Time Window Constraints Research Articles

A Two-Stage Distributed Task Assignment Algorithm Based on Contract Net Protocol for Multi-UAV Cooperative Reconnaissance Task Reassignment in Dynamic Environments.

Multi-UAV systems have been widely used in reconnaissance, disaster relief, communication, and other fields. However, many dynamic events can cause a partial failure of the original mission during the mission execution process, in which case task reassignment should be carried out. How to reassign resources and tasks in multi-dynamic, multi-target, and multi-constraint events becomes a core issue in the enhancement of combat efficiency. This paper establishes a model of multi-UAV cooperative reconnaissance task reassignment that comprehensively considers various dynamic factors such as UAV performance differences, size of target areas, and time window constraints. Then, a two-stage distributed task assignment algorithm (TS-DTA) is presented to achieve multi-task reassignment in dynamic environments. Finally, this paper verifies the effectiveness of the TS-DTA algorithm through simulation experiments and analyzes its performance through comparative experiments. The experimental results show that the TS-DTA algorithm can efficiently solve the task reassignment problem in dynamic environments while effectively reducing the communication burden of UAV formations.

Dynamic Route Optimization for Chinese E-Commerce Logistics Based on Ant Colony Algorithm

Abstract The limited nature of logistics and distribution vehicles and the variability of customer acceptance service time limit the service efficiency and quality of logistics and distribution. To optimize a logistics distribution path under capacity and time window constraints, a mathematical model of the problem is first developed in this study, with the lowest cost as the model’s objective function. The logistics distribution issue with soft time windows is then addressed using an ant colony algorithm, and a logistics path optimization strategy based on the maximum minimal ant colony system is suggested. Then, the heuristic function is rebuilt to improve the ant colony algorithm’s solution speed, and the pheromone update approach is included. Finally, experimental approaches are used to test the model’s and optimization algorithm’s efficacy for customer sizes of 30, 50, and 100. The experimental results show that the optimized ant colony algorithm has the best value of 2 for α and 3 for β, which can converge earlier. The improved ant colony algorithm also finds the best solution faster than the conventional ant colony method in just 23 rounds. In the mathematical model of the logistics distribution path optimization issue, this study suggests that the optimized ant colony method has the optimization algorithm’s rationality, efficacy, and stability.

Vehicle re-routing under disruption in cross-dock network with time constraints

This study expands on the investigation of the vehicle routing problem with cross-dock (VRPCD) by incorporating disruption in a network with suppliers, customers, and a single cross-dock. It is assumed that the disruption (e.g., traffic accident or mechanical failure) impacts the scheduled pickup times of vehicles carrying freight; hence, pickup or delivery is affected by late vehicle arrivals to suppliers, cross-docks, and customers as well as increased transportation cost for the carrier. To help decision makers mitigate the impact of this type of disruption, mixed-integer linear programs (MILPs) are developed to model VRPCD with time-window constraints under both normal and disrupted conditions that will quantify the impact of disruption. The Golden Ball metaheuristic is adapted to provide an optimal or near-optimal solution of the MILPs in a short time period. To demonstrate the effectiveness of the proposed models and algorithms, numerical experiments are performed on hypothetical networks containing a realistic number of pickup and delivery locations. Results from numerical experiments and ANOVA analysis showed that: 1) the cross-dock is able to cope with the disruption when the number of disrupted nodes is small, 2) early breakdown of an inbound vehicle with fewer nodes on its routes causes smaller service delays, 3) as the number of disrupted nodes increases, it is advantageous to reroute outbound vehicles, and 4) when the breakdown occurs earlier in the planning horizon, it is better to reroute both inbound and outbound vehicles whereas only outbound vehicles should be rerouted when the breakdown happens later in the day.

Comparison Between an Exact and a Heuristic-Based Traveling Salesman Problem with Time Window Constraints

Abstract This work aims to compare two distinct approaches for solving a Travelling Salesman Problem with time window constraints. Given an environment with a fixed number of cities (points of interest), a robot must determine a route such that each city is visited in an imposed time interval. Both of the examined techniques have the objective of identifying the path with the lowest cost in terms of the distance traveled. The initial approach employs an exact method by defining the requirements as a mixed integer linear programming (MILP) optimization problem. The second method involves a meta-heuristic approach, using an ant colony procedure to solve the optimization problem. Besides qualitative information, the performed quantitative comparison relies on multiple numerical simulations performed in a MATLAB environment. We thus highlight the advantages and disadvantages of both methods, by taking into consideration criteria as the simulation time and the relative difference between the obtained costs versus the number of cities.

