Numerical solution of continuous-time mean–variance portfolio selection with nonlinear constraints

An investment problem is considered with dynamic mean–variance (M–V) portfolio criterion under discontinuous prices described by jump-diffusion processes. Some investment strategies are restricted in the study. This M–V portfolio with restrictions can lead to a stochastic optimal control model. The corresponding stochastic Hamilton–Jacobi–Bellman equation of the problem with linear and nonlinear constraints is derived. Numerical algorithms are presented for finding the optimal solution in this article. Finally, a computational experiment is to illustrate the proposed methods by comparing with M–V portfolio problem which does not have any constraints.     

Multiobjective portfolio optimization of ARMA–GARCH time series based on experimental designs

The modern portfolio theory has been trying to determine how an investor might allocate assets among the possible investments options. Since the seminal contribution provided by Harry Markowitz’s theory of portfolio selection, several other tools and procedures have been proposed to deal with return-risk trade-off. Furthermore, diversification across sources of returns and risks based on entropy indexes is another pivotal aspect in portfolio management. An efficient approach to model these portfolio properties with the proportion of each asset can be obtained according to mixture design of experiments. Desirability method can be applied to optimize this nonlinear multiobjective problem. Nevertheless, a tuning procedure is required, since preference articulation parameters in desirability algorithm are unknown a priori. As a result, a computer-aided desirability tuning method is proposed to find an optimal portfolio with time series of returns and risks modeled by ARMA–GARCH models. To assess the proposal feasibility, the method is tested with a heteroskedastic dataset formed by weekly world crude oil spot prices and returns. Computer-aided desirability tuning was able to enhance the global desirability by 79% in relation to the result with no tuning procedure.     

An evolutionary approach for multi-objective optimization of the integrated location–inventory distribution network problem in vendor-managed inventory

Vendor-managed inventory (VMI) is one of the emerging solutions for improving the supply chain efficiency. It gives the supplier the responsibility to monitor and decide the inventory replenishments of their customers. In this paper, an integrated location–inventory distribution network problem which integrates the effects of facility location, distribution, and inventory issues is formulated under the VMI setup. We presented a Multi-Objective Location–Inventory Problem (MOLIP) model and investigated the possibility of a multi-objective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA2) for solving MOLIP. To assess the performance of our approach, we conduct computational experiments with certain criteria. The potential of the proposed approach is demonstrated by comparing to a well-known multi-objective evolutionary algorithm. Computational results have presented promise solutions for different sizes of problems and proved to be an innovative and efficient approach for many difficult-to-solve problems.     

Heuristic and exact algorithms for the max–min optimization of the multi-scenario knapsack problem

We are concerned with a variation of the standard 0–1 knapsack problem, where the values of items differ under possible S scenarios. By applying the ‘pegging test’ the ordinary knapsack problem can be reduced, often significantly, in size; but this is not directly applicable to our problem. We introduce a kind of surrogate relaxation to derive upper and lower bounds quickly, and show that, with this preprocessing, the similar pegging test can be applied to our problem. The reduced problem can be solved to optimality by the branch-and-bound algorithm. Here, we make use of the surrogate variables to evaluate the upper bound at each branch-and-bound node very quickly by solving a continuous knapsack problem. Through numerical experiments we show that the developed method finds upper and lower bounds of very high accuracy in a few seconds, and solves larger instances to optimality faster than the previously published algorithms.     

