Nenad Mladenovic

Khalifa University | KU · Department of Engineering Systems and Management

Several problems are emerging in the context of communication networks and most of them must be solved in reduced computing time since they affect to critical tasks. In this research, the monitor placement problem is tackled. This problem tries to cover the communications of an entire network by locating a monitor in specific nodes of the network,...

K-means plays a vital role in data mining and is the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its performance drastically drops when applied to vast amounts of data. Therefore, it is crucial to improve K-means by scaling it to big data using as few of the following computat...

Motivated by the practical research and development (R&D) process in high-end equipment manufacturing, this study investigates a bi-level scheduling problem in a complex R&D project network, where each project contains multiple modules with a complete task network. In the bi-level scheduling problem, the upper-level problem is that the R&D project...

K-means clustering plays a vital role in data mining. However, its performance drastically drops when applied to huge amounts of data. We propose a new heuristic that is built on the basis of regular K-means for faster and more accurate big data clustering using the "less is more" and MSSC decomposition approaches. The main advantage of the propose...

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 NP\documentclass[12pt]{minimal} \usep...

Commercial flights nearly halted due to the COVID-19 pandemic in the second quarter of 2020. Consequently, several countries have had to schedule repatriation flights to return their citizens stranded in other countries. Flight routes and schedules are known in normal circumstances, and passengers buy seats on these flights; however, the reverse st...

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...

This special issue of Optimization Letters presents selected, peer-reviewed, papers that were accepted for presentation in the two recent International Conferences on Variable Neighborhood Search. The “ICVNS 2018” was held in Sithonia, Halkidiki, Greece, during October 4-7, 2018 and the “ICVNS 2019” was held in Rabat, Morocco, during October 3–5, 2...

In the field of automatic programming (AP), the solution of a problem is a program, which is usually represented by an AP-tree. A tree is built using functional and terminal nodes. For solving AP problems, we propose a new neighborhood structure that adapts the classical “elementary tree transformation” (ETT) into this specific AP-tree. The ETT is...

Nowadays more and more complex railway systems can operate efficiently only if we have tools for planning their maintenance. Train accidents are mainly caused by infrastructure problems, or more specifically by track geometry failures.In this paper, we present a support decision system for forecasting the deterioration of track geometry. Two types...

This paper examines a new model for hub location known as the hub location routing problem. The problem shares similarities with the well studied uncapacitated single allocation p-hub median problem except that the hubs are now connected to each other by a cyclical path (or tour) known as the global route, each cluster of non-hub nodes and assigned...

During major infectious disease outbreak, such as COVID-19, the goods and parcels supply and distribution for the isolated personnel has become a key issue worthy of attention. In this study, we propose a delivery problem that arises in the last-mile delivery during major infectious disease outbreak. The problem is to construct a Hamiltonian tour o...

The main idea of Less is more approach (LIMA) is using as fewer as possible ingredients to provide the best possible outcome. This approach has been used successfully almost in all the scientific and art disciplines. Recently, the idea has also been successfully explored in solving hard optimization problems. In this note we first define the domina...

Diversity and dispersion problems consists of selecting a subset of elements from a given set so that their diversity is maximized. The one of most recently proposed variant is the MaxMin dispersion problem with capacity and cost constraints. This variant usually called the generalized dispersion problem. In this paper we propose variant of tabu se...

The p-center problem is a well-known and highly studied problem pertaining to the identification of p of the potential n center locations in such a way as to minimize the maximum distance between the users and the closest center. As opposed to the p-center, the p-second center problem minimizes the maximum sum of the distances from the users to the...

The topic of this paper is the Capacitated Dispersion Problem (CDP). To solve the problem, variable neighborhood search (VNS) based heuristics are proposed. The proposed heuristics are Basic VNS, General VNS and General Skewed VNS. Their performances are assessed on the benchmark instances from the literature and compared against the state-of-the-a...

This paper examines the uncapacitated multiple allocation p-hub median problem (UMApHMP) in a general setting where a given network may violate the triangle inequality, thus leading to flow paths with more than two hubs connecting origin-to-destination pairs. We present two improved flow formulations using a new ”augmented graph” that allows a subs...