Models and Algorithms for Multiagent Hierarchical Routing with Time Windows

The problem of modeling real logistics systems arranged in a hierarchical manner is considered. Clusters of lower level consumers are formed that meet the time window (TW) constraints for each consumer and the cluster as a whole. In each such cluster, a traveling salesman’s route is constructed and the vertex closest to the central node, which is the vertex of reloading goods from heavy vehicles (Vs) to light Vs serving consumer clusters, is selected. The transshipment vertices, in turn, are combined into higher level traveling salesmen’s routes, taking into account TWs for routes of this level. The software implementation is tested on well-known networks. The technique is applicable for the synthesis of the central distribution center and system distribution centers of the lower level, as well as for calculating the required number of vehicles (agents).

Research on the Bias Sampling RRT Algorithm for Supermarket Chain Distribution Routes under the O2O Model

The purpose of this article is to propose the Bias Sampling RRT algorithm and use it as an optimization algorithm for supermarket chain distribution routes under the O2O model. As a retail store in the transformation and upgrading of chain stores, the actual terrain factors in distribution directly affect the timely delivery of goods from online to offline. The Bias Sampling RRT algorithm, as a path search method for time window vehicle routing problems, can find the optimal path that meets time window constraints. The applicability and effectiveness of the Bias Sampling RRT algorithm have been demonstrated through map simulation calculations. The simulation results show that compared with the RRT method, the Bias Sampling RRT algorithm has a shorter distribution path and shorter distribution time. This method is very suitable for the distribution activities of chain supermarkets or single store retail enterprises in the complex transformation and upgrading of actual terrain.

The Multi-Trip Autonomous Mobile Robot Scheduling Problem with Time Windows in a Stochastic Environment at Smart Hospitals

Autonomous mobile robots (AMRs) play a crucial role in transportation and service tasks at hospitals, contributing to enhanced efficiency and meeting medical demands. This paper investigates the optimization problem of scheduling strategies for AMRs at smart hospitals, where the service and travel times of AMRs are stochastic. A stochastic mixed-integer programming model is formulated to minimize the total cost of the hospital by reducing the number of AMRs and travel distance while satisfying constraints such as AMR battery state of charge, AMR capacity, and time windows for medical requests. To address this objective, some properties of the solutions with time window constraints are identified. The variable neighborhood search (VNS) algorithm is adjusted by incorporating the properties of the AMR scheduling problem to solve the model. Experimental results demonstrate that VNS generates high-quality solutions. Both enhanced efficiency and the meeting of medical demands are achieved through intelligently arranging the driving routes of AMRs for both charging and service requests, resulting in substantial cost reductions for hospitals and enhanced utilization of medical resources.

Speed optimization of inland sea vessels based on C.W saving algorithm

Abstract This paper constructs a mathematical model of the C-W saving algorithm based on the characteristics of the C-W saving algorithm and the collection and data analysis of energy efficiency data of ships without time window constraints. On this basis, the mathematical model with the time window constraint is further constructed by adding the time window constraint and fully considering some factors in the transportation of inland vessels that cannot be ignored, such as fuel price, carbon tax price and sailing speed. Finally, the impact of fuel, charter, and carbon tax prices on the optimization scheme is analyzed by the sensitivity analysis method. With the lowest total sailing cost as the target, the total sailing cost of the whole voyage is reduced by $4,749, which is 3.75% less than last year. Optimization with the lowest carbon emission as the goal, the whole voyage CO2 emissions reduced by 4.22t, a year-on-year reduction of 10.76%. Considering the total voyage cost and carbon emission for multi-objective optimization, the total voyage cost is reduced by 1210, which is 0.91% less than the same period last year. Therefore, the speed optimization method for inland sea vessels based on the C-W saving algorithm can provide a basis for shipowners and speed companies in making sailing plans.

Membership‐based aircraft parking stand allocation system with time window constraints: An event‐based time–space separated algorithm

AbstractOutsourcing maintenance service providers are vital to guarantee safe operation in airline industry. To reduce the workload and avoid incompatible arrangement schemes in traditional manual arrangement, this article constructs an intelligent system with a novelly designed model and algorithm for membership‐based aircraft parking stand allocation problem. This problem arises from outsourcing maintenance service providers. They need to first serve membership orders, while guaranteeing punctual delivery of other orders. In particular, mutual collision should be strictly avoided between aircrafts. To solve this problem, first, a mathematical model is constructed to optimize timetable and aircraft parking stand allocation scheme. Second, to quickly obtain feasible scheduling scheme, three kinds of mechanisms, including information guidance mechanism, boundary arrangement mechanisms and local optimal adjustment mechanism, are novelly proposed. Moreover, event‐based time–space separated heuristic algorithm is subtly designed based on time–space separation characteristics. In addition, coding schemes are proposed through problem analysis. Finally, three cases with different scales are utilized and six comparison algorithms are selected to illustrate the superiority of our proposed algorithm.

