scispace - formally typeset
Search or ask a question

Showing papers on "Assignment problem published in 2019"


Journal ArticleDOI
TL;DR: The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.
Abstract: In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

421 citations


Journal ArticleDOI
TL;DR: This paper proposes an efficient incentive mechanism based on contract theoretical modeling to minimize the network delay from a contract-matching integration perspective and demonstrates that significant performance improvement can be achieved by the proposed scheme.
Abstract: Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on the base station and reduce the processing delay during the peak time. The computation tasks can be offloaded from the base station to vehicular fog nodes by leveraging the under-utilized computation resources of nearby vehicles. However, the wide-area deployment of VFC still confronts several critical challenges, such as the lack of efficient incentive and task assignment mechanisms. In this paper, we address the above challenges and provide a solution to minimize the network delay from a contract-matching integration perspective. First, we propose an efficient incentive mechanism based on contract theoretical modeling. The contract is tailored for the unique characteristic of each vehicle type to maximize the expected utility of the base station. Next, we transform the task assignment problem into a two-sided matching problem between vehicles and user equipment. The formulated problem is solved by a pricing-based stable matching algorithm, which iteratively carries out the “propose” and “price-rising” procedures to derive a stable matching based on the dynamically updated preference lists. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

263 citations


Journal ArticleDOI
TL;DR: This paper presents a novel approach for the GCI fusion of LMO densities that is both robust to label inconsistencies and computationally efficient and shows how the label matching problem can be formulated as a linear assignment problem of finite length.
Abstract: This paper addresses multi-agent multi-object tracking with labeled random finite sets via Generalized Covariance Intersection (GCI) fusion. While standard GCI fusion of Labeled Multi-Object (LMO) densities is labelwise and hence fully parallelizable, previous work unfortunately revealed that its fusion performance is highly sensitive to the unavoidable label inconsistencies among different agents. In order to overcome the label inconsistency sensitivity problem, we present a novel approach for the GCI fusion of LMO densities that is both robust to label inconsistencies and computationally efficient. The novel approach consists of, first, finding the best matching between labels of different agents by minimization of a suitable label inconsistency indicator, and, then, performing GCI fusion labelwise according to the obtained label matching. Furthermore, it is shown how the label matching problem, which is at the core of the proposed method, can be formulated as a linear assignment problem of finite length (efficiently solvable in polynomial time by the Hungarian algorithm), exactly for Labeled Multi-Bernoulli densities and approximately for arbitrary LMO densities. Simulation experiments are carried out to demonstrate the robustness and effectiveness of the proposed approach in challenging tracking scenarios.

67 citations


Journal ArticleDOI
TL;DR: In this paper, the authors apply optimal transport to the comparison of the graph of the data rather than the data itself, where each channel of the oscillatory data is interpreted as a discrete point cloud and the corresponding misfit function can be computed through the solution of series of linear sum assignment problem (LSAP), while, based on the adjoint state technique, its gradient can be calculated from the assignment solution of these LSAP.
Abstract: Optimal transport distance is an appealing tool to measure the discrepancy between datasets in the frame of inverse problems, for its ability to perform global comparisons and its convexity with respect to shifted patterns in the compared quantities. However, solving inverse problems might require to compare signed quantities, while the optimal transport theory has been developed for the comparison of probability measures. In this study we propose to circumvent this difficulty by applying optimal transport to the comparison of the graph of the data rather than the data itself. We investigate this approach in the frame of seismic imaging, where each channel of the oscillatory data is interpreted as a discrete point cloud. We demonstrate that the corresponding misfit function can be computed through the solution of series of linear sum assignment problem (LSAP), while, based on the adjoint state technique, its gradient can be computed from the assignment solution of these LSAP. We illustrate how this approach yields a convex misfit function in the frame of seismic imaging using the full waveform. We show how an efficient strategy, based on a specific LSAP solver, the auction algorithm, can be designed. We illustrate the interest of the approach on a realistic 2D visco-acoustic seismic imaging problem. The proposed strategy relaxes the constraint on the accuracy of the initial model, outperforming the conventional least-squares approach and a previously proposed optimal transport based approach. The computational time increases by only a few percents compared with the least-squares approach, opening the way to applications to 3D field data in the near future.