This paper reviews the papers on the applications of VNS in the health care area by analyzing the characteristics of VNS in different problems. In the health care field, many complex optimization problems need to be tackled in a short time considering multiple influencing factors, such as personnel preferences, resources limitations, etc. As a meta...

This article investigates a novel physician scheduling problem. Different types of tasks can be performed by physicians with certain qualifications. Tasks have different properties depending on their types, lengths, and starting times. Physicians performing tasks can yield different values of benefit and cost according to their qualifications and t...

This article presents a new conceptual approach for the interpretative topic modeling problem. It uses sentences as basic units of analysis, instead of words or n-grams, which are commonly used in the standard approaches.The proposed approach’s specifics are using sentence probability evaluations within the text corpus and clustering of sentence em...

Energy-efficient scheduling has drawn wide attention in the last few decades from both academia and industry due to its significance in energy saving and environmental protection. However, dynamic disruptions in practical sce- narios such as dynamic arrival of orders, release time of raw materials, and machine breakdown, make it challenging to find...

This work presents a method for summarizing scientific articles from the arXive dataset using Variable Neighborhood Search (VNS) heuristics to automatically find the best summaries in terms of ROUGE-1 score we could assemble from scientific article text sentences. Then vectorizing the sentences using BERT pre-trained language model and augmenting t...

This paper considers a fast solving the practical problem in railway planning and scheduling. i.e., the problem of assigning given arrival and departure railway paths to routs. This problem is to execute as fully as possible the train traffic across the railway station, using a fixed amount of the resources. It appears that the problem may be solve...

Automatic programming is an efficient technique that has contributed to an important development in the artificial intelligence and machine learning fields. In this chapter, we introduce the technique called Variable Neighborhood Programming (VNP) that was inspired by the principle of the Variable Neighborhood Search (VNS) algorithm. VNP starts fro...

One can expect that the life expectancy of people in a city or geographical region depends on health-care infrastructure in that city or region, as well as on investment devoted to it. In this paper we wanted to check the influence of health-care supports of different kind on the life expectancy. Data are collected on all 85 geographical districts...

This volume constitutes the proceedings of the 8th International Conference on Variable Neighborhood Search, ICVNS 2021, held in Abu Dhabi, United Arab Emirates, in March 2021. The 14 full papers presented in this volume were carefully reviewed and selected from 27 submissions. The papers describe recent advances in methods and applications of vari...

Parallel-batching processing and job deterioration are universal in the real industry. Scholars have deeply investigated the problem of parallel-batching scheduling and the problem of scheduling with deteriorating jobs separately. However, the situations where both parallel-batching processing and job deterioration exist simultaneously were seldom...

This paper investigates an integrated production and assembly scheduling problem with the practical manufacturing features of serial batching and the effects of deteriorating and learning. The problem is divided into two stages. During the production stage, there are several semi-product manufacturers who first produce ordered product components in...

Internet of things (IoT) covers various aspects of collecting and exchanging data between diverse entities. From IoT provider’s perspective, one of the most significant issues is how to set the price that maximizes its revenue while meeting users’ requirements. In this paper, we focus on revenue maximization of the IoT service provider by applying...

The Variable Neighborhood Search (VNS) metaheuristic is based on systematic changes in the neighborhood structure within a search. It has been successfully applied for the solution of various global and combinatorial optimization problems. The aim of this special issue of Journal of Global Optimization (JOGO) is to present some recent methodologica...

Well-known and widely applied k-means clustering heuristic is used for solving Minimum Sum-of-Square Clustering problem. In solving large size problems, there are two major drawbacks of this technique: (i) since it has to process the large input dataset, it has heavy computational costs and (ii) it has a tendency to converge to one of the local min...

The paper focuses on the problem of chemical kinetics, calculation of variations in the concentration of substances in the reactions over time, and creation of a mass kinetic solver to solve the problem using modern parallelization technologies. A mathematical model of variation in the concentration of substances in a system with a one-dimensional...

Hub location problems generally assume that the triangle inequality applies on the edges of a complete graph. Hence the flow between any pair of nodes requires at most two hubs for the transfer process. Here we relax the triangle inequality restriction and present two new formulations of the uncapacitated multiple allocation p-hub median problem th...

