Framework For Exact Solutions Research Articles

Generalized Riskiness Index in Vehicle Routing Under Uncertain Travel Times: Formulations, Properties, and Exact Solution Framework

We consider a vehicle routing problem with time windows under uncertain travel times where the goal is to determine routes for a fleet of homogeneous vehicles to arrive at the locations of customers within their stipulated time windows to the maximum extent while ensuring that the total travel cost does not exceed a prescribed budget. Specifically, a novel performance measure that accounts for the riskiness associated with late arrivals at the customers, called the generalized riskiness index (GRI), is optimized. The GRI covers several existing riskiness indices as special cases and generates new ones. We demonstrate its salient managerial and computational properties to motivate it better. We propose alternative set partitioning-based models of the problem. To obtain the optimal solution, we develop an exact solution framework combining route enumeration and branch-price-and-cut algorithms, in which the GRI is dealt with in route enumeration and column generation subproblems. We mainly reduce the solution space by exploiting the GRI and budget constraints’ properties without losing optimality. The proposed method is tested on a collection of instances derived from the literature. The results show that a new instance of the GRI outperforms several existing riskiness indices in mitigating lateness. The exact method can solve instances with up to 100 nodes to optimality. It can consistently solve instances involving up to 50 nodes, outperforming state-of-the-art methods by more than doubling the manageable instance size. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72101187, 72371204, 72021002, and 71901180], the Qatar National Research Fund [Grant ARG01-0430-230029], Natural Science Foundation of Sichuan Province [24NSFSC6232], and Guanghua Talent Project of the Southwestern University of Finance and Economics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0345 .

Endowing black holes with beyond-Horndeski primary hair: An exact solution framework for scalarizing in every dimension

This work outlines a straightforward mechanism for endorsing primary hair into Schwarzschild black holes, resulting in a unique modification within the framework of a special scalar-tensor theory, the so-called beyond-Horndeski gravity. The derived solutions are exact, showcase primary hair with a regular scalar field profile everywhere, and continuously connect with the vacuum geometry. Initially devised to introduce primary hair in spherically symmetric solutions within general relativity in any dimension, our investigation explores the conditions under which spherically symmetric black holes in alternative gravitational theories become amenable to the endowment of primary hair through a similar pattern. As a preliminary exploration, we embark on the process of endowing primary hair to the Reissner-Nordström black hole. Subsequently, we extend our analysis to encompass spherically symmetric solutions within Lovelock and cubic quasitopological gravity theories. Published by the American Physical Society 2024

An automated exact solution framework towards solving the logistic regression best subset selection problem

An automated logistic regression solution framework (ALRSF) is proposed to solve a mixed integer programming (MIP) formulation of the well known logistic regression best subset selection problem. The solution framework firstly determines the optimal number of independent variables that should be included in the model using an automated cardinality parameter selection procedure. The cardinality parameter dictates the size of the subset of variables and can be problem-specific. A novel regression parameter fixing heuristic that utilises a Benders decomposition algorithm is applied to prune the solution search space such that the optimal regression parameter values are found faster. An optimality gap is subsequently calculated to quantify the quality of the final regression model by considering the distance between the best possible log-likelihood value and a log-likelihood value that is calculated using the current parameter values. Attempts are then made to reduce the optimality gap by adjusting regression parameter values. The ALRSF serves as a holistic variable selection framework that enables the user to consider larger datasets when solving the best subset selection logistic regression problem by significantly reducing the memory requirements associated with its mixed integer programming formulation. Furthermore, the automated framework requires minimal user intervention during model training and hyperparameter tuning. Improvements in quality of the final model (when considering both the optimality gap and computing resources required to achieve a result) are observed when the ALRSF is applied to well-known real-world UCI machine learning datasets. Keywords: Best subset selection, Independent variable selection, Logistic regression, Mixed integer programming

Dock assignment and truck scheduling problem; consideration of multiple scenarios with resource allocation constraints

The notion of ‘resource’ plays an important role in the overall efficiency and performance of most cross-docks. The processing time can often be described in terms of the resources allocated to different trucks. Conversely, for a given processing time, different combinations of resources can be prescribed. We study the problem of truck scheduling and dock assignment in the presence of resource constraints. In the absence of a closed-form (or well-defined) linear formulation describing the processing times as a function of resources, expert’ knowledge has been mobilized to enable modelling of the problem as an integer linear model. Two cases are taken into account: In the first one, the expert believes in his/her estimation of the processing time for every truck and only proposes a different combination of resources for his/her estimation, while in the second one the expert proposes a limited number of resource deployment scenarios for serving trucks, each of which has a different combination of resources and different processing times. We propose a novel compact integer programming formulation for the problem, which is particularly designed with an embedded structure that can be exploited in dual decomposition techniques with a remarkably computationally efficient column generation approach in this case. The case in which a scenario with invariant processing time is considered and modelled as a special case of the proposed model. Since a direct application of commercial solvers such as CPLEX to solve instances of this problem is not realistic, we propose a branch-and-price framework and, moreover, several classes of valid inequalities. Our extensive computational experiments confirm that the proposed exact solution framework is very efficient and viable in solving real-size instances of the practice and in a reasonable amount of time.

