Time Window Constraints Research Articles

Integrated rescheduling optimization of berth allocation and quay crane allocation with shifting strategies

Container terminal operations are a core aspect of vessel operations in ports and necessitate an intelligent rescheduling approach to manage complex uncertainty scenarios. This paper investigates the problem of berth allocation and quay crane (QC) assignment with shifting strategies. A variable-length rolling mechanism (VLRM) is designed to identify uncertainty scenarios and determine the optimal rescheduling time point. Notably, we introduce a novel cross-wharf shifting movements strategy (CWSM) and develop a rescheduling model that accounts for constraints related to tidal time windows. The proposed Adaptive Large Neighborhood Search algorithm (ALNS) is tailored to solve this problem effectively. Computational results from various instance sizes validate the efficacy of both the model and the ALNS algorithm. These results demonstrate that the ALNS algorithm exhibits distinct advantages in solution quality and computational time for large-scale instances. Furthermore, the inclusion of tidal time window constraints significantly amplifies the complexity of uncertainty scenarios, emphasizing their importance and the impossibility of neglecting them. The cross-wharf shifting movements strategy, presented herein, effectively mitigates the impact of uncertainty scenarios within a defined scope while enhancing adjustment flexibility.

Using intermediate points in parcel delivery operations with truck‐based autonomous drones

AbstractAutonomous drones are no longer science fiction but are becoming reality. Prior studies have investigated how an autonomous drone can be used in conjunction with a parcel delivery truck, but they all restricted the drones’ launch/recovery sites to customer nodes visited by a truck. In practice, parcel carriers are considering the use of intermediate points (IPs), the sites found along the arcs connecting customer nodes, as drones' launch/recovery points. This means that the academic literature currently lags the industry practice. This article extends the previous works on truck‐and‐drone last‐mile delivery by investigating the conditions under which the use of IPs is beneficial by using both theoretical and empirical approaches. Our results show that, although using IPs is an effective concept that can save the cost (time) of package deliveries by 2.189% on average, the cost saving realized by using IPs can vary notably across carriers depending on their network characteristics. Specifically, our results suggest that the benefit of using IPs becomes high when a network has the following characteristics: (1) low customer density (a small number of customers served per square mile), and (2) large number of time‐sensitive packages (subject to time window constraints). Based on these findings, we provide normative implications on if, when, where, and to what extent IPs can be beneficial for the truck‐and‐drone joint operations in last‐mile logistics.

Impact of Uncertain Flight Time on Heterogeneous UAVs’ Task Planning with Temporal Constraints

Heterogeneous multi-UAV systems offer distinct advantages through their complementary and coordinated use of their diverse capabilities. However, this complexity poses significant challenges in task planning, particularly in considering temporal constraints among tasks. As task dependencies evolve from simple linear chains to complex networked associations, uncertainties in flight times can have a substantial impact on the overall schedule. To address these challenges, this study introduces a rapid estimation method that recursively calculates task completion times, derives their probability distributions, and assesses the robustness of the plan. Furthermore, a neighborhood search algorithm guided by dynamic time windows is designed to effectively evaluate the consequences of task insertions, precisely to adjust high-risk tasks, and reduce blindness in enumerative neighborhood exploration. Simulation results demonstrate that the proposed approach effectively accounts for inherent randomness in the problem and exhibits strong adaptability to changes in the problem scale, flight time fluctuations, and variations in time window constraints.

Recourse strategy for the routing problem of mobile parcel lockers with time windows under uncertain demands

It is expected that mobile parcel lockers (MPLs) will provide more responsive and flexible service to the increasing variation of demand than fixed parcel lockers. However, the service provided by MPLs following planned routes can be degraded and even fail due to the demand uncertainty, such as demand volume and pickup time. In this study, we propose an optimization approach integrated with the recourse strategies to overcome this barrier. Three types of uncertain demands and two types of service failures are addressed in this study, with consideration of uncertain demands and time window constraint. To optimally determine the a priori routes for a set of MPLs, we propose a tailored tabu search algorithm by taking into account the recourse cost. A series of numerical experiments were conducted to examine the effectiveness of the proposed approach in enhancing route reliability with a comparison to ordinary express vehicles. The results reveal that the service reliability can be significantly improved with only a marginal increase in the operational cost by the proposed approach.

Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

Exact and heuristic max-plus strategies for makespan minimization in permutation flow shops with time-window constraints

We consider permutation flow shops with time-window constraints, which consist of several jobs to be processed in a sequence of machines in the same order, where no job overtaking is allowed, and with restrictions on the timing of the operations. The investigated class of permutation flow shops is larger than the one typically considered in scheduling literature. This paper introduces two strategies, based on previous results in the max-plus algebra, to obtain a schedule minimizing the makespan: an exact branch and bound method, and a heuristics inspired from receding-horizon control. Simulations demonstrate the advantages and limitations of the proposed approaches.

A Hybrid Heuristic-Exact Optimization for Large-Scale Home Health Care Problem.

During the COVID-19 pandemic, numerous people experiencing illness or senescence choose to receive home health care (HHC) services. However, a rapid increase in patients makes it a challenge to reasonably allocate nurses to provide HHC services under the condition of a paucity of nurse resources and patient time window constraints. To solve the large-scale HHC problem, a hybrid heuristic-exact optimization algorithm is proposed with three novel contributions. First, a framework of hybrid heuristic-exact optimization is designed to solve the large-scale problem where a reasonable solution is difficult to obtain under constraints. Second, a multi-objective mixed-integer linear programming modelization is formulated to get a more diverse nurse assignment. Finally, an improved branch and bound algorithm is proposed to speed up computation for the large-scale problem. Computational results on different HHC instances from 25 to 1000 patients demonstrate that the proposed algorithm can optimize the HHC problem with more than 100 patients and can provide various assignments for different numbers of nurses, which the common algorithm cannot optimize.

考虑碳排放成本的多车型农资配送路径优化研究

Agriculture is the foundation of the national economy, and agricultural materials are the basis of agricultural development. As the three rural issues (agriculture, countryside, and farmers) become increasingly important, the distribution of agricultural materials attracts extensive attention. Given the slow development of rural logistics, the traditional agricultural material distribution process encounters many problems, such as cumbersome distribution links, high distribution costs, and low profit for enterprises, which in turn cause high production costs and low income for farmers. In consideration of battery energy consumption and soft time window constraints, this study adopted the agricultural material distribution route as the study object and established an optimization model of the agricultural material distribution route with fixed , transportation, energy consumption, time window penalty, and carbon emission costs as the objective functions. With regard to the algorithm, the operation of differential update and chaotic disturbance was innovatively enhanced and applied to the improved ant colony algorithm to simulate the model and obtain the optimal distribution route optimization model. Results show that the traditional ant colony algorithm improved by differential updating and chaotic disturbance has the advantages of low distribution cost, reasonable route, small number of activated vehicles, and short convergence time. Compared with the traditional ant colony algorithm, the improved ant colony algorithm can converge to the global optimum faster. This study provides guidance and suggestions on route selection and vehicle configuration to reduce costs and increase efficiency and offers certain theoretical support to alleviate urban traffic pollution and implement carbon trading policies in the future.

Multiserver time window allowance schedules for virtual visits with uncertain time-dependent no-shows and service times

Virtual care serves as a new mode that can divert non-urgent visits from traditional office visits. Whether virtual service can improve the access to medical treatment and reduce the burden of traditional office services, the key issue is to generate efficient appointment schedules with the lowest operation cost. In this paper, considering the uncertainty of time-dependent no-shows and service times, we investigate a multiserver time window allowance appointment scheduling problem, where time window constraints that restricts virtual visits to be served during the particular period are explicitly modeled. We formulate the problem as a stochastic mixed-integer program to optimize decisions of physician allocation and appointment time simultaneously. Based on the sample average approximation, a stabilized Benders decomposition algorithm is developed by incorporating acceleration techniques, such as cut aggregation and feasibility cuts. Numerical results based on real data indicate the effectiveness of the proposed multiserver time window allowance schedules (MTWAS) and algorithm. Comparing with the off-the-shelf solver Gurobi, the developed algorithm demonstrates high performance in terms of computation speed and solution quality. Under different time-dependent no-show patterns of virtual and office visits, the obtained MTWAS perform better than previous solutions in almost all test cases. In addition, we offer useful managerial insights to aid the virtual service provider in making better scheduling decisions.