Dynamic community partitioning for e-commerce last mile delivery with time window constraints

Community logistics (CL) is a recently proposed delivery strategy designed to deal with e-commerce last-mile delivery scheduling by dynamically assigning vehicles to designated delivery regions partitioned into “communities”. Since optimizing vehicle routes is not mandatory in the CL spectrum, the delivery solution format and optimization process can be greatly simplified. Nevertheless, abandoning vehicle routes means vehicle arrival time at each customer specified delivery destination is unknown, resulting in the inability of handling time window constraints of e-commerce orders. To expand the application scope of CL, this study introduces community time window, an aggregation of identical or adjacent order time windows. Once the community time window for a delivery community is satisfied, all orders in this community can be received within designated time windows without determining vehicle routes. With this new concept, the application range of the CL is extended to e-commerce last mile delivery contexts where order time window constraints are considered. A dynamic community partitioning problem with the time window is presented based on the Markov decision process (MDP). An efficient heuristic solution framework based on policy function approximation is proposed to solve the MDP model. Numerical results show that the CL is very effective in dealing with the time window constraints of e-commerce orders.

Strong cutting planes for the capacitated multi-pickup and delivery problem with time windows

In this paper, we propose an improved 2-index mixed integer linear programming formulation for capacitated multi-pickup and delivery problems with time windows. We develop new families of valid inequalities, present some simple separation heuristics and solve the benchmark problems using a cutting plane approach. Specifically, we propose and demonstrate the effectiveness of incompatible request sets based valid inequalities, exploiting thereby the time window and capacity constraints. Further, we introduce two sub-families of these inequalities: node-based and request-based incompatible request set cuts, along with their separation heuristics. The computational results show that the incompatible request set cuts are highly useful as cutting planes for the benchmark instances. Overall, there is a marked improvement in solution performance vis-à-vis the different formulations and solution approaches used in extant literature. The approach in this paper is able to solve 12%, 37% and 43% more instances to optimality, improve the root gap by 18%, 33% and 36%, and final gap by 36%, 45% and 43% vis-à-vis the asymmetric representative formulation, three-index and the two-index formulations respectively.

An improved particle swarm optimization with particle refactor operator for perishable food delivery problems by electric vehicles

ABSTRACT The increasing demand for perishable food and the popularity of electric vehicles have promoted the integration research of perishable food delivery services and electric vehicles. Aiming at minimizing the total delivery cost, a new model is formulated for perishable food delivery problems by electric vehicles (PFDP-EV), which considers vehicle capacity constraints, travel time constraints, time window constraints, and so on. An improved particle swarm optimization with particle refactor operator (IPSO-PRO) is developed to solve the proposed model. For the IPSO-PRO, a particle refactor operator is designed to help reconstruct the unqualified particles, and an elite selection strategy and an adaptive weighted strategy are used to improve the performance. Then, extensive efforts are conducted to verify the proposed method. First, the parameters of IPSO-PRO are tuned based on the Taguchi method. Second, small-scale, medium-scale, and large-scale perishable food delivery instances (19 instances) are simulated to evaluate the performance, and the results show that IPSO-PRO achieves the best average gap of 0%. Finally, based on a simulation case, the result and sensitivity analysis are conducted to reveal insightful management insights, which provides decision support for perishable food delivery problems.

Comparing approaches for a multi-level planning problem in a mining industry

This research investigates a global planning problem that integrates production, storage, and transportation decisions. The studied problem occurs in a leading mining industry. The logistics and production system is a capacitated multi-site, multi-level, and multi-product network where products are manufactured, stored, and transported, with time windows constraints, via a single railway to final stocks. To solve this problem, we propose an integrated mathematical model and three heuristic procedures, namely, relax and fix, top-down, and bottom-up. We generate several realistic instances and compare the performance of the heuristics to the exact method. The computational experiments show that the relax and fix heuristic allows good-quality solutions regardless of the considered instance. The top-down and bottom-up approaches reveal some counterintuitive managerial insights related to subsystem trade-offs and interactions. Finally, we demonstrate the high impact of train transport on the performance of the global problem.