63 citations


Journal ArticleDOI
TL;DR: This paper uses worker confidence to represent the reliability of successfully completing the assigned sensing tasks, and formulates two optimization problems, maximum reliability assignment (MRA) under a recruitment budget and minimum cost Assignment (MCA)under a task reliability requirement.
Abstract: The large quantity of mobile devices equipped with various built-in sensors and the easy access to the high-speed wireless networks have made spatial crowdsourcing receive much attention in the research community recently. Generally, the objective of spatial crowdsourcing is to outsource location-based sensing tasks (e.g., traffic monitoring and pollution monitoring) to ordinary mobile workers (e.g., users carrying smartphones) efficiently. In this paper, we study a reliable task assignment problem for spatial crowdsourcing in a large worker market. Specifically, we use worker confidence to represent the reliability of successfully completing the assigned sensing tasks, and we formulate two optimization problems, maximum reliability assignment (MRA) under a recruitment budget and minimum cost assignment (MCA) under a task reliability requirement. We reveal the special structure properties of these problems, based on which we design effective approaches to assign tasks to the most suitable workers. The performances of the proposed algorithms are verified by theoretic analysis and experimental results on both real and synthetic datasets.

61 citations


Journal ArticleDOI
TL;DR: Simulation results show that the proposed relay assignment approaches yield good performances in terms of the global transmission satisfaction and fairness, and it is proved that two proposed algorithms can both achieve the stable matching results.
Abstract: In this paper, we study the distributed relay assignment problem in multi-channel multi-radio unmanned aerial vehicle (UAV) communication networks. Multi-UAVs are driven by the overall task and fly in certain formation, where UAVs with different tasks have various transmission requirements. Source UAVs equipped with multi-radio can select more than one relay radios to achieve high data rate, and each relay radio can be shared by multiple source UAVs. We construct distributed game models to promote the global transmission performance by self-organizing coordination among UAVs. Specifically, the channel competition relationship between relay UAVs is modeled as a congestion game model, while the task-driven relay selection among UAVs is modeled as a many-to-many matching market without substitutability. With the proposed game models, the optimizing of local optimized process will lead to the improvement of global transmission results. After that, we design algorithms for the stable and changeable topology structures, respectively. Based on the given formation shape of UAVs, a learning matching algorithm is proposed to reach the optimum result with a large probability. A fast potential matching algorithm is propose to deal with the topological change of UAV networks. We prove that two proposed algorithms can both achieve the stable matching results. Simulation results show that the proposed relay assignment approaches yield good performances in terms of the global transmission satisfaction and fairness. Particularly, the result of the learning algorithm is close to the global optimum and the fast potential matching approach is robust to the perturbation of UAV networks.

58 citations


Proceedings ArticleDOI
01 Oct 2019
TL;DR: This paper proposes a graph neural network model to transform coordinates of feature points into local features and shows promising results on both synthetic and real datasets demonstrate the effectiveness of the proposed method.
Abstract: The feature matching problem is a fundamental problem in various areas of computer vision including image registration, tracking and motion analysis. Rich local representation is a key part of efficient feature matching methods. However, when the local features are limited to the coordinate of key points, it becomes challenging to extract rich local representations. Traditional approaches use pairwise or higher order handcrafted geometric features to get robust matching; this requires solving NP-hard assignment problems. In this paper, we address this problem by proposing a graph neural network model to transform coordinates of feature points into local features. With our local features, the traditional NP-hard assignment problems are replaced with a simple assignment problem which can be solved efficiently. Promising results on both synthetic and real datasets demonstrate the effectiveness of the proposed method.

