A novel method of restoration path optimization for the AC-DC bulk power grid after a major blackout

The restoration control of the modern alternating current-direct current (AC-DC) hybrid power grid after a major blackout is difficult and complex. Taking into account the interaction between the line-commutated converter high-voltage direct current (LCC-HVDC) and the AC power grid, this paper proposes a novel optimization method of restoration path to reconfigure the skeleton network for the blackout power grid. Based on the system strength, the supporting capability of the AC power grid for the LCC-HVDC is first analysed from the aspects of start-up and operation of LCC-HVDCs. Subsequently, the quantitative relationship between the restoration path and the restoration characteristic of LCC-HVDC is derived in detail based on the system strength indices of the short-circuit capacity and the frequency regulation capability. Then, an optimization model of restoration path considering non-tree paths is formulated and a feasible optimization algorithm is proposed to achieve the optimal path restoration scheme. A modified IEEE 39-bus system and a partial power grid of Southwest China are simulated to show that the proposed method is suitable for the restoration of AC-DC power grids and can improve restoration efficiency. This research can be an important guidance for operators to rapidly restore the AC-DC power grid.Comment: Accepted by IET Generation, Transmission & Distributio

A genetic algorithm for a bicriteria supplier selection problem

Abstract In this paper, we discuss the problem of selecting suppliers for an organisation, where a number of suppliers have made price offers for supply of items, but have limited capacity. Selecting the cheapest combination of suppliers is a straightforward matter, but purchasers often have a dual goal of lowering the number of suppliers they deal with. This second goal makes this issue a bicriteria problem -minimisation of cost and minimisation of the number of suppliers. We present a mixed integer programming (MIP) model for this scenario. Quality and delivery performance are modelled as constraints. Smaller instances of this model may be solved using an MIP solver, but large instances will require a heuristic. We present a multipopulation genetic algorithm for generating Pareto-optimal solutions of the problem. The performance of this algorithm is compared against MIP solutions and Monte Carlo solutions

Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources

This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a machine m. These additional resources are limited, and this made the UPMR a difficult problem to solve. In this study, the maximum completion time of jobs makespan must be minimized. Here, we proposed genetic algorithm (GA) to solve the UPMR problem because of the robustness and the success of GA in solving many optimization problems. An enhancement of GA was also proposed in this work. Generally, the experiment involves tuning the parameters of GA. Additionally, an appropriate selection of GA operators was also experimented. The guide genetic algorithm (GGA) is not used to solve the unspecified dynamic UPMR. Besides, the utilization of parameters tuning and operators gave a balance between exploration and exploitation and thus help the search escape the local optimum. Results show that the GGA outperforms the simple genetic algorithm (SGA), but it still didn't match the results in the literature. On the other hand, GGA significantly outperforms all methods in terms of CPU time

A hybrid genetic algorithm application for a bi-objective, multi-project, multi-mode, resource-constrained project scheduling problem

Here we consider a bi-objective, multi-project, multi-mode, resource-constrained project-scheduling problem. The objectives were to minimize the makespan, minimize the mean of the flow times for individual projects, minimize the mean completion times for individual projects and maximize the total net present value of all projects. As a solution method, we used the non-dominated sorting genetic algorithm II (NSGA-II). To improve NSGA-II, a backward–forward pass (BFP) procedure was proposed for post-processing and for new population generation. Different alternatives for implementing BFP were tested with the results reported for different objective function combinations. To increase diversity, an injection procedure was introduced and implemented. Both the BFP and injection procedures led to improved objective function values. Moreover, the injection procedure generated a significantly higher number of non-dominated solutions with more diversity. A detailed fine-tuning process was conducted by employing a response surface optimization method. An extensive computational study was performed. Managerial insights are presented

Diseño de una técnica de solución para el problema de planeación de la generación y programación del consumo de energía en una escuela rural de Cundinamarca

Los recientes cambios climáticos han disminuido las fuentes de recursos fósiles, generando la necesidad de buscar fuentes alternas de energía renovable. En Colombia, muchas personas viven en condiciones de pobreza energética, generalmente en comunidades rurales. En este contexto, el Departamento de Ingeniería Electrónica de la Pontificia Universidad Javeriana diseñó un sistema de generación de energía solar que será implementado en una escuela rural de Caparrapí, Cundinamarca. El problema por resolver en este proyecto es la planeación de la generación y programación del consumo de energía del sistema. Este problema se abordará mediante la formulación de un modelo matemático y el diseño de una técnica de solución, los cuales se compararán en términos de tiempo computacional y resultado de la función objetivo en instancias de diferentes tamaños.Recent climate changes have reduced the sources of fossil resources, generating the need to look for alternative sources of renewable energy. In Colombia, many people live in conditions of energy poverty, usually in rural communities. In this context, the Department of Electronic Engineering of the Pontificia Universidad Javeriana designed a solar energy generation system that will be implemented in a rural school in Caparrapí, Cundinamarca. The problem to solve in this project is the planning of the generation and programming of the energy consumption of the system. This problem will be addressed by the formulation of a mathematical model and the design of a solution technique, which will be compared in terms of computational time and result of the objective function in instances of different sizes.Ingeniero (a) IndustrialPregrad

