Showing papers on "Assignment problem published in 2017"

Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment

Abstract: Display Omitted An improved adaptive PSO based on Alpha-stable distribution and dynamic fractional calculus is studied.A new multi-objective optimization model of gate assignment problem is proposed.The actual data are used to demonstrate the effectiveness of the proposed method. Gate is a key resource in the airport, which can realize rapid and safe docking, ensure the effective connection between flights and improve the capacity and service efficiency of airport. The minimum walking distances of passengers, the minimum idle time variance of each gate, the minimum number of flights at parking apron and the most reasonable utilization of large gates are selected as the optimization objectives, then an efficient multi-objective optimization model of gate assignment problem is proposed in this paper. Then an improved adaptive particle swarm optimization(DOADAPO) algorithm based on making full use of the advantages of Alpha-stable distribution and dynamic fractional calculus is deeply studied. The dynamic fractional calculus with memory characteristic is used to reflect the trajectory information of particle updating in order to improve the convergence speed. The Alpha-stable distribution theory is used to replace the uniform distribution in order to escape from the local minima in a certain probability and improve the global search ability. Next, the DOADAPO algorithm is used to solve the constructed multi-objective optimization model of gate assignment in order to fast and effectively assign the gates to different flights in different time. Finally, the actual flight data in one domestic airport is used to verify the effectiveness of the proposed method. The experiment results show that the DOADAPO algorithm can improve the convergence speed and enhance the local search ability and global search ability, and the multi-objective optimization model of gate assignment can improve the comprehensive service of gate assignment. It can effectively provide a valuable reference for assigning the gates in hub airport.

Pareto-Optimization for Scheduling of Crude Oil Operations in Refinery via Genetic Algorithm

Abstract: With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.

Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

Abstract: In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve energy-efficient collaborative task executions at network-edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.

An Efficient Online Algorithm for Dynamic SDN Controller Assignment in Data Center Networks

Abstract: Software defined networking is increasingly prevalent in data center networks for it enables centralized network configuration and management. However, since switches are statically assigned to controllers and controllers are statically provisioned, traffic dynamics may cause long response time and incur high maintenance cost. To address these issues, we formulate the dynamic controller assignment problem (DCAP) as an online optimization to minimize the total cost caused by response time and maintenance on the cluster of controllers. By applying the randomized fixed horizon control framework, we decompose DCAP into a series of stable matching problems with transfers, guaranteeing a small loss in competitive ratio. Since the matching problem is NP-hard, we propose a hierarchical two-phase algorithm that integrates key concepts from both matching theory and coalitional games to solve it efficiently. Theoretical analysis proves that our algorithm converges to a near-optimal Nash stable solution within tens of iterations. Extensive simulations show that our online approach reduces total cost by about 46%, and achieves better load balancing among controllers compared with static assignment.

A Distributed Version of the Hungarian Method for Multirobot Assignment

Abstract: In this paper, we propose a distributed version of the Hungarian method to solve the well-known assignment problem. In the context of multirobot applications, all robots cooperatively compute a common assignment that optimizes a given global criterion (e.g., the total distance traveled) within a finite set of local computations and communications over a peer-to-peer network. As a motivating application, we consider a class of multirobot routing problems with “spatiotemporal” constraints, i.e., spatial targets that require servicing at particular time instants. As a means of demonstrating the theory developed in this paper, the robots cooperatively find online suboptimal routes by applying an iterative version of the proposed algorithm in a distributed and dynamic setting. As a concrete experimental test bed, we provide an interactive “multirobot orchestral” framework, in which a team of robots cooperatively plays a piece of music on a so-called orchestral floor.

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment.

Abstract: Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

Trichromatic Online Matching in Real-Time Spatial Crowdsourcing

Abstract: The prevalence of mobile Internet techniques and Online-To-Offline (O2O) business models has led the emergence of various spatial crowdsourcing (SC) platforms in our daily life. A core issue of SC is to assign real-time tasks to suitable crowd workers. Existing approaches usually focus on the matching of two types of objects, tasks and workers, or assume the static offline scenarios, where the spatio-temporal information of all the tasks and workers is known in advance. Recently, some new emerging O2O applications incur new challenges: SC platforms need to assign three types of objects, tasks, workers and workplaces, and support dynamic real-time online scenarios, where the existing solutions cannot handle. In this paper, based on the aforementioned challenges, we formally define a novel dynamic online task assignment problem, called the trichromatic online matching in real-time spatial crowdsourcing (TOM) problem, which is proven to be NP-hard. Thus, we first devise an efficient greedy online algorithm. However, the greedy algorithm can be trapped into local optimal solutions easily. We then present a threshold-based randomized algorithm that not only guarantees a tighter competitive ratio but also includes an adaptive optimization technique, which can quickly learn the optimal threshold for the randomized algorithm. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Improved formulations and an Adaptive Large Neighborhood Search heuristic for the integrated berth allocation and quay crane assignment problem

