Showing papers on "Assignment problem published in 2021"

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

Abstract: This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

Explicit Evolutionary Multitasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem

Abstract: Recently, evolutionary multitasking (EMT) has been proposed in the field of evolutionary computation as a new search paradigm, for solving multiple optimization tasks simultaneously. By sharing useful traits found along the evolutionary search process across different optimization tasks, the optimization performance on each task could be enhanced. The autoencoding-based EMT is a recently proposed EMT algorithm. In contrast to most existing EMT algorithms, which conduct knowledge transfer across tasks implicitly via crossover, it intends to perform knowledge transfer explicitly among tasks in the form of task solutions, which enables the employment of task-specific search mechanisms for different optimization tasks in EMT. However, the autoencoding-based explicit EMT can only work on continuous optimization problems. It will fail on combinatorial optimization problems, which widely exist in real-world applications, such as scheduling problem, routing problem, and assignment problem. To the best of our knowledge, there is no existing effort working on explicit EMT for combinatorial optimization problems. Taking this cue, in this article, we thus embark on a study toward explicit EMT for combinatorial optimization. In particular, by using vehicle routing as an illustrative combinatorial optimization problem, the proposed explicit EMT algorithm (EEMTA) mainly contains a weighted $l_{1}$ -norm-regularized learning process for capturing the transfer mapping, and a solution-based knowledge transfer process across vehicle routing problems (VRPs). To evaluate the efficacy of the proposed EEMTA, comprehensive empirical studies have been conducted with the commonly used vehicle routing benchmarks in multitasking environment, against both the state-of-the-art EMT algorithm and the traditional single-task evolutionary solvers. Finally, a real-world combinatorial optimization application, that is, the package delivery problem (PDP), is also presented to further confirm the efficacy of the proposed algorithm.

Hierarchical Multiobjective Heuristic for PCB Assembly Optimization in a Beam-Head Surface Mounter.

Abstract: This article proposes a hierarchical multiobjective heuristic (HMOH) to optimize printed-circuit board assembly (PCBA) in a single beam-head surface mounter. The beam-head surface mounter is the core facility in a high-mix and low-volume PCBA line. However, as a large-scale, complex, and multiobjective combinatorial optimization problem, the PCBA optimization of the beam-head surface mounter is still a challenge. This article provides a framework for optimizing all the interrelated objectives, which has not been achieved in the existing studies. A novel decomposition strategy is applied. This helps to closely model the real-world problem as the head task assignment problem (HTAP) and the pickup-and-place sequencing problem (PAPSP). These two models consider all the factors affecting the assembly time, including the number of pickup-and-place (PAP) cycles, nozzle changes, simultaneous pickups, and the PAP distances. Specifically, HTAP consists of the nozzle assignment and component allocation, while PAPSP comprises place allocation, feeder set assignment, and place sequencing problems. Adhering strictly to the lexicographic method, the HMOH solves these subproblems in a descending order of importance of their involved objectives. Exploiting the expert knowledge, each subproblem is solved by an elaborately designed heuristic. Finally, the proposed HMOH realizes the complete and optimal PCBA decision making in real time. Using industrial PCB datasets, the superiority of HMOH is elucidated through comparison with the built-in optimizer of the widely used Samsung SM482.

Optimal User-Edge Assignment in Hierarchical Federated Learning based on Statistical Properties and Network Topology Constraints

Abstract: Learning-based applications have demonstrated practical use cases in ubiquitous environments and amplified interest in exploiting the data stored on users' mobile devices. Distributed optimization algorithms aim to leverage such distributed and diverse data to learn a global phenomena by performing training amongst participating devices and repeatedly aggregating their local models' parameters into a global model. Federated Averaging is a promising solution that allows for extending local training before aggregating the parameters, offering better communication efficiency. However, in the cases where the participants' data are strongly skewed (i.e., local distributions are different), the model accuracy can significantly drop. To face this challenge, we leverage the edge computing paradigm to design a hierarchical learning system that performs Federated Gradient Descent on the user-edge layer and Federated Averaging on the edge-cloud layer. In this hierarchical architecture, the users might be assigned to different edges, leading to different edge-level data distributions. We formalize and optimize this user-edge assignment problem to minimize classes' distribution distance between edge nodes, which enhances the Federated Averaging performance. Our experiments on multiple real datasets show that the proposed optimized assignment is tractable and leads to faster convergence of models towards a better accuracy value.

Dynamic Type Matching

Abstract: Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of...

