This paper contemplates how branch-price-and-cut solvers can be employed along with the robust optimization paradigm to address parametric uncertainty in the context of vehicle routing problems. In this setting, given postulated uncertainty sets for customer demands and vehicle travel times, one aims to identify a set of cost-effective routes for vehicles to traverse, such that the vehicle capacities

Motivated by the parameter identification problem of a reaction-diffusion transport model in a vapor phase infiltration processes, we propose a Bayesian optimization procedure for solving the inverse problem that aims to find an input setting that achieves a desired functional output. The proposed algorithm improves over the standard single-objective Bayesian optimization by (i) utilizing the generalized

The mathematical theory for optimal switching is by now relatively well developed, but the number of concrete applications of this theoretical framework remains few. In this paper, we bridge parts of this gap by applying optimal switching theory to a conceptual production planning problem related to hydropower. In particular, we study two examples of small run-of-river hydropower plants and provide

Computing sparse solutions to overdetermined linear systems is a ubiquitous problem in several fields such as regression analysis, signal and image processing, information theory and machine learning. Additional non-negativity constraints in the solution are useful for interpretability. Most of the previous research efforts aimed at approximating the sparsity constrained linear least squares problem

The honeybee has a significant impact on industry and nature. In recent years, a mysterious disease make honeybees die, often losses reach 80–100% of the apiaries. The causal syndrome of such massive die-off is called Colony Collapse Disorder. The model adopted in the paper is constituted by a system of three ordinary differential equations that account for the change in time of the population size

This paper presents an efficient and compact MATLAB code for three-dimensional stress-based sensitivity analysis. The 146 lines code includes the finite element analysis and p-norm stress sensitivity analysis based on the adjoint method. The 3D sensitivity analysis for p-norm global stress measure is derived and explained in detail accompanied by corresponding MATLAB code. The correctness of the analytical

Palletization, a core activity in warehousing and distribution, involves the solution of a three-dimensional bin packing problem with side constraints. This problem is known as the mixed-case palletization problem. Motivated by the fact that solving industry-size instances is still very challenging for existing methodology, we propose a data-driven solution approach that combines data analysis at the

In this work, we advocate using Bayesian techniques for inversely identifying material parameters for multiscale crystal plasticity models. Multiscale approaches for modeling polycrystalline materials may significantly reduce the effort necessary for characterizing such material models experimentally, in particular when a large number of cycles is considered, as typical for fatigue applications. Even

In the present work, firstly, we use a minimax equality to prove the existence of a solution to a certain system of varitional equations providing a numerical approximation of such a solution. Then, we propose a numerical method to solve a collage-type inverse problem associated with the corresponding system, and illustrate the behaviour of the method with a numerical example.

Numerous decision-making problems are expressed in the form of uncertain linguistic variables in real life. Expressing qualitative decision-making information in quantitative form can make the decision making process clearer and simpler. In this paper, we transform the 2-dimension uncertain linguistic variable into a cloud model which converses the qualitative information to the quantitative information

Global warming and climate change call for urgent minimization of the impact of human activities on the environment. There is a great need for the improvement of resource efficiencies by integrating various life-supporting systems. The challenge is on the energy, water and environment systems to integrate and become more sustainable. This research field has received increased attention over the past

In this work, metamodel-based robust optimization is performed using measured scatter of noise variables. Principal component analysis is used to describe the input noise using linearly uncorrelated principal components. Some of these principal components follow a normal probability distribution, others however deviate from a normal probability distribution. In that case, for more accurate description

The article presents the study of Particulate Matter air pollution with PM1, PM2,5 and PM10 by means of a low-cost sensors mounted on Unmanned Aerial Vehicles. The article is divided into two parts. In first part pollution measurement system is described. In second part expert system for optimization of flight parameters is described. The research was conducted over a municipal cemetery area in Poland

This paper describes a novel method to select the optimal member sizes and connection types for steel frame structures to minimize total installed cost. The proposed dimension increasing search (DIS) method addresses geometric constraints associated with connection, safety, and serviceability constraints. Solving this optimization problem requires addressing a non-convex objective function of total

This paper provides a tutorial overview of robust optimization in power systems, including robust optimization and adaptive robust optimization. We also introduce distributionally robust optimization. For illustration purposes, we describe and analyze a short-term operation problem and a long-term planning one. The operation problem allows identifying the transmission line whose failure has the higher

