In order to increase the driving range of battery electric vehicles, while maintaining a high level of thermal comfort inside the passenger cabin, it is necessary to design an energy management system which optimally synthesizes multiple control actions of heating, ventilation and air-conditioning (HVAC) system. To gain an insight into optimal control actions and set a control benchmark, the paper

In this paper, a new class of kernel functions is introduced. We show that most existing kernel functions belong to this class. All functions in the new class are eligible in the sense of Bai et al. (SIAM J Optim 15(1):101–128, 2004), and hence the analysis of the resulting interior-point methods can follow the scheme proposed in Bai et al. (2004). We introduce five new kernel functions and by using

We define a regularized variant of the dual dynamic programming algorithm called DDP-REG to solve nonlinear dynamic programming equations. We extend the algorithm to solve nonlinear stochastic dynamic programming equations. The corresponding algorithm, called SDDP-REG, can be seen as an extension of a regularization of the stochastic dual dynamic programming (SDDP) algorithm recently introduced which

The testing and verification of a complex hardware or software system, such as modern integrated circuits found in everything from smartphones to servers, can be a difficult process. One of the most difficult and time-consuming tasks a verification team faces is reaching coverage closure, or hitting all events in the coverage space. Coverage-directed-generation (CDG), or the automatic generation of

In this work, a design optimisation strategy is presented for expensive and uncertain single- and multi-objective problems. Computationally expensive design fitness evaluations prohibit the application of standard optimisation techniques and the direct calculation of risk measures. Therefore, a surrogate-assisted optimisation framework is presented. The computational budget limits the number of high-fidelity

Given an infeasible, unbounded, or pathological convex optimization problem, a natural question to ask is: what is the smallest change we can make to the problem’s parameters such that the problem becomes solvable? In this paper, we address this question by posing it as an optimization problem involving the minimization of a convex regularization function of the parameters, subject to the constraint

Energy management can play a significant role in energy savings and temperature control of buildings, which consume a major share of energy resources worldwide. Model predictive control (MPC) has become a popular technique for energy management, arguably for its ability to cope with complex dynamics and system constraints. The MPC algorithms found in the literature are mostly centralized, with a single

The advantages of multidisciplinary design are well understood, but not yet fully adopted by the industry where methods should be both fast and reliable. For such problems, minimum computational cost while providing global optimality and extensive design information at an early conceptual stage is desired. However, such a complex problem consisting of various objectives and interacting disciplines

When solving for optimal strategies, a financial engineer needs to take into consideration of preferences heterogeneities, which involve not only present bias, but also future-focused preferences. We provide a reusable tool (i.e. algorithm) for explicitly solving optimal strategy in the presence of preferences variation over time, decision-makers and goods. In this framework, a new discount function

The IAPWS-IF97 (Wagner et al. (2000) J Eng Gas Turbines Power 122:150) is the state-of-the-art model for the thermodynamic properties of water and steam for industrial applications and is routinely used for simulations of steam power cycles and utility systems. Its use in optimization-based design, however, has been limited because of its complexity. In particular, deterministic global optimization

In this paper, we propose a new iterative method for finding an element of the solution set of a split variational inclusion problem in real Hilbert spaces. The iterative scheme is based on a well-known Mann-type method to obtain strong convergence and an inertial method to speed up the convergence rate. We also apply the proposed algorithm to studying the split feasibility problem. Finally, we give

Hard-capacitated k-means (HCKM) is one of the fundamental problems remaining open in combinatorial optimization and engineering. In HCKM, one is required to partition a given n-point set into k disjoint clusters with known capacity so as to minimize the sum of within-cluster variances. It is known to be at least APX-hard, and most of the work on it has been done from a meta heuristic or bi-criteria

The physical and virtual connectivity of systems via flows of energy, material, information, etc., steadily increases. This paper deals with systems of sub-systems that are connected by networks of shared resources that have to be balanced. For the optimal operation of the overall system, the couplings between the sub-systems must be taken into account, and the overall optimum will usually deviate

Wastewater treatment process design involves the optimization of multiple conflicting objectives. The detection of different equivalent solutions in terms of objective values is crucial for designers in order to efficiently switch to the new optimal operation policies if changes in the process conditions or new constraints occur. In this work, the dynamic multi-objective optimization of a municipal

