# Fred GloverUniversity of Colorado Boulder | CUB · School of Engineering & Leeds School of Business

Fred Glover

Doctor of Philosophy

## About

603

Publications

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Introduction

My wife Diane and I live in Boulder, Colorado and are fond of travel and outdoor activities. On the personal side, apart from research, my favorite pursuits are hiking, biking and speculative writing.

## Publications

Publications (603)

Quantum Bridge Analytics relates generally to methods and systems for hybrid classical-quantum computing, and more particularly is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future. This is the fi...

Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing, and is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future. This is the second of a two-part tutorial...

Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking. As a useful model for a variety of practical applications, however, it is a computationally challenging problem. In this paper, we propose an effective hybrid evolutionary ranking algorithm to solve the rank ag...

The minimum connected dominating set (MCDS) problem consists of selecting a minimum set of vertices from an undirected graph, such that each vertex not in this set is adjacent to at least one of the vertices in it, and the subgraph induced by this vertex set is connected. This paper presents a fast vertex weighting (FVW) algorithm for solving the M...

Tabu search is an optimization methodology that guides a local heuristic search procedure to explore the solution space beyond local optimality. It is substantiated by the hypothesis that an intelligent solving algorithm must incorporate memory to base its decisions on information collected during the search. The method creates in this way a learni...

Focal distance tabu search modifies a standard tabu search algorithm for binary optimization by augmenting a periodic diversification step that drives the search away from a current best (or elite) solution until the objective function deteriorates beyond a specified threshold or until attaining a lower bound on the distance from the originating so...

We propose a new algorithm for fixed‐charge network flow problems based on ghost image (GI) processes as proposed in Glover (1994) and adapted to fixed‐charge transportation problems in Glover et al. (2005). Our GI algorithm iteratively modifies an idealized representation of the problem embodied in a parametric GI, enabling all steps to be perform...

Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing, and is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future.This is the second of a two-part tutorial t...

We propose a new self-organizing algorithm for fixed-charge network flow problems based on ghost image (GI) processes as proposed in Glover (1994) and adapted to fixed-charge transportation problems in Glover, Amini and Kochenberger (2005). Our self-organizing GI algorithm iteratively modifies an idealized representation of the problem embodied in...

Scatter Search is an evolutionary metaheuristic introduced by Glover (1977) as a heuristic for integer programming and was joined with a directional rounding strategy for 0–1 Mixed Integer Programming (MIP) problems based on Star Paths in Glover (1995). In this paper, we address directional rounding both independently and together with these other...

Clustering consists of partitioning data objects into subsets called clusters according to some similarity criteria. This paper addresses a structure for generating overlapping clusters, where data objects can belong to more than one subset, which we join with bi-objective optimization and link to biclustering for problems with binary data. Biclust...

Clustering consists of partitioning data objects into subsets called clusters according to some similarity criteria. This paper addresses a generalization called quasi-clustering that allows overlapping of clusters, and which we link to biclustering. Biclustering simultaneously groups the objects and features so that a specific group of objects has...

The multiple vehicle pickup and delivery problem is a generalization of the traveling salesman problem that has many important applications in supply chain logistics. One of the most prominent variants requires the route durations and the capacity of each vehicle to lie within given limits, while performing the loading and unloading operations by a...

Quantum Bridge Analytics relates generally to methods and systems for hybrid classical-quantum computing, and more particularly is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future. This is the fi...

Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing, and is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future.
This is the second of a two-part tutoria...

Critical node problems involve identifying a subset of critical nodes from an undirected graph whose removal results in optimizing a pre-defined measure over the residual graph. As useful models for a variety of practical applications, these problems are computational challenging. In this paper, we study the classic critical node problem (CNP) and...

Diversification-Based Learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization. We show that the DBL framework goes significantly beyond that of the more recent Opposition-based learning (OBL) framework introduced in Tizhoosh (2005), which...

We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search. Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greate...

A long-standing challenge in the metaheuristic literature is to devise a way to select parent solutions in evolutionary population-based algorithms to yield better offspring, and thus provide improved solutions to populate successive generations. We identify a way to achieve this goal that simultaneously improves the efficiency of the evolutionary...

Evolutionary computing is a general and powerful framework for solving difficult optimization problems, including those arising in expert and intelligent systems. In this work, we investigate for the first time two hybrid evolutionary algorithms incorporating tabu search for solving the generalized max-mean dispersion problem (GMaxMeanDP) which has...

