Comparison of Multiobjective Evolutionary Algorithms for operations scheduling under machine availability constraints

Many of the problems that arise in production systems can be handled with Multi-Objective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary Multi-Objective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2 and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Bahia Blanca. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina;Fil: Méndez, M.. Universidad de Las Palmas de Gran Canaria. Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería : España;Fil: Tohme, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Bahia Blanca. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina;Fil: Broz, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - CONICET - Bahia Blanca. Instituto de Investigaciones Económicas y Sociales del Sur; Argentina

The Three-Objective Optimization Model of Flexible Workshop Scheduling Problem for Minimizing Work Completion Time, Work Delay Time, and Energy Consumption

In recent years, the optimal design of the workshop schedule has received much attention with the increased competition in the business environment. As a strategic issue, designing a workshop schedule affects other decisions in the production chain. The purpose of this thesis is to design a three-objective mathematical model, with the objectives of minimizing work completion time, work delay time and energy consumption, considering the importance of businesses attention to reduce energy consumption in recent years. The developed model has been solved using exact solution methods of Weighted Sum (WS) and Epsilon Constraint (Ɛ) in small dimensions using GAMS software. These problems were also solved in large-scale problems with NSGA-II and SFLA meta-heuristic algorithms using MATLAB software in single-objective and multi-objective mode due to the NP-Hard nature of this group of large and real dimensional problems. The standard BRdata set of problems were used to investigate the algorithms performance in solving these problems so that it is possible to compare the algorithms performance of this research with the results of the algorithms used by other researchers. The obtained results show the relatively appropriate performance of these algorithms in solving these problems and also the much better and more optimal performance of the NSGA-II algorithm compared to the performance of the SFLA algorithm

Energy and labor aware production scheduling for industrial demand response using adaptive multi-objective memetic algorithm

Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices to reduce cost. This paper introduces a multi-objective optimization model which schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to the trade-off between energy and labor costs. An adaptive multi-objective memetic algorithm is proposed to leverage feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. Thus, adaptive balance remains between exploration of the NSGA-II and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer demonstrate the effectiveness and efficiency of the algorithm. The proposed scheduling method enables intelligent production systems, where production loads and human workers are mutually matched and jointly adapted to real-time electricity pricing for cost-efficient production

A research survey: review of flexible job shop scheduling techniques

In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results. A total of 404 distinct publications were found addressing the FJSSP. Some of the research papers presented more than one technique/algorithm to solve the problem that is categorized into 410 different applications. Selected time period of these research papers is between 1990 and February 2014. Articles were searched mainly on major databases such as SpringerLink, Science Direct, IEEE Xplore, Scopus, EBSCO, etc. and other web sources. All databases were searched for “flexible job shop” and “scheduling” in the title an