Determination of a suitable index for a solvent via two-column extractive distillation using a heuristic method

The traditional approach to solvent selection in the extractive distillation process strictly focuses on the change in the relative volatility of light-heavy components induced by the solvent. However, the total annual cost of the process may not be minimal when the solvent induces the largest change in relative volatility. This work presents a heuristic method for selecting the optimal solvent to minimize the total annual cost. The functional relationship between the relative volatility and the total annual cost is established, where the main factors, such as the relative volatility of the light-heavy components and the relative volatility of the heavy-component solvent, are taken into account. Binary azeotropic mixtures of methanol-toluene and methanol-acetone are separated to verify the feasibility of the model. The results show that using the solvent with the minimal two-column extractive distillation index, the process achieves a minimal total annual cost. The method is conducive for sustainable advancements in chemistry and engineering because a suitable solvent can be selected without simulation verification.     

p-Median and p-dispersion problems: A bi-criteria analysis

Given a collection of n locations and a symmetric measure of distance (difference) between each pair of locations, we seek to identify (select) a subset of p locations so as to achieve two distinct objectives. The first objective is to use the selected locations as centers (medians) of p groups that would partition the entire collection and minimize the total distance between the locations and their respective group medians. The second objective is to maximize the minimum distance (diversity) among the selected locations themselves. We study this problem as a multi-objective optimization problem and propose an iterative algorithm to obtain its non-dominated frontier. At each iteration we construct and solve a 0–1 integer programming problem. Through a computational experiment we show that this algorithm is computationally effective for small to medium size instances of the problem. We also propose a Lagrangian heuristic algorithm for solving larger instances of this problem.     

An optimization algorithm for the workforce management in a retail chain

This work proposes a scheduling problem for the workforce management in a chain of supermarkets operating in Italy. We focus on determining the ideal mix of full-time and part-time workers which are needed every week to guarantee a satisfactory service level during the check-out operations. The generation of working shifts, to be assigned to retail workers, is subject to several constraints imposed by both labour laws and enterprise bargaining agreements.We present a mathematical formulation of the problem followed by an exact solution approach which relies on the definition of feasible daily working shifts. The number of feasible daily shifts, that are combined to determine feasible weekly shifts, could drastically increase, depending on the selected planning interval. In addition, there may exist additional constraints, that are difficult to incorporate into the mathematical model. For these reasons, a hybrid heuristic, which does not require the generation of all feasible weekly shifts, is proposed in this paper.Using appropriate statistical techniques, a sensitivity analysis is performed to test the design of the hybrid heuristic. Computational tests are carried out by solving several real instances provided by the retail firm. The results obtained by the heuristic are compared both with an exact approach and with the solutions adopted by the retail company, which have been determined by using a naïf approach. Our hybrid heuristic exhibits excellent performance finding optimal or near optimal solutions in a very limited CPU time.     

An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production

Energy-efficient scheduling is highly necessary for energy-intensive industries, such as glass, mould or chemical production. Inspired by a real-world glass-ceramics production process, this paper investigates a bi-criteria energy-efficient two-stage hybrid flow shop scheduling problem, in which parallel machines with eligibility are at stage 1 and a batch machine is at stage 2. The performance measures considered are makespan and total energy consumption. Time-of-use (TOU) electricity prices and different states of machines (working, idle and turnoff) are integrated. To tackle this problem, a mixed integer programming (MIP) is formulated, based on which an augmented ε-constraint (AUGMECON) method is adopted to obtain the exact Pareto front. A problem-tailored constructive heuristic method with local search strategy, a bi-objective tabu search algorithm and a bi-objective ant colony optimisation algorithm are developed to deal with medium- and large-scale problems. Extensive computational experiments are conducted, and a real-world case is solved. The results show effectiveness of the proposed methods, in particular the bi-objective tabu search.     