Sparser spiking activity can be better: Feature Refine-and-Mask spiking neural network for event-based visual recognition

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μs level temporal resolution, has prominent advantages in many specific visual scenarios and gained much research interest. Spiking neural network (SNN) is naturally suitable for dealing with event streams due to its temporal information processing capability and event-driven nature. However, existing works SNN neglect the fact that the input event streams are spatially sparse and temporally non-uniform, and just treat these variant inputs equally. This situation interferes with the effectiveness and efficiency of existing SNNs. In this paper, we propose the feature Refine-and-Mask SNN (RM-SNN), which has the ability of self-adaption to regulate the spiking response in a data-dependent way. We use the Refine-and-Mask (RM) module to refine all features and mask the unimportant features to optimize the membrane potential of spiking neurons, which in turn drops the spiking activity. Inspired by the fact that not all events in spatio-temporal streams are task-relevant, we execute the RM module in both temporal and channel dimensions. Extensive experiments on seven event-based benchmarks, DVS128 Gesture, DVS128 Gait, CIFAR10-DVS, N-Caltech101, DailyAction-DVS, UCF101-DVS, and HMDB51-DVS demonstrate that under the multi-scale constraints of input time window, RM-SNN can significantly reduce the network average spiking activity rate while improving the task performance. In addition, by visualizing spiking responses, we analyze why sparser spiking activity can be better. Code

Urban On-Demand Delivery via Autonomous Aerial Mobility: Formulation and Exact Algorithm

The implementation of the autonomous unmanned aerial mobility is a game changer for the on-demand delivery service in the crowded urban setting. In this study, the first of its kind commercial unmanned aerial vehicle (UAV) urban delivery program in China is targeted. Different from the traditional ground pickup and delivery services, the aerial mode considers not only the time window constraints, but also the spatial conflicts incurred during the take-off and landing operations of UAVs. To obtain the optimal flying routes of the focused problem, a mixed integer programming model is formulated. Due to its inherent complexity, the optimal schedule cannot be attained within acceptable time via the off-the-shelf solvers. To help speed up the solving process, a branch-and-cut based exact algorithm is proposed, together with a series of customized valid inequalities. To further accelerate, a greedy insertion heuristic is designed to secure high-quality initial solutions. In the numerical section, it is observed that the algorithm proposed in this paper can help solve the real-life on-demand UAV delivery problem to near optimum (within 5% optimality gap) within reasonable computation time (in 5 minutes). <i>Note to Practitioners</i>&#x2014;With the increase of labor cost, the distribution cost increases very rapidly. In the meantime, the employment of automated vehicles for logistics reshapes the landscape of the urban last-mile delivery. As an efficient courier carrier, the unmanned aerial vehicle (UAV) is trending the autonomous delivery endeavour. When integrating UAVs into the urban delivery program, practitioners need to pay special attention to the scheduling of UAVs at the operational level in addition to the hardware of the UAVs. To help solve the UAV dispatch problem, we propose an online scheduling scheme, considering the spatial conflict constraints in the actual UAV operations. And an exact algorithm is designed to accelerate the solving process. Numerical experiments demonstrate that the proposed algorithm can achieve near optimal dispatch plan with 5% optimality gap in 5 minutes. Furthermore, it is discovered that the demand pooling is an essential decision to make for UAV-based delivery. Longer pooling time can increase the UAV efficiency with more realized demand information, but too much pooling could lead to prolonged customer waiting and a low service level.

EV Charging Path Distribution Solution Based on Intelligent Network Connection

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper’s model can save the distribution cost of electric vehicles.

A novel multimodal multi-objective optimization algorithm for multi-robot task allocation

Multi-robot task allocation (MRTA) is widely used in various fields and plays an important role in some complex task environments due to its ability to distribute parallel processing tasks. However, the multi-robot cooperative system is susceptible to actual environments or preferences of decision-makers. Therefore, providing enough solutions/schemes in the MRTA is important. To improve the reliability and feasibility of obtained solution set, an improved multimodal multi-objective differential evolution algorithm hybrid with a simulated annealing algorithm (IMMODE-SA) is proposed to solve MRTA problems in this study. In the proposed IMMODE-SA, a novel population initialization method is used to improve the population quality, and a redundant solution deletion method is employed to delete redundant solutions during the search process. Moreover, a simulated annealing algorithm is utilized to improve the exploitation capability in the last generation of evolutionary process. To verify the performance of the proposed algorithm, extensive simulation experiments are conducted on three MRTA instances. Experimental results show that the proposed algorithm performs better than other competitors on MRTA instances in terms of Hypervolume (HV). Also, the validity of the proposed algorithm is demonstrated via three experiments and experimental analysis results indicate that the IMMODE-SA can provide more equivalent optimal schemes to decision makers. Finally, it is crucial to solve MRTA problems with time window constraints.