Phaseless inverse scattering problems appear often in practical applications since phaseless data are relatively easier to measure than the phased data, but they are also numerically more difficult to solve due to the translation invariance property. Based on three distinct noisy measurements of phaseless far-field data, the phase information can be approximately reconstructed by formulating it as

This study focuses on the optimization of a refinery scheduling process with the help of an adiabatic quantum computer, and more concretely one of the quantum annealers developed by D-Wave Systems. We present an algorithm for finding a global optimal solution of a MILP that leans on a solver for QUBO problems, and apply it to various possible cases of refinery scheduling optimization. We analyze the

Topology optimization is an effective approach for the efficient layout design of structures and their components. This approach is well-established in the solid mechanics’ domain for linear elastic and small-displacement conditions. However, the topology optimization of elastic structures under large-displacement conditions has been marginally addressed in the literature. This study proposes a numerical

Given an edge-weighted graph and a subset of the vertices called terminals, the multiway cut problem aims to find a minimum weight set of edges that separates each terminal from all the others. This problem is known to be NP-hard even for \(k=3\). Computational experiments on social networks obtained from the Indian e-commerce company Flipkart show that an integer programming formulation (referred

In this paper, we study uncertainty set construction for robust optimization using various polyhedral norms. We first introduce the classical symmetric polyhedral-norms induced uncertainty sets and the corresponding robust counterparts of a linear uncertain constraint. Then, we introduce a novel method for asymmetric uncertainty set construction based on the distributional information of the uncertain

We develop tightening and solution methods for optimization problems containing bilinear terms. We focus on the bilinear term \(w=xy\) with nonnegative variables \(x\in \left[{x}^{\mathrm{L}},{x}^{\mathrm{U}}\right]\) and \(y\in [{y}^{\mathrm{L}},{y}^{\mathrm{U}}]\), where \(w\) is semi-continuous and upper and lower bounded by \({w}^{\mathrm{U}}\) and \({w}^{\mathrm{L}}\) when positive. \({w}^{\mathrm{U}}\)

We perform a comprehensive study on the performance of derivative free optimization (DFO) algorithms for the generation of targeted black-box adversarial attacks on Deep Neural Network (DNN) classifiers assuming the perturbation energy is bounded by an \(\ell _\infty\) constraint and the number of queries to the network is limited. This paper considers four pre-existing state-of-the-art DFO-based algorithms

In Plug-in hybrid electric vehicles (PHEVs) typically combine several power sources, which are coordinated by means of an optimal energy management strategy. When considering the so-called blended mode, in which the engine is regularly used over a trip, the shape of battery state-of-charge (SoC) trajectory over travelled distance is of particular importance for achieving minimum fuel consumption. The

We apply a robust steady state optimization method for stiff delay differential equations to the economic optimization of a fluidized catalytic cracking unit. Stiff systems of differential equations appear in this case due to the different time scales in the gas and fluid phase. Delays result from the catalyst hold-ups in the standpipes connecting riser and regenerator. We show that the proposed robust

At present, black-box and simulation-based optimization problems with multiple objective functions are becoming increasingly common in the engineering context. In many cases, the functional relationships that define the objective and constraints are only known as black-boxes, cannot be differentiated accurately, and may be subject to unexpected failures. Directional direct search techniques, in particular

The goals of this paper are: (1) to bring attention to the existence and utility of multiple optimal rankings for the linear ordering problem, (2) to make the case for finding some or all of these multiple optimal rankings, (3) to provide an efficient algorithm that determines the existence of multiple optimal rankings, (4) to provide algorithms that find a sample of all optimal rankings, and (5) to

Designing inspection frequency to efficiently track stochastic dynamics is a fundamental engineering problem. Especially, tracking environmental variables like water quantity and quality is of vital importance for sustainable and sound development. However, theoretical understanding of methodologies to design cost-efficient inspection schemes of environmental dynamics is still limited. To tackle this

Domain Decomposition Methods (DDM) are a set of numerical techniques that efficiently implement parallel computing for the structural analysis of large domains. This work presents the implementation of mixed DDM for linear elasticity problems along with non-linear problems such as crack propagation. In addition, optimization algorithms have been used to find the optimal parameters of the mixed domain

Buses account for almost 60% of the total public transport services in Europe, and most of the vehicles are diesel fuelled. Regional transport administrators, under pressure by governments to introduce zero-emission buses, require analytical tools for identifying optimal solutions. In literature, few models combine location analysis, least cost planning, and emission assessment, taking into account

