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  Subjects -> STATISTICS (Total: 130 journals)
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Optimization Letters
Journal Prestige (SJR): 0.721
Citation Impact (citeScore): 1
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1862-4480 - ISSN (Online) 1862-4472
Published by Springer-Verlag Homepage  [2468 journals]
  • A self adaptive inertial algorithm for solving variational inequalities
           over the solution set of the split variational inequality problem

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      Abstract: Abstract The purpose of this paper is to investigate a new inertial self-adaptive iterative algorithm for solving variational inequalities over the solution set of the split variational inequality problem with multiple output sets in real Hilbert spaces. The strong convergence result is given under some mild conditions widely used in the convergence analysis. Our algorithm is accelerated by the inertial technique and eliminates the dependence on the norm of the transformation operators and the strongly monotone and Lipschitz continuous constants of the involved operator. Two applications in the network bandwidth allocation problem and the image classification problem are shown, and some numerical experiments are presented to show the advantages of the proposed algorithm.
      PubDate: 2023-12-07
       
  • Two-machine job shop scheduling with optional job rejection

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      Abstract: Abstract We investigate a two-machine job shop scheduling problem with optional job rejection. The target is to look for a feasible schedule for the set of accepted jobs so that the sum of the makespan of the accepted jobs and the total penalty of the rejected jobs is minimized. We propose an exact pseudo-polynomial dynamic programming algorithm, a greedy \(\frac{\sqrt{5}+1}{2}\) -approximation algorithm, an LP-based \(\frac{e}{e-1}\) -approximation algorithm, and a fully polynomial time approximation scheme to solve it. We demonstrate that the proportionate case is \(\mathcal{N}\mathcal{P}\) -hard and provide an \(O(n^2)\) -time algorithm for the agreeable case.
      PubDate: 2023-12-06
       
  • An improved model for estimating optimal VRP solution values

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      Abstract: Abstract Since it is computationally expensive to solve the vehicle routing problem (VRP) optimally, as this problem is NP-hard, in this technical note we study how to accurately approximate the optimal VRP tour length. In our previous papers, we developed a linear regression model including the mean and standard deviation of the modified Clarke and Wright heuristic solution values, which was able to predict the optimal VRP tour length fairly well. In this note, we find that by doing a small amount of extra work to include the minimum of the modified Clarke and Wright heuristic solution values, we can improve the predictive results substantially.
      PubDate: 2023-12-06
       
  • Randomized Lagrangian stochastic approximation for large-scale constrained
           stochastic Nash games

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      Abstract: Abstract In this paper, we consider stochastic monotone Nash games where each player’s strategy set is characterized by possibly a large number of explicit convex constraint inequalities. Notably, the functional constraints of each player may depend on the strategies of other players, allowing for capturing a subclass of generalized Nash equilibrium problems (GNEP). While there is limited work that provide guarantees for this class of stochastic GNEPs, even when the functional constraints of the players are independent of each other, the majority of the existing methods rely on employing projected stochastic approximation (SA) methods. However, the projected SA methods perform poorly when the constraint set is afflicted by the presence of a large number of possibly nonlinear functional inequalities. Motivated by the absence of performance guarantees for computing the Nash equilibrium in constrained stochastic monotone Nash games, we develop a single timescale randomized Lagrangian multiplier stochastic approximation method where in the primal space, we employ an SA scheme, and in the dual space, we employ a randomized block-coordinate scheme where only a randomly selected Lagrangian multiplier is updated. We show that our method achieves a convergence rate of \(\mathcal {O}\left( \frac{\log (k)}{\sqrt{k}}\right)\) for suitably defined suboptimality and infeasibility metrics in a mean sense.
      PubDate: 2023-12-04
       
  • Mixed lattice structures and cone projections

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      Abstract: Abstract Problems related to projections on closed convex cones are frequently encountered in optimization theory and related fields. To study these problems, various unifying ideas have been introduced, including asymmetric vector-valued norms and certain generalized lattice-like operations. We propose a new perspective on these studies by describing how the problem of cone projection can be formulated using an order-theoretic formalism developed in this paper. The underlying mathematical structure is a partially ordered vector space that generalizes the notion of a vector lattice by using two partial orderings and having certain lattice-type properties with respect to these orderings. In this note we introduce a generalization of these so-called mixed lattice spaces, and we show how such structures arise quite naturally in some of the applications mentioned above.
      PubDate: 2023-12-02
       
  • Correction to: A no-delay single machine scheduling problem to minimize
           total weighted early and late work