Placement of Public Fast-Charging Station and Solar Distributed Generation with Battery Energy Storage in Distribution Network Considering Uncertainties and Traffic Congestion

Abstract: In this paper, a sustainable solution for the allocation of Public Fast-Charging Stations (PFCSs) and Solar Distributed Generations (SDGs) along with Battery Energy Storages (BESs) and its scheduling is proposed. The solution is obtained with minimization of Energy loss, voltage deviation index, and investment as well as operation maintenance costs of PFCS, SDG, BES, considering battery degradation. Moreover, the associate relevant factors such as; number of charging ports, capacities of the PFCS and EV flow captured by the PFCS are evaluated. Two-stage optimization is employed to getting the solutions. The first stage of optimization deals with PFCS's location, SDG's locations with sizes and BES scheduling. On the other hand, second stage looks after the assignment of EVs to the apt PFCSs considering the shortest distances with traffic congestions in view of reducing energy consumption of the EVs. As a test case, a 33 node radial distribution network is chosen with the corresponding traffic network. The allocation problem is solved by using Harris Hawks Optimization (HHO) and Grey Wolf Optimizer (GWO). Four other established optimization techniques are used to authenticate the solutions. The EV assignment problem is tackled by Integer Linear Programming (ILP). 2m Point Estimation Method (2m PEM) is applied to deal with the uncertainties associated with EVs, traffic flow and SDG.

Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints

Abstract: Cooperative multiple task assignment problem is an essential issue in the collaboration of multiple unmanned aerial vehicles (UAVs). Consensus-based bundle algorithm (CBBA) is a decentralized task assignment method that only considers homogeneous agents and independent tasks. Thus, we develop an extended CBBA with task coupling constraints (CBBA-TCC) in this paper to solve the multi-task assignment problem with task coupling constraints in the heterogeneous multi-UAV system. CBBA is a two-stage iteration algorithm with inner and outer consensus stages. The inner consensus stage is designed as a modified version of CBBA in this paper. A Can-do list is firstly raised at the beginning of bundle construction phase on each agent to record the tasks that can be performed by this agent without violating the task precedence constraints. Hence, at the inner consensus stage, each agent will only bid on the Can-do list. Then, we adopt a task performing time list for each agent to store the performing times of its assigned tasks. With associate consensus strategy of task performing time list at the conflict resolution phase, the precedence constraint of coupled tasks can be guaranteed. After reaching inner consensus, the outer consensus stage introduces an insert-position feasibility index to determine whether the assigned tasks satisfy the coupling constraints and resolve the constraint violation conflicts. Through the iterations of inner and outer consensus stages, CBBA will reach global consensus and obtain conflict-free task assignment results. Numerical simulations demonstrate the feasibility and reliability of CBBA in various search and rescue scenarios.

Dynamic Controller Assignment in Software Defined Internet of Vehicles Through Multi-Agent Deep Reinforcement Learning

Abstract: In this article, we introduce a novel dynamic controller assignment algorithm targeting connected vehicle services and applications, also known as Internet of Vehicles (IoV). The proposed approach considers a hierarchically distributed control plane, decoupled from the data plane, and uses vehicle location and control traffic load to perform controller assignment dynamically. We model the dynamic controller assignment problem as a multi-agent Markov game and solve it with cooperative multi-agent deep reinforcement learning. Simulation results using real-world vehicle mobility traces show that the proposed approach outperforms existing ones by reducing control delay as well as packet loss.

Quality Inference Based Task Assignment in Mobile Crowdsensing

Abstract: With the increase of mobile devices, Mobile Crowdsensing (MCS) has become an efficient way to ubiquitously sense and collect environment data. Comparing to traditional sensor networks, MCS has a vital advantage that workers play an active role in collecting and sensing data. However, due to the openness of MCS, workers and sensors are of different qualities. Low quality sensors and workers may yield noisy data or even inaccurate data. Which gives the importance of inferring the quality of workers and sensors and seeking a valid task assignment with enough total qualities for MCS. To solve the problem, we adopt truth inference methods to iteratively infer the truth and qualities. Based on the quality inference, this paper proposes a task assignment problem called quality-bounded task assignment with redundancy constraint (QTAR). Different from traditional task assignment problem, redundancy constraint is added to satisfy the preliminaries of truth inference, which requires that each task should be assigned a certain or more amount of workers. We prove that QTAR is NP-complete and propose a $ (2+\epsilon)$ ( 2 + e ) - approximation algorithm for QTAR, called QTA. Finally, experiments are conducted on both synthesis data and real dataset. The results of the experiments prove the efficiency and effectiveness of our algorithms.

