Panos Pardalos

University of Florida | UF · Department of Industrial and Systems Engineering

This paper presents a comprehensive survey of methods which can be utilized to search for solutions to systems of nonlinear equations (SNEs). Our objectives with this survey are to synthesize pertinent literature in this field by presenting a thorough description and analysis of the known methods capable of finding one or many solutions to SNEs, an...

This paper presents a comprehensive survey of methods which can be utilized to search for solutions to systems of nonlinear equations (SNEs). Our objectives with this survey are to synthesize pertinent literature in this field by presenting a thorough description and analysis of the known methods capable of finding one or many solutions to SNEs, an...

Consider a graph with nonnegative node weight. A vertex subset is called a CDS (connected dominating set) if every other node has at least one neighbor in the subset and the subset induces a connected subgraph. Furthermore, if every other node has at least m neighbors in the subset, then the node subset is called a [Formula: see text]CDS. The minim...

Supply chain (SC) resiliency and risk management have garnered increasing attention recently. While several studies have explored the use of scale-free network models to design and optimise SC networks, there remains a lack of a generalised stress-testing method that can be applied to various types and sizes of SCs. To address this, we propose a no...

This paper provides a thorough exploration of the absolute value equations Ax−|x|=b, a seemingly straightforward concept that has gained heightened attention in recent years. It is an NP-hard and nondifferentiable problem and equivalent with the standard linear complementarity problem. Offering a comprehensive review of existing literature, the stu...

With the recent advances in machine learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in the literature, with a lack of studies on the application of deep learning sequence models such as Recurrent Neur...

In this paper, we consider the counting function $\QEnum(y) = |\PC_{y} \cap \ZZ^{n_x}|$ for a parametric polyhedron $\PC_{y} = \{ x \in \RR^{n_x} \colon A x \leq b + B y\}$, where $y \in \RR^{n_y}$. We give a new representation of $\QEnum(y)$, called a \emph{piece-wise step-polynomial with periodic coefficients}, which is a generalization of piece-...

In this paper, an integrated service planning and physician scheduling problem in the outpatient department is investigated, considering the re-consultation of patients as well as multiple types of physicians and consultation services. The problem is to determine 1) the number of patients to be served for each type of consultation service in each s...

Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error of variational autoencoder (VAE) by maximizing the evidence lower bound. We revisit VAE from the perspective of information theory to provide some theoretical fo...

Aiming to meet increasing energy demand and reduce carbon emissions caused by fossil fuel consumption, China is vigorously supporting the diffusion of photovoltaic (PV) generation equipment. The government and banks are recognized as playing irreplaceable and important roles in promoting PV investment. Therefore, this study applies a tripartite evo...

Global vaccine revenues are projected at $59.2 billion, yet large-scale vaccine distribution remains challenging for many diseases in countries around the world. Poor management of the vaccine supply chain can lead to a disease outbreak, or at worst, a pandemic. Fortunately, a large number of those challenges, such as decision-making for optimal al...

In portfolio optimization, we may be dealing with misspecification of a known distribution, that stock returns follow it. The unknown true distribution is considered in terms of a Wasserstein-neighborhood of P to examine the tractable formulations of the portfolio selection problem. This study considers a distributionally robust portfolio optimizat...

Predicting corporate bankruptcy is one of the fundamental tasks in credit risk assessment. In particular, since the 2007/2008 financial crisis, it has become a priority for most financial institutions, practitioners, and academics. The recent advancements in machine learning (ML) enabled the development of several models for bankruptcy prediction....

This paper deals with a parallel machine scheduling problem with linearly increasing energy consumption cost. Maintenance activities are considered in the problem. After maintenance, the machine energy consumption cost returns to the normal level. Thus, an important decision is how to determine a reasonable number of maintenance activities to enabl...

Service-oriented manufacturing has been a prevalent trend in integrating product collaborative development with maintenance service, whereas the final products are frequently trapped into severe quality defects lacking appropriate contracting incentives. This paper establishes a tractable framework investigating the alignment of inspection and warr...

The optimal operation of a conveyor network used in distribution centers is critical to delivery service quality. Optimizing conveyor operations entails routing a given number of items from loading locations to unloading locations in the shortest possible time, called makespan. Developing an efficient model for conveyor operations is necessary to a...