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      PubDate: 2023-12-01
       
  • Special issue dedicated to the 8th International Conference on Variable
           Neighborhood Search (ICVNS 2021)

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      Abstract: Abstract This special issue contains 15 papers submitted by the participants of the 8th International Conference on Variable Neighborhood Search (ICVNS 2021), which was held in Abu Dhabi, U.A.E., online due to COVID-19 restrictions, on March 22–24, 2021.
      PubDate: 2023-12-01
       
  • A no-delay single machine scheduling problem to minimize total weighted
           early and late work

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      Abstract: Abstract This paper investigates the no-delay single machine scheduling problem to minimize the total weighted early and late work. This criterion is one of the most important objectives in practice but has not been studied so far in the literature. First, we formulate the problem as a 0–1 integer programming formulation. Since the complexity of the problem is proven to be NP-hard, we propose two metaheuristics, namely General Variable Neighborhood Search and hybrid GRASP-VND, based on new neighborhood structures. The results demonstrate that all proposed methods can solve optimally instances up to 30 jobs. However, extensive computational experimentation, on large-sized instances, show that the quality of the solutions given by GVNS is better than that obtained by hybrid GRASP-VND algorithm.
      PubDate: 2023-12-01
       
  • Multi-objective home health care routing: a variable neighborhood search
           method

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      Abstract: Abstract Health and convenience are two indispensable indicators of the society promotion. Nowadays, to improve community health levels, the comfort of patients and those in need of health services has received much attention. Providing Home Health Care (HHC) services is one of the critical issues of health care to improve the patient convenience. However, manual nurse planning which is still performed in many HHC institutes results in a waste of time, cost, and ultimately lower efficiency. In this research, a multi-objective mixed-integer model is presented for home health care planning so that in addition to focusing on the financial goals of the institution, other objectives that can help increase productivity and quality of services are highlighted. Therefore, four different objectives of the total cost, environmental emission, workload balance, and service quality are addressed. Taking into account medical staff with different service levels, and the preferences of patients in selecting a service level, as well as different vehicle types, are other aspects discussed in this model. The epsilon-constraint method is implemented in CPLEX to solve small-size instances. Moreover, a Multi-Objective Variable Neighborhood Search (MOVNS) composed of nine local neighborhood moves is developed to solve the practical-size instances. The results of the MOVNS are compared with the epsilon-constraint method, and the strengths and weaknesses of the proposed algorithm are demonstrated by a comprehensive sensitivity analysis. To show the applicability of the algorithm, a real example is designed based on a case study, and the results of the algorithm over real data are evaluated.
      PubDate: 2023-12-01
       
  • Feature selection in machine learning via variable neighborhood search

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      Abstract: Abstract The generalization ability of machine learning methods can be improved via feature selection. In this work a novel heuristic framework for feature selection in machine learning is proposed. The framework is built on the Variable Neighborhood Search (VNS) heuristic. The proposed framework is generic, and can be applied to any existing supervised machine learning methods. Implementation of the proposed framework that encapsulates conventional regression and classification problems is illustrated in this paper. Numerical experiments with real datasets display the applicability of the proposed framework.
      PubDate: 2023-12-01
       
  • A general variable neighborhood search approach for the minimum load
           coloring problem

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      Abstract: Abstract The minimum load coloring problem consists of finding a 2-coloring function that assign either a color red or blue to each node of a graph such that the (maximum) load is minimized, i.e., to reduce as much as possible the number of edges with, at least, one endpoint colored in red (symmetrically, in blue). This \(\mathcal {NP}\) -complete problem arises in Wavelength Division Multiplexing (WDM) technology and it has been used for broadcast WDM networks. In this paper, several procedures based on the Variable Neighborhood Search (VNS) methodology are proposed and compared on a set of random graphs and DIMACS benchmarks. Experimental results show that the proposed VNS variant exhibits a remarkable performance in comparison with the state-of-the-art methods. In particular, our approach achieves the best results in 48 out of 52 considered instances by employing, on average, less than 7 seconds. These results are further confirmed by conducting statistical tests.
      PubDate: 2023-12-01
       
  • General variable neighborhood search for the minimum stretch spanning tree
           problem

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      Abstract: Abstract Given an undirected graph, the minimum stretch spanning tree problem (MSSTP) deals with finding a spanning tree such that the maximum distance in the tree for adjacent nodes in the original graph, called the stretch, is minimum. This is an NP-hard problem with many applications in transportation and communication networks. We propose a general variable neighborhood search (GVNS) algorithm based on a balance between solution generation and improvement. To achieve this balance, we consider different construction heuristics and neighborhood strategies to efficiently explore the search space. To assess the merit of our proposal, we perform extensive experimentation on various classes of graphs consisting of 214 instances. A comparison in terms of solution quality and execution time with the best previous method, namely an artificial bee colony (ABC) algorithm, shows the superiority of GVNS. Results are compared using statistical tests to draw significant conclusions.
      PubDate: 2023-12-01
       