Abstract: This paper focuses on the integrated berth allocation and quay crane assignment problem in container terminals. We consider the decrease in the marginal productivity of quay cranes and the increase in handling time due to deviation from the desired position. We consider a continuous berth, discretized in small equal-sized sections. A number of enhancements over the state-of-the-art formulation and an Adaptive Large Neighborhood Search (ALNS) heuristic are presented. Computational results reveal that the enhancements improve many of the best-known bounds, and the ALNS outperforms the state-of-the-art heuristics for many instances. We also conduct further analysis on a new larger benchmark.

Bayesian Estimation of Bipartite Matchings for Record Linkage

Abstract: The bipartite record linkage task consists of merging two disparate datafiles containing information on two overlapping sets of entities. This is nontrivial in the absence of unique identifiers and it is important for a wide variety of applications given that it needs to be solved whenever we have to combine information from different sources. Most statistical techniques currently used for record linkage are derived from a seminal article by Fellegi and Sunter in 1969. These techniques usually assume independence in the matching statuses of record pairs to derive estimation procedures and optimal point estimators. We argue that this independence assumption is unreasonable and instead target a bipartite matching between the two datafiles as our parameter of interest. Bayesian implementations allow us to quantify uncertainty on the matching decisions and derive a variety of point estimators using different loss functions. We propose partial Bayes estimates that allow uncertain parts of the bipartite...

Interviewing in two‐sided matching markets

Abstract: We introduce the interview assignment problem, which generalizes the oneto-one matching model of Gale and Shapley (1962) by including a stage of costly information acquisition. Agents do not know their preferences over potential partners unless they choose to conduct costly interviews. Although there may exist many equilibria in which all agents are assigned the same number of interviews, we show the e‐ciency of the resultant match can vary signiflcantly depending on the degree of overlap { the number of common interview partners among agents { exhibited by the interview assignment. Among all such equilibria, the one with the highest degree of overlap yields the highest probability of being matched for any agent. Our analysis is used to motivate new and explain existing coordinating mechanisms prevalent in markets with interviewing.

Negative-weight shortest paths and unit capacity minimum cost flow in Õ(m10/7 log W) time: (extended abstract)

Abstract: In this paper, we study a set of combinatorial optimization problems on weighted graphs: the shortest path problem with negative weights, the weighted perfect bipartite matching problem, the unit-capacity minimum-cost maximum flow problem, and the weighted perfect bipartite b-matching problem under the assumption that ||b||1 = O(m). We show that each of these four problems can be solved in O(m10/7 log W) time, where W is the absolute maximum weight of an edge in the graph, providing the first polynomial improvement in their sparse-graph time complexity in over 25 years.At a high level, our algorithms build on the interior-point method-based framework developed by Madry (FOCS 2013) for solving unit-capacity maximum flow problem. We develop a refined way to analyze this framework, as well as provide new variants of the underlying preconditioning and perturbation techniques. Consequently, we are able to extend the whole interior-point method-based approach to make it applicable in the weighted graph regime.

Cost-effective barrier coverage formation in heterogeneous wireless sensor networks

Abstract: Barrier coverage is a critical issue in wireless sensor networks (WSNs) for security applications, which however cannot be guaranteed to be formed after initial random deployment of sensors. Existing work on barrier coverage mainly focus on homogeneous WSNs, while little effort has been made on exploiting barrier coverage formation in heterogeneous WSNs where different types of sensors are deployed with different sensing models and costs. In this paper, we study how to efficiently form barrier coverage by leveraging multiple types of mobile sensors to fill in gaps between pre-deployed stationary sensors in heterogeneous WSNs. The stationary sensors are grouped into clusters and a cluster-based directional barrier graph is proposed to model the barrier coverage formation problem. We prove that the minimum cost of mobile sensors required to form a barrier with stationary sensors is the length of the shortest path on the graph. Moreover, we propose a greedy movement algorithm for heterogeneous WSNs to efficiently schedule different types of mobile sensors to different gaps while minimizing the total moving cost. In particular, we formulate the movement problem for homogeneous WSNs as a minimum cost bipartite assignment problem, and solve it in polynomial time using the Hungarian algorithm. Extensively experimental results on homogeneous and heterogeneous WSNs demonstrate the effectiveness of the proposed algorithms.