Population-based evolutionary algorithms usually manage a large number of individuals to maintain the diversity of the search, which is complex and time-consuming. In this paper, we propose an evolutionary algorithm using only two individuals, called master-apprentice evolutionary algorithm (MAE), for solving the flexible job shop scheduling proble...

Recent years have witnessed the remarkable discovery that the Quadratic Unconstrained Binary Optimization (QUBO) model unifies a wide variety of combinatorial optimization problems, and moreover is the foundation of adiabatic quantum computing and a subject of study in neuromorphic computing. Through these connections, QUBO models lie at the heart...

Meta-analytics represents the unification of metaheuristics and analytics, two fields of the foremost interest and practical importance. While metaheuristics provide a modern framework and an arsenal of cutting-edge techniques to handle complex, real-world problems, Analytics embodies the use of prediction and optimization techniques in practical c...

We present a new clustering algorithm for handling complexities encountered in analysing data sets of hotel ratings and analyse its performance in a clustering case study. In the setting we address, business constraints and coordinates (among other individual attributes of objects) are unknown and only distances between objects are available to the...

The minimum differential dispersion problem is a NP-hard combinatorial optimization problem with numerous relevant applications. In this paper, we propose an intensification-driven tabu search algorithm for solving this computationally challenging problem by integrating a constrained neighborhood, a solution-based tabu strategy, and an intensified...

The Cross-Docking Assignment Problem (CDAP) is a challenging optimization problem in supply chain management with important practical applications in the trucking industry. The goal is to assign incoming trucks (outgoing trucks) to inbound (outbound) doors to minimize the material handling cost within a cross-docking platform while respecting the c...

This chapter describes the history of metaheuristics in five distinct periods, starting long before the first use of the term and ending a long time in the future. The field of metaheuristics has undergone several paradigm shifts that have changed the way researchers look upon the development of heuristic methods. Most notably, there has been a shi...

The 0–1 multidimensional knapsack problem is a well-known NP-hard combinatorial optimization problem with numerous applications. In this work, we present an effective two-phase tabu-evolutionary algorithm for solving this computationally challenging problem. The proposed algorithm integrates two solution-based tabu search methods into the evolution...

The bipartite boolean quadratic programming problem with partitioned variables (BQP-PV) is an NP-hard combinatorial optimization problem that accommodates a variety of real-life applications. We propose an adaptive tabu search with strategic oscillation (ATS-SO) approach for BQP-PV, which employs a multi-pass search framework where each pass consis...

Integer programming models for clustering have applications in diverse fields addressing many problems such as market segmentation and location of facilities. Integer programming models are flexible in expressing objectives subject to some special constraints of the clustering problem. They are also important for guiding clustering algorithms that...

The maximum min-sum dispersion problem (Max-Minsum DP) is an important representative of a large class of dispersion problems. Having numerous applications in practice, the NP-hard Max-Minsum DP is however computationally challenging. This paper introduces an effective solution-based tabu search (SBTS) algorithm for solving the Max-Minsum DP approx...

We introduce a new class of assignment-based neighborhoods for symmetric and asymmetric traveling salesman problems that exhibits a combinatorial leverage property, by which a tour can be generated in polynomial time that dominates an exponential number of other tours. The ejection chain perspective motivating the new neighborhoods differs from tha...

A cross docking facility is a type of warehouse in supply chain management that allows orders to be prepared with or without going through the phase of storing products in the warehouse and subsequently selecting them for delivery. The goods are unloaded from incoming trucks called origins on inbound doors of a cross-docking facility platform and,...

A cross docking facility is a type of warehouse in supply chain management that allows orders to be prepared with or without going through the phase of storing products in the warehouse and subsequently selecting them for delivery. The goods are unloaded from incoming trucks called origins on inbound doors of a cross-docking facility platform and,...

Pseudo-Centroid Clustering replaces the traditional concept of a centroid expressed as a center of gravity with the notion of a pseudo-centroid (or a coordinate free centroid) which has the advantage of applying to clustering problems where points do not have numerical coordinates (or categorical coordinates that are translated into numerical form)...

We develop a series of theorems about the graph structure of the classical Minimum Linear Arrangement (MinLA) problem which disclose properties that can be exploited by Multi-Neighborhood Search (MNS) algorithms. As a foundation, we differentiate between swaps of labels attached to adjacent and non-adjacent nodes to create two new neighborhood clas...

This is the latest version of our paper though the type of the file was mislabeled.

The emergence of high-dimensional data requires the design of new optimization methods. Indeed, conventional optimization methods require improvements, hybridization, or parameter tuning in order to operate in spaces of high dimensions. In this paper, we present a new adaptive variant of a pattern search algorithm to solve global optimization probl...