Research on the Distribution of Emergency Supplies during the COVID-19 Epidemic

The COVID-19 virus spreads quickly and is highly contagious, and the timeliness of emergency supplies is extremely demanding, so adequate and efficient emergency supplies have become a necessary prerequisite for strict prevention and control during the outbreak. However, the available research results are still relatively few, and using the brain storm optimization algorithm to solve the emergency supplies distribution problem is still blank. In order to optimize the emergency supplies distribution system in the context of COVID-19, this paper studies the open vehicle routing problem by considering the load weight constraints, demand constraints, and time window constraints. With the shortest total distribution distance as the objective function, a mathematical model of mixed integer linear programming is established, and the crossover operation of the genetic algorithm, the destruction operator and the repair operator of the large neighborhood search algorithm are introduced in the brain storm optimization algorithm to enhance the diversity of the solution while making the solution toward the optimal direction. Through the verification of two sets of examples, we found that compared with the other 13 solution algorithms, the solution capability and solution efficiency of the brain storm optimization algorithm are better than the comparison algorithms, which can significantly reduce the length of the distribution route and lower the distribution cost, and has excellent convergence and stability. Meanwhile, it can effectively improve the distribution efficiency of emergency supplies, which is of great significance to help the emergency reception and regular prevention and control during the outbreak of the COVID-19 epidemic. In addition, it has the necessary guidance for the improvement of personnel efficiency in the supply chain and logistics industry.

Multi-agent planning and coordination for automated aircraft ground handling

Inspired by the vision of fully autonomous airside operations at Schiphol airport, this study aims to contribute to the short-term goal of automated aircraft ground handling. In this research, we design and evaluate a multi-agent system for planning of automated ground handling. There are two main components in the system: task allocation optimization and multi-agent path planning. To allocate tasks to ground support equipment (GSE) vehicles, an auction mechanism inspired by temporal sequential single item (TeSSI) auction is proposed. Ground handling tasks scheduling for GSE vehicles is modeled as several single-vehicle pickup and delivery optimization problems (SPDP), and the values of the objective functions are used to generate bids for GSE vehicle agents in the auction. Prioritized safe interval path planning for large agents (LA-SIPP) is used to plan collision-free paths for GSE vehicle agents in the model to execute tasks. The aim is to increase the success rates of allocating tasks and finding collision free paths without causing flight delays, given the limited resources such as a small number of available GSE vehicles, time windows constraints and conflicting interests of different agents. Due to the results, even for the instances with frequent flights and the most limited resources, the success rates of allocation and path planning were higher than 81% and 98%, respectively. Furthermore, periodic task allocation and path planning of the ground handling tasks for flights in three aircraft stands during a planning time window of the day, as well as replanning in case of disruptions were performed in a short CPU time. There is a lack of research dealing with the complete process of ground handling, since existing studies concerning the automation of ground handling operations involve fleet assignment or task scheduling models without an integration of detailed path planning. Our main contribution is to present a framework that combines task allocation and path planning for automation of ground handling operations and provides solutions using a multi-agent perspective.

Cooperative aerial and ground vehicle trajectory planning for post-disaster communications: An attention-based learning approach

The ground infrastructures are highly susceptible to disruption after disasters, which causes the paralysis of communication. In this case, solutions besides original architecture are needed to meet the requirements of communication. Since unmanned aerial vehicle (UAV) can be quickly sent to disaster events to provide temporary connection due to its agility and mobility, it is suitable for performing disaster relief. Nevertheless, the limited onboard energy restricts the UAVs from fulfilling such persistent tasks. To this end, we introduce a ground vehicle carrying backup batteries to handle the energy issue of the UAV. Considering a time-constrained disaster-affected area, we propose a cooperative trajectory planning scheme to provide emergency communications swiftly and timely. The goal of our optimization task is to minimize the total cost of the mission, which consists of the operation cost for mission completion and the penalty cost for latency. We show that the task can be regarded as an extension of traveling salesman problem with soft time window constraints, which is NP-hard in general, and we propose a novel attention-based deep reinforcement learning with a sequential model strategy to learn the policy for the UAV's visiting order, based on which the trajectories of the UAV and ground vehicle are jointly designed. Numerical results show that our proposed attention-based trajectory planning scheme is effective and efficient, providing a guideline for the system design of post-disaster communications.