Scheduling Decisions Research Articles

Flexible resource demand response scheduling strategy under 5G-V2X

Electric vehicles (EV) and 5 G base stations, functioning as flexible load resources, contribute significantly to the provision of ample reserve capacity for the power grid. The emergence of 5 G Vehicle-to-Everything (V2X) technology has fostered a coupling between EV and base stations, further enhancing their roles within the power grid. Building upon this foundation, this paper proposes a scheduling strategy for demand response in flexibility resources under the 5 G-V2X framework. Initially, the dispatchable load of EV is examined in conjunction with the traffic network. Subsequently, we consider the communication power consumption resulting from the connected vehicle model, enabling us to devise path planning and charging navigation strategies for EV. Lastly, we establish a scheduling decision model that aims to minimize both the operation costs of base stations and the variance in grid load. The simulation is validated using real-world road conditions in the primary urban area of a city. The findings illustrate that the proposed dispatching strategy not only achieves a mutually beneficial outcome for EV users and aggregators, without disrupting EV charging and discharging processes, but also enhances the capability of demand response, thereby ensuring reliable grid operation.

Irrigation Scheduling Impacts Vegetative Growth, Seed Yield, and Fungal Diseases of Spinach Seed Crops in a Maritime Mediterranean Climate

Although irrigation scheduling has been studied for diverse vegetable crops, much less attention has been given to irrigation scheduling for the seed crops on which these production systems rely. In spinach, for which irrigation scheduling needs are likely to vary greatly between seed and leaf production, this leaves seed producers without adequate resources to make irrigation scheduling decisions. Our research sought to fill this gap by evaluating two alternative irrigation scheduling strategies (a publicly available decision-support tool and soil moisture sensors) and four soil moisture thresholds for irrigation for their impacts on vegetative growth, marketable seed yield, seed quality, and the severity of Stemphylium leaf spot (caused by Stemphylium vesicarium and Stemphylium beticola), a common foliar disease of spinach, under sprinkler irrigation. We found that in all 3 years of the study, earlier and more frequent irrigation increased vegetative growth. However, marketable seed yield only increased relative to the control treatment based on farmers’ standard irrigation practices in 1 of the 3 years—a year with an abnormally late planting date. This indicates that vegetative growth is more responsive than seed yield to earlier and more frequent irrigation, and that increases in vegetative growth do not translate directly to increased marketable seed yield. Contrary to the expected increase in Stemphylium leaf spot severity with increasing irrigation, the severity decreased in both years it was measured, likely as a result of the small stature of the spinach seed parent lines used in our study and opportunistic pathogenicity on moisture-stressed plants. These results provide a useful foundation from which spinach seed producers can make irrigation management decisions for their crops that underpin a valuable global industry.

Adaptive Transmissions for Batteryless Periodic Sensing

Batteryless, self-sustaining embedded sensing devices are key enablers for scalable and long-term operations of Internet of Things (IoT) applications. While advancements in both energy harvesting and intermittent computing have helped pave the way for building such batteryless IoT devices, a present challenge is a system design that can utilize intermittent energy to meet data requirements from IoT applications. In this paper, we take the requirement of periodic data sensing and describe the hardware and software of a batteryless IoT device with its model, design, implementation, and evaluation. A key finding is that, by estimating the non-linear hardware charging and discharging time, the device software can make scheduling decisions that both maintain the selected sensing period and improve transmission goodput. A hardware–software prototype was implemented using an MSP430 development board and LoRa radio communication technology. The proposed design was empirically compared with one that does not consider the non-linear hardware characteristics. The result of the experiments illustrated the nuances of the batteryless device design and implementation, and it demonstrated that the proposed design can cover a wider range of feasible sensing rates, which reduces the restriction on this parameter choice. It was further demonstrated that, under an intermittent supply of power, the proposed design could still keep the device functioning as required.