In this paper, a Positron Emission Tomography/Computed Tomography (PET/CT) examination scheduling problem considering multi-stage processes is studied. Before the actual examination process, imaging agents (a drug with radioactivity) need to be injected into patients. The radioactivity of the imaging agents continuously decays, which results in the...

In this paper, an extension of the minimum cost flow problem is considered in which multiple incommensurate weights are associated with each arc. In the minimum cost flow problem, flow is sent over the arcs of a graph from source nodes to sink nodes. The goal is to select a subgraph with minimum associated costs for routing the flow. The problem is...

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...

We consider the restricted bounded inverse optimal value problem on minimum spanning tree. In a connected undirected network G=(V,E,w), we are given a spanning tree T0, a weight vector w, a lower bound vector l, an upper bound vector u, a cost vector c and a value K. We aim to obtain a new weight vector w̄ satisfying the lower and upper bounds such...

In view of some shortcomings of traditional vertex 1-center (V1C), we introduce a vertex quickest 1-center (VQ1C) problem on a tree, which aims to find a vertex such that the maximum transmission time to transmit σ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \...

1 Preface This special issue contains 11 papers submitted by the participants of the International Conference “Mathematical Optimization Theory and Operations Research” (MOTOR 2020) which was held online from July 6 to July 10, 2020. Like previous MOTOR conferences, this event was intended to bring together researchers and practitioners working in...

In the real world, there are many set function optimization problems with objective function and/or constraint which is neither submodular nor supermodular. Usually, it is hard to study their approximation solutions. In this chapter, we summarize existing efforts in the literature.

In this chapter, we study the incremental method which is very different from those methods in the previous chapters. This method does not use the self-reducibility. It starts from a feasible solution, and in each iteration, computation moves from a feasible solution to another feasible solution by improving the objective function value. The increm...

Linear programming (LP) is an important combinatorial optimization problem, and in addition, it is an important tool to design and to understand algorithms for other problems. In this chapter, we introduce LP theory starting from Simplex Algorithm, which is an incremental method.

Self-reducibility is the backbone of each greedy algorithm in which self-reducibility structure is a tree of special kind, i.e., its internal nodes lie on a path. In this chapter, we study algorithms with such a self-reducibility structure and related combinatorial theory supporting greedy algorithms.

The divide-and-conquer is an important technique for design of algorithms. In this chapter, we will employ several examples to introduce this technique, including the rectilinear minimum spanning tree, the Fibonacci search method, and the sorting problem. Sorting is not a combinatorial optimization problem. However, it appears in algorithms very of...

Greedy is an important strategy to design approximation algorithms, especially in the study of submodular optimization problems. In this chapter, we will explore this strategy together with important results in submodular optimization.

A divide-and-conquer algorithm consists of many iterations. Usually, each iteration contains three steps. In the first step (called the divide step), divide the problem into smaller subproblems. In the second step (called conquer step), solve those subproblems. In the third step (called the combination step), combine solutions for subproblems into...

There are three types of incremental methods, primal, dual, and primal-dual. In Chap. 6, we touched all of them for linear programming (LP). This chapter is contributed specially to primal-dual methods for further exploring techniques about primal-dual with a special interest in the minimum cost flow. Actually, the minimum cost flow is a fundamenta...

Restriction is a major technique in design of approximation algorithms. The Steiner minimum tree is a classic NP-hard combinatorial optimization problem. In the study of the Steiner minimum tree and its variations, restriction plays an important role.

The relaxation is a powerful technique to design approximation algorithms. It is similar to restriction, in terms of making a change on feasible domain; however, in an opposite direction, i.e., instead of shrinking the feasible domain, enlarge it by relaxing certain constraint. There are various issues about relaxation. In this chapter, we study so...

The class P consists of all polynomial-time solvable decision problems. What is the class NP? There are two popular misunderstandings: (1) NP is the class of problems which are not polynomial-time solvable. (2) A decision problem belongs to the class NP if its answer can be checked in polynomial-time.

