rolling horizon strategy that solves a constrained optimization problem at each transferring sample time. The proposed method considers the effect of network congestion and impatient passengers and drivers. We show that the proactiveness of the NMPC incentivizes the drivers to follow the transferring commands instead of random cruising during idle periods. In microsimulation experiments, the designed controller improves the performance of the ride-sourcing system by reducing passengers’ average unassigned time (-20.4%) and waiting times (-12.4%), vehicles’ average waiting times (-8.8%), the fleet size (-18.6%), and increasing the number of the served trip requests (9.7%).

Chronological operation simulation for power system with peak regulation ancillary service market in China: Formulation, solving, and validation

Peak regulation ancillary service market (PRSM) is an emerging and effective mechanism for renewable energy accommodation in China, especially under Carbon Neutral. To comprehensively analyze its long-term impact on the power system, a chronological operation simulation (COS) model is proposed. Besides, the computational-burden balancing technique based on the time-domain decomposition is developed to improve the computational efficiency of COS. The real-world northeast China power system with PRSM has been analyzed. Case studies validate the simulation with published statistical data and demonstrate the accuracy. Besides, 52.16% computation time is reduced compared to the widely used rolling horizon method. The necessity of PRSM for renewable energy accommodation is verified through chronological simulation. The relationship between peak regulation costs and renewable energy penetration is also given, which demonstrates its applicability.

A combined adaptive entropy‐TOPSIS and model predictive control strategy for mixed loading and delay operations in the reheating furnace

SummaryIn this paper, a combined adaptive entropy‐TOPSIS and model predictive control strategy is proposed to deal with the mixed loading and delay operations in the reheating furnace. Firstly, the mathematical models consistent with the behaviour of the real reheating furnace are built to describe the complicated heat exchange process. Secondly, a dynamical optimization problem for the mixed loading operation and delay operation in the walking beam reheating furnace is obtained. To adjust the weighting factors of the optimization problem in real time, the adaptive entropy‐TOPSIS method is proposed. Then, the rolling horizon approach is applied to solve the proposed optimization problem. Finally, numerical experiments and simulation analysis are undertaken to verify the reliability and accuracy of the proposed strategy. The simulation results demonstrate that the proposed strategy can deal with three typical cases of delays effectively and the control accuracy is successfully improved from 74.79% to 99.17%.

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

An On-Demand Dynamic Taxi Crowdshipping Model for Urban Parcel Delivery

Abstract In this article, a dynamic crowdshipping model is presented to use the vacant space of taxis to deliver parcels while serving passengers. A mixed integer linear programming (MILP) model within a rolling horizon framework that periodically updates input information is formulated. The objective function aims to maximize the number of matched trips while controlling the total travel time in the system. The developed model is tested using the taxi ridership and the household travel survey data in New York City, ensuring the credibility of the proposed model in addressing the goals of the study and its potential application to mitigate transportation externalities.

Co-optimization of decarbonized operation of coal-fired power plants and seasonal storage based on green ammonia co-firing

Coal-fired power plants (CFPP) can provide significant inertia and flexibility support for power systems with a high share of renewable energy (RE). The ammonia–coal co-firing technology can effectively reduce carbon emissions (CE) from CFPP. We propose a low-carbon power supply and multi-timescale energy storage system in combination with this technology. The system consists of batteries, fuel cells and the CFPP equipped with the ammonia–coal co-combustion module. It can also cope with short and long term fluctuations in RE. We use the Mixed Integer Linear Programming method to construct an optimization model for this system. We then set up six sub-cases according to the power systems in Texas, USA. Based on weekly and annual simulations of these cases, we analyze the economic and environmental performances of the proposed system with different RE shares, as well as exploring the characteristics of different energy storage methods. Compared to batteries or fuel cells only scenarios, CFPP configured with green ammonia co-firing can significantly reduce the total operating cost (TOC), CE and RE curtailment. We use the rolling horizon optimization method to simulate and calculate the distribution of ammonia storage levels throughout the year. The ammonia storage levels are high in spring and autumn and low in summer. Finally, the sensitivity and efficiency analysis of the proposed system is carried out. As far as ammonia co-firing technology allows, the higher the maximum ammonia–coal mixing ratio, the lower TOC, CE and RE curtailment. This phenomenon is more significant at a higher RE share. The overall efficiency of green ammonia synthesis and combustion for power generation is around 13.92%. It can work in synergy with batteries and fuel cells to improve efficiency and reduce the marginal cost of long-term energy storage.