A queueing-based approach for integrated routing and appointment scheduling

This paper aims to address the integrated routing and appointment scheduling (RAS) problem for a single service provider. The RAS problem is an operational challenge faced by operators that provide services requiring home attendance, such as grocery delivery, home healthcare, or maintenance services. While considering the inherently random nature of service and travel times, the goal is to minimize a weighted sum of the operator’s travel times and idle time, and the client’s waiting times. To handle the complex search space of routing and appointment scheduling decisions, we propose a queueing-based approach to effectively deal with the appointment scheduling decisions. We use two well-known approximations from queueing theory: first, we use an approach based on phase-type distributions to accurately approximate the objective function, and second, we use an heavy-traffic approximation to derive an efficient procedure to obtain good appointment schedules. Combining these two approaches results in a fast and sufficiently accurate hybrid approximation, thus essentially reducing RAS to a routing problem. Moreover, we propose the use of a simple yet effective large neighborhood search metaheuristic to explore the space of routing decisions. The effectiveness of our proposed methodology is tested on benchmark instances with up to 40 clients, demonstrating an efficient and accurate methodology for integrated routing and appointment scheduling.

Research on optimization of Dispatching System of Military Disaster relief Material Transport Vehicles

With the rapid development of informatization, a more efficient dispatching system is needed in the process of military logistics and transportation management to provide guarantee for emergency needs. From China’s point of view, in the face of natural disasters, the military’s participation in rescue is a common guarantee task. How to optimize the effective vehicle scheduling in the process of specific disaster relief material transportation is of great significance to promote the timeliness and effectiveness of rescue. This article establishes a vehicle scheduling model based on time windows by combining the characteristics of emergency transportation, in order to function by the time function, and can be solved by using genetic algorithm, according to the example analysis algorithm in overcome the problem of convergence time, can effectively promote the transportation vehicle scheduling, relief materials transportation scheduling decisions about the army have corresponding reference value.

An Optimization Model for Flight Rescheduling from an Airport’s Centralized Perspective for Better Management of Demand and Capacity Utilization

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This attitude towards maximizing throughput calls for tactical flight rescheduling to comply with airports’ capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays at peak hours. The optimization model is used to make better scheduling intervention decisions considering airport resource constraints and safety of operation. A simulation algorithm is also developed to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those of the optimization model. Computational results from a case study demonstrate that a reduction of 52.6% and 61% in total delay times for arrival and departure flights, respectively, can be achieved. The optimization model also facilitates the implementation of a collaborative decision-making system for better coordination of airport traffic flow management with commercial airlines.

Peak shaving and self-consumption maximization in home energy management systems: A combined integer programming and reinforcement learning approach

This paper proposes a novel framework for Home Energy Management System based on the combination of integer programming and Reinforcement Learning (RL) for achieving efficient home-based Demand Response (DR). In particular, RL is exploited to manage the charge and discharge of Battery Energy Storage System (BESS), and Mixed Integer Linear Programming is exploited for load scheduling. The idea is to focus the RL specifically on BESS management, as its behavior is stochastic and is mainly affected by Photovoltaic (PV) production and user behavior changes. The scheduling decisions of household appliances, Electric Vehicles (EVs), and charging/discharging batteries can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e., to minimize the electricity bill as well as the DR-induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV, PV panels, and electric storage. The test results demonstrate the effectiveness of the proposed home energy management framework under the application of different demand-side flexibility strategies.

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Considering the requirements of the actual production scheduling process, the utilization of the genetic programming hyper-heuristic (GPHH) approach to automatically design dispatching rules (DRs) has recently emerged as a popular optimization approach. However, the decision objects and decision environments for routing and sequencing decisions are different in the dynamic flexible job shop scheduling problem (DFJSSP), leading to different required feature information. Traditional algorithms that allow these two types of scheduling decisions to share one common feature set are not conducive to the further optimization of the evolved DRs, but instead introduce redundant and unnecessary search attempts for algorithm optimization. To address this, some related studies have focused on customizing the feature sets for both routing and sequencing decisions through feature selection when solving single-objective problems. While being effective in reducing the search space, the selected feature sets also diminish the diversity of the obtained DRs, ultimately impacting the optimization performance. Consequently, this paper proposes an improved GPHH with dual feature weight sets for the multi-objective energy-efficient DFJSSP, which includes two novel feature weight measures and one novel hybrid population adjustment strategy. Instead of selecting suitable features, the proposed algorithm assigns appropriate weights to the features based on their multi-objective contribution, which could provide directional guidance to the GPHH while ensuring the search space. Experimental results demonstrate that, compared to existing studies, the proposed algorithm can significantly enhance the optimization performance and interpretability of energy-efficient DRs.

Robust Drone Delivery with Weather Information

Problem definition: Drone delivery has recently garnered significant attention due to its potential for faster delivery at a lower cost than other delivery options. When scheduling drones from a depot for delivery to various destinations, the dispatcher must take into account the uncertain wind conditions, which affect the delivery times of drones to their destinations, leading to late deliveries. Methodology/results: To mitigate the risk of delivery delays caused by wind uncertainty, we propose a two-period drone scheduling model to robustly optimize the delivery schedule. In this framework, the scheduling decisions are made in the morning, with the provision for different delivery schedules in the afternoon that adapt to updated weather information available by midday. Our approach minimizes the essential riskiness index, which can simultaneously account for the probability of tardy delivery and the magnitude of lateness. Using wind observation data, we characterize the uncertain flight times via a cluster-wise ambiguity set, which has the benefit of tractability while avoiding overfitting the empirical distribution. A branch-and-cut (B&C) algorithm is developed for this adaptive distributionally framework to improve its scalability. Our adaptive distributionally robust model can effectively reduce lateness in out-of-sample tests compared with other classical models. The proposed B&C algorithm can solve instances to optimality within a shorter time frame than a general modeling toolbox. Managerial implications: Decision makers can use the adaptive robust model together with the cluster-wise ambiguity set to effectively reduce service lateness at customers for drone delivery systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101049 and 72232001], the Natural Science Foundation of Liaoning Province [Grant 2023-BS-091], the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045], and the Major Project of the National Social Science Foundation [Grant 22&ZD151]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0339 .

Integrated charging scheduling and operational control for an electric bus network

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. Operational control is carried out through speed control of the vehicles and bus holding at the terminal. We propose a hierarchical control framework to solve this integrated problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules.

Integrated decision of production scheduling and condition-based maintenance planning for multi-unit systems with variable replacement thresholds

In practice, production scheduling and maintenance planning are two popular technologies that are interdependent, but they are most often performed independently. In this study, we have developed an integrated model for coordinating single-machine scheduling and condition-based maintenance planning. Firstly, we build upon existing scheduling research that often views the system as a whole and further consider the degradation and maintenance of multiple units within the system during the scheduling process. Secondly, in contrast to traditional degradation modeling which usually assumes a single threshold to trigger maintenance/replacement, we also consider the variability of replacement thresholds caused by the heterogeneity of scheduling tasks. Based on this foundation, we propose an integrated policy that considers both scheduling and condition-based maintenance. Furthermore, we have developed an integrated decision model for multi-unit systems. The model aims to minimize the total weighted expected completion time over the entire scheduling time horizon. Moreover, the genetic algorithm was employed to optimize the integrated model. Finally, we present an example of an SA adjustable multi-axis machine and conduct a sensitivity analysis of key parameters to validate the integration policy.

Impact of Insurance Type on Access to Pediatric Surgical Care.

This study aimed to measure the impact of insurance type on access to pediatric surgical care, clinical and surgical scheduling decisions, provider-driven cancelations, and missed care opportunities (MCOs). We hypothesize that patients with public health insurance experience longer scheduling delays and more frequently canceled surgical appointments compared with patients with private health insurance. This retrospective study reviewed the demographics and clinical characteristics of patients who underwent a surgical procedure within the plastic and oral surgery department at our institution in 2019. Propensity score matching and linear regressions were used to estimate the effect of insurance type on hospital scheduling and patient access outcomes while controlling for procedure type and sex. A total of 457 patients were included in the demographic and clinical characteristics analyses; 354 were included in propensity score matching analyses. No significant differences in the number of days between scheduling and occurrence of initial consultation or number of clinic cancelations were observed between insurance groups (P > 0.05). However, patients with public insurance had a 7.4 times higher hospital MCO rate (95% CI [5.2-9.7]; P < 0.001) and 4.7 times the number of clinic MCOs (P = 0.007). No significant differences were found between insurance groups in timely access to surgical treatment or cancelations. Patients with public insurance had more MCOs than patients with private insurance. Future research should investigate how to remove barriers that impact access to care for marginalized patients.

A reinforcement learning approach for selecting infill drilling locations considering long-term production planning in mining complexes with supply uncertainty

Simultaneous stochastic optimisation frameworks provide a method for optimising long-term production schedules in mining complexes that aim to maximise net present value and manage risk related to supply uncertainty. The uncertainty and local variability related to the quality and quantity of material in the mineral deposits are modelled with a set of stochastic orebody simulations, an input into the simultaneous stochastic optimisation framework. Infill drilling provides opportunities to collect additional information associated with the mineral deposits, which can inform future production scheduling decisions. A framework is developed for optimising infill drilling locations with a criterion that seeks areas that directly affect long-term planning decisions and requires the use of geostatistical simulations. Actor-critic reinforcement learning is applied to identify infill drilling locations in a copper mining complex using this criterion. The case study demonstrates that adapting production scheduling decisions given additional information has the potential to improve the associated production and financial forecasts and identifies a stable area for infill drilling.

Modeling of sustainable integrated supply chains under the consideration of European Union regulations

AbstractThis work introduces a multi-period, multi-commodity, inventory-routing problem with strategic fleet scheduling decisions, under the consideration of speed limits, as well as strict European Union regulations on truck drivers’ working and driving time. To address the new problem, a mixed integer linear programming model was developed. Several artificial but realistic problem instances were randomly generated following relative guidelines from the open literature, to validate and assess the performance of the novel mathematical model. Furthermore, in an effort to produce useful managerial insights, several sensitivity analyses were performed considering different fluctuation rates on key model parameters.

The dynamics of neurobehavioral impairment and recovery sleep: improved biomathematical modeling for fatigue risk management in operational settings

Adequate sleep is essential for maintaining health, safety, and neurobehavioral functioning in 24/7 operational settings. Loss of sleep causes fatigue, which results in neurobehavioral impairment. Neurobehavioral impairment builds up disproportionately under conditions of total sleep deprivation compared to sustained sleep restriction; and recuperation due to recovery sleep is disproportionately fast after total sleep deprivation compared to sustained sleep restriction. These sleep schedule-dependent dynamics have been captured in a previously published, biomathematical model of fatigue, which includes positive feedback regulation of a relatively fast (hours to days) sleep/wake homeostatic process by a slower (days to weeks) allostatic process—a feature that suggests adenosinergic mechanisms are involved. However, the previously published model underestimates the rate of recuperation due to recovery sleep after acute total sleep deprivation as well as after consecutive days of sleep restriction. The objective of the present research is to modify the model to improve the accuracy of its predictions for recuperation due to recovery sleep. This can be accomplished by including in the model an additional, reciprocal feedback mechanism, presumed to be predominantly adenosinergic in nature, which provides feedback from the faster homeostatic process back onto the slower allostatic process. Adding a single new model parameter and refitting three existing model parameters significantly improves the predictions for recuperation due to recovery sleep after both acute total sleep deprivation and sustained sleep restriction. This model modification also improves the predictions of the build-up of neurobehavioral impairment across days of sustained sleep restriction, without adversely affecting the accuracy of the model in other scenarios including circadian misalignment and sleep inertia. The modified model preserves the previously developed capability to predict the differential dynamics of fatigue for objective performance impairment and subjective sleepiness. With the improved predictions for recuperation due to recovery sleep, the expanded model can be used to provide quantitative estimates for potentially impactful work scheduling decisions, such as the duration of time off needed before workers would be safe to return to the work floor. This enhances the usefulness of the model as a tool for predicting and managing neurobehavioral functioning and safety in 24/7 operational settings.

