N-list-enhanced heuristic for distributed three-stage assembly permutation flow shop scheduling

System-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses the production planning of a distributed manufacturing system consisting of three stages: production of parts (subcomponents), assembly of components in Original Equipment Manufacturer (OEM) factories, and final assembly of products at the product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is the most recent development in the literature of distributed scheduling problems, which has seen very limited development for possible industrial applications. This research introduces a highly efficient constructive heuristic to contribute to the literature on DTrSAPFSP. Numerical experiments considering a comprehensive set of operational parameters are undertaken to evaluate the performance of the benchmark algorithms. It is shown that the N-list-enhanced Constructive Heuristic algorithm performs significantly better than the current best-performing algorithm and three new metaheuristics in terms of both solution quality and computational time. It can, therefore, be considered a competitive benchmark for future studies on distributed production scheduling and computing

On the use of biased-randomized algorithms for solving non-smooth optimization problems

Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

A New Energy-Aware Flexible Job Shop Scheduling Method Using Modified Biogeography-Based Optimization

Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS)

Internet of Things (IoT) Utilization to Improve Performance and Productivity of Internal Supply Chain

The inevitable transformations brought about by the rapidly changing Internet of Things (IoT) impact all aspects of life today, including management and businesses. Specifically, areas of businesses depending mainly on internal supply chain capacity are experiencing a paradigm shift to ensure effective company performance regarding purchases, production, company sales, and product distribution. This shift means that challenges faced by the internal chain supply unit can be solved by adopting and adapting IoT as a new way to minimize work delays and save time. Moreover, IoT automatically leads to performance and productivity increases. Therefore, the present paper aims to justify adopting and adapting IoT applications in Indonesian companies, including retail businesses. Most companies’ internal supply chain units face several difficulties during and after the devastating peak of COVID-19, which has led to a total global lockdown. These problems' complexity is exponential and requires innovative ways to solve their prevailing challenges. This study used observation, interview, and documentary research methods through a large-scale survey. The survey obtained the necessary information regarding how companies utilize IoT to improve their performance and productivity without hindering their internal supply chain and production units. The study concluded that the adoption of IoT, if well implemented, leads to a sustainable company and uninterrupted supply chain performance, indicating the proper performance of the organization. Doi: 10.28991/esj-2021-SP1-017 Full Text: PD

Algoritmos para la programación de la producción en un entorno de flujo regular distribuido de permutación

Este proyecto implica la documentación, estudio y posterior implementación de un Algoritmo de Optimización basado en la Biogeografía para un tipo Flow-Shop de permutación distribuido. Estos tipos de problemas consta de dos etapas: producción y montaje (en este documento nos centramos en la etapa de producción), en la que los productos se producen de forma secuencial. Éstos se elaboran por partes en distintas fábricas para que en el caso de que fallase alguna, la producción no se detenga. Para ello, habrá que tener claro en qué se fundamenta la optimización basada en la biogeorafía y como funciona el Algoritmo. Después de hacer la correspondiente implementación se transcurrirá a la comparación de resultados entre los distintos algoritmos que existen para resolver este problema: algoritmo HBBO y la Heurística basada en la inserción (HBI) de Hatami (2013). Antes de describir el problema es necesario tener unos conocimientos previos, como por ejemplo qué es un Flow-Shop, qué tipos de entornos existen en la programación de operaciones, los tipos de restricciones que se pueden dar y qué tipo de objetivos se plantean en las fábricas. Todas estas explicaciones están elaboradas en el capítulo 2 del documento. En la descripción de los algoritmos que se ha realizado en el apartado 3 se han incorporado la explicación de las funciones más importantes utilizadas, además de alguna suposición que se ha tomado debido a qué no se han tenido los datos suficientes para abordarlo o se han detectado errores en el documento Jian y Shuai (2016). En la implementación se ha utilizado un progama llamado Code::Blocks (comentado en el objeto del problema) que se utiiza para codificar en C, junto con la librería , para hacer más sencilla la imlementación, ya que esta libería posee una serie de funciones que son utilizadas en la elaboración del documento y facilita la inicialización de elementos como vectores o matrices. Para la comparación de estos dos métodos de programación, se han utilizado una serie de datos que han sido recopilados del documento Jian y Shuai (2016) y que se expresan en el capítulo 4 del proyecto. Los datos a emplear son el número de trabajos (n), el número de máquinas (m), el número de fábricas (F), la cantidad de instancias (smax) y el número de iteraciones máximas a realizar en cada experimento (Itermax). Estos datos tienen dos bloques diferenciados: instancias de pequeño tamaño e instancias de gran tamaño. Los experimentos a elaborar consistirán en la combinación del número de trabajo, máquinas y fábricas (n x m x F) teniendo en cuenta que para cada tamaño de instancias se usa una cantidad de hábitats y número de iteraciones diferentes. Para cada experimento se obtendrá las soluciones que serán comparadas con el fin de comprobar la eficacia del algoritmo. Todos los resultados que se han obtenido se pueden apreciar en el apartado 4 de la memoria, que con la ayuda del Excel para crear tablas y gráficas se han podido realizar la comparación entre ambos métodos y sacar una conclusión. La conclusión final que se puede deducir es que para pequeñas instancias (número de trabajos, máquinas y fábricas pequeños) el algoritmo HBBO es más eficiente que el algoritmo HBI, pero para instancias de gran tamaño (número de trabajos, máquinas y fábricas más grandes) el algoritmo HBBO da peores resultados que el algoritmo HBI. En conclusión, si se trabaja con instancias de pequeño tamaño es más adecuado la utilización del algoritmo HBBO para obtener la mejor solución, pero para instancias de gran tamaño, el uso de algoritmo HBBO resulta ineficiente y el tiempo de obtención de los resultados es mucho mayor, siendo así la utilización del algoritmo HBI una mejor opción.Universidad de Sevilla. Grado en Ingeniería de Tecnologías Industriale

A Hybrid Algorithm Based on Comprehensive Search Mechanisms for Job Shop Scheduling Problem

The research on complex workshop scheduling methods has important academic significance and has wide applications in industrial manufacturing. Aiming at the job shop scheduling problem, a hybrid algorithm based on comprehensive search mechanisms (HACSM) is proposed to optimize the maximum completion time. HACSM combines three search methods with different optimization scales, including fireworks algorithm (FW), extended Akers graphical method (LS1+_AKERS_EXT), and tabu search algorithm (TS). FW realizes global search through information interaction and resource allocation, ensuring the diversity of the population. LS1+_AKERS_EXT realizes compound movement with Akers graphical method, so it has advanced global and local search capabilities. In LS1+_AKERS_EXT, the shortest path is the core of the algorithm, which directly affects the encoding and decoding of scheduling. In order to find the shortest path, an effective node expansion method is designed to improve the node expansion efficiency. In the part of centralized search, TS based on the neighborhood structure is used. Finally, the effectiveness and superiority of HACSM are verified by testing the relevant instances in the literature

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms