Preferred Time Windows Research Articles

A branch-and-price-and-cut algorithm for the home health care routing and scheduling problem with multiple prioritized time windows

This paper addresses a variant of home health care routing and scheduling problem with endogenous time window options for patients. The objective is to minimize the total operational cost while ensuring a specified level of service satisfaction regarding patients’ prioritized time windows. We formulate the problem as a set-covering model and propose a branch-and-price-and-cut (BPC) algorithm to tackle it. Specifically, we devise a column generation procedure to obtain the lower bounds in the branch-and-bound framework, in which we employ a labeling algorithm with a multiple time windows processing mechanism to deal with the pricing sub-problems. Additionally, the lower bound is strengthened by incorporating 2-path inequalities and subset-row inequalities. The effectiveness of the BPC algorithm is demonstrated through numerical experiments conducted on the modified Solomon benchmark instances. Furthermore, we analyze the problem and derive the following managerial insights: i) Incorporating the assignment decisions of time windows for patients into the problem can significantly reduce nurse costs and travel costs, even when accounting for patients’ preferences for time windows. ii) There is a negative correlation between cost savings and the overall satisfaction levels of patients’ preferred time windows, with marginal cost savings decreasing as the overall satisfaction level declines. iii) Besides the total satisfaction level, the operational cost is also influenced by factors such as the distribution of patient locations, the width of time windows, and the proportion of patients with multiple time windows.

Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

ABSTRACT This paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows to offer flexibility to help route planners find more fuel-efficient routes (“green delivery”). Customers can rank their preferred time windows as first, second, and third. The optimization model aims to reduce tour costs, promote electromobility over fossil fuels, such as diesel, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives, which are overall cost, use of fossil fuel, and customer satisfaction. For the new problem, a set of realistic benchmark problems is created and four mainstream solvers are applied for the Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA. These algorithms are compared in terms of their effectiveness in achieving the objectives of minimizing travel costs, promoting electromobility, and meeting customer preferences. The study uses five different problems of single-vehicle route planning. Two major findings are that the selection of the metaheuristic can make a big difference in terms of algorithm performance. The resulting 3-D Pareto fronts reveal the nature of this new class of problems: Interestingly, in the new model with flexible time windows, most users can still be delivered in their most preferred time windows with only small concessions to the other objectives. However, using only one time window per user can lead to an increasingly drastic cost and fossil fuel consumption.

Flexible Scheduling Model of Bus Services between Venues of the Beijing Winter Olympic Games

Traditional buses travel on fixed routes and areas, which cannot satisfy the flexible demands of athletes in the context of COVID-19 and the closed-loop traffic management policy during the 2022 Beijing Winter Olympic Games (BWOG). This study predicts the travel demands based on the characteristics of athletes’ daily travel demands and then presents a flexible bus service scheduling model for cross-region scheduling among Beijing, Yanqing, and Zhangjiakou to provide high-level service. The flexible bus service is point-to-point and avoids unnecessary contact, which reduces the risk of spreading COVID-19 and ensures athletes’ safety. In this study, the flexible bus scheduling model is established to optimize scheduling schemes, whose object is to minimize the cost of the system based on some realistic constraints. These constraints consider not only the preferred time windows of athletes’ demand but also the vehicle’s capacity, depot, minimum load factor, total demands, etc. In addition, a genetic-simulated annealing hybrid algorithm (GSAHA) is designed to solve the model based on the characteristics of the genetic algorithm (GA) and simulated annealing. To assess the feasibility and efficiency of the model and algorithm, a case study is conducted in the Beijing-Yanqing area. Furthermore, the travel time of the flexible bus is compared to that of the traditional bus, according to the results of the case study. Moreover, the sensitivity of the model and algorithm are analyzed. The experimental results show that the proposed model and algorithm can dispatch buses with superior flexibility and high-level services during the BWOG.

Improving Efficiency in Congested Traffic Networks: Pareto-Improving Reservations through Agent-Based Timetabling

In an urban transportation network, congestion occurs in the form of a queue behind a bottleneck. Many studies have considered a reservation-based optimization approach for queuing systems. To control the traffic density behind a bottleneck so that it does not exceed the link capacity, and to reduce the emissions and improve the sustainability of cities, we propose a new mobility service system to offer a Pareto-improving schedule for both the portion of agents making reservations and others with fixed departure time schedules. This reservation system takes the agents’ (i.e., users or vehicles here) actual arrival and departure times from a conventional system without reservations as the preferred time windows at both the origins and destinations. Such a centralized mobility service system could maintain or improve the end-to-end traveling performance for all users. The proposed reservation and end-to-end timetabling problem is formulated as a multicommodity flow optimization problem in a discretized space–time network. We use a modified dynamic programming method for the reservation strategy on the space–time network and further adopt the alternative direction method of multiplier (ADMM) based on prime and dual theory to solve the large-scale instances. A comprehensive discussion is also provided regarding the technical challenges and potential solutions when operating such a system in a real-world setting.

Per1/Per2-Igf2 axis-mediated circadian regulation of myogenic differentiation.

Circadian rhythms regulate cell proliferation and differentiation, but circadian control of tissue regeneration remains elusive at the molecular level. Here, we show that proper myoblast differentiation and muscle regeneration are regulated by the circadian master regulators Per1 and Per2. Depletion of Per1 or Per2 suppressed myoblast differentiation in vitro and muscle regeneration in vivo, demonstrating their nonredundant functions. Both Per1 and Per2 were required for the activation of Igf2, an autocrine promoter of myoblast differentiation, accompanied by Per-dependent recruitment of RNA polymerase II, dynamic histone modifications at the Igf2 promoter and enhancer, and the promoter-enhancer interaction. This circadian epigenetic priming created a preferred time window for initiating myoblast differentiation. Consistently, muscle regeneration was faster if initiated at night, when Per1, Per2, and Igf2 were highly expressed compared with morning. This study reveals the circadian timing as a significant factor for effective muscle cell differentiation and regeneration.

Vehicle routing with private and shared delivery locations

The rapid growth of e-commerce has led to an increase of home delivery requests. Providing efficient distribution systems for services on the last mile has become a challenging issue for logistics companies, where a trade-off between the classical approaches, attended home delivery (AHD) and usage of shared delivery locations (SDLs) has been identified. AHD provides a higher quality of service but implies very high costs for the company, while usage of SDL requires customers to perform the very last mile by themselves. For companies, this bears the risk of a decrease in the perceived service quality. However, due to consolidation effects, transportation costs can be considerably mitigated. We propose a mixed delivery approach, which combines AHD and SDL usage in an innovative way. Customers can either be served at home during their preferred time window, or they can be asked to pick up their parcel at one of the SDLs. For each customer served using an SDL, the company pays a compensation price in order to reduce the perceived decrease in service quality. The newly introduced decision problem is formulated mathematically. We propose two matheuristics to efficiently solve large instances. In an extensive numerical study, we show that the new approach clearly outperforms standard ones. We observe an increase in solution quality of up to 40%, while customers’ perceived quality of service is not affected. Additionally, the results reveal that the obtained improvements are robust for different customer-specific time-window preferences, accepted travel times needed to reach an SDL, and different compensation schemes.

Data driven course scheduling to ensure timely graduation

ABSTRACT With progressively decreasing state funding in the last two decades, timely graduation has become an imperative yet challenging problem for many public universities. Our research empirically studies students' enrolment and performance data during an 8-year period in a large college at the California State University Long Beach. Through data analytics, we identify four fundamental issues that lead to delayed graduation. We propose innovative solutions that directly tackle each of the four identified issues while systematically matching capacity and demand. Specifically, we propose major-specific degree roadmaps tailored to increase the chance students can successfully complete all required courses within the timely graduation window. Given major migration behaviours, we design robust roadmaps that proactively prepare students for possible major change later without delaying graduation. The well-crafted degree roadmap provides students with a clear path to degree attainment, as well as guidance on the timing of course enrolments. Further, to maximise students' access to courses as well as capacity utilisation, we develop an optimisation model to determine the class schedule in which all students are guaranteed a seat within their preferred time window in all required classes. The proposed approach is widely applicable to many institutions facing the timely graduation challenge.