Nowadays most cancer patients are treated by radiotherapy. The treatment duration of patients will grow longer over time because of the half-life decaying effect of the radioactive source, which can be regarded as a continuous non-linear deteriorating effect. How to sequence the treatment of the patients before the radioactivity is reduced to the l...

In this paper, we study an integrated physician planning and scheduling problem (IPPSP) considering multiple types of services, which can be done by multiple qualifications of physicians with different costs and revenues. The problem of physician planning is to assign the working periods to each physician, based on which the problem of physician sc...

Human Learning Optimization (HLO) is a simple yet efficient binary meta-heuristic, in which three learning operators, i.e. the random learning operator (RLO), individual learning operator (ILO) and social learning operator (SLO), are developed to mimic human learning mechanisms to solve optimization problems. Among these three operators, RLO direct...

The max+sum spanning tree (MSST) problem is to determine a spanning tree T whose combined weight max e∈Tw(e) + ∑ e∈Tc(e) is minimum for a given edge-weighted undirected network G(V, E, c, w). This problem can be solved within O(mlog n) time, where m and n are the numbers of edges and nodes, respectively. An inverse MSST problem (IMSST) aims to dete...

This paper investigates the collaborative scheduling problem of research and development (R&D) and manufacturing processes in the context of high-end equipment, and the objective is to minimize the makespan. A unique feature is the dual usage of a limited resource budget, which can increase the quantity of both researchers and assembly lines. This...

In this review, we present the main approaches developed around satellite and airborne Synthetic Aperture Radar (SAR) imagery. The great range of SAR imagery applications is summarized in this paper. We organize the most popular methods and their applications in a cohesive manner. SAR data applications are classified into earth observation and obje...

Algorithm portfolios are multi-algorithmic schemes that combine a number of solvers into a joint framework for solving global optimization problems. A crucial part of such schemes is the resource allocation process that is responsible for assigning computational resources to the constituent algorithms. We propose a resource allocation process based...

Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization...

Public companies in the US stock market must annually report their activities and financial performances to the SEC by filing the so-called 10-K form. Recent studies have demonstrated that changes in the textual content of the corporate annual filing (10-K) can convey strong signals of companies’ future returns. In this study, we combine natural la...

Given a graph, the maximum clique problem (MCP) asks for determining a complete subgraph with the largest possible number of vertices. We propose a new exact algorithm, called CliSAT, to solve the MCP to proven optimality. This problem is of fundamental importance in graph theory and combinatorial optimization due to its practical relevance for a w...

Human Learning Optimization (HLO) is a simple yet powerful meta-heuristic developed based on a simplified human learning model. Many cognitive activities of humans contain an element of reasoning, and with reasoning, humans can gain deeper information on problems to boost learning performance. Inspired by this fact, this paper proposes a novel huma...

In this paper, we investigate the chance-constrained support vector machine (SVM) problem in which the data points are virtually uncertain although some properties of distributions are available. Thus the robust joint chance-constrained SVM is applied to consider the probability of any existing misclassification in the uncertain data. We transform...

One of the big concerns when planning the expansion of power distribution systems (PDS) is reliability. This is defined as the ability to continuously meet the load demand of consumers in terms of quantity and quality. In a scenario in which consumers increasingly demand high supply quality, including few interruptions and continuity, it becomes es...

In practical manufacturing systems, the job processing time usually varies with the performance change of manufacturing resources, among which the learning and deteriorating effects are typical characteristics. Due to the interests from both academic exploration and industrial innovation, the research on scheduling problems with these effects is ab...

We study hierarchical games where the second stage consists of a finite noncooperative game. To ensure that the lower level problem admits solutions, its mixed extension is considered. By using the Shannon entropy, a regularization scheme for the two-stage game is introduced and some properties are presented, as the asymptotic subgame perfectness.

Symbolic Regression has been widely used during the last decades for inferring complex models. The foundation of its success is due to the ability to recognize data correlations, defining non-trivial and interpretable models. In this paper, we apply Symbolic Regression to explore possible uses and obstacles for describing stochastic financial proce...

In this paper, two distributed multi-proximal primal–dual algorithms are proposed to deal with a class of distributed nonsmooth resource allocation problems. In these problems, the global cost function is the summation of local convex and nonsmooth cost functions, each of which consists of one twice differentiable function and multiple nonsmooth fu...

