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

The Nelder-Mead (NM) method is a popular derivative-free optimization algorithm owing to its fast convergence and robustness. However, it is known that the method often fails to converge or costs a long time for a large-scale optimization. In the present study, the NM method has been improved using direct inversion in iterative subspace (DIIS). DIIS is a technique to accelerate an optimization method

Nowadays, renewable energies are important sources for supplying electric power demand and a key entity of future energy markets. Therefore, wind power producers (WPPs) in most of the power systems in the world have a key role. On the other hand, the wind speed uncertainty makes WPPs deferent power generators, which in turn causes adequate bidding strategies, that leads to market rules, and the functional

This paper presents a state-of-the-art overview of water and energy optimisation methods with applications to Kraft pulp mills. The main conclusions are highlighted, and several research gaps are identified and proposed for future research. Kraft processes have the potential to be adapted to biorefineries for producing biofuels and other high-value products from wood biomass. Biorefineries enable opportunities

Neutrosophic sets have been commenced as a generalization of crisp, fuzzy and intuitionistic fuzzy sets to depict vague, incompatible and deficient information about a real world dilemma. Interval-valued fuzzy sets have widely been acknowledged as more proficient in modeling suspicions and practical in assigning an interval of values where allotting an accurate and precise number to an expert’s outlook

Bi-modulus constitutive law assumes that material constants have different values in tension and compression. It is known that finding an equilibrium state of an elastic body consisting of a bi-modulus material is recast as a semidefinite programming problem, which can be solved with a primal-dual interior-point method. As an alternative approach, this paper presents a fast first-order optimization

In this paper, we introduce a new relaxed extrgadient algorithm with alternated inertial extrapolation step and self adaptive variable stepsizes for solving variational inequality problems whose cost operator is pseudomonotone operator in Hilbert spaces. We establish the weak convergence of the proposed algorithm and linear convergence under some standard assumptions. Numerical experiments are given

Industrial buildings play a major role in sustainable development, producing and expending a significant amount of resources, energy and waste. Due to product individualization and accelerating technological advances in manufacturing, industrial buildings strive for highly flexible building structures to accommodate constantly evolving production processes. However, common sustainability assessment

We consider a setting in which it is desired to find an optimal complex vector \({\mathbf {x}}\in {\mathbb {C}}^N\) that satisfies \({\mathcal {A}}({\mathbf {x}}) \approx {\mathbf {b}}\) in a least-squares sense, where \({\mathbf {b}} \in {\mathbb {C}}^M\) is a data vector (possibly noise-corrupted), and \({\mathcal {A}}(\cdot ): {\mathbb {C}}^N \rightarrow {\mathbb {C}}^M\) is a measurement operator

The modular open-source framework GRAMPC-D for model predictive control of distributed systems is presented in this paper. The modular concept allows to solve optimal control problems in a centralized and distributed fashion using the same problem description. It is tailored to computational efficiency with the focus on embedded hardware. The distributed solution is based on the alternating direction

This manuscript investigates the design-for-control (DfC) problem of minimizing pressure induced leakage and maximizing resilience in existing water distribution networks. The problem consists in simultaneously selecting locations for the installation of new valves and/or pipes, and optimizing valve control settings. This results in a challenging optimization problem belonging to the class of non-convex

Piecewise relaxations are powerful techniques to compute strong dual bounds for (mixed-integer) quadratically constrained problems and provide starting points that can be used by local nonlinear solvers to generate high-quality primal solutions. They work by first partitioning the domain of one of the variables in every quadratic or bilinear term and then selecting the optimal interval in the partition

Aircraft ejection systems are lifesaving devices used, in case of emergencies for military pilots. As per current literature the success rate of ejections happening in low altitudes (below 150 m) is 51.2%. The success rate of an ejection depends on various parameters such as height for parachute full deployment, ejection posture, spinal alignment, restraints etc. This paper proposes a multi objective

The draft tube of a hydraulic turbine plays an important role for the efficiency and power characteristics of the overall system. The shape of the draft tube affects its performance, resulting in an increasing need for data-driven optimisation for its design. In this paper, shape optimisation of an elbow-type draft tube is undertaken, combining Computational Fluid Dynamics and a multi-objective Bayesian