A Novel Model for Designing a Demand- Responsive Connector (DRC) Transit System With Consideration of Users’ Preferred Time Windows

This article presents a mathematical model to design a demand-responsive connector (DRC) bus operational network for improving the service quality and accessibility of public transportation systems. The proposed model features an integrated framework that simultaneously guides passengers to reach their nearest bus stops and routes buses to transport passengers at selected bus stops to connected stations of major transit systems. Passengers' preferred time for pick up is fully considered in the model. For this purpose, this study proposes a multi-objective mixed-integer linear programing model to effectively capture the interactions between users with their predefined service time windows and DRC bus network. This study further develops a three-stage heuristic to yield suboptimal solutions to the model in a reasonable time. Case study results demonstrate the effectiveness of the proposed model.

Flexible Route Optimization for Demand-Responsive Public Transit Service

Traditional customized buses travel on fixed routes, which cannot satisfy passengers’ flexibility and convenience requirements. This paper studies a demand-responsive transit (DRT) service that can continuously adjust the path based on passengers’ dynamic demand. The path optimization model is established with more realistic constraints to create a bus travel plan within a specified area, and the model not only considers the preferred time windows of passengers but also maximizes the benefits of the system. Based on simulated annealing, a dynamic genetic algorithm is designed to generate the static initial travel path, and the dynamic travel path is continuously updated to satisfy the real-time demand. To evaluate the proposed model and algorithm, a case study in a typical residential community of Beijing, China, is conducted based on transit smart card records. According to the case study results, the convenience, travel time, and economic and environmental benefits of the DRT service are assessed via comparison with traditional buses and private cars. The analysis results demonstrate the feasibility and significance of the method, and it can be used by transit planners to design a superior DRT service.

Quantifying Recreational Use of an Estuary: A Case Study of Three Bays, Cape Cod, USA.

Estimates of the types and number of recreational users visiting an estuary are critical data for quantifying the value of recreation and how that value might change with variations in water quality or other management decisions. However, estimates of recreational use are minimal and conventional intercept surveys methods are often infeasible for widespread application to estuaries. Therefore, a practical observational sampling approach was developed to quantify the recreational use of an estuary without the use of surveys. Designed to be simple and fast to allow for replication, the methods involved the use of periodic instantaneous car counts multiplied by extrapolation factors derived from all-day counts. This simple sampling approach can be used to estimate visitation to diverse types of access points on an estuary in a single day as well as across multiple days. Evaluation of this method showed that when periodic counts were taken within a preferred time window (from 11am-4:30pm), the estimates were within 44 percent of actual daily visitation. These methods were applied to the Three Bays estuary system on Cape Cod, USA. The estimated combined use across all its public access sites is similar to the use at a mid-sized coastal beach, demonstrating the value of estuarine systems. Further, this study is the first to quantify the variety and magnitude of recreational uses at several different types of access points throughout the estuary using observational methods. This work can be transferred to the many small coastal access points used for recreation across New England and beyond.

A Hybrid Multi-objective Genetic Algorithm for Bi-objective Time Window Assignment Vehicle Routing Problem

Providing a satisfying delivery service is an important way to maintain the customers’ loyalty and further expand profits for manufacturers and logistics providers. Considering customers’ preferences for time windows, a bi-objective time window assignment vehicle routing problem has been introduced to maximize the total customers’ satisfaction level for assigned time windows and minimize the expected delivery cost. The paper designs a hybrid multi-objective genetic algorithm for the problem that incorporates modified stochastic nearest neighbour and insertion-based local search. Computational results show the positive effect of the hybridization and satisfactory performance of the metaheuristics. Moreover, the impacts of three characteristics are analysed including customer distribution, the number of preferred time windows per customer and customers’ preference type for time windows. Finally, one of its extended problems, the bi-objective time window assignment vehicle routing problem with time-dependent travel times has been primarily studied.