New pricing strategies and an effective exact solution framework for profit-oriented ring arborescence problems

We study novel solution methods for a class of network optimization problems that find application in strategic planning of telecommunication infrastructure. These directed network design models are used to compute centralized two-level networks under capacity constraints: Circuits on the inner level and directed trees in the periphery.We develop non-compact arc-based and set-packing formulations and integrate cuts to strengthen polyhedral descriptions of their relaxations. Theoretical results on polyhedral structure are provided that serve as the foundation of our branch-and-cut-and-price methods. For the column generation component, we introduce new relaxations of the pricing problem that are based on not necessarily elementary ring arborescences: q-d-Steiner-arb and ng-ring-Steiner-arbs. These models are related to q-arbs and ng-route relaxations known to be state-of-the-art for related network optimization models including vehicle routing. Our effective algorithmic framework incorporates iterated local search based primal heuristics and exact pricing strategies that can be applied to all considered model variants and related network design problems.The novel methods clearly outperform existing approaches from the literature regarding both solution time and solution quality. In a computational study, we show that our algorithms are capable of closing most optimality gaps for unsolved literature instances with up to 51 vertices. Furthermore, we provide results for new larger instances with up to 101 vertices.

Large-displacement symmetrical foldable cones.

A foldable cone, or f-cone, is a discrete vertex folded from flat with curved faces, and is an exemplar of nonrigid origami. We determine the exact equilibrium shape of a well-folded symmetrical f-cone by geometrical considerations alone, by treating as cones intersecting along original fold lines expressing equal fold angles. When moderate displacements prevail, the shape is found alternatively from the spherical image of the vertex, from deploying Gauss's mapping. The analytical working is much less compared to direct linearization of the exact solution framework; expressions for shape parameters are availed in closed form, and some of their variation is accurately retained compared to the exact case.

On the phase velocity simulation of the multi curved viscoelastic system via an exact solution framework

The analysis of the wave propagation behavior of a sandwich structure with a soft core and multi-hybrid nanocomposite (MHC) face sheets is carried out in the framework of the higher-order shear deformation theory (HSDT). In order to take into account the viscoelastic influence, the Kelvin-Voight model is presented. In this paper, the constituent material of the core is made of an epoxy matrix which is reinforced by both macro- and nano-size reinforcements, namely carbon fiber (CF) and carbon nanotube (CNT). The effective material properties like Young's modulus or density are derived utilizing a micromechanical scheme incorporated with the Halpin–Tsai model. Then, on the basis of an energy-based Hamiltonian approach, the equations of motion are derived. The detailed parametric study is conducted, focusing on the combined effects of the viscoelastic foundation, CNT' weight fraction, core to total thickness ratio, small radius to total thickness ratio, and carbon fiber angle on the wave propagation behavior of sandwich structure. The results show that as well as increasing the phase velocity of the sandwich structure by increasing the wave number, this influence will be much more effective by increasing the damping factor. It is also observed that there is a critical value for the viscoelastic foundation that the relation between wave number and phase velocity will change from direct to indirect. The presented study outputs can be used in ultrasonic inspection techniques and structural health monitoring.

An Exact Solution Framework for Multitrip Vehicle-Routing Problems with Time Windows

The need to reduce pollution and traffic in city centers requires the use of small vans, electric vehicles, and drones to distribute goods. Because of autonomy and capacity issues, these vehicles need to perform multiple trips from/to the depot during the day. The category of decision-making problems modeling such distribution problems are known as multitrip vehicle-routing problems (MTVRPs), which generalize the well-known vehicle-routing problem by allowing vehicles to perform multiple trips per day. Several MTVRPs are solved in the literature with different mathematical models and algorithms. In “An Exact Solution Framework for Multitrip Vehicle-Routing Problems with Time Windows,” R. Paradiso, R. Roberti, D. Laganà, and W. Dullaert propose a single algorithm that can solve, to optimality, the MTVRP with capacity and time windows constraints and four variants of this problem featuring additional operational constraints. The proposed framework significantly outperforms the state-of-the-art algorithms from the literature.