Large scale multi-dimensional time series can be found in many disciplines, including finance, econometrics, biomedical engineering, and industrial engineering systems. It has long been recognized that the time dependent components of the vector time series often reside in a subspace, leaving its complement independent over time. In this paper we develop a method for projecting the time series onto

District heating and cooling networks are a key infrastructure to decarbonise the heating and cooling sector. Besides the design of new networks according to the principles of the 4th and 5th generation, operational aspects may significantly contribute to improve the efficiency of existing networks from both economic and environmental standpoints. This article is the second step of a work that aims

Large fleets of engineering assets that are subject to ongoing degradation are posing the challenge of how and when to perform maintenance. For a given case study, this paper proposes a formulation for combined scheduling and planning of maintenance actions. A hierarchical approach and a two-stage approach (with either uniform or non-uniform time grid) are considered and compared to each other. The

The amounts of mixed municipal waste in the EU countries differ during the last decades. Waste composition is influenced especially by the recent trends in the packaging of products that are daily purchased and the attitude towards sorting of municipal solid waste. Waste composition is one of the most important factors that shape future waste management planning. The sortable waste fraction in the

This paper addresses the synthesis of combined evaporation–crystallisation systems for the recovery of valuable materials from waste in line with sustainable development and circular economy concepts. The primary focus of this work is the utilisation of distiller waste from the Solvay process, which comprises sodium chloride (NaCl), calcium chloride (\({\text {CaCl}}_2\)) and water (\(\hbox{H}_2\)O)

Waste-to-energy treatment is an efficient energy recovery method and an important countermeasure against global warming. The supply of steam from waste treatment plants to industrial sectors presents a higher energy recovery efficiency than traditional waste-to-energy methods. However, its technical potential and economic benefits are not fully understood by policymakers. Furthermore, the regional

In the last years, Control Moment Gyros (CMGs) are widely used for high-speed attitude control, since they are able to generate larger torque compared to “classical” actuation systems, such as Reaction Wheels . This paper describes the attitude control problem of a spacecraft, using a Model Predictive Control method. The features of the considered linear MPC are: (i) a virtual reference, to guarantee

Data-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in model-based optimization of design and/or operation of processes. Thus, it is critical to ensure that data-driven models are not evaluated outside their validity domain during process optimization. We propose a method to learn

Air contamination and road congestion are two major problems in modern cities. Both are closely related and present the same source: traffic flow. To deal with these problems, governments impose traffic restrictions preventing the entry of vehicles into sensitive areas, with the final goal of decreasing pollution levels. Unfortunately, these restrictions force drivers to look for alternative routes

This note is concerned with inverse wave scattering in one and two dimensional domains. It seeks to recover an unknown function based on measurements collected at the boundary of the domain. For one-dimensional problem, only one point of the domain is assumed to be accessible. For the two dimensional domain, the outer boundary is assumed to be accessible. It develops two iterative algorithms, in which

A correction to this paper has been published: https://doi.org/10.1007/s11081-021-09631-1

A correction to this paper has been published: https://doi.org/10.1007/s11081-021-09635-x

We consider the problem of modifying \(L^2\)-based approximations so that they “conform” in a better way to Weber’s model of perception: Given a greyscale background intensity \(I > 0\), the minimum change in intensity \(\varDelta I\) perceived by the human visual system (HVS) is \(\varDelta I / I^a = C\), where \(a > 0\) and \(C > 0\) are constants. A “Weberized distance” between two image functions

Mineral value chains, also known as mining complexes, involve mining, processing, stockpiling, waste management and transportation activities. Their optimization is typically partitioned into separate stages, considered sequentially. An integrated stochastic optimization of these stages has been shown to increase the net present value of the related mining projects and operations, reduce risk in meeting

In this paper the problem of the concurrent topological optimization of two different bodies sharing a region of the design space is dealt with. This design problem focuses on the simultaneous optimization of two bodies (components) where not only the material distribution of each body has to be optimized but also the design space has to be divided among the two bodies. This novel optimization formulation

We propose, implement, and test two approaches for dispatching trucks in an open-pit mining operation. The first approach relies on a nonlinear optimization model that incorporates queueing effects to set target average flow rates between mine locations. The second approach is based on a time-discretized mixed integer programming (MIP) model. The MIP model is difficult to solve exactly in real-time

We present three core principles for engineering-oriented integrated modeling and optimization tool sets—intuitive modeling contexts, systematic computer-aided reformulations, and flexible solution strategies—and describe how new developments in Pyomo.GDP for Generalized Disjunctive Programming (GDP) advance this vision. We describe a new logical expression system implementation for Pyomo.GDP allowing