An almost robust optimization model for integrated berth allocation and quay crane assignment problem

Abstract: The integrated berth allocation and quay crane assignment problem is an important issue for the operations management in container terminals. This issue primarily considers the assignment of berthing time, position, and the number of quay cranes in each time segment to ships that must be discharged and loaded at terminals. This study examines such a problem by considering uncertainties in the late arrival of ships and inflation of container quantity. Based on historical data, we first divide the uncertainty set into K non-overlapping full-dimensional clusters via K -means clustering, and the weight of each cluster is calculated. Then, we formulate an almost robust model by introducing the weighted max penalty function with the objective of minimizing the total cost, which is caused by the deviations from the expected berthing location and departure time. The concept of robustness index is introduced to investigate the trade-off between the changes in the objective value and the penalty violation. A decomposition method, which contains a deterministic master problem and a stochastic subproblem, is proposed to solve the problem. In each iteration, the subproblem checks the master problem under different realizations, adds scenarios, and cuts into the master problem if needed. Numerical experiments demonstrate that (i) the proposed method can solve the model efficiently, (ii) the robustness index shows that a significant improvement in objective can be achieved at the expense of a small amount of penalty, (iii) the proposed model can handle uncertainties better than the deterministic, fully robust, and worst-case models in terms of total expected cost, total vessel delays, and utilization rates of the berth and quay crane, and (iv) the proposed method becomes more attractive compared with the first come first served approach as the congestion situation or uncertainty degree increase.

Auction-Based Multichannel Cooperative Spectrum Sharing in Hybrid Satellite-Terrestrial IoT Networks

Abstract: In this article, we investigate the multichannel cooperative spectrum sharing in hybrid satellite-terrestrial Internet of Things (IoT) networks with the auction mechanism, which is designed to reduce the operational expenditure of the satellite-based IoT (S-IoT) network while alleviating the spectrum scarcity issues of terrestrial-based IoT (T-IoT) network. The cluster heads of selected T-IoT networks assist the primary satellite users transmission through cooperative relaying techniques in exchange for spectrum access. We propose an auction-based optimization problem to maximize the sum transmission rate of all primary S-IoT receivers with the appropriate secondary network selection and corresponding radio resource allocation profile by the distributed implementation while meeting the minimum transmission rate of secondary receivers of each T-IoT network. Specifically, the one-shot Vickrey–Clarke–Groves (VCG) auction is introduced to obtain the maximum social welfare, where the winner determination problem is transformed into an assignment problem and solved by the Hungarian algorithm. To further reduce the primary satellite network decision complexity, the sequential Vickrey auction is implemented by sequential fashion until all channels are auctioned. Due to incentive compatibility with those two auction mechanisms, the secondary T-IoT cluster yields the true bids of each channel, where both the nonorthogonal multiple access (NOMA) and time division multiple access (TDMA) schemes are implemented in cooperative communication. Finally, simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection. Moreover, the influence of key factors on the performance of the proposed scheme is analyzed in detail.

Integrated proactive and reactive strategies for sustainable berth allocation and quay crane assignment under uncertainty

Abstract: The berth allocation and quay crane assignment problem (BACAP) is a complex port operation planning problem susceptible to uncertainties, such as vessel arrival time fluctuation to its estimated time of arrival and maritime markets. For promoting reliability and sustainability of container terminals, this paper addresses the optimization of BACAP under the uncertain vessels’ arrival times and fluctuation of loading and unloading volumes. We propose a proactive BACAP strategy considering minimum recovery cost under uncertainty using a reactive strategy. A stochastic programming model is formulated to minimize the basic cost in the baseline schedule, and the recovery cost in real uncertain scenarios. A two-stage meta-heuristic framework based on GA is developed for solving this problem. Numerical experiments and scenario analysis are conducted to validate the effectiveness of the proposed model and the proposed solution approaches.

