A Genetic Algorithm Based Approach for Scheduling of Dual-Resource Constrainded Manufacturing Systems

ElMaraghy, Hoda A.; Patel, Vishvas; Abdallah, Imed Ben

doi:10.1016/s0007-8506(07)63204-1

Cited by 25 publications

(4 citation statements)

References 9 publications

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“…Huang et al Engineering and Operations Management, Melbourne, Australia, November 14-16, 2023 © IEOM Society International (2014) worked on scheduling the optimization strategy for dual-resource job-shop scheduling (DR-JSP) with heterogeneous workers by using a pheromone branch genetic algorithm. Elmaraghy et al (1999) applied the genetic algorithm to compare various rules and heuristics in machine and worker assignments and schedules for DR-JSP and JSP. Chaudhry and Drake (2009) gave a generalized GA implementation for DR-JSP for identical parallel machines and worker assignments.…”

Section: Literature Reviewmentioning

confidence: 99%

Mixed-Integer Linear Programming Model for the Dual-Resource Flexible Job-Shop Scheduling Problem

Agrawal,

Vital-Soto,

Olivares-Aguila

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Scheduling is a vital function for efficiently operating flexible job-shop systems. Traditionally, that function considers assigning jobs to machines and their sequence. However, machines need to be operated by another set of resources (i.e., workers). Due to the fact that operators are skilled workers, the available pool is limited. Hence, the interaction of machines and humans needs to be studied in an integral manner to address the scheduling problem. In this article, a novel precedence variable-based, mixed-integer linear programming model is developed for the dual-resource flexible job-shop problem. The mathematical formulation deals with the optimal assignment of machines and workers to operations and the operation sequence in both resources by minimizing