Uncertainty often plays an important role in dynamic flow problems. In this paper, we consider both, a stationary and a dynamic flow model with uncertain boundary data on networks. We introduce two different ways how to compute the probability for random boundary data to be feasible, discussing their advantages and disadvantages. In this context, feasible means, that the flow corresponding to the random

We consider the problem of modifying \(L^2\)-based approximations so that they “conform” in a better way to Weber’s model of perception: Given a greyscale background intensity \(I > 0\), the minimum change in intensity \(\varDelta I\) perceived by the human visual system is \(\varDelta I / I^a = C\), where \(a > 0\) and \(C > 0\) are constants. A “Weberized distance” between two image functions u and

Given a nonlinear, univariate, bounded, and differentiable function f(x), this article develops a sequence of Mixed Integer Linear Programming (MILP) and Linear Programming (LP) relaxations that converge to the graph of f(x) and its convex hull, respectively. Theoretical convergence of the sequence of relaxations to the graph of the function and its convex hull is established. For nonlinear non-convex

Three-dimensional (3D) printing, also known as additive manufacturing (AM), has emerged in the last decades as an innovative technology to build complex structures. It enables increasing design complexity and low-cost customization with a vast range of materials. AM capabilities contributed to a widespread acceptance of 3D printing in different industries such as the aerospace and the automotive. However

In this paper, a wide neighborhood arc-search interior-point algorithm for convex quadratic programming with box constrains and linear constraints (BLCQP) is presented. The algorithm searches the optimizers along the ellipses that approximate the entire central path. Assuming a strictly feasible initial point is available, we show that the algorithm has \(O(n^{\frac{3}{4}}\log \frac{{({x^0} - l)^T}{s^0}

Real-world decision making problems in various fields including engineering sciences are becoming ever more challenging to address. The consideration of various competing criteria related to, for example, business, technical, workforce, safety and environmental aspects increases the complexity of decision making and leads to problems that feature multiple competing criteria. A key challenge in such

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is considered among the most frequently used techniques to deal with multi-criteria group decision-making (MCGDM) conflicts. In this article, we have presented an extended TOPSIS technique in the framework of interval type-2 trapezoidal Pythagorean fuzzy numbers (IT2TrPFN). We first projected a novel approach to evaluate the

So far, controlling the solutions of PDE’s on fractal sets has not been much explored. Whereas those structures bear interesting properties in terms of heat diffusion. We hereafter discuss the extension of classical results of control theory to self-similar sets, and apply them to the benchmark case of the Sierpiński Gasket. Our results show that this path will gain being developped.

Multidomain mixed pairs of coupled stationary constrained boundary value problems, are formulated variationally in frameworks of reflexive Banach spaces as macro-hybrid systems, and analyzed. Interior synchronizing and interface coupling transmission constraints are modeled in terms of Lagrange multipliers as solutions of dual variational subpotential maximal monotone inclusions. Existence and uniqueness

This paper proposes a method for the solution of constrained min-max problems. The method is tested on a benchmark of representative problems presenting different structures for the objective function and the constraints. The particular min-max problem addressed in this paper finds application in optimisation under uncertainty when the constraints need to be satisfied for all possible realisations

The ground station scheduling problem is a complex scheduling problem involving multiple objectives. Evolutionary techniques for multi-objective optimization are becoming popular among different fields, due to their effectiveness in obtaining a set of trade-off solutions. In contrast to some conventional methods, that aggregate the objectives into one weighted-sum objective function, multi-objective

This paper proposes a novel geometric programming based formulation to solve a gate-sizing and retiming problem in the context of circuit optimization. The gate-sizing aspect of the problem involves continuous variables while the retiming problem involves the placement of registers in the circuit and can be naturally modeled using discrete variables. Our formulation is solved using first-order convex

We consider the problem of jointly estimating multiple related zero-mean Gaussian distributions from data. We propose to jointly estimate these covariance matrices using Laplacian regularized stratified model fitting, which includes loss and regularization terms for each covariance matrix, and also a term that encourages the different covariances matrices to be close. This method ‘borrows strength’

CO2 capture and storage (CCS) is a climate change mitigation strategy that aims to reduce the amount of CO2 vented into the atmosphere from industrial processes. Designing cost-effective CCS infrastructure is critical in meeting CO2 emission reduction targets and is a computationally challenging problem. We formalize the computational problem of designing cost-effective CCS infrastructure and detail