This volume constitutes the post- conference proceedings of the 7th International Conference on Variable Neighborhood Search, ICVNS 2019, held in Rabat, Morocco, in October 2019. The 13 full papers presented in this volume were carefully reviewed and selected from 46 submissions. The papers describe recent advances in methods and applications of va...

https://www.springer.com/journal/11590/updates/17535960

This book presents recent theoretical and practical advances in operational research (OR). The papers focus on a number of key areas including combinatorial optimization, integer programming, heuristics, and mathematical programming. In addition, this volume highlights OR applications in different areas such as financial decision making, marketing,...

This special issue of the International Transactions in Operational Research focuses on Matheuristics and Metaheuristics and is the largest published to date, highlighting the importance of the field and the broad scope of these methods and the reach of their applications. Academicians and practitioners responded with enthusiasm to three parallel c...

We consider the NP-hard problem of synthesis of optimal spanning communication subgraph in a given arbitrary simple edge-weighted graph. This problem occurs in the wireless networks while minimizing total transmission power consumptions. We propose a new method based on the variable neighborhood decomposition search metaheuristic for the approximat...

The Traveling Repairman Problem with profits generalizes the Traveling Repairman Problem, by taking into account the variability of the repairman’s profit over different time steps in order to maximize the total profit. In this paper, we first analyze the complexities of several neighborhood structures and the efficient updating of objective values...

We consider the stochastic linear programming problem with quantile criterion and continuous distribution of random parameters. Using the sample approximation, we obtain a stochastic programming problem with discrete distribution of random parameters. It is known that the solution to this problem provides an approximate solution to the problem with...

Having defined a complete bipartite graph G, with weights associated with both vertices and edges, the Bipartite Quadratic Programming problem (BQP) consists in selecting a subgraph that maximizes the sum of the weights associated with the chosen vertices and the edges that connect them. Applications of the BQP arise in mining discrete patterns fro...

In this paper, we propose the continuous variable neighborhood search method for finding all the solutions to a nonlinear system of equations (NSEs). We transform the NSE problem into an equivalent optimization problem, and we use a new objective function that allows us to find all the zeros. Instead of the usual sum-of-squares objective function,...

The goal of the less is more approach (LIMA) for solving optimization problems that has recently been proposed in Mladenović et al. (2016) is to find the minimum number of search ingredients that make a heuristic more efficient than the currently best. In this paper, LIMA is successfully applied to solve the obnoxious p‐median problem (OpMP). More...

In this paper we propose a new variant of the Variable Neighborhood Decomposition Search (VNDS) heuristic for solving global optimization problems. We call it Ascent-Descent VNDS since it performs “boundary effect”, or local search step, even if the improvement in solving the subproblem has not been obtained. We apply it in detecting communities in...

Variable neighborhood search (VNS) is a proven heuristic framework for finding good solutions to combinatorial and global optimization problems. In this paper two VNS-based heuristics are proposed for solving the capacitated clustering problem. The first follows a standard VNS approach, and the second a skewed VNS that allows moves to inferior solu...

We study the single-processor scheduling problem with time restrictions in order to minimize the makespan. In this problem, n independent jobs have to be processed on a single processor, subject only to the following constraint: During any time period of length \(\alpha >0\) the number of jobs being executed is less than or equal to a given integer...

This paper extends some explanations about the convergence of a type of Evolution Strategies guided by Neighborhood Structures, the Neighborhood Guided Evolution Strategies. Different well-known Neighborhood Structures commonly applied to Vehicle Routing Problems are used to highlight the evolution of the move operators during the evolutionary proc...

Clustering is an automated and powerful technique for data analysis. It aims to divide a given set of data points into clusters which are homogeneous and/or well separated. A major challenge with clustering is to define an appropriate clustering criterion that can express a good separation of data into homogeneous groups such that the obtained clus...

We consider a two-stage stochastic programming problem with a bilinear loss function and a quantile criterion. The problem is reduced to a single-stage stochastic programming problem with a quantile criterion. We use the method of sample approximations. The resulting approximating problem is considered as a stochastic programming problem with a dis...

We propose a new matching problem for combinatorial optimization in financial markets. The problem studied here has arisen from the financial regulators that collect transaction data across regulated assets classes. Unlike previous matching problems, our focus is to identify any unhedged/unmatched derivative, Contract for Difference (CFD) with its...