Comparison of different approaches to multistage lot sizing with uncertain demand

AbstractWe study a new variant of the classical lot sizing problem with uncertain demand where neither the planning horizon nor demands are known exactly. This situation arises in practice when customer demands arriving over time are confirmed rather lately during the transportation process. In terms of planning, this setting necessitates a rolling horizon procedure where the overall multistage problem is dissolved into a series of coupled snapshot problems under uncertainty. Depending on the available data and risk disposition, different approaches from online optimization, stochastic programming, and robust optimization are viable to model and solve the snapshot problems. We evaluate the impact of the selected methodology on the overall solution quality using a methodology‐agnostic framework for multistage decision‐making under uncertainty. We provide computational results on lot sizing within a rolling horizon regarding different types of uncertainty, solution approaches, and the value of available information about upcoming demands.

Coupled Control of Traffic Signal and Connected Autonomous Vehicles at Signalized Intersections

To enhance the traffic operation efficiency at signalized intersections, a model for coupled control of traffic signals and connected autonomous vehicles at isolated signalized intersections is proposed. This model estimates the time that CAVs reach stop lines with real-time information about the speed and position of CAVs. The arrival time is leveraged to optimize traffic signal timing by rolling horizon, with the maximization of phase saturation as the optimization objective. Based on the optimized traffic signal timing, the speed profile of CAVs is optimized by a linear integer programming, with the maximization of speed at the moment of reaching the stop line as the optimization objective. Through the coupled control of travel speed and the traffic signal, CAVs can pass through the intersection safely, efficiently, and smoothly. NetLogo, a multiagent microscopic simulator, is developed to test this strategy, and an intersection in Weihai is taken for verification and analysis lastly. The simulation results demonstrate that, compared with the fixed traffic signal timing control and the model optimizing only speed profile of CAVs, the proposed model can reduce the average number of stops by 47.0% and the queuing time by 41.3%. In addition, the optimization is better during off-peak hours, about 10% higher than the peak hours.

Joint optimization for autonomous intersection management and trajectory smoothing design with connected automated vehicles

Trajectory smoothing design (TSD) may significantly reduce fuel consumption and improve driving comfort at intersections. In this paper, a mixed integer linear programming (MILP) model with discrete time is formulated to jointly optimize autonomous intersection management and TSD, aiming to improve traffic efficiency, fuel economy and driving comfort simultaneously. Driving safety of car-following and collision avoidance at conflict points, diverge points and converge points, as well as constraints of acceleration and jerk are considered. To reasonably describe vehicle movement within intersection areas, the vehicle trajectory within the intersection is treated as a channel considering the vehicle width. A rolling horizon framework is used to solve the model. We have compared the traffic efficiency, fuel economy, monetary cost and driving comfort of the joint optimization model with that of the state-of-the-art two-stage strategy. Finally, sensitivity analysis with respect to left-turn ratio, weighted coefficient of TSD and control zone length is conducted.

Forecast and production order accuracy for stochastic forecast updates with demand shifting and forecast bias correction

In this paper a general demand model for the supplier is developed based on practically observed customer forecasting behaviours. A rolling horizon information update approach is assumed where customers provide their forecasts for a predefined information horizon. In the basic situation, a MMFE (martingale model of forecast evolution) demand model is assumed where information updates are unbiased. This setting is extended to a situation where updates are biased, i.e., a forecast update does not necessarily imply an information improvement. Furthermore, the model is extended by a demand shifting behaviour, i.e., demands for certain periods may be shifted to other periods. For this practically motivated demand model, the forecast accuracy related to periods before delivery is calculated and a measure for production order accuracy is developed assuming a simplified material requirements planning structure. Finally, a correction method for reducing the negative effects of biased demand forecasts is introduced and its performance is evaluated based on simulation and real company data.