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

Abstract: In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.

Destination-aware Task Assignment in Spatial Crowdsourcing

Abstract: With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. Our empirical studies demonstrate that our proposed technique is quite effective and settle the problem nicely.

On the minimization of traffic congestion in road networks with tolls

Abstract: Population growth and the massive production of automotive vehicles have lead to the increase of traffic congestion problems. Traffic congestion today is not limited to large metropolitan areas, but is observed even in medium-sized cities and highways. Traffic engineering can contribute to lessen these problems. One possibility, explored in this paper, is to assign tolls to streets and roads, with the objective of inducing drivers to take alternative routes, and thus better distribute traffic across the road network. This assignment problem is often referred to as the tollbooth problem and it is NP-hard. In this paper, we propose mathematical formulations for two versions of the tollbooth problem that use piecewise-linear functions to approximate congestion cost. We also apply a biased random-key genetic algorithm on a set of real-world instances, analyzing solutions when computing shortest paths according to two different weight functions. Experimental results show that the proposed piecewise-linear functions approximate the original convex function quite well and that the biased random-key genetic algorithm produces high-quality solutions.

Flight gate assignment and recovery strategies with stochastic arrival and departure times

Abstract: We consider the problem of assigning flights to airport gates. We examine the general case in which an aircraft serving a flight may be assigned to different gates for arrival, parking, and departure processing. The objectives can be divided into deterministic and stochastic goals. The former include maximization of the total assignment preference score, a minimal number of unassigned flights during overload periods, and minimization of the number of tows. A special focus lies on the stochastic objectives, which aim at minimizing the expected number of any kind of constraint violations, i.e. not respecting gate closures, violation of shadow restrictions (a situation in which gate assignments may cause blocking of neighboring gates) or of tow time restrictions and classical gate conflicts in which two aircraft are assigned to the same gate and are at the airport at the same time. We show that the minimization of expected gate conflicts can be modeled in a graph theoretical approach using the clique partitioning problem (CPP). We furthermore show that the classical (deterministic) flight gate assignment problem, which can also be modeled using a CPP, can be integrated such that a simple though powerful model emerges, which no longer needs including a dummy gate, which is often used in practical gate assignment models. As constraint violations cannot fully be prevented, recovery strategies become necessary. We present a procedure for recovery planning that has proved its practical relevance at numerous airports. Finally, in an extensive numerical study we test our results on practical data, which contain a statistical analysis of flight arrival and departure times. The tests include a detailed comparison of current robustness measures and state-of-the-art approaches found in literature.

Solving the Group Multirole Assignment Problem by Improving the ILOG Approach

Abstract: Role assignment is a critical element in the role-based collaboration process. There are many different requirements to be considered when undertaking this task. This correspondence paper formalizes the group multirole assignment (GMRA) problem; proves the necessary and sufficient condition for the problem to have a feasible solution, provides an improved IBM ILOG CPLEX optimization package solution, and verifies the proposed solution with experiments. The contributions of this paper include: 1) the formalization of an important engineering problem, i.e., the GMRA problem; 2) a theoretical proof of the necessary and sufficient condition for GMRA to have a feasible solution; and 3) an improved ILOG solution to such a problem.

Hesitant Fuzzy Multiattribute Matching Decision Making Based on Regret Theory with Uncertain Weights

Abstract: An approach based on regret theory with hesitant fuzzy analysis is presented in a context of multiattribute matching decision making where the relative weights are uncertain. There are two steps being addressed in this approach. First, we put forward a maximizing differential model to determine the relative weights of hesitant fuzzy attributes, and calculate collective utilities of each attribute according to regret theory. The matching satisfaction degrees (MSDs) are then acquired by aggregating the collective utilities with relative weights. Secondly, an optimal matching model is programmed to generate the matching results based on the MSDs. This model belongs to a sort of multiobjective assignment problem and can be solved using the min–max method. A case study of matching outsourcing contractors and providers in Fuzhou National Hi-tech Zone is conducted to demonstrate the proposed approach and its potential applications.