Coordinated formation control for intelligent and connected vehicles in multiple traffic scenarios

Abstract: In this paper, a unified multi-vehicle formation control framework for Intelligent and Connected Vehicles (ICVs) that can apply to multiple traffic scenarios is proposed. In the one-dimensional scenario, different formation geometries are analyzed and the interlaced structure is mathematically modelized to improve driving safety while making full use of the lane capacity. The assignment problem for vehicles and target positions is solved using Hungarian Algorithm to improve the flexibility of the method in multiple scenarios. In the two-dimensional scenario, an improved virtual platoon method is proposed to transfer the complex two-dimensional passing problem to the one-dimensional formation control problem based on the idea of rotation projection. Besides, the vehicle regrouping method is proposed to connect the two scenarios. Simulation results prove that the proposed multi-vehicle formation control framework can apply to multiple typical scenarios and have better performance than existing methods.

Exact and heuristic solution approaches for the airport gate assignment problem

Abstract: In this study, we consider an airport gate assignment problem that assigns a set of aircraft to a set of gates The aircraft that cannot be assigned to any gate are directed to an apron We aim to make aircraft-gate assignments so as to minimize the number of aircraft assigned to apron and among the apron usage minimizing solutions, we aim to minimize total walking distance travelled by all passengers The problem is formulated as a mixed-integer nonlinear programming model and then it is linearized A branch and bound algorithm, beam search and filtered beam search algorithms that employ powerful lower and upper bounding mechanisms are developed The results of the computational experiment have shown the satisfactory performance of the algorithms

Distributed Task Assignment for Multiple Robots Under Limited Communication Range

Abstract: This article investigates the task assignment problem in which multiple dispersed robots need to visit a set of target locations while trying to minimize the robots' total travel distance. Each robot initially has the position information of all the targets and of those robots that are within its limited communication range, and each target demands a robot with some specified capability to visit it. We propose a decentralized auction algorithm which first employs an information consensus procedure to merge the local information carried by each communication-connected (CC) robot subnetwork. Then, we apply a marginal-cost-based strategy to construct conflict-free target assignments for the CC robots. When the communication network of the robots is not connected, we demonstrate that the robots' total travel distance might in fact increase when their communication range grows, and more importantly, such a somewhat counterintuitive fact holds for a range of algorithms. Furthermore, the proposed algorithm guarantees that the total travel distance of the robots is at most twice of the optimal when the communication network is initially connected. Finally, Monte Carlo simulation results demonstrate the satisfying performance of the proposed algorithm.

A parametric approach of partial eigenstructure assignment for high-order linear systems via proportional plus derivative state feedback

Abstract: In this paper, a partial eigenstructure assignment problem for the high-order linear time-invariant (LTI) systems via proportional plus derivative (PD) state feedback is considered. By partitioning the open-loop system into two parts (the altered part and the unchanged part) and utilizing the solutions to the high-order generalized Sylvester equation (HGSE), complete parametric expressions of the feedback gain matrices of the closed-loop system are established. Meanwhile, a group of arbitrary parameters representing the degrees of freedom of the proposed method is provided and optimized to satisfy the stability of the system and robustness criteria. Finally, a numerical example and a three-axis dynamic flight motion simulator system example with the simulation results are offered to illustrate the effectiveness and superiority of the proposed method.

A Cooperative Dynamic Task Assignment Framework for COTSBot AUVs

Abstract: This paper presents a cooperative dynamic task assignment framework for a certain class of Autonomous Underwater Vehicles (AUVs) employed to control outbreak of Crown-Of-Thorns Starfish (COTS) in Australia's Great Barrier Reef. The problem of monitoring and controlling the COTS is transcribed into a constrained task assignment problem in which eradicating clusters of COTS, by the injection system of COTSbot AUVs, is considered as a task. A probabilistic map of the operating environment including seabed terrain, clusters of COTS, and coastlines is constructed. Then, a novel heuristic algorithm called Heuristic Fleet Cooperation (HFC) is developed to provide a cooperative injection of the COTSbot AUVs to the maximum possible COTS in an assigned mission time. Extensive simulation studies together with quantitative performance analysis are conducted to demonstrate the effectiveness and robustness of the proposed cooperative task assignment algorithm in eradicating the COTS in the Great Barrier Reef.

Zonal-based flexible bus service under elastic stochastic demand

Abstract: This study investigates flexible bus that provides door-to-door service with multiple passengers sharing the vehicle, which reduces congestion on the urban network. The service area is divided into zones, and flexible buses are assigned to zonal routes based on the historical demand characteristics before demand realization. Passengers are served by either regular service or ad hoc service after the demand realization to minimize the sum of ad hoc service cost and detour time cost. The elastic and stochastic natures of demand are captured in the formulation by the volume stochasticity of demand, detour time stochasticity, and elasticity of demand with respect to the flexible bus service price and quality. The profit of the flexible bus service is maximized while accounting for the detour time cost. To effectively solve the problem, volume and detour time reliability measures are introduced to separate the problem into a vehicle-to-route assignment problem and a passenger-to-vehicle assignment problem. A gradient-based solution approach is devised to determine the flexible bus routing plans by optimizing the associated reliability measures. The solution approach is further improved by combining the gradient-based approach with a greedy search solution approach and relaxing the formulations. The formulation and solution approaches are implemented using real data in Chengdu, China, with promising results.