Integrated Models and Algorithms for Automotive Supply Chain Optimization

The automotive industry is one of the most important economic sectors, and the efficiency of its supply chain is crucial for ensuring its profitability. Developing and applying techniques to optimize automotive supply chains can lead to favorable economic outcomes and customer satisfaction. In this dissertation, we develop integrated models and algorithms for automotive supply chain optimization. Our objective is to explore methods that can increase the competitiveness of the automotive supply chain via maximizing efficiency and service levels. Based on interactions with an automotive industry supplier, we define an automotive supply chain planning problem at a detailed operational level while taking into account realistic assumptions such as sequence-dependent setups on parallel machines, auxiliary resource assignments, and multiple types of costs. We model the research problem of interest using mixed-integer linear programming. Given the problem’s NP-hard complexity, we develop a hybrid metaheuristic approach, including a constructive heuristic and an effective encoding-decoding strategy, to minimize the total integrated cost of production setups, inventory holding, transportation, and production outsourcing. Furthermore, since there are often conflicting objectives of interest in automotive supply chains, we investigate simultaneously optimizing total cost and customer service level via a multiobjective optimization methodology. Finally, we analyze the impact of adding an additional transportation mode, which offers a cost vs. delivery time option to the manufacturer, on total integrated cost. Our results demonstrate the promising performance of the proposed solution approaches to analyze the integrated cost minimization problem to near optimality in a timely manner, lowering the cost of the automotive supply chain. The proposed bicriteria, hybrid metaheuristic offers decision makers several options to trade-off cost with service level via identified Pareto-optimal solutions. The effect of the available additional transportation mode’s lead time is found to be bigger than its cost on the total integrated cost measure under study

MINIMUM FLOW TIME SCHEDULE GENETIC ALGORITHM FOR MASS CUSTOMIZATION MANUFACTURING USING MINICELLS

Minicells are small manufacturing cells dedicated to an option family and organized in a multi-stage configuration for mass customization manufacturing. Product variants, depending on the customization requirements of each customer, are routed through the minicells as necessary. For successful mass customization, customized products must be manufactured at low cost and with short turn around time. Effective scheduling of jobs to be processed in minicells is essential to quickly deliver customized products. In this research, a genetic algorithm based approach is developed to schedule jobs in a minicell configuration by considering it as a multi-stage flow shop. A new crossover strategy is used in the genetic algorithm to obtain a minimum flow time schedule

Study of hybrid strategies for multi-objective optimization using gradient based methods and evolutionary algorithms

Most of the optimization problems encountered in engineering have conflicting objectives. In order to solve these problems, genetic algorithms (GAs) and gradient-based methods are widely used. GAs are relatively easy to implement, because these algorithms only require first-order information of the objectives and constraints. On the other hand, GAs do not have a standard termination condition and therefore they may not converge to the exact solutions. Gradient-based methods, on the other hand, are based on first- and higher-order information of the objectives and constraints. These algorithms converge faster to the exact solutions in solving single-objective optimization problems, but are inefficient for multi-objective optimization problems (MOOPs) and unable to solve those with non-convex objective spaces. The work in this dissertation focuses on developing a hybrid strategy for solving MOOPs based on feasible sequential quadratic programming (FSQP) and nondominated sorting genetic algorithm II (NSGA-II). The hybrid algorithms developed in this dissertation are tested using benchmark problems and evaluated based on solution distribution, solution accuracy, and execution time. Based on these performance factors, the best hybrid strategy is determined and found to be generally efficient with good solution distributions in most of the cases studied. The best hybrid algorithm is applied to the design of a crushing tube and is shown to have relatively well-distributed solutions and good efficiency compared to solutions obtained by NSGA-II and FSQP alone

Methods to Support the Project Selection Problem With Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies

The United States Air Force relies upon information production activities to gain insight regarding uncertainties affecting important system configuration and in-mission task execution decisions. Constrained resources that prevent the fulfillment of every information production request, multiple information requestors holding different temporal-sensitive objectives, non-constant marginal value preferences, and information-product aging factors that affect the value-of-information complicate the management of these activities. This dissertation reviews project selection research related to these issues and presents novel methods to address these complications. Quantitative experimentation results demonstrate these methods’ significance