Customized bus routing problem with time window restrictions: model and case study

Considering that, in reality, passengers usually hold preferred time windows when waiting for a bus at a station, this study develops a mixed integer programming model for the customized bus routing problem (CBRP) with full spatial–temporal constraints based on one of our previous studies. Specifically, bus routing and passenger assignment are simultaneously optimized with better vehicle capacity utilization and more realistic considerations of partial service, characteristics of customized bus service, and a range of operational constraints. To solve the formulated model, an exact algorithm (i.e. the branch-and-cut algorithm) and two heuristics (i.e. the genetic and tabu search algorithms) are numerically compared through an illustrative example, and subsequently, a case study in Beijing is conducted to assess the proposed approach. A comparison with the practical customized bus system shows that the proposed approach realizes effective vehicle usage on several routes.

Adiabatic quantum dynamics under decoherence in a controllable trapped-ion setup

Suppressing undesired nonunitary effects is a major challenge in quantum computation and quantum control. In this work, by considering the adiabatic dynamics in presence of a surrounding environment, we theoretically and experimentally analyze the robustness of adiabaticity in open quantum systems. More specifically, by considering a decohering scenario, we exploit the validity conditions of the adiabatic approximation as well as its sensitiveness to the resonance situation, which typically harm adiabaticity in closed systems. As an illustration, we implement an oscillating Landau-Zener Hamiltonian, which shows that decoherence may drive the resonant system with high fidelities to the adiabatic behavior of open systems. Moreover we also implement the adiabatic quantum algorithm for the Deutsch problem, where a distinction is established between the open system adiabatic density operator and the target pure state expected in the computation process. Preferred time windows for obtaining the desired outcomes are then analyzed. We experimentally realize these systems through a single trapped Ytterbium ion $^{171}$Yb$^+$, where the ion hyperfine energy levels are used as degrees of freedom of a two-level system, with both driven field and decohering strength efficiently controllable.

Formation and incidence of shell galaxies in the Illustris simulation

Shells are low surface brightness tidal debris that appear as interleaved caustics with large opening angles, often situated on both sides of the galaxy center. In this paper, we study the incidence and formation processes of shell galaxies in the cosmological gravity+hydrodynamics Illustris simulation. We identify shells at redshift z=0 using stellar surface density maps, and we use stellar history catalogs to trace the birth, trajectory and progenitors of each individual star particle contributing to the tidal feature. Out of a sample of the 220 most massive galaxies in Illustris ($\mathrm{M}_{\mathrm{200crit}}>6\times10^{12}\,\mathrm{M}_{\odot}$), $18\%\pm3\%$ of the galaxies exhibit shells. This fraction increases with increasing mass cut: higher mass galaxies are more likely to have stellar shells. Furthermore, the fraction of massive galaxies that exhibit shells decreases with increasing redshift. We find that shell galaxies observed at redshift $z=0$ form preferentially through relatively major mergers ($\gtrsim$1:10 in stellar mass ratio). Progenitors are accreted on low angular momentum orbits, in a preferred time-window between $\sim$4 and 8 Gyrs ago. Our study indicates that, due to dynamical friction, more massive satellites are allowed to probe a wider range of impact parameters at accretion time, while small companions need almost purely radial infall trajectories in order to produce shells. We also find a number of special cases, as a consequence of the additional complexity introduced by the cosmological setting. These include galaxies with multiple shell-forming progenitors, satellite-of-satellites also forming shells, or satellites that fail to produce shells due to multiple major mergers happening in quick succession.

Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

This paper presents a mixed-integer linear programming model for demand-responsive feeder transit services to assign vehicles located at different depots to pick up passengers at the demand points and transport them to the rail station. The proposed model features passengers’ one or several preferred time windows for boarding vehicles at the demand point and their expected ride time. Moreover, passenger satisfaction that was related only to expected ride time is fully accounted for in the model. The objective is to simultaneously minimize the operation costs of total mileage and maximize passenger satisfaction. As the problem is an extension of the nondeterministic polynomial problem with integration of the vehicle route problem, this study further develops an improved bat algorithm to yield meta-optimal solutions for the model in a reasonable amount of time. When this was applied to a case study in Nanjing City, China, the mileage and satisfaction of the proposed model were reduced by 1.4 km and increased by 7.1%, respectively, compared with the traditional model. Sensitivity analyses were also performed to investigate the impact of the number of designed bus routes and weights of objective functions on the model performance. Finally, a comparison of Cplex, standard bat algorithm, and group search optimizer is analyzed to verify the validity of the proposed algorithm.

Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

Emerging transportation network services, such as customized buses, hold the promise of expanding overall traveler accessibility in congested metropolitan areas. A number of internet-based customized bus services have been planned and deployed for major origin-destination (OD) pairs to/from inner cities with limited physical road infrastructure. In this research, we aim to develop a joint optimization model for addressing a number of practical challenges for providing flexible public transportation services. First, how to maintain minimum loading rate requirements and increase the number of customers per bus for the bus operators to reach long-term profitability. Second, how to optimize detailed bus routing and timetabling plans to satisfy a wide range of specific user constraints, such as passengers’ pickup and delivery locations with preferred time windows, through flexible decision for matching passengers to bus routes. From a space-time network modeling perspective, this paper develops a multi-commodity network flow-based optimization model to formulate a customized bus service network design problem so as to optimize the utilization of the vehicle capacity while satisfying individual demand requests defined through space-time windows. We further develop a solution algorithm based on the Lagrangian decomposition for the primal problem and a space-time prism based method to reduce the solution search space. Case studies using both the illustrative and real-world large-scale transportation networks are conducted to demonstrate the effectiveness of the proposed algorithm and its sensitivity under different practical operating conditions.

A simulation model with multi-attribute utility functions for energy consumption scheduling in a smart grid

When consumers’ use of electricity is mainly driven by convenience, coincident demand occurs, resulting in electric load peaks. Consequently, the undesirable large gaps between peak and off-peak loads will adversely affect the system’s efficiency due to unused capacity during off-peak hours and extra ancillary generators required during peak hours. Demand response (DR) has long been proposed to reduce peak load by providing incentives to encourage consumers to shift their peak loads to off-peak periods. In most DR literature, incentive schemes are purely financial in assuming that cost is the only parameter to influence consumers’ load-shifting behavior. In this paper, we assume that in addition to cost, convenience of energy usage is also an important factor when consumers respond to DR programs. Hence, we use multi-attribute utility functions consisting of both cost and convenience factors to model consumer behavior on energy consumption for home appliances. The “convenience” herein is defined as being able to use an appliance during one’s preferred time window. A simulation model is developed to study a residential population consisting of heterogeneous households with varying preference of convenience over cost. We study the effects of time-of-use pricing structure on users’ utility-based load shifting behaviors and subsequently on system-wide performances such as peak to average ratio (PAR) and load variance (LV). We also describe a method of design of experiment (DOE) for determining an optimal time-of-use rate structure that minimizes both PAR and LV for the system.

The time locked theta response reflects interindividual differences in human memory performance

Recent research indicates that an increase in theta band power is related to episodic memory performance. In this study with human subjects, the evoked (time locked) and induced (not time locked) theta response is analyzed in a recognition task. The results show a strong evoked theta response during an early retrieval period of up to 400 ms. Only for good memory performers theta is strictly time locked, indicating that theta peaks appear in preferred time windows after a target is presented. This effect – which coincides with a large P3 – suggests that good performance requires a strict timing of different processing stages that correspond to cycles of theta activity.