57 citations



Proceedings ArticleDOI
Xiaohui Zeng1, Renjie Liao1, Li Gu1, Yuwen Xiong2, Sanja Fidler1, Raquel Urtasun2 
01 Oct 2019
TL;DR: A differentiable matching layer which unrolls a projected gradient descent algorithm in which the projection step exploits the Dykstra's algorithm and it is proved that under mild conditions, the matching is guaranteed to converge to the optimal one.
Abstract: In this paper, we propose the differentiable mask-matching network (DMM-Net) for solving the video object segmentation problem where the initial object masks are provided. Relying on the Mask R-CNN backbone, we extract mask proposals per frame and formulate the matching between object templates and proposals as a linear assignment problem where thA heading inside a blocke cost matrix is predicted by a deep convolutional neural network. We propose a differentiable matching layer which unrolls a projected gradient descent algorithm in which the projection step exploits the Dykstra's algorithm. We prove that under mild conditions, the matching is guaranteed to converge to the optimal one. In practice, it achieves similar performance compared to the Hungarian algorithm during inference. Meanwhile, we can back-propagate through it to learn the cost matrix. After matching, a U-Net style architecture is exploited to refine the matched mask per time step. On DAVIS 2017 dataset, DMM-Net achieves the best performance without online learning on the first frames and the 2nd best with it. Without any fine-tuning, DMM-Net performs comparably to state-of-the-art methods on SegTrack v2 dataset. At last, our differentiable matching layer is very simple to implement; we attach the PyTorch code in the supplementary material which is less than $50$ lines long.

53 citations


Journal ArticleDOI
TL;DR: Results of the study shows that the proposed approach using the mathematical model produce much more favourable results for the warehouse management, and system throughputs and customer satisfaction is increased.

53 citations


Journal ArticleDOI
TL;DR: This paper first formulate an optimization problem and the objective is to minimize the energy consumption of microgrid-enabled MEC networks’ energy supply plan, and shows that the problem is an NP-hard problem.
Abstract: The computational tasks at multiaccess edge computing (MEC) are unpredictable in nature, which raises uneven energy demand for MEC networks. Thus, to handle this problem, microgrid has the potentiality to provides seamless energy supply from its energy sources (i.e., renewable, nonrenewable, and storage). However, supplying energy from the microgrid faces challenges due to the high uncertainty and irregularity of the renewable energy generation over the time horizon. Therefore, in this paper, we study about the microgrid-enabled MEC networks’ energy supply plan, where we first formulate an optimization problem and the objective is to minimize the energy consumption of microgrid-enabled MEC networks. The problem is a mixed integer nonlinear optimization with computational and latency constraints for tasks fulfillment, and also coupled with the dependencies of uncertainty for both energy consumption and generation. Therefore, we show that the problem is an NP-hard problem. As a result, second, we decompose our formulated problem into two subproblems: 1) energy-efficient tasks assignment problem for MEC into community discovery problem and 2) energy supply plan problem into Markov decision process. Third, we apply a low complexity density-based spatial clustering of applications with noise to solve the first subproblem for each base station distributedly. Sequentially, we use the output of the first subproblem as a input for solving the second subproblem, where we apply a model-based deep reinforcement learning. Finally, the simulation results demonstrate the significant performance gain of the proposed model with a high accuracy energy supply plan.

Journal ArticleDOI
TL;DR: This paper proposes an algorithm that provides an assignment profile with utility at least 1∕(1+κ) of the optimal utility, where κ∈[0,1] is a parameter for the curvature of the submodular utility functions.

Journal ArticleDOI
TL;DR: A multi-objective evolutionary algorithm is developed to find the Pareto optimal or near-optimal solutions for the sheet-strip sequencing problem and the results demonstrate the effectiveness of the proposed algorithms for solving the hot-rolling scheduling problem under consideration.

Journal ArticleDOI
01 Mar 2019
TL;DR: The deterministic variant of the dynamic assignment problem is considered and the task of finding the path of maximum cost is investigated.
Abstract: The deterministic variant of the dynamic assignment problem is considered. The task of finding the path of maximum cost is investigated. Examples of solution the dynamic assignment problem and solving the problem of finding the path of maximum cost are given.

Book ChapterDOI
18 Mar 2019
TL;DR: This work uses real world data from a mid-sized German airport as well as simulation based data to extract typical instances small enough to be amenable to the D-Wave machine, and employs bin packing on the passenger numbers to reduce the precision requirements of the extracted instances.
Abstract: Optimal flight gate assignment is a highly relevant optimization problem from airport management. Among others, an important goal is the minimization of the total transit time of the passengers. The corresponding objective function is quadratic in the binary decision variables encoding the flight-to-gate assignment. Hence, it is a quadratic assignment problem being hard to solve in general. In this work we investigate the solvability of this problem with a D-Wave quantum annealer. These machines are optimizers for quadratic unconstrained optimization problems (QUBO). Therefore the flight gate assignment problem seems to be well suited for these machines. We use real world data from a mid-sized German airport as well as simulation based data to extract typical instances small enough to be amenable to the D-Wave machine. In order to mitigate precision problems, we employ bin packing on the passenger numbers to reduce the precision requirements of the extracted instances. We find that, for the instances we investigated, the bin packing has little effect on the solution quality. Hence, we were able to solve small problem instances extracted from real data with the D-Wave 2000Q quantum annealer.