A fin serves as an extended surface to enhance the heat transfer from a larger heated mass to which it is attached. We use a multiobjective optimization (MO) approach for a fin in steady-state to analyze the dynamics of effectiveness and efficiency simultaneously. Our approach is based on a piecewise linear approximation of the design. We use the \(\epsilon\)-constraint method of MO to identify Pareto

In this paper we consider optimal control of nonlinear time-dependent fluid structure interactions. To determine a time-dependent control variable a BFGS algorithm is used, whereby gradient information is computed via a dual problem. To solve the resulting ill conditioned linear problems occurring in every time step of state and dual equation, we develop a highly efficient monolithic solver that is

Mine operations are supported by a short-term production schedule, which defines where and when mining activities are performed. However, deviations can be observed in this short-term production schedule because of several sources of uncertainty and their inherent complexity. Therefore, schedules that are more likely to be reproduced in reality should be generated so that they will have a high adherence

This article presents a novel stochastic optimization model that simultaneously optimizes the short-term extraction sequence, shovel relocation, scheduling of a heterogeneous hauling fleet, and downstream allocation of extracted materials in open-pit mining complexes. The proposed stochastic optimization formulation considers geological uncertainty in addition to uncertainty related to equipment performances

A special class of quadratic programming (QP) problems is considered in this paper. This class emerges in simulation of assembly of large-scale compliant parts, which involves the formulation and solution of contact problems. The considered QP problems can have up to 20,000 unknowns, the Hessian matrix is fully populated and ill-conditioned, while the matrix of constraints is sparse. Variation analysis

We present a two-stage stochastic program with recourse to construct covered call portfolios. To maximize the expected utility of a covered call portfolio, the model selects equity positions and call option overwriting weights for varying strike prices and expiry dates. Since the model has linear constraints and risk-averse utility functions are concave, the optimization problem is convex. The model

The ideas in this paper are motivated by an increased need for systematic data-enabled resource management of large-scale electric energy systems. The basic control objective is to manage uncertain disturbances, power imbalances in particular, by optimizing available power resources. To that end, we start with a centralized optimal control problem formulation of system-level performance objective subject

This paper develops a hybrid optimization approach for multi-criteria optimal design of a compliant positioning platform for nanoindentation tester. The platform mimics the biomechanical behavior of beetle so as to allow a linear motion. Structure of the beetle-liked mechanism consists of six legs arranging in a symmetric topology. Amplification ratio and static characteristics of the platform are

Recovering the end-of-life (EOL) products helps companies reduce the purchasing cost for goods and materials that can be removed from EOL products and reused. This also contributes to the efforts aiming at reducing the environmental consequences of hazardous materials. Disassembly lines play a vital role in the disassembling process of EOL products. This research introduces the Type-E multi-manned

Concentrating solar power (CSP) tower technologies capture thermal radiation from the sun utilizing a field of solar-tracking heliostats. When paired with inexpensive thermal energy storage (TES), CSP technologies can dispatch electricity during peak-market-priced hours, day or night. The cost of utility-scale photovoltaic (PV) systems has dropped significantly in the last decade, resulting in inexpensive

Recently, Bouafia et al. (J Optim Theory Appl 170:528–545, 2016) investigated a new efficient kernel function that differs from self-regular kernel functions. The kernel function has a trigonometric barrier term. This paper introduces a new efficient twice parametric kernel function that combines the parametric classic function with the parametric kernel function trigonometric barrier term given by

The continuous adjoint method is formulated and utilized for the optimization of a static mixing device. The CFD tool used for the simulations is based on a two-phase model governing flows of two miscible fluids. The formulation of the corresponding continuous adjoint problem is presented and the computed gradients are utilized in an optimization loop. In specific, a multi-objective optimization problem

In this paper, we study the Fritz John necessary and sufficient optimality conditions for weak efficient solutions of vector equilibrium problem with constraints via contingent hypoderivatives in finite-dimensional spaces. Using the stability of objective functions at a given optimal point and assumming, in addition, that the regularity condition (RC) holds, some primal and dual necessary optimality

This paper discusses joint rectangular chance or probabilistic constrained geometric programs. We present a new reformulation of the joint rectangular chance constrained geometric programs where the random parameters are elliptically distributed and pairwise independent. As this reformulation is not convex, we propose new convex approximations based on the variable transformation together with piecewise