Two-stage stochastic programming for multi-objective optimization of sustainable utility systems integrating with combined heat and power units

Utility systems provide thermal energy and power for industrial processes while emitting significant amounts of carbon dioxide. This study investigates the integration of Combined Heat and Power (CHP) units and renewable energy sources into utility systems in the context of diverse energy demands and low-carbon operations. A two-stage stochastic programming model (TSSP) is proposed for sustainable utility systems. The main objective of the model is to minimize the annual total cost and the other two objectives are to maximize renewable energy penetration rate and minimize grid net interaction level. The model tackles renewable energy sources’ uncertainty and evaluates environmental metrics by computing carbon emission costs. In TSSP, the first stage involves new equipment capacity planning. The second stage involves considering uncertainties related to wind speed and solar radiation. Monte Carlo simulations and probability density functions were used to generate a variety of scenarios for the optimization of operational scheduling decisions. Finally, a case study was conducted to validate the effectiveness of the proposed model. Compared to traditional deterministic optimization in utility systems, the two-stage stochastic programming of sustainable utility systems can reduce carbon emissions by 5.6 percent annually and overall costs by 8,907,733 CNY. It provides decision-makers with a range of reference options by balancing multiple objectives, including economic factors, renewable energy penetration rate, and grid interaction level.

Optimising O&M scheduling in offshore wind farms considering weather forecast uncertainty and wake losses

Offshore wind farms, while strategically positioned to maximise wind energy capture, are exposed to harsh environments, impacting their reliability and accessibility. Low reliability and accessibility in turn translate into high Operation and Maintenance (O&M) costs, which represent approximately 30% of the total lifetime costs of the project. One approach to reduce O&M costs is to improve maintenance planning decisions. The present paper presents a novel O&M scheduling methodology for offshore wind farm operations based on wind and wave forecasts. The methodology forecasts farm yield considering farm wake effects to identify optimal operation schedules, vessel routing, and turbine maintenance sequences that minimise costs and revenue losses due to downtime. Weather forecast uncertainty is modelled and integrated into the decision-making process to minimise weather risks. A case study inspired by the WindFloat Atlantic floating wind project was used to demonstrate the optimisation potential. Results suggest that adopting the proposed methodology over business-as-usual scheduling significantly boosts offshore wind farm operational profits during the forecasted period, with increases ranging from 2% to 24%, depending on the scenario considered. It was found that integrating the wind farm wake losses into the scheduling decisions can improve the optimal scheduling solution and further minimise total operation costs.

Becoming a freelancer or contractor? Drivers’ contractual mode and schedule decisions in a dual sourcing market

In the ride-hailing market, the platforms could recruit contractors subject to prescriptive schedules to reduce the labor supply uncertainty and balance the demand and supply, and the drivers are entitled to become a freelancer or a contractor and choose the schedule they would like to provide the service. This paper proposes an analytical framework to capture the drivers’ decisions of becoming a freelancer and contractor as well as how to arrange their work schedules in a dual sourcing market. The utility structures for two kinds of drivers, freelancers and contractors, are defined and a time-expanded network is adopted to portray the drivers’ work schedule choices. With this setting, the drivers’ simultaneous contractual mode and work schedule selection model is formulated and the algorithm is designed. The numerical examples are conducted to investigate the characteristics of the equilibrium solutions. The results show that a dual-sourcing market is always advantageous to contractors and conditionally beneficial to the platform, freelancers and customers. When there is low potential demand, higher fixed salary is preferred for the platform to attract contractor and complement the labor supply. When the market is thriving, the effect of the profits’ increment by dual-sourcing is not obvious. This work could provide new insights on how to manage a dual-sourcing market for the platform.

Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay. In this work, we focus on the timeliness of computational-intensive updates, measured by Age-of-Information (AoI), and study how to jointly optimize the task updating and offloading policies for AoI with fractional form. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. The uncertain edge load dynamics, the nature of the fractional objective, and hybrid continuous-discrete action space (due to the joint optimization) make this problem challenging and existing approaches not directly applicable. To this end, we propose a fractional reinforcement learning (RL) framework and prove its convergence. We further design a model-free fractional deep RL (DRL) algorithm, where each device makes scheduling decisions with the hybrid action space without knowing the system dynamics and decisions of other devices. Experimental results show that our proposed algorithms reduce the average AoI by up to 57.6% compared with several non-fractional benchmarks.

Utilization of data and information flows for enhancing a chambermaid scheduling decision making in a smart hospitality supply chain

Utilization of data and information flows for enhancing a chambermaid scheduling decision making in a smart hospitality supply chain