The optimization landscape of hybrid quantum–classical algorithms: From quantum control to NISQ applications

This review investigates the landscapes of hybrid quantum–classical optimization algorithms that are prevalent in many rapidly developing quantum technologies, where the objective function is computed by either a natural quantum system or an engineered quantum ansatz, but the optimizer is classical. In any particular case, the nature of the underlying control landscape is fundamentally important for systematic optimization of the objective. In early studies on the optimal control of few-body dynamics, the optimizer could take full control of the relatively low-dimensional quantum systems to be manipulated. Stepping into the noisy intermediate-scale quantum (NISQ) era, the experimentally growing computational power of the ansatz expressed as quantum hardware may bring quantum advantage over classical computers, but the classical optimizer is often limited by the available control resources. Across these different scales, we will show that the landscape’s geometry experiences morphological changes from favorable trap-free landscapes to easily trapping rugged landscapes, and eventually to barren-plateau landscapes on which the optimizer can hardly move. This unified view provides the basis for understanding classes of systems that may be readily controlled out to those with special consideration, including the difficulties and potential advantages of NISQ technologies, as well as seeking possible ways to escape traps or plateaus, in particular circumstances.     

The (black) art of runtime evaluation: Are we comparing algorithms or implementations?

Any paper proposing a new algorithm should come with an evaluation of efficiency and scalability (particularly when we are designing methods for “big data”). However, there are several (more or less serious) pitfalls in such evaluations. We would like to point the attention of the community to these pitfalls. We substantiate our points with extensive experiments, using clustering and outlier detection methods with and without index acceleration. We discuss what we can learn from evaluations, whether experiments are properly designed, and what kind of conclusions we should avoid. We close with some general recommendations but maintain that the design of fair and conclusive experiments will always remain a challenge for researchers and an integral part of the scientific endeavor.     

The capacitated multi-facility location–allocation problem with probabilistic customer location and demand: two hybrid meta-heuristic algorithms

A new mathematical model for the capacitated multi-facility location–allocation problem with probabilistic customers' locations and demands is developed in this article. The model is formulated into the frameworks of the expected value model (EVM) and the chance-constrained programming (CCP) based on two different distance measures. In order to solve the model, two hybrid intelligent algorithms are proposed, where the simplex algorithm and stochastic simulation are the bases for both algorithms. However, in the first algorithm, named SSGA, a special type of genetic algorithm is combined and in the second, SSVDO, a vibration-damping optimisation (VDO) algorithm is united. The Taguchi method is employed to tune the parameters of the two proposed algorithms. Finally, some numerical examples are given to illustrate the applications of the proposed methodologies and to compare their performances.     

The active learning hypothesis of the job–demand–control model: an experimental examination

The active learning hypothesis of the job–demand–control model [Karasek, R. A. 1979. “Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign.” Administration Science Quarterly 24: 285–307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed–accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.     

A novel group decision model based on mean–variance–skewness concepts and interval-valued fuzzy sets for a selection problem of the sustainable warehouse location under uncertainty

Recently, sustainable warehouse location has been regarded as one of the most critical and significant decision problems for long-term planning in the supply chain. This strategic decision can be effected by different quantitative and qualitative evaluation criteria via three dimensions of the sustainability. Main theme of the paper is to select the most optimal location decision from a number of potential sustainable warehouse candidates. For this purpose, this paper presents a novel multi-criteria decision-making model by a group of supply chain experts or decision makers with interval-valued fuzzy setting and asymmetric uncertainty information. Concepts of mean, variance and skewness are introduced into the proposed group decision model, and their mathematical relations are defined based on a fuzzy possibilistic statistical approach. Then, new relations in this model are presented for obtaining ideal solutions under uncertainty with two high and low values of the possibilistic mean and possibilistic standard deviation, along with the possibilistic cube root of skewness. In addition, novel separation measures and new fuzzy ranking index of hybridized relative closeness coefficients are presented to provide final preference order of warehouse location candidates under uncertain conditions. Finally, a sustainable warehouse location selection problem in a pharmaceutical company is presented and solved by the proposed group decision model to demonstrate its applicability and suitability.