Assembly industry plays a key role in Central and Eastern Europe. Large companies and their subcontractors manufacture automotive and electronic products from components, employing a significant number of human resources. Due to the growing labor shortage, it is critical that the production lines should be optimally loaded, i.e., the tasks have to be evenly distributed among the workstations according

The performance of an organic Rankine cycle (ORC) relies on process design and operation. Simultaneous optimization of design and operation for a range of working fluids (WFs) is therefore a promising approach for WF selection. For this, deterministic global process optimization can guarantee to identify a global optimum, in contrast to local or stochastic global solution approaches. However, providing

As a consequence of the liberalisation of the European gas market in the last decades, gas trading and transport have been decoupled. At the core of this decoupling are so-called bookings and nominations. Bookings are special capacity right contracts that guarantee that a specified amount of gas can be supplied or withdrawn at certain entry or exit nodes of the network. These supplies and withdrawals

For an electric power mix subject to uncertainty, the stochastic unit-commitment problem finds short-term optimal generation schedules that satisfy several system-wide constraints. In regulated electricity markets, this very practical and important problem is used by the system operator to decide when each unit is to be started or stopped, and to define how to generate enough energy to meet the load

In this paper, we propose a predictor–corrector primal–dual approach for the doubly modified logarithmic barrier function (\(\hbox {M}^{2}\hbox {BF}\)) method in order to solve Optimal Reactive Power Flow (ORPF) problems. The \(\hbox {M}^{2}\hbox {BF}\) is a modification of the Polyak’s modified logarithmic barrier function (MBF) and is also a particular element of a recent family of nonquadratic penalty

A multistatic sonar system consists of one or more sources that are able to emit underwater sound, and receivers that listen to the reflected sound waves. Knowing the speed of sound in water, the time when the sound was sent from a source, and the arrival time of the sound at one or more receivers, it is possible to determine the location of surrounding objects. The propagation of underwater sound

In this paper sharp bounds for the probability of union of n arbitrary events following unknown probability distribution are found, when only \(m(m < n)\) out of n binomial moments of the events are given. The bounds are found using a class of special matrices and their inverses. Generalized closed form probabilistic bounds are found for the probability of occurrence of at least r out of n events by

This paper proposes a decentralized optimization algorithm for the triple-hierarchical constrained convex optimization problem of minimizing a sum of strongly convex functions subject to a paramonotone variational inequality constraint over an intersection of fixed point sets of nonexpansive mappings. The existing algorithms for solving this problem are centralized optimization algorithms using all

Internet traffic continues to grow relentlessly, driven largely by increasingly high resolution video content. Although studies have shown that the majority of packets processed by Internet routers are pass-through traffic, they nonetheless have to be queued and routed at every hop in current networks, which unnecessarily adds substantial delays and processing costs. Such pass-through traffic can be

The exploitation of subsurface hydrocarbon reservoirs is achieved through the control of production and injection wells (i.e., by prescribing time-varying pressures and flow rates) to create conditions that make the hydrocarbons trapped in the pores of the rock formation flow to the surface. The design of production strategies to exploit these reservoirs in the most efficient way requires an optimization

In this work a 3D printing system based on the use of stereolithography and able to print parts made of two materials is studied. The 3D printing problem is divided into two subproblems. First, the problem of locating UV light emitters capable of reaching all areas of the polymer that constitutes the part to be printed is analyzed with the goal of minimizing the number of used emitters. Next, for each

In this paper, we first propose a new finite exponential-trigonometric kernel function that has finite value at the boundary of the feasible region. Then by using some simple analysis tools, we show that the new kernel function has exponential convexity property. We prove that the large-update primal-dual interior-point method based on this kernel function for solving linear optimization problems has

The lightweight design of a thin-walled tube under torsion is addressed in the paper. A multi-objective optimization approach is adopted to minimize the mass while maximizing the structural stiffness of the thin walled tube. Constraints on available room (maximum diameter), safety (admissible stress), elastic stability (buckling), minimum thickness (forced by manufacturing technologies) are included

Semidefinite relaxation techniques have shown great promise for nonconvex optimal power flow problems. However, a number of independent numerical experiments have led to concerns about scalability and robustness of existing SDP solvers. To address these concerns, we investigate some numerical aspects of the problem and compare different state-of-the-art solvers. Our results demonstrate that semidefinite