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

This paper addresses the complex issue of managing medical waste transportation using electric vehicles, with the goal of minimizing both energy consumption and the risks associated with hazardous waste. A multi-objective mixed-integer linear programming model is introduced, incorporating practical factors such as time windows, partial recharge policy, load-dependent discharge, infection risk, and trips to waste disposal facilities. Our proposed method, a combination of the multi-objective evolutionary algorithm using decomposition (MOEA/D) with adaptive large neighborhood search (ALNS) and local search (LS) techniques, is referred to as MOEA/D-ALNS. This method demonstrates superior performance compared with the non-dominated sorting genetic algorithm, NSGA-II, modified MOEA/D and MOEA/D-LNS in benchmark instances with realistic assumptions. Our experimental results revealed an inverse correlation between energy consumption and risk objectives. Sensitivity analyses showed that eliminating time-window constraints results in more energy-efficient and safer routes while maintaining a slightly lower battery energy level can strike an ideal balance between energy consumption, risk, and battery health. This research contributes to the understanding of infectious medical waste management with its consideration of electric vehicles and waste disposal. It lays a solid foundation for future studies aiming to improve the sustainability and efficiency of medical waste routing practices.

Customized passenger path optimization for airport connections under carbon emissions restrictions

In response to the challenge of optimizing customized passenger transport paths for airport connections while taking carbon emissions constraints into account, this paper proposes an optimization model that minimizes the total cost by addressing passenger time window constraints, determining optimal passenger transport paths, and optimizing factors like the number of drop-off stations and vehicle occupancy rates. The total cost comprises the operational expenses of customized passenger transport businesses and travel time costs per passenger. We develop an annealing genetic algorithm to solve the model and provide a case analysis. Our findings indicate that the algorithm and the model empower decision-makers to swiftly select passenger transport path schemes that minimize the total cost with their specific requirements.

Valid inequalities for the non-unit demand capacitated minimum spanning tree problem with arc time windows and flow costs

In this paper, we introduce the non-unit demand capacitated minimum spanning tree problem with arc time windows and flow costs. The problem is a variant of the capacitated minimum spanning tree problem with arc time windows (CMSTP_ATW). We devise a mixed integer programming (MIP) formulation to model the problem and solve it using CPLEX. Furthermore, we propose three sets of inequalities, and we prove that they are valid. These valid inequalities tighten the model and lead to better lower bounds. To examine the quality of the solutions obtained, we convert the original data sets of Solomon (1987, “Algorithms for the Vehicle Routing and Scheduling Problem with Time Window Constraints.” Operations Research 35 (2): 254–265. https://doi.org/10.1287/opre.35.2.254) to approximate the non-unit demand CMSTP_ATW instances and provide results for the problems with 100 nodes. We execute extensive computational experiments, and the results show the positive effect of the inclusion of valid inequalities in the MIP.

Route optimization via improved ant colony algorithm with graph network

<p>Route optimization problem using vehicle routing problem (VRP) and time window constraint is explained as finding paths for a finite count of vehicles to provide service to a huge number of customers and hence, optimizing the path in a given duration of the time window. The vehicles in the loop have restricted intake of capacity. This path initiates from the depot, delivers the goods, and stops at the depot. Each customer is to serve exactly once. If the arrival of the vehicle is before the time window “opens” or when the time window “closes,” there will be waiting for cost and late cost. The challenge involved over here is to scheduling visits to customers who are only available during specific time windows. Ant colony optimization (ACO) algorithm is a meta-heuristic algorithm stimulated by the growing behaviour of real ants. In this paper, we combine the ACO algorithm with graph network henceforth increasing the number of vehicles in a particular depot for increasing the efficiency for timely delivery of the goods in a particular time width. This problem is solved by, an efficient technique known as the ACO+graph algorithm.</p>

Impact of priority assignment on schedule-based attacks in real-time embedded systems