     

Identification of Wiener–Hammerstein models: Two algorithms based on the best split of a linear model applied to the SYSID'09 benchmark problem

This paper describes the identification of Wiener–Hammerstein models and two recently suggested algorithms are applied to the SYSID'09 benchmark data. The most difficult step in the identification process of such block-oriented models is to generate good initial values for the linear dynamic blocks so that local minima are avoided. Both of the considered algorithms obtain good initial estimates by using the best linear approximation (BLA) which can easily be estimated from data. Given the BLA, the two algorithms differ in the way the dynamics are separated into two linear parts. The first algorithm simply considers all possible splits of the dynamics. Each of the splits is used to initialize one Wiener–Hammerstein model using linear least-squares and the best performing model is selected. In the second algorithm, both linear blocks are initialized with the entire BLA model using basis function expansions of the poles and zeros of the BLA. This gives over-parameterized linear blocks and their order is decreased in a model reduction step. Both algorithms are explained and their properties are discussed. They both give good, comparable models on the benchmark data.     

The model matching problem for a class of polynomial non-linear discrete input–output systems with cross-products. An algorithmic approach

In this paper we examine the model matching problem that concerns non-linear input–output discrete systems, containing products among delays of input and output signals, through a special factorisation. The algebraic framework of δε-operators and the star-product that we adopt describe these systems. Moreover, a certain procedure that resembles the Euclidean division allows us to discover the linear factors of those systems, with respect to the above mentioned operations. The entire approach is symbolically algorithmic and involves the use of suitable software.     

An evaluation of the Mn–Ga system: Phase diagram,crystal structure,magnetism, and thermodynamic properties

The Ni–Mn-Ga alloy system is attractive due to its functional properties with potentials in various applications. However, the fundamental alloy thermodynamics of the binary Mn–Ga system is still lack of investigation. Therefore, a comprehensive evaluation of the Mn–Ga is performed in this work. Different versions of Mn–Ga phase diagrams available in the literature are reviewed. A new version of the Mn–Ga phase diagram is recommended along with possible invariant reactions. The crystal structure, magnetic transition, and thermochemical properties of intermetallic compounds are reviewed by considering available experimental and modeling such as ab initio calculations. In fact, more experimental information on the Mn-rich side is required in order to perform CALPHAD thermodynamic modeling for a reliable database. Further experiments are recommended to study the high-temperature phase equilibria between liquid, (γMn), (δMn) and Mn₂Ga(h), phase reactions between Mn₈Ga₅ and Mn₇Ga₆, and invariant reactions involving the MnGa phase. Nevertheless, the summarized information on phase equilibria, phase diagram, crystallography, magnetic transition temperature, magnetic moment, heat capacity, and enthalpy of formation can support the future thermodynamic investigation of the Mn–Ga system, which is critical for the materials design and discovery of Ni–Mn-Ga alloys.     

Systematic layout planning in human–system interface: An evaluation of alarm displays with spatial proximity for accidents diagnosis of advanced boiling water reactor

To ensure safety in nuclear power plants, this study investigated the interface design of a digital alarm system by analyzing the diagnosing process of operating information. This study focused on the layout planning of alarm windows and followed proximity compatibility principle and nuclear human–system interface design review guidelines to improve the human–system interface. This study adopt systematic layout planning to modify the design of alarm windows. The time of alarm handling, and accuracy, as well as human reliability were evaluated to compare the original layout with the improved one. In the end, according to the results of the comparison, an alternative alarm window layout human–system interface, which fits diagnosing process, is suggested.Relevance to industryThe similar layout planning process could be applied to design control centers in high reliability organizations, in particular which need decision-making by person.     

Genotype–phenotype mapping implications for genetic programming representation: Commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham,Grant Dick,and James Maclaurin

Here we comment on the article “On the mapping of genotype to phenotype in evolutionary algorithms,” by Peter A. Whigham, Grant Dick, and James Maclaurin. The article reasons about analogies from molecular biology to evolutionary algorithms and discusses conditions for biological adaptations in the context of grammatical evolution, which provide a useful perspective to GP practitioners. However, the connection of the listed implications for GP is not sufficiently convincing for the reader . Therefore this commentary will (1) examine the proposed principles one by one, challenging the authors to provide more supporting evidence where felt that this was needed, and (2) propose a methodical way to GP practitioners to apply these principles when designing GP representations.