Journal ArticleDOI
TL;DR: It is observed that the number of occupied frequency slots and the average SNR of the network depend on the transmission power, and there is an optimum value for transmission power that maximizes the spectral efficiency.
Abstract: In this paper, we study the impairment-aware manycast routing, modulation level, and spectrum assignment problem in elastic optical networks. We formulate a mixed-integer linear program (MILP) that serves all the given manycast requests at once, referred to as a joint impairment-aware MILP. The formulated MILP can be used to jointly serve diverse traffic types, i.e., manycast, multicast, anycast, and unicast requests. In this formulation, nonlinear interference noise generated in fibers, the noise of optical amplifiers, the limitation of the maximum splitting degree of multicast-capable nodes (MCNs), and the power penalty of splitting the data flow into multiple branches at MCNs are considered. Furthermore, by modifying the joint MILP, two decomposed MILPs and corresponding heuristic algorithms are proposed to find a light-tree and assign modulation level and spectrum to the given requests, sequentially. We simulated the proposed heuristic algorithms and joint MILP (as a benchmark) and compared them with their distance-adaptive (DA) alternatives by considering a small-scale network. Our results reveal that our heuristic algorithms perform close to the joint MILP and have lower spectrum consumption than the DA alternatives. Furthermore, the performance of the heuristic algorithms is evaluated by considering a large-scale network. We observe that the number of occupied frequency slots and the average SNR of the network depend on the transmission power, and there is an optimum value for transmission power that maximizes the spectral efficiency. Moreover, the results show that the impairment-aware schemes outperform their DA alternatives in terms of spectrum consumption.

Journal ArticleDOI
TL;DR: The concept of the demand correlation pattern (DCP) is introduced to describe the correlation among items, based on which a new model is constructed to address the storage location assignment problem (SLAP).

Journal ArticleDOI
TL;DR: Simulation results demonstrate that the UCAV formation can choose the best algorithm according to the real battlefield environment, which can solve the cooperative task assignment and path planning problems quickly and effectively to meet the demand of the cooperative combat.
Abstract: Multi-model techniques have shown an outstanding effectiveness in the cooperative task assignment and path planning of the unmanned combat aerial vehicle(UCAV) formation. With cooperative decision making and control, the cooperative combat of the UCAV formation are described and the mathematical model of the UCAV formation is built. Then, the task assignment model of the UCAV formation is developed according to flight characteristics of the UCAV formation and constraints in battlefield. The cooperative task assignment problem is solved using the improved particle swarm optimization(IPSO), ant colony algorithm(ACA) and genetic algorithm(GA) respectively. The comparative analysis is conducted in the aspects of the precision and the search speed. The path planning model of the UCAV formation is constructed considering the oil cost, threat cost, crash cost and time cost. The cooperative path planning problem is solved based on the evolution algorithm(EA), in which unique coding scheme of chromosomes is designed, and the crossover operator and mutation operator are redefined. Simulation results demonstrate that the UCAV formation can choose the best algorithm according to the real battlefield environment, which can solve the cooperative task assignment and path planning problems quickly and effectively to meet the demand of the cooperative combat.

Journal ArticleDOI
Tao Hu, Peng Yi, Zehua Guo1, Julong Lan, Yuxiang Hu 
TL;DR: This paper proposes a dynamic slave controller assignment that prevents the network crash by planningslave controller assignment ahead of the controller failures, and can decrease the worst case latency under controller failures by 35.1% averagely, and reduce the probability of network crash.

Journal ArticleDOI
12 Feb 2019-Sensors
TL;DR: The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.
Abstract: This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.

Journal ArticleDOI
TL;DR: The numerical results show that the parabola function, which uses the information about the solution quality, outperforms all other proposed heuristics and is very efficient and are not only useful for reducing the infield operations costs of small growers, but also for efficient management of the inbound logistics equipment and machinery of the sugarcane supply system.