In this paper, we describe an algorithmic framework for the optimal operation of transient gas transport networks consisting of a hierarchical MILP formulation together with a sequential linear programming inspired post-processing routine. Its implementation is part of the KOMPASS decision support system, which is currently used in an industrial setting. Real-world gas transport networks are controlled

Various versions of inertial subgradient extragradient methods for solving variational inequalities have been and continue to be studied extensively in the literature. In many of the versions that were proposed and studied, the inertial factor, which speeds up the convergence of the method, is assumed to be less than 1, and in many cases, stringent conditions are also required in order to obtain convergence

The optimization of the locations of a large number of wells in an oil or gas field represents a challenging computational problem. This is because the number of optimization variables scales with the maximum number of wells considered. In this work, we develop and test a new two-stage strategy for large-scale oil field optimization problems. In the first stage, wells are constrained to lie in repeated

In engineering applications one often has to trade-off among several objectives as, for example, the mechanical stability of a component, its efficiency, its weight and its cost. We consider a biobjective shape optimization problem maximizing the mechanical stability of a ceramic component under tensile load while minimizing its volume. Stability is thereby modeled using a Weibull-type formulation

Stratified models depend in an arbitrary way on a selected categorical feature that takes K values, and depend linearly on the other n features. Laplacian regularization with respect to a graph on the feature values can greatly improve the performance of a stratified model, especially in the low-data regime. A significant issue with Laplacian-regularized stratified models is that the model is K times

In this paper, we develop a novel evolutionary interactive method called interactive K-RVEA, which is suitable for computationally expensive problems. We use surrogate models to replace the original expensive objective functions to reduce the computation time. Typically, in interactive methods, a decision maker provides some preferences iteratively and the optimization algorithm narrows the search

This paper offers a novel approach for computing globally optimal solutions to the pump scheduling problem in drinking water distribution networks. A tailored integer linear relaxation of the original non-convex formulation is devised and solved by branch and bound where integer nodes are investigated through non-linear programming to check the satisfaction of the non-convex constraints and compute

Global warming and climate change call for urgent minimization of human activities on the environment. Therefore, 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 field of research has received increased attention over the past

This work presents multi-objective optimization of an aspirating smoke detection system which uses the pipeline to transport air sample from the sampling points to the analysing module. On the basis of 3D computational fluid dynamics simulation it has been shown that the smoke transport will not always take place in the centre of the pipe and that one dimensional analysis is not able to provide information

Short-term open pit planners have to deal with the task of designing a feasible production schedule. This schedule must fulfill processing, mining and operational constraints and, at the same time, maximize the profit or total metal produced. It also must comply with the long-term production schedule and must incorporate new blasthole sampling data. This task is performed with little support of optimization

Distributed optimization using multiple computing agents in a localized and coordinated manner is a promising approach for solving large-scale optimization problems, e.g., those arising in model predictive control (MPC) of large-scale plants. However, a distributed optimization algorithm that is computationally efficient, globally convergent, amenable to nonconvex constraints remains an open problem

In this work, we provide a new Black–Scholes model, where the weak formulation at stake is done in the case of a general class of finite Radon measures. A numerical estimation of the parameters, by means of a gradient algorithm, shows that the estimated model is better as regards option pricing quality than the classical Black–Scholes one.

We present the closed-form solution to the problem of hedging price and quantity risks for energy retailers (ER), using financial instruments based on electricity price and weather indexes. Our model considers an ER who is intermediary in a regulated electricity market. ERs buy a fixed quantity of electricity at a variable cost and must serve a variable demand at a fixed cost. Thus ERs are subject

Variable order structures model situations in which the comparison between two points depends on a point-to-cone map. In this paper, inexact projected gradient methods for solving smooth constrained vector optimization problems on variable ordered spaces are presented. It is shown that every accumulation point of the generated sequences satisfies the first-order necessary optimality condition. Moreover

The impact of intermittent power production by Photovoltaic (PV) systems to the overall power system operation is constantly increasing and so is the need for advanced forecasting tools that enable understanding, prediction, and managing of such a power production. Solar power production forecasting is one of the enabling technologies, which can accelerate the transition to sustainable energy environment