Algorithmic expedients for the S-labeling problem

Graph labeling problems have been widely studied in the last decades and have a vast area of application. In this work, we study the recently introduced S-labeling problem, in which the nodes get labeled using labels from 1 to |V| and for each edge the contribution to the objective function, called S-labeling number of the graph, is the minimum label of its end-nodes. The goal is to find a labeling with minimum value. The problem is NP-hard for planar subcubic graphs, although for many other graph classes the complexity status is still unknown.In this paper, we present different algorithmic approaches for tackling this problem: We develop an exact solution framework based on Mixed-Integer Programming (MIP) which is enhanced with valid inequalities, starting and primal heuristics and specialized branching rules. We show that our MIP formulation has no integrality gap for paths, cycles and perfect n-ary trees, and, to the best of our knowledge, we give the first polynomial-time algorithm for the problem on n-ary trees as well as a closed formula for the S-labeling number for such trees. Moreover, we also present a Lagrangian heuristic and a constraint programming approach. A computational study is carried out in order to (i) investigate if there may be other special graph classes, where our MIP formulation has no integrality gap, and (ii) assess the effectiveness of the proposed solution approaches for solving the problem on a dataset consisting of general graphs.

An exact solution framework for the multiple gradual cover location problem

Facility and covering location models are key elements in many decision aid tools in logistics, supply chain design, telecommunications, public infrastructure planning, and many other industrial and public sectors. In many applications, it is likely that customers are not dichotomously covered by facilities, but gradually covered according to, e.g., the distance to the open facilities. Moreover, customers are not served by a single facility, but by a collection of them, which jointly serve them. In this paper we study the recently introduced multiple gradual cover location problem (MGCLP). The MGCLP addresses both of the issues described above.We propose the first exact approach to solve the MGCLP. In particular, we provide four different mixed-integer programming formulations for the MGCLP, all of them exploiting the submodularity of the objective function and developed a branch-and-cut framework based on these formulations. The framework is further enhanced by starting and primal heuristics and preprocessing procedures based on domination between facilities.The computational results show that our approach allows to effectively address different sets of instances. We provide equal or better solution values for 33 out of 40 instances from the literature, and provide proof of optimality for 20 of them (15 instances more with respect to previously published results). Many of these instances can be solved within a minute. We also introduce new instances with different characteristics and analyze the dependence of the structure of the obtained solutions with respect to such characteristics.

A decomposition approach to solve the selective graph coloring problem in some perfect graph families

Graph coloring is the problem of assigning a minimum number of colors to all vertices of a graph such that no two adjacent vertices receive the same color. The selective graph coloring problem is a generalization of the standard graph coloring problem; given a graph with a partition of its vertex set into clusters, the objective is to choose exactly one vertex per cluster so that, among all possible selections, the number of colors necessary to color the vertices in the selection is minimum. This study focuses on a decomposition based exact solution framework for selective coloring in some perfect graph families: in particular, permutation, generalized split, and chordal graphs where the selective coloring problem is known to be NP‐hard. Our method combines integer programming techniques and combinatorial algorithms for the graph classes of interest. We test our method on graphs with different sizes and densities, present computational results and compare them with solving an integer programming formulation of the problem by CPLEX, and a state‐of‐the art algorithm from the literature. Our computational experiments indicate that our decomposition approach significantly improves solution performance in low‐density graphs, and regardless of edge‐density in the class of chordal graphs.

A Benders decomposition-based framework for solving quay crane scheduling problems

In this paper, we study the Quay Crane Scheduling Problem (QCSP) in container terminals. We describe a new mathematical formulation for the QCSP and by addressing the structure of workload assignments we develop an easier way to handle non-crossing constraints. The proposed mathematical formulation is used in an exact solution framework based on logic-based Benders decomposition. The proposed approach decomposes the problem into a workload-assignment master problem and operation-sequence slave subproblems. Logic-based cuts are proposed to ensure the convergence of the approach. Computational results show the effectiveness of the proposed solution approach.

Mathematical programming formulations for the efficient solution of the k-sum approval voting problem

In this paper we address the problem of electing a committee among a set of m candidates on the basis of the preferences of a set of n voters. We consider the approval voting method in which each voter can approve as many candidates as he likes by expressing a preference profile (boolean m-vector). In order to elect a committee, a voting rule must be established to ‘transform’ the n voters’ profiles into a winning committee. The problem is widely studied in voting theory; for a variety of voting rules the problem was shown to be computationally difficult and approximation algorithms and heuristic techniques were proposed in the literature. In this paper we follow an Ordered Weighted Averaging approach and study the k-sum approval voting (optimization) problem in the general case 1 ≤ k < n. For this problem, we provide different mathematical programming formulations that allow us to solve it in an exact solution framework. We provide computational results showing that our approach is efficient for medium size test problems (n up to 200, m up to 60), since in all tested cases it was able to find the exact optimal solution in very short computational times.