Journal ArticleDOI
TL;DR: This paper addresses the joint selective maintenance and repairperson assignment problem (JSM–RAP) for complex multicomponent systems more complex than the series-parallel systems commonly used in previous SM models and proposes two nonlinear formulations and their corresponding binary integer programming models.
Abstract: This paper addresses the joint selective maintenance and repairperson assignment problem (JSM–RAP) for complex multicomponent systems. The systems perform consecutive missions separated by scheduled finite duration breaks and are imperfectly maintained during the breaks. Current selective maintenance (SM) models usually assume that only one repair channel is available or that the repairperson assignment optimisation can be done at a subsequent stage. Using a generalised reliability function for k-out-of-n systems, we formulate the JSM–RAP for multicomponent systems more complex than the series-parallel systems commonly used in previous SM models. Two nonlinear formulations and their corresponding binary integer programming models are then proposed and optimally solved. Numerical experiments show the added value of the proposed approach and highlight the benefit of jointly carrying out the selection of the components to be maintained, the maintenance level to be performed and the assignment of the maintenance tasks to repairpersons. It is also shown that the flexibility provided by mixed skill cohorts of repairpersons over uniform cohorts can yield higher performance levels when the skillsets are significantly different.

Journal ArticleDOI
TL;DR: This article simulates the Quantum Approximate Optimization Algorithm applied to instances of this problem derived from real world data and finds that repeated runs of the QAOA identify the feasible solution with close to unit probability for all instances.
Abstract: Airlines today are faced with a number of large scale scheduling problems. One such problem is the tail assignment problem, which is the task of assigning individual aircraft to a given set of flights, minimizing the overall cost. Each aircraft is identified by the registration number on its tail fin. In this article, we simulate the Quantum Approximate Optimization Algorithm (QAOA) applied to instances of this problem derived from real world data. The QAOA is a variational hybrid quantum-classical algorithm recently introduced and likely to run on near-term quantum devices. The instances are reduced to fit on quantum devices with 8, 15 and 25 qubits. The reduction procedure leaves only one feasible solution per instance, which allows us to map the tail assignment problem onto the Exact Cover problem. We find that repeated runs of the QAOA identify the feasible solution with close to unit probability for all instances. Furthermore, we observe patterns in the variational parameters such that an interpolation strategy can be employed which significantly simplifies the classical optimization part of the QAOA. Finally, we empirically find a relation between the connectivity of the problem graph and the single-shot success probability of the algorithm.

Journal ArticleDOI
TL;DR: This work considers a flexible job shop scheduling problem with sequence-dependent setup times that incorporates heterogeneous machine operator qualifications by taking account of machine- and operator-dependent processing times, and presents exact and heuristic decomposition-based solution approaches.
Abstract: We consider a flexible job shop scheduling problem with sequence-dependent setup times that incorporates heterogeneous machine operator qualifications by taking account of machine- and operator-dependent processing times. We analyze two objective functions, minimizing the makespan and minimizing the total tardiness, and present exact and heuristic decomposition-based solution approaches. These approaches divide the scheduling problem into a vehicle routing problem with precedence constraints and an operator assignment problem, and connect these problems via logic inequalities. We assess the quality of our solution methods in an extensive computational study that is based on randomly generated as well as real-world problem instances.

Journal ArticleDOI
TL;DR: This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time.
Abstract: This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP-hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal.

Journal ArticleDOI
TL;DR: The software-based approach for solving intuitionistic fuzzy solid assignment problem (IFSAP) is presented, and social issue (real-life problem) is converted into a mathematical model and it is solved by the proposed method.
Abstract: This paper sustains a sound mathematical and computing background. In this paper, the software-based approach for solving intuitionistic fuzzy solid assignment problem (IFSAP) is presented. The IFSAP is formulated and it is solved by using Lingo 17.0 software tool. Theorems related to IFSAP is proved. The IFSAP and its crisp solid assignment problem both are solved at a time and their optimal solution is obtained. In addition, the optimal objective values of both the IFSAP and its crisp solid assignment problem (SAP) are estimated with the help of substituting the optimal solution(s) to their respective decision variables in the objective functions. Some new and important results are proposed. To illustrate the efficiency of the proposed method the numerical example is presented. The reliability of the proposed results are verified by using the numerical example. Strengths and weakness of the paper is mentioned. The novelty of the analysis is given into a coherent, concise, and meaningful manner of analysis. Social issue (real-life problem) is converted into a mathematical model and it is solved by the proposed method. At the end, the advantages of the proposed algorithm is explained.