A dynamic dispatching problem for autonomous mine trucks in open-pit mines considering endogenous congestion

The introduction of autonomous mine trucks can improve the efficiency, productivity, and safety of open-pit mines, but may require more reasonable dynamic truck dispatching system to guide routes and schedules than manual driven mine trucks. In practical operations, for security consideration, only one autonomous mine truck is allowed to pass through each intersection at a time, which may lead to endogenous congestion at intersections. In this paper, we first introduce the autonomous mine trucks and endogenous congestion at intersections in the dynamic truck dispatching problem. We propose the concept of road resource which denotes the time-extended intersection and give the time–space network with road resources. The rolling horizon framework is applied to model the dynamic feature. In each horizon, the dispatching of autonomous mine trucks is formulated as a mixed integer linear programming model on the time–space network with road resources. To solve the dispatching problem in each horizon, we propose a Lagrangian relaxation algorithm to decompose the problem into a series of shortest path problem, which is solved by the designed label setting algorithm. Moreover, to yield high-quality feasible solution in each iteration of Lagrangian relaxation algorithm, a repairing algorithm with an adaptive initialization strategy is designed. A case study of an open-pit mine in China is involved to show the efficiency of the designed model with road resources and the proposed algorithm. The discussions about the productions, waiting time, travel distance, and serving time are given by sensitivity analysis.

Rolling horizon wind-thermal unit commitment optimization based on deep reinforcement learning

Rolling horizon wind-thermal unit commitment optimization based on deep reinforcement learning

Provision of multiple services with controllable loads as multi-area thermal energy storage

Power systems are experiencing a decrease of synchronous generation along with increased penetration of inverter based renewable generation leading to reduced system inertia and a need for flexible resources. Non-generating resources such as thermostatically controlled loads (TCLs) are flexible due to their thermal energy storage capacity. When aggregated, TCLs can arbitrage energy prices and provide reserves to the power system. We approach the operational flexibility of the TCLs by modeling a risk-averse aggregator that controls decentralized TCLs and aims to maximize its own profit. The high number and low power rating of residential TCLs makes it difficult to model and assess their flexibility potential on national level. Thus, we make use of a high-level thermal energy storage model for aggregations of TCLs to quantify their flexibility potential. We present a method to aggregate temperature, TCL parameters, and building stock data into a thermal battery equivalent. We propose a multi-period multi-market multi-zonal two-stage chance constrained rolling horizon optimization problem formulation for the risk-averse day-ahead self-scheduling problem of a price-taker TCL aggregator bidding in energy and reserve markets under uncertainty and recast the problem as a linear program. We perform several case studies in the Swedish power system based on a survey of single- and two-family dwellings with electric heating and assess the flexibility potential. Additionally, a sensitivity analysis provides insights regarding market design and policy implications.

A dynamic rescheduling and speed management approach for high-speed trains with uncertain time-delay

This paper studies the problem of rescheduling trains with speed management in uncertain disruptions that lead to temporary speed restrictions (TSRs) in high-speed railway systems. The disruption uncertainties are that the coverage and speed limit value of TSRs change randomly over time, and the start and end times of TSRs are unknown in advance. First, a mixed-integer linear programming model is formulated to reduce the total train traveling times and improve the passenger comfortability. The solutions of the model provide simultaneously the optimal train rescheduling strategies and train speed control strategies. Second, a rolling horizon algorithm is applied in consideration of the disruption uncertainties. The rolling horizon algorithm updates and solves the model in every time horizon, according to the disruption information newly detected. Therefore, the train rescheduling orders and speed control orders can be adjusted according to the real-time situation to guarantee effectiveness. The rolling horizon approach is terminated automatically once the disruption ends. Finally, based on the practical data of the Beijing-Tianjin intercity high-speed railway line, numerical experiments are carried out to test the effectiveness and efficiency of the proposed approach.