In this paper, we investigate the impact of priority assignment on schedule-based attacks in fixed-priority real-time systems. Through the schedule-based attacks, attackers may manipulate the input and output buffers of victim tasks, cause instability, and/or steal hidden information. The success of those attacks highly depends on the proper positioning of the malicious task with respect to the victim tasks in the actual schedule. Firstly, we develop an optimal priority assignment algorithm that incorporates the user specified priority preferences that reflect task types (anterior task, posterior task, or victim task), while still meeting all the deadlines. Secondly, we propose a dynamic delaying algorithm to further reduce posterior schedule-based attacks at run-time. Finally, we derive and compare the performance of six attack-aware priority-assignment policies as well as their dynamic extensions through comprehensive simulations. Our results suggest that the well-known RMS algorithm can be easily outperformed in terms of reducing schedule-based attacks, by properly specifying the relative priorities of malicious, anterior, and posterior tasks through our optimal algorithm. In particular, the AMP assignment, which assigns the anterior, malicious, and posterior tasks execution priorities in decreasing order, is shown to offer rather robust and consistent performance even at high system utilizations. Our framework explicitly incorporates the logical execution time and attack effective window constraints to model schedule-based attacks in realistic manner.

Terminal-based zonal bus: A novel flexible transit system design and implementation

The pursuit of individual mobilities expedites the development of demand-responsive systems, while the balance between service flexibilities and operational cost is becoming particularly important. This paper proposes a method for operating a novel demand-responsive bus system termed terminal-based zonal bus (TZB). It is a flexible system with vehicles executing a terminal-based zone routing scheme in specified service zones. The terminal-based routing scheme intends to generate TZB lines, and each line is operated by one vehicle that travels along a series of terminal stations arranged in order and serves enroute passenger requests between any two consecutive terminal stations. As instant response is the main trait of demand-responsive systems, TZB is able to address instance response by incorporating reserve capacity, which is employed to indicate the vehicle responsive ability of new real-time requests. Further, a tailored heuristic algorithm is designed to solve the proposed model, which simultaneously incorporates several practical restrictions, including the capability of adopting real-time requests, terminal stations’ fleet-size constraints, time window constraints, and vehicle capacity. Computational experiments are conducted based upon the newly developed modern city Xiong An, China which confirm that the proposed method is capable of obtaining high-quality solutions efficiently, and TZB can provide flexible service at a relatively low operational cost.

Optimization of perishable agricultural products logistics distribution path based on IACO-time window constraint

With the development of modern logistics industry, the cold chain logistics distribution of perishable agricultural products has become an important link to ensure food quality and safety. Aiming at the complicated situation in the process of perishable agricultural products transportation path planning, a cold chain logistics distribution path optimization model based on Ant Colony Optimization (ACO) was proposed. Based on the traditional soft and hard time window constraint model, an optimized time window constraint model was proposed, and the update function of pheromone increment was substituted to optimize the traditional ant colony algorithm. An Improved Ant Colony Optimization (IACO)-time-window constrained path optimization model was proposed. The test results showed that in the convergence training experiment in Matlab environment, the research method only needed 19 iterations to reach the lowest level. In the experiment of application effect, the length of the path generated by the research method for car a was 188.93 km. Car Group a could complete 60.38 % offload at the fourth distribution node. The experimental results showed that the proposed route optimization model had better iteration efficiency and could effectively generate a shorter distance distribution path, which provides a new technical support for the route optimization of cold chain distribution of perishable agricultural products.

Simulated annealing with reinforcement learning for the set team orienteering problem with time windows