新型树启发式搜索算法的机器人路径规划

面对三维空间移动机器人从起始点到终止点的最短路径问题,提出一种新型的边缘点树启发式搜索(TreeEP)算法,该方法将地图空间进行密度可调的三维离散化处理,根据障碍安全距离筛选出障碍物的可靠边缘点信息,再利用树扩散架构选出最能引导搜索方向的潜力点进行扩散搜索,最终得出最短路径。提出局部调整策略,得到改进的Tree-EP算法。实验结果表明,在带障碍复杂地形最短路径搜索应用中,提出的Tree-EP算法与已有方法相比,能找到更短的移动路径。     

Efficient recursive least squares parameter estimation algorithm for accurate nanoCMOS variable gain amplifier performances

ABSTRACT

With the aggressive scaling of integrated circuit technology, parametric estimation is a critical task for designers who looked for solutions to the challenges of some Nanoscale CMOS parameters. This paper presented the prediction of primary parameters of CMOS transistor for 16 nm to 10 nm process nodes using both of Bisquare Weights (BW) method and a novel recursive least squares (RLS) parameter estimation algorithm. The proposed RLS algorithm consists of the minimisation of a quadratic criterion relating to the prediction error in order to attain the best estimated parameters of the developed mathematical model. The obtained results thanks to the proposed RLS algorithm were better than those reached using the BW method. Comparisons between Predictive Technology Model (PTM) data and parameters estimated with RLS algorithm were made to check the validity and the consistency of the proposed algorithm. These predicted primary parameters were helpful to estimate and to optimise the performances of the Variable Gain Amplifier (VGA) which was a basic circuit element with a key role in the design of new upcoming receivers.     

Accurate estimation of cost function parameters for thermal power plants using a novel optimization approach

Accurate parameter estimation of the input-output characteristics in thermal power plants is an important issue in power system because these characteristics directly affect the economic dispatch calculations. Parameter estimation is an optimization problem in which the optimal values of the unknown parameters should be estimated by an optimization technique. By considering the valve-point effect, the parameter estimation will be more difficult since the fitness function of this optimization problem turns into a non-smooth and non-convex function in which finding the global optimal is a challenging task. In this paper, a recently proposed metaheuristic approach, crow search algorithm (CSA), is proposed for accurate estimation of the input-output characteristics of thermal power plants with and without valve-point effect. Simulation results show that CSA finds more promising results than least squares method (LSM), particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC).     

基于伪布尔模型和启发式算法求解无容量设施选址问题

采用伪布尔模型和启发式算法来求解无容量设施选址问题。首先给出了问题的伪布尔(pseudo-Boolean)表示，然后基于Khumawala规则对问题进行预处理，最后提出两种启发式分支准则来求解问题。实验结果表明所提算法简单有效。     

Multi-Objective Approach for Reconfiguration of Distribution Systems with Distributed Generations

This paper presents a fuzzy multi-objective based heuristic algorithm for network reconfiguration of distribution systems considering distributed generations (DGs). The objectives of reduction of real power loss, branch current carrying capacity limit, maximum and minimum voltage constraints, and feeder load balancing are considered for performance enhancement of the distribution system. Since these objectives are non-commensurable and difficult to solve simultaneously using conventional approaches, they are converted into fuzzy domain and a fuzzy multi-objective function is formulated. A sensitivity analysis based on voltage profile improvement and real power loss reduction is used for obtaining optimal locations of DGs and genetic algorithm is used for optimal sizing of DGs. The proposed reconfiguration algorithm is implemented in two stages, initially in the first stage without incorporating DGs and in the second stage incorporating DGs for obtaining an optimal distribution system network reconfiguration. The advantage of the proposed method is demonstrated through a seventy node four feeders and a sixteen node three feeders distribution systems.     

A Heuristic Combinatorial Optimization Algorithm for Load-Leveling and Peak Demand Reduction using Energy Storage Systems

A method for applying combinatorial optimization algorithms to Energy Storage System (ESS) scheduling is presented in this paper. Scheduling is essential for the integration of ESS in electrical networks at grid level or at consumer level to achieve the objectives of integration such as constraint management or energy cost reduction and for efficient storage dispatch. It also shows that for a time-of-use (ToU) tariff scheme based on the shape of the demand profile with higher prices tied to peak periods, effective load-leveling, and peak demand reduction always leads to energy cost reduction. While other methods usually require more information such as generation cost curves or ToU tariffs to schedule ESS, the proposed method uses only demand profile information and ESS parameters to achieve load-leveling and peak demand reduction and also considers the entire optimization time horizon. This is done by combining heuristic bin packing and subset sum algorithms with specific modifications to the standard forms and through transformations. A case study is presented in which the algorithm is used to schedule household ESS with repurposed electric vehicle batteries and the results are compared to a demand response scheme on the same setup.