  • Efficient metaheuristics for the home (health)-care routing and scheduling
           problem with time windows and synchronized visits

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      Abstract: Abstract Home healthcare and home care centers are facing increasing demands and costs all over the world. Researchers are attracted by several organizational issues related to home care centers’ activities, in particular by the daily routing and scheduling issue. The challenge is to deal with the assignment of visits to home caregivers and the design of sequences of visits execution, minimizing several objective functions separately such as traveling time, preferences and workload balance. This problem is presented in the literature as a vehicle routing problem with synchronization and time window constraints. A benchmark is available, and two efficient metaheuristics have been provided in the literature: a simulated annealing based algorithm (SA-ILS) and an adaptive large neighborhood search (ALNS). In this paper, new metaheuristics; a genetic algorithm, several variants of variable neighborhood descent: three nested and two mixed, and a hybrid genetic algorithm, are provided. Considering fairness as objective function, numerical results show the superiority of the two mixed variable neighborhood descent and the hybrid genetic algorithm, in comparison to SA-ILS and ALNS. Considering preference and traveling time as objective functions, numerical results show that the two mixed variable neighborhood descent outperform the SA-ILS and are competitive with the ALNS.
      PubDate: 2023-12-01
       
  • General variable neighborhood search for the parallel machine scheduling
           problem with two common servers

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      Abstract: Abstract We address in this paper the parallel machine scheduling problem with a shared loading server and a shared unloading server. Each job has to be loaded by the loading server before being processed on one of the available machines and unloaded immediately by the unloading server after its processing. The objective function involves the minimization of the overall completion time, known as the makespan. This important problem raises in flexible manufacturing systems, automated material handling, healthcare, and many other industrial fields, and has been little studied up to now. To date, research on it has focused on the case of two machines. The regular case of this problem is considered. A mixed integer programming formulation based on completion time variables is suggested to solve small-sized instances of the problem. Due to its \(\mathcal{NP}\mathcal{}\) -hardness, we propose two greedy heuristics based on the minimization of the loading, respectively unloading, server waiting time, and an efficient General Variable Neighborhood Search (GVNS) algorithm. In the computational experiments, the proposed methods are compared using 120 new and publicly available instances. It turns out that, the proposed GVNS with an initial solution-finding mechanism based on the unloading server waiting time minimization significantly outperforms the other approaches.
      PubDate: 2023-12-01
       
  • A VNS based framework for early diagnosis of the Alzheimer's disease
           converted from mild cognitive impairment

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      Abstract: Abstract Mild cognitive impairment (MCI) is an intermediate stage between age-related cognitive decline. Alzheimer's disease (AD) is a more serious decline in dementia. Early identification of mild cognitive impairment with a high risk of Alzheimer's disease is very important for increasing the success rate of the treatment. In this study, we present a Variable Neighborhood Search (VNS) based framework that uses Magnetic Resonance Imaging (MRI) data to diagnose early conversion from MCI to AD. The proposed framework has been built in three main phases: preparing dataset, feature selection, and classification. After preparing the dataset, a VNS algorithm selects the most predictive MRI features for classification. Then, a Linear Support Vector Machine is utilized to classify the selected features. All data in this study are obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with 860 subjects, eight different monthly periods, and 286 features in each period. The results obtained from the framework outperform those of previous research in terms of accuracy, sensitivity, and specificity values. The results of this study demonstrate that our framework has a huge potential for early prediction and detection of mild cognitive impairment to Alzheimer's disease conversion.
      PubDate: 2023-12-01
       
  • Variable neighborhood search for the single machine scheduling problem to
           minimize the total early work

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      Abstract: Abstract In this paper, the single machine scheduling problem to minimize the total early work is studied. The aim of this problem is to minimize the total amount of processing performed on the jobs before their due dates. We have proposed a better implementation of the existing dynamic programming method and thus solved large problems in an exact way. In particular, we can currently resolve instances of sizes 10,000 exactly instead of 200 with a pseudo-polynomial dynamic programming algorithm. Since the considered problem was proven to be NP-hard, a heuristic based on the Variable Neighborhood Search (VNS) method is proposed to solve larger instances. Computational experiments show that both methods are efficient.
      PubDate: 2023-12-01
       