An adaptive large neighborhood search heuristic for solving a robust gate assignment problem

Abstract: Robust gate assignment problem with transfer passengers and tows is considered.A large neighborhood search heuristic is proposed to solve the integrated problem.Extensive experiments are conducted to justify the performance of our approach. With the rapid growth of air traffic demand, airport capacity becomes a major bottleneck within the air traffic control systems. Minor disturbances may have a large impact on the airport surface operations due to the overly tight schedules, which results in frequent gate conflict occurrences during airports daily operations. A robust gate schedule that is resilient to disturbances is essential for an airport to maintain a good performance. Unfortunately, there is no efficient expert system available for the airport managers to simultaneously consider the traditional cost (the aircraft tow cost, transfer passenger cost) and the robustness. To fill this gap, in this paper, we extend the traditional gate assignment problem and consider a wider scope, in which the traditional costs and the robustness are simultaneously considered. A mathematical model is first built, which leads to a complex non-linear model. To efficiently solve this model, an adaptive large neighborhood search (ALNS) algorithm is then designed. We novelly propose multiple local search operators by exploring the characteristics of the gate assignment problem. The comparison with the benchmark algorithm shows the competitiveness of proposed algorithm in solving the considered problem. Moreover, the proposed methodology also has great potential from the practical perspective since it can be easily integrated into current expert systems to help airport managers make satisfactory decisions.

A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing

Abstract: This paper investigates an integrated production and transportation scheduling problem in an MTO supply chain. A harmony search-based memetic optimization model is developed to handle this problem, in which certain heuristic procedures are proposed to convert the investigated problem into an order assignment problem. A novel improvisation process is also proposed to improve the optimum-seeking performance. The effectiveness of the proposed model is validated by numerical experiments. The experimental results show that (1) the proposed model can solve the investigated problem effectively and that (2) the proposed memetic optimization process exhibits better optimum-seeking performance than genetic algorithm-based and traditional memetic optimization processes.

Assessment of efficiency of assignment of vehicles to tasks in supply chains: a case study of a municipal company

Abstract: The main purpose of the paper is to present criteria of efficiency of assignment of vehicles to tasks at municipal companies, which collect garbage from city inhabitants. Three types of criteria are introduced in the paper: garbage collection time, length of route allocation, and utilization of resources. A two-stage method of optimization of taskroutes is proposed. It generates tasks at the first stage and assigns vehicles to the tasks at the second stage. At municipal companies that are responsible for garbage, collection tasks are not pre-defined, and consequently tasks must be designated before the workday. The proposed method is based on genetic algorithm, which is used for the purpose of optimization of the assignment problem. The obtained by the algorithm optimal assignment is compared with assignments obtained in the random way. Criteria of evaluation of efficiency of the obtained route of different mutually conflicting dimensions were introduced, such as is task realization time, distances travelled on particular routes, and number of vehicles involved in garbage collection. Efficiency of the obtained assignment appeared to be sufficiently good. First published online: 17 Jan 2017

A Novel Model and Decomposition Approach for the Integrated Airline Fleet Assignment, Aircraft Routing, and Crew Pairing Problem

Abstract: Given a daily flight schedule and a set of aircraft fleets, the airline scheduling problem assigns individual aircraft and groups of crew to each flight based on specific considerations of aircraft maintenance requirements and crew work rules, respectively. Traditionally, this problem has been sequentially broken down into several stages, where the fleet assignment problem, which is solved first, partitions the entire flight network into subnetworks according to fleet types, followed by respectively solving the aircraft routing and crew pairing problems to generate suitable aircraft and crew rotations. However, this sequential approach ignores the interdependencies among the stages, leading to suboptimal, or even infeasible, solutions. In this paper, we propose an integrated model and solution approach that incorporates the fleet assignment (with itinerary-based demands), aircraft routing, and crew pairing problems within a single framework. We solve the resulting formulation of the problem by using a Ben...

An Efficient Globally Optimal Algorithm for Asymmetric Point Matching

Abstract: Although the robust point matching algorithm has been demonstrated to be effective for non-rigid registration, there are several issues with the adopted deterministic annealing optimization technique. First, it is not globally optimal and regularization on the spatial transformation is needed for good matching results. Second, it tends to align the mass centers of two point sets. To address these issues, we propose a globally optimal algorithm for the robust point matching problem in the case that each model point has a counterpart in scene set. By eliminating the transformation variables, we show that the original matching problem is reduced to a concave quadratic assignment problem where the objective function has a low rank Hessian matrix. This facilitates the use of large scale global optimization techniques. We propose a modified normal rectangular branch-and-bound algorithm to solve the resulting problem where multiple rectangles are simultaneously subdivided to increase the chance of shrinking the rectangle containing the global optimal solution. In addition, we present an efficient lower bounding scheme which has a linear assignment formulation and can be efficiently solved. Extensive experiments on synthetic and real datasets demonstrate the proposed algorithm performs favorably against the state-of-the-art methods in terms of robustness to outliers, matching accuracy, and run-time.