The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a unifying model for representing a wide range of combinatorial optimization problems, and for linking a variety of disciplines that face these problems. A new class of quantum annealing computer that maps QUBO onto a physical qubit network structure with specific size and ed...

The quadratic unconstrained binary optimization (QUBO) problem arises in diverse optimization applications ranging from Ising spin problems to classical problems in graph theory and binary discrete optimization. The use of preprocessing to transform the graph representing the QUBO problem into a smaller equivalent graph is important for improving s...

The well-known K-means clustering algorithm has been employed widely in different application domains ranging from data analytics to logistics applications. However, the K-means algorithm can be affected by factors such as the initial choice of centroids and can readily become trapped in a local optimum. In this paper, we propose an improved K-mean...

We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search. Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greate...

This chapter describes the history of metaheuristics in five distinct periods, starting long before the first use of the term and ending a long time in the future.

We present a new clustering algorithm for handling complexities encountered in modeling hotel ratings data sets and analyze its performance in a clustering case study. In the setting we address, business constraints and coordinates (among other individual attributes of objects) are unknown and only distances between objects are available to the clu...

This paper presents tabu search and memetic search algorithms for solving the minimum differential dispersion problem. The tabu search algorithm employs a neighborhood decomposition candidate list strategy and a rarely used solution-based tabu memory. Unlike the typical attribute-based tabu list, the solution-based tabu strategy leads to a more hig...

In recent years, the general binary quadratic programming (BQP) model has been widely applied to solve a number of combinatorial optimization problems. In this paper, we recast the maximum vertex weight clique problem (MVWCP) into this model which is then solved by a probabilistic tabu search algorithm designed for the BQP. Experimental results on...

Supply chains often experience significant economic losses from disruptions such as facility breakdowns, transportation mishaps, natural calamities, and intentional attacks. To help respond and recover from a disruption, we investigate adjustments in order activity across four echelons including assembly. Simulation experiments reveal that the impa...

This paper proposes a learning-based path relinking algorithm (LPR) for solving the bandwidth coloring problem and the bandwidth multicoloring problem. Based on the population path-relinking framework, the proposed algorithm integrates a learning-driven tabu optimization procedure and a path-relinking operator. LPR is assessed on two sets of 66 com...

We propose new iterated improvement neighborhood search algorithms for metaheuristic optimization by exploiting notions of conditional influence within a strategic oscillation framework. These approaches, which are unified within a class of methods called multi-wave algorithms, offer further refinements by memory based strategies that draw on the c...

The capacitated single assignment hub location problem with modular link capacities is a variant of the classical hub location problem in which the cost of using edges is not linear but stepwise, and the hubs are restricted in terms of transit capacity rather than in the incoming traffic. We propose a metaheuristic algorithm based on strategic osci...

The capacitated arc routing problem (CARP) is a difficult combinatorial optimization problem that has been intensively studied in the last decades. We present a hybrid metaheuristic approach (HMA) to solve this problem which incorporates an effective local refinement procedure, coupling a randomized tabu thresholding procedure with an infeasible de...

Unconstrained binary quadratic programming (UBQP) provides a unifying modeling and solution framework for solving a remarkable range of binary optimization problems, including many accompanied by constraints. Current methods for solving UBQP problems customarily rely on neighborhoods consisting of flip moves that select one or more binary variables...

State-of-the-art branch and bound algorithms for mixed integer programming
make use of special methods for making branching decisions. Strategies that
have gained prominence include modern variants of so-called strong branching
(Applegate, et al.,1995) and reliability branching (Achterberg, Koch and
Martin, 2005; Hendel, 2015), which select variabl...

The quadratic multiple knapsack problem (QMKP) is a challenging combinatorial optimization problem with numerous applications. In this paper, we propose the first evolutionary path relinking approach (EPR) for solving the QMKP approximately. This approach combines advanced features both from the path relinking (PR) method and the responsive thresho...

An equitable k-coloring of an undirected graph G=(V,E) is a partition of its vertices into k disjoint independent sets, such that the cardinalities of any two independent sets differ by at most one. As a variant of the graph coloring problem (GCP), the equitable coloring problem (ECP) concerns finding a minimum k for which an equitable k-coloring e...

The generalized vertex cover problem (GVCP) was recently introduced in the literature and modeled as a binary linear program. GVCP extends classic vertex cover problems to include both node and edge weights in the objective function. Due to reported difficulties in finding good solutions for even modest sized problems via the use of exact methods (...