  • A two-phase multi-objective metaheuristic for a green UAV grid routing
           problem

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      Abstract: Abstract This paper deals with Unmanned Aerial Vehicle (UAV) routing in dynamic grid scenarios with limited battery autonomy and multiple charging stations. Inspired by a multi-criteria view of real systems, we consider different objective functions, while respecting the navigation over forbidden areas and also a real-time flight autonomy. A multi-objective variant of Variable Neighborhood Search is considered for finding sets of non-dominated solutions. Twelve neighborhood structures were developed in order to explore the solution space, including learning techniques. The latter stores known routes in order to speed up the search. A case of study was developed where UAVs have to serve clients spread throughout a grid, representing a map. Each UAV starts in a given grid point with a given battery charge, where the grid is composed by four different kinds of points: a regular one and three special (prohibited, recharge and client). Any update can happen on the routes on real-time, so the metaheuristic should handle real-time information and update the plan and graphics user interface accordingly. Any sequence of valid adjacent points forms a route, but since this yields a huge number of combinations, a preprocessing technique is proposed to pre-compute distances in a given dynamic scenario. Computational results demonstrate the performance of different variants of the proposed algorithms.
      PubDate: 2023-12-01
       
  • Fat-tailed distributions for continuous variable neighborhood search

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      Abstract: Abstract Using the Gaussian normal distribution on the whole solution space in the continuous variable neighborhood search method has shown similar success as the use of traditional bounded neighborhoods, but with less parameters to be specified. For unbounded problems with distant optimal solutions, although not limited by bounded geometrical neighborhoods, it showed to be inefficient due to the exponential decrease of the normal density function. In order to reach distant solutions more efficiently, six more “fat-tailed” distributions, which can be easily generated, are tested in this paper. The experiments on test functions showed greater efficiency for most new distributions opposite to a normal distribution. Moreover, following the “less is more approach”, this paper presents a very efficient algorithm for both close and distant optimal solutions. It combines two neighborhood structures: one being efficient for near solutions, and the other more efficient for distant solutions. This approach, with a reduced number of parameters the user must define in advance, has shown to be robust when the position of the optimal point is unknown.
      PubDate: 2023-12-01
       
  • General variable neighborhood search approach to group steiner tree
           problem

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      Abstract: Abstract In this paper, we consider the Group Steiner Tree (GST) problem that can be stated as follows: For a given non-negative edge weighted graph \(G = (V, E)\) , an integer k, and the corresponding family \(g_1, \ldots , g_k\) containing non-empty subsets of V called groups, we need to find a minimum cost tree \(T = (V_T, E_T)\) where \(V_T \subseteq V\) and \(E_T\subseteq E\) that spans at least one vertex from each of the groups. Numerous applications of this NP-hard problem initiated researchers to study it from both theoretical and algorithmic aspects. One of the challenges is to provide a good heuristic solution within the reasonable amount of CPU time. We propose the application of metaheuristic framework based on Variable Neighborhood Search (VNS) and related approaches. One of our main objectives is to find a neighborhood structure that ensures efficient implementation. We develop Variable Neighborhood Descend (VND) algorithm that can be the main ingredient of several local search approaches. Experimental evaluation involves comparison of our heuristic to exact approach based on Integer Linear Programming solvers and other metaheuristic approaches, such as genetic algorithm. The obtained results show that the proposed method always outperforms genetic algorithm. Exact method is outperformed in the case of instances with large number of groups.
      PubDate: 2023-12-01
       
  • Combining variable neighborhood search and machine learning to solve the
           vehicle routing problem with crowd-shipping

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      Abstract: Abstract Crowd-shipping is an innovative delivery model, based on the sharing economy concept. In this framework, delivery operations are carried out by using existing underused resources, i.e., ordinary people who usually travel on the roads with their own vehicles and have empty space to share, in addition to the company’s conventional vehicles. We refer to these non-professional couriers as “occasional drivers”. Occasional drivers are not company’s employees: they are common people who may decide to perform a delivery service during their free time, for a small compensation. Usually, this process is possible thanks to a crowd-shipping platform, which connects the company, the occasional drivers, and the customers. In this paper, we tackle the crowd-shipping model, by developing an approach inspired to variable neighborhood search (VNS) approach, where several machine learning techniques are used to explore the most promising areas of the search space. VNS is a well-known meta-heuristic already used in crowd-shipping applications. In this paper, the learning strategies embedded into the framework have shown to improve the effectiveness of the basic framework.
      PubDate: 2023-12-01
       
 
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