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...

Discrete sequential data is the collection of an ordered series of discrete events or abstractions from a set of data points collected at different time periods. These processes are one of the most common and important process types encountered in various domains. It is customary to discover similarities and to detect the indicators of anomalies in...

In this paper, we investigate a manufacturer selection and composition problem for Distributed Virtual Manufacturing Network (DVMN) with order acceptance and scheduling of deteriorating jobs, where potential manufacturers include proprietary plants and outsourced co-manufacturers. In such a problem, at the beginning of planning horizon, a manufactu...

In this study, we discuss and develop a distributionally robust joint chance-constrained optimization model and apply it for the shortest path problem under resource uncertainty. In sch a case, robust chance constraints are approximated by constraints that can be reformulated using convex programming. Since the issue we are discussing here is of th...

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...

This volume offers a wealth of interdisciplinary approaches to artificial intelligence, machine learning and optimization tools, which contribute to the optimization of urban features towards forming smart, sustainable, and livable future cities. Special features include: • New research on the design of city elements and smart systems with respect...

Increasing in-farm efficiency and productivity is the main concern of farmers across the world considering the increasing demand of agricultural products and decreasing farmland. Towards that direction, the agricultural sector is being transformed in a rapid pace. Precision agriculture uses innovative technologies to increase crop yield while using...

Purchase order financing (POF) and buyer direct financing (BDF) are both innovative financing schemes aiming to help financial constrained suppliers secure financing for production. In this paper, we investigate the interaction mechanism between suppliers’ financing strategy selection and manufacturers’ loans offering strategy adoption under two in...

Realistic epidemic spreading is usually driven by traffic flow in networks, which is not captured in classic diffusion models. Moreover, the progress of a node's infection from mild to severe phase has not been particularly addressed in previous epidemic modeling. To address these issues, we propose a novel traffic-driven epidemic spreading model b...

In this paper, we discuss the medical staff scheduling problem in the Mobile Cabin Hospital (MCH) during the pandemic outbreaks. We investigate the working contents and patterns of the medical staff in the MCH of Wuhan during the outbreak of Covid-19. Two types of medical staff are considered in the paper, i.e., physicians and nurses. Besides, two...

In this special issue, we call for rigorous research that borrows from various disciplines and presents relevant and original work related to the disruption of illicit markets using OR and analytics approaches. This can be rendered in various forms, such as a new way of framing the issue via problematization, design approaches and constraint induce...

The goal of this special issue is thus to improve the research and practice in issues related to analytics AI and OR solutions for social goods (AI&OR4SG) by mutually benefit from practitioners, researchers, and policymakers international collaborations; promoting the development of new methodology and metrics to address the specific challenges rel...

Objectives Acute kidney injury (AKI) affects up to one-quarter of hospitalised patients and 60% of patients in the intensive care unit (ICU). We aim to understand the baseline characteristics of patients who will develop distinct AKI trajectories, determine the impact of persistent AKI and renal non-recovery on clinical outcomes, resource use, and...

This Chapter aims to highlight the technological tools that help to apply the principles of circular economy in agriculture that embody the key concepts of sustainable development (environment, society, economy). The definition of circular economy, mainly environmentally oriented, is being examined emphasizing to energy flows and the need to reduce...

We consider the maximum shortest path interdiction problem by upgrading edges on trees under Hamming distance (denoted by (MSPITH)), which has wide applications in transportation network, network war and terrorist network. The problem (MSPITH) aims to maximize the length of the shortest path from the root of a tree to all its leaves by upgrading ed...

The development of digital technology, such as data mining and analysis techniques, has enabled e-commerce platforms to use the data generated in their ecosystems and forecast the online demand more accurately. By sharing the forecast information, platforms help their partners reduce the demand uncertainty. To examine the effects of the shared info...

In this research, stochastic geometric programming with joint chance constraints is investigated with elliptically distributed random parameters. The constraint’s random coefficient vectors are considered dependent, and the dependence of the random vectors is handled through copulas. Moreover, Archimedean copulas are used to derive the random rows...