An exact solution framework for the minimum cost dominating tree problem

The minimum cost dominating tree problem is a recently introduced NP-hard problem, which consists of finding a tree of minimal cost in a given graph, such that for every node of the graph, the node or one of its neighbours is in the tree. We present an exact solution framework combining a primal–dual heuristic with a branch-and-cut approach based on a transformation of the problem into a Steiner arborescence problem with an additional constraint. The effectiveness of our approach is evaluated on testbeds proposed in literature containing instances with up to 500 nodes. Our framework manages to solve all but four instances from literature to proven optimality within 3 h (most of them in a few seconds). We provide optimal solution values for 69 instances from literature for which the optimal solution was previously unknown.

A branch-and-cut framework for the consistent traveling salesman problem

We develop an exact solution framework for the Consistent Traveling Salesman Problem. This problem calls for identifying the minimum-cost set of routes that a single vehicle should follow during the multiple time periods of a planning horizon, in order to provide consistent service to a given set of customers. Each customer may require service in one or multiple time periods and the requirement for consistent service applies at each customer location that requires service in more than one time period. This requirement corresponds to restricting the difference between the earliest and latest vehicle arrival-times, across the multiple periods, to not exceed some given allowable limit. We present three mixed-integer linear programming formulations for this problem and introduce a new class of valid inequalities to strengthen these formulations. The new inequalities are used in conjunction with traditional traveling salesman inequalities in a branch-and-cut framework. We test our framework on a comprehensive set of benchmark instances, which we compiled by extending traveling salesman instances from the well-known TSPLIB library into multiple periods, and show that instances with up to 50 customers, requiring service over a 5-period horizon, can be solved to guaranteed optimality. Our computational experience suggests that enforcing arrival-time consistency in a multi-period setting can be achieved with merely a small increase in total routing costs.

New Models for Commercial Territory Design

In this work, a series of novel formulations for a commercial territory design problem motivated by a real-world case are proposed. The problem consists on determining a partition of a set of units located in a territory that meets multiple criteria such as compactness, connectivity, and balance in terms of customers and product demand. Thus far, different versions of this problem have been approached with heuristics due to its NP-completeness. The proposed formulations are integer quadratic programming models that involve a smaller number of variables than heretofore required. These models have also enabled the development of an exact solution framework, the first ever derived for this problem, that is based on branch and bound and a cut generation strategy. The proposed method is empirically evaluated using several instances of the new quadratic models as well as of the existing linear models. The results show that the quadratic models allow solving larger instances than the linear counterparts. The former were also observed to require fewer iterations of the exact method to converge. Based on these results the combination of the quadratic formulation and the exact method are recommended to approach problem instances associated with medium-sized cities.

An exact solution framework for a broad class of vehicle routing problems

This paper presents an exact solution framework for solving some variants of the vehicle routing problem (VRP) that can be modeled as set partitioning (SP) problems with additional constraints. The method consists in combining different dual ascent procedures to find a near optimal dual solution of the SP model. Then, a column-and-cut generation algorithm attempts to close the integrality gap left by the dual ascent procedures by adding valid inequalities to the SP formulation. The final dual solution is used to generate a reduced problem containing all optimal integer solutions that is solved by an integer programming solver. In this paper, we describe how this solution framework can be extended to solve different variants of the VRP by tailoring the different bounding procedures to deal with the constraints of the specific variant. We describe how this solution framework has been recently used to derive exact algorithms for a broad class of VRPs such as the capacitated VRP, the VRP with time windows, the pickup and delivery problem with time windows, all types of heterogeneous VRP including the multi depot VRP, and the period VRP. The computational results show that the exact algorithm derived for each of these VRP variants outperforms all other exact methods published so far and can solve several test instances that were previously unsolved.

Run-up of solitary waves on slopes with different profiles

The problem of sea-wave run-up on a beach is discussed within the framework of exact solutions of a nonlinear theory of shallow water. Previously, the run-up of solitary waves with different forms (Gaussian and Lorentzian pulses, a soliton, special-form pulses) has already been considered in the literature within the framework of the same theory. Depending on the form of the incident wave, different formulas were obtained for the height of wave run-up on a beach. A new point of this study is the proof of the universality of the formula for the maximum height of run-up of a solitary wave on a beach for the corresponding physical choice of the determining parameters of the incident wave, so that the effect of difference in form is eliminated. As a result, an analytical formula suitable for applications, in particular, in problems related to tsunamis, has been proposed for the height of run-up of a solitary wave on a beach.

Feynman–Schwinger representation method for bound states

In nuclear and particle physics one is often faced with problems where perturbation theory is not applicable. An example of this is the description of bound states. Therefore, an exact solution of field theory to all orders is an unavoidable and interesting problem. Path integrals provide a framework for exact solutions in field theory. In this talk I will present an economical method of evaluating path integrals using the Feynman-Schwinger representation (FSR).