805 research outputs found
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling
The following interdisciplinary article presents a memetic algorithm with
applying deep reinforcement learning (DRL) for solving practically oriented
dual resource constrained flexible job shop scheduling problems (DRC-FJSSP).
From research projects in industry, we recognize the need to consider flexible
machines, flexible human workers, worker capabilities, setup and processing
operations, material arrival times, complex job paths with parallel tasks for
bill of material (BOM) manufacturing, sequence-dependent setup times and
(partially) automated tasks in human-machine-collaboration. In recent years,
there has been extensive research on metaheuristics and DRL techniques but
focused on simple scheduling environments. However, there are few approaches
combining metaheuristics and DRL to generate schedules more reliably and
efficiently. In this paper, we first formulate a DRC-FJSSP to map complex
industry requirements beyond traditional job shop models. Then we propose a
scheduling framework integrating a discrete event simulation (DES) for schedule
evaluation, considering parallel computing and multicriteria optimization.
Here, a memetic algorithm is enriched with DRL to improve sequencing and
assignment decisions. Through numerical experiments with real-world production
data, we confirm that the framework generates feasible schedules efficiently
and reliably for a balanced optimization of makespan (MS) and total tardiness
(TT). Utilizing DRL instead of random metaheuristic operations leads to better
results in fewer algorithm iterations and outperforms traditional approaches in
such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202
A New Multicommodity Flow Model for the Job Sequencing and Tool Switching Problem
Artigo cientĂfico.In this paper a new multicommodity flow mathematical model for the Job Sequencing and Tool Switching Problem (SSP) is presented. The proposed model has a LP relaxation lower bound equal to the number of tools minus the tool machine’s capacity. Computational tests were performed comparing the new model with the models of the literature. The proposed model performed better, both in execution time and in the number of instances solved to optimality.Coordenação de Aperfeiçoamento de Pessoal de NĂvel Superior (CAPES
On the role of metaheuristic optimization in bioinformatics
Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics
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
Satisfying flexible due dates in fuzzy job shop by means of hybrid evolutionary algorithms
This paper tackles the job shop scheduling problem with fuzzy sets modelling uncertain durations and flexible due dates. The objective is to achieve high-service level by maximising due-date satisfaction, considering two different overall satisfaction measures as objective functions. We show how these functions model different attitudes in the framework of fuzzy multicriteria decision making and we define a measure of solution robustness based on an existing a-posteriori semantics of fuzzy schedules to further assess the quality of the obtained solutions. As solving method, we improve a memetic algorithm from the literature by incorporating a new heuristic mechanism to guide the search through plateaus of the fitness landscape. We assess the performance of the resulting algorithm with an extensive experimental study, including a parametric analysis, and a study of the algorithm’s components and synergy between them. We provide results on a set of existing and new benchmark instances for fuzzy job shop with flexible due dates that show the competitiveness of our method.This research has been supported by the Spanish Government under research grant TIN2016-79190-R
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