This research investigates the Set Team Orienteering Problem with Time Windows (STOPTW), a new variant of the well-known Team Orienteering Problem with Time Windows and Set Orienteering Problem. In the STOPTW, customers are grouped into clusters. Each cluster is associated with a profit attainable when a customer in the cluster is visited within the customer’s time window. A Mixed Integer Linear Programming model is formulated for STOPTW to maximizing total profit while adhering to time window constraints. Since STOPTW is an NP-hard problem, a Simulated Annealing with Reinforcement Learning (SARL) algorithm is developed. The proposed SARL incorporates the core concepts of reinforcement learning, utilizing the ε-greedy algorithm to learn the fitness values resulting from neighborhood moves. Numerical experiments are conducted to assess the performance of SARL, comparing the results with those obtained by CPLEX and Simulated Annealing (SA). For small instances, both SARL and SA algorithms outperform CPLEX by obtaining eight optimal solutions and 12 better solutions. For large instances, both algorithms obtain better solutions to 28 out of 29 instances within shorter computational times compared to CPLEX. Overall, SARL outperforms SA by resulting in lower gap percentages within the same computational times. Specifically, SARL outperforms SA in solving 13 large STOPTW benchmark instances. Finally, a sensitivity analysis is conducted to derive managerial insights.

Container Drayage Transportation Scheduling With Foldable and Standard Containers

Traditional container drayage scheduling with standard containers has been studied for years. In this article, we focus on a novel container drayage transportation problem involving foldable and standard containers simultaneously. Four types of container tasks are required to be scheduled among a terminal, a depot, and a number of customers in an export-dominant area. The optimizing objective is to minimize the total working time of trucks in operation, which reveals the total cost for trucking companies to some certain extents. An improved truck-state transition method is designed to analyze the state of trucks. A mathematical model based on this method is built to describe the problem. A large neighborhood search algorithm considering time window constraints and the state transition is used to solve the drayage problem. The mathematical model and the algorithm are evaluated and validated based on a large number of randomly generated instances. Experimental results show that the developed algorithm is effective in solving the problem. With the increase of the maximum number of empty foldable containers that a truck can carry at a time, the total working time of trucks in operation decreases.

An Exact Method for a First-Mile Ridesharing Problem

Motivated by the worldwide development of shared mobility, we investigate a vehicle routing problem with time windows and deadlines called the first-mile ridesharing problem (FMRSP). The FMRSP involves routing a fleet of vehicles, each servicing customers within specific time windows. The FMRSP generalizes the well-known vehicle routing problem with time windows (VRPTW), additionally imposing that each vehicle route arrives at the destination before the earliest deadline associated with the set of customers served by the route. The FMRSP is also related to the VRPTW and release dates, where in addition to time window constraints, a release date is associated with each customer defining the earliest time that the order is available to leave the depot for delivery. For the FMRSP, we present an exact method based on a branch-price-and-cut (BPC) algorithm combining state-of-the-art techniques and an innovative pricing algorithm. The pricing algorithm is based on a bidirectional bucket graph-based labeling algorithm, in which the backward extension of a label is computed in a constant time. Effective dominance rules used to speed up the computation are also described. Extensive computational studies demonstrate that our proposed BPC algorithm can solve optimality-modified Solomon benchmark instances involving up to 100 customers and real-world instances involving up to 290 customers. Funding: This research was supported by the National Natural Science Foundation of China [Grants 71831003, 72171043, 71831006, and 71901180]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0139 .

Value of Cargo Pooling in On-Demand Intra-City Freight Logistics

On-demand intra-city freight logistics (ICFL) has recently emerged as a new freight service, where shippers can submit their shipping requests using smartphones and be matched to drivers in real time based on their locations and drivers’ availability. A major challenge faced by on-demand ICFL platforms is the shortage of vehicles during peak demand periods. Cargo pooling, the cargo version of carpooling, offers as a promising way to increase supply: cargoes heading in the same direction would share the cargo compartment of the same vehicle and be serviced simultaneously, which is achieved by careful sequencing of the pickup and delivery locations of the cargoes. We investigate models for cargo pooling for on-demand ICFL and quantify its benefit, which is new to the literature. The major difference between existing studies on ICFL and ours is that we no longer assume that demands are known beforehand. Instead, the demands arrive gradually throughout the day and we need to periodically match requests to drivers and re-optimize vehicle routes. We formulate the matching problem as a dynamic pickup and delivery problem with three-dimensional loading and time window constraints. To solve this model, we develop an algorithm based on large neighborhood search and tree search. The algorithm is tested with real freight data in a city in the Yangtze River Delta. Results show that the algorithm can reduce the total cost by 21.4% and reduce the total vehicle miles traveled by 36.0%.