Posted Content
TL;DR: This work provides a general ADP method that can learn from the ILP based assignment found in ride-pooling and handles the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and showing a connection to Deep Reinforcement Learning that allows it to learn this value-function with increased stability and sample-efficiency.
Abstract: On-demand ride-pooling (e.g., UberPool) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies. Unlike in Taxi on Demand (ToD) services -- where a vehicle is only assigned one passenger at a time -- in on-demand ride-pooling, each (possibly partially filled) vehicle can be assigned a group of passenger requests with multiple different origin and destination pairs. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current time step without considering its effect on future assignments. This is because even a myopic assignment in ride-pooling involves considering what combinations of passenger requests that can be assigned to vehicles, which adds a layer of combinatorial complexity to the ToD problem. A popular approach that addresses the limitations of myopic assignments in ToD problems is Approximate Dynamic Programming (ADP). Existing ADP methods for ToD can only handle Linear Program (LP) based assignments, however, while the assignment problem in ride-pooling requires an Integer Linear Program (ILP) with bad LP relaxations. To this end, our key technical contribution is in providing a general ADP method that can learn from ILP-based assignments. Additionally, we handle the extra combinatorial complexity from combinations of passenger requests by using a Neural Network based approximate value function and show a connection to Deep Reinforcement Learning that allows us to learn this value-function with increased stability and sample-efficiency. We show that our approach outperforms past approaches on a real-world dataset by up to 16%, a significant improvement in city-scale transportation problems.

Journal ArticleDOI
TL;DR: A privacy-preserving reverse auction based assignment model which consists of a reverse auction task assignment algorithm, which is a truthful incentive mechanism, to encourage workers to offer authentic data and theoretically show that the proposed model is secure against semi-honest adversaries.
Abstract: The ubiquity of mobile device and wireless networks flourishes the market of spatial crowdsourcing, in which location constrained tasks are sent to workers and expected to be performed in some designated locations. To obtain a global optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this process, there is a significant security concern, that is, the platform may not be trustworthy, so it brings about a threat to workers location privacy. In this paper, to tackle the privacy-preserving task assignment problem, we propose a privacy-preserving reverse auction based assignment model which consists of two key parts. In the first part, we generalize private location to travel cost and protect it by an anonymity based data aggregation protocol. In the second part, we propose a reverse auction task assignment algorithm, which is a truthful incentive mechanism, to encourage workers to offer authentic data. We theoretically show that the proposed model is secure against semi-honest adversaries. Experimental results show that our model is efficient and can scale to real SC applications.

Journal ArticleDOI
TL;DR: Two novel heuristics based on Probabilistic Tabu Search utilizing a new neighborhood structure applicable both to CDAP and related problems are developed and outperform recent state-of-the-art approaches.

Journal ArticleDOI
TL;DR: A bilevel mathematical optimization model is formulated to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow and crossing elimination at intersections.
Abstract: Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this article, a bilevel mathematical optimization model is formulated to address this issue, and our research objective is to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow (also referred to as lane reversal) and crossing elimination at intersections. Mathematical models are developed to represent the two reconfiguration schemes and characterize the interactions between traffic operators and passengers. Specifically, traffic operators act as leaders to determine the optimal system reconfiguration to minimize the total travel time for all the users (both evacuees and regular commuters), while passengers act as followers by freely choosing the path with the minimum travel time, which eventually converges to a user equilibrium state. For each given network reconfiguration, the lower-level problem is formulated as a traffic assignment problem (TAP) where each user tries to minimize his/her own travel time. To tackle the lower-level optimization problem, a gradient projection method is leveraged to shift the flow from other nonshortest paths to the shortest path between each origin-destination pair, eventually converging to the user equilibrium traffic assignment. The upper-level problem is formulated as a constrained discrete optimization problem, and a probabilistic solution discovery algorithm is used to obtain the near-optimal solution. Two numerical examples are used to demonstrate the effectiveness of the proposed method in restoring the traffic system performance.