BASE Layers: Simplifying Training of Large, Sparse Models

Abstract: We introduce a new balanced assignment of experts (BASE) layer for large language models that greatly simplifies existing high capacity sparse layers. Sparse layers can dramatically improve the efficiency of training and inference by routing each token to specialized expert modules that contain only a small fraction of the model parameters. However, it can be difficult to learn balanced routing functions that make full use of the available experts; existing approaches typically use routing heuristics or auxiliary expert-balancing loss functions. In contrast, we formulate token-to-expert allocation as a linear assignment problem, allowing an optimal assignment in which each expert receives an equal number of tokens. This optimal assignment scheme improves efficiency by guaranteeing balanced compute loads, and also simplifies training by not requiring any new hyperparameters or auxiliary losses. Code is publicly released at this https URL

Multiple Task Assignment and Path Planning of a Multiple Unmanned Surface Vehicles System Based on Improved Self-Organizing Mapping and Improved Genetic Algorithm

Abstract: This paper addresses multiple task assignment and path-planning problems for a multiple unmanned surface vehicle (USVs) system. Since it is difficult to solve multi-task allocation and path planning together, we divide them into two sub-problems, multiple task allocation and path planning, and study them separately. First, to resolve the multi-task assignment problem, an improved self-organizing mapping (ISOM) is proposed. The method can allocate all tasks in the mission area, and obtain the set of task nodes that each USV needs to access. Second, aiming at the path planning of the USV accessing the task nodes, an improved genetic algorithm (IGA) with the shortest path is proposed. To avoid USV collision during navigation, an artificial potential field function (APFF) is proposed. A multiple USV system with multi-task allocation and path planning is simulated. Simulation results verify the effectiveness of the proposed algorithms.

A Bi-Objective Constrained Robust Gate Assignment Problem: Formulation, Instances and Algorithm

Abstract: The gate assignment problem (GAP) aims at assigning gates to aircraft considering operational efficiency of airport and satisfaction of passengers. Unlike the existing works, we model the GAP as a bi-objective constrained optimization problem. The total walking distance of passengers and the total robust cost of the gate assignment are the two objectives to be optimized, while satisfying the constraints regarding the limited number of flights assigned to apron, as well as three types of compatibility. A set of real instances is then constructed based on the data obtained from the Baiyun airport (CAN) in Guangzhou, China. A two-phase large neighborhood search (2PLNS) is proposed, which accommodates a greedy and stochastic strategy (GSS) for the large neighborhood search; both to speed up its convergence and to avoid local optima. The empirical analysis and results on both the synthetic instances and the constructed real-world instances show a better performance for the proposed 2PLNS as compared to many state-of-the-art algorithms in literature. An efficient way of choosing the tradeoff from a large number of nondominated solutions is also discussed in this article.

Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Abstract: The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

Abstract: Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Power efficiency in cloud data centers (DCs) has become an important topic in recent years as more and larger DCs have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of DCs. Virtual machine (VM) assignment is the key in server consolidation. In the past few years, many methods to VM assignment have been proposed, but existing VM assignment approaches to the VM assignment problem consider the energy consumption by physical machines (PM). In current paper a new approach is proposed that using a combination of the sine cosine algorithm (SCA) and ant lion optimizer (ALO) as discrete multi-objective and chaotic functions for optimal VM assignment. First objective of our proposed model is minimizing the power consumption in cloud DCs by balancing the number of active PMs. Second objective is reducing the resources wastage by using optimal VM assignment on PMs in cloud DCs. Reducing SLA levels was another purpose of this research. By using the method, the number of increase of migration of VMs to PMs is prevented. In this paper, several performance metrics such as resource wastage, power consumption, overall memory utilization, overall CPU utilization, overall storage space, and overall bandwidth, a number of active PMs, a number of shutdowns, a number of migrations, and SLA are used. Ultimately, the results obtained from the proposed algorithm were compared with those of the algorithms used in this regard, including First Fit (FF), VMPACS and MGGA.