Local search heuristic that explores several neighborhood structures in a deterministic way is called variable neighborhood descent (VND). Its success is based on the simple fact that different neighborhood structures do not usually have the same local minimum. Thus, the local optima trap problem may be resolved by deterministic change of neighborh...

Variable neighborhood search (VNS) is a metaheuristic for solving combinatorial and global optimization problems. Its basic idea is systematic change of neighborhood both within a descent phase to find a local optimum and in a perturbation phase to get out of the corresponding valley. In this chapter we present the basic schemes of variable neighbo...

In the field of Automatic Programming (AP), the solution of a problem is a program, which is usually presented by a tree with a specific structure. This tree contains different types of nodes, and is called an AP tree. For solving AP problems, we propose a new local search procedure that adapts the known ‘elementary tree transformation’ (ETT) into...

Nelder-Mead method (NM) for solving continuous non-linear optimization problem is probably the most cited and the most used method in the optimization literature and in practical applications, too. It belongs to the direct search methods, those which do not use the first and the second order derivatives. The popularity of NM is based on its simplic...

Let G=(V,E,L) be an edge-labeled graph. Let V be the set of vertices of G, E the set of edges, L the set of labels (colors) such that each edge e∈E has an associated label L(e). The goal of the minimum labeling global cut problem (MLGCP) is to find a subset L′⊆L of labels such that G′=(V,E′,L\L′) is not connected and |L′| is minimized. In this work...

One of the most popular topics in analyzing complex networks is the detection of its community structure. In this paper, we introduce a new criterion for community detection, called the E‐quality function. The quality of an individual community is defined as a difference between its benefit and its cost, where both are exponential functions of the...

In this chapter, we consider The Multiprocessor Scheduling Problem with Communication Delays. We propose a new Mixed Integer Program (MIP) formulation for this problem taking into account the precedence constraints and the communication delays—delays that depend on the network and the tasks. The new proposed formulation reduces both the number of v...

This paper presents a review of the well-known K-means, H-means, and J-means heuristics, and their variants, that are used to solve the minimum sum-of-squares clustering problem. We then develop two new local searches that combine these heuristics in a nested and sequential structure, also referred to as variable neighborhood descent. In order to s...

Based on the system dynamics (SD) model, this paper puts forward a quantitative method to evaluate the earthquake emergency plan in China. Firstly, we analyze the disaster system structure and the content of plan. Using the analysis results, we establish a system dynamics (SD) model and then carry out its simulation. According to the simulation res...

k-means is a benchmark algorithm used in cluster analysis. It belongs to the large category of heuristics based on location-allocation steps that alternately locate cluster centers and allocate data points to them until no further improvement is possible. Such heuristics are known to suffer from a phenomenon called degeneracy in which some of the c...

Clustering addresses the problem of finding homogeneous and well-separated subsets, called clusters, from a set of given data points. In addition to the points themselves, in many applications, there may exist constraints regarding the size of the clusters to be found. Particularly in balanced clustering, these constraints impose that the entities...

The balanced clustering problem consists of partitioning a set of n objects into K equal-sized clusters as long as n is a multiple of K. A popular clustering criterion when the objects are points of a q-dimensional space is the minimum sum of squared distances from each point to the centroid of the cluster to which it belongs. We show in this paper...

In this paper we study the k-labelled spanning forest (kLSF) problem: given an undirected graph whose edges are labelled, and an integer positive value k, the aim is to find a spanning forest of the input graph with the minimum number of connected components and the upper bound k on the number of labels. The problem is related to the minimum labell...

Abstract In this paper we propose a new local search method for solving automatic programming problem and we use it within recent Variable Neighborhood Programming (VNP) method. In automatic programming area the solution is a program and the most common way is to present it by a tree T with the specific structure (i.e., it has different types of no...

In this paper we review a recently proposed variant of variable neighborhood search (VNS) referred to as nested variable neighborhood search (NVNS) and propose a generalization of this approach. In addition, we develop a heuristic stemming from this general framework and apply it on the capacitated clustering problem (CCP). Based on obtained result...

Among the methods to deal with optimization tasks, parallel metaheuristics have been used in many real-world and scientific applications to efficiently solve these kind of problems. This paper presents a novel Multi Improvement strategy for dealing with the Minimum Latency Problem (MLP), an extension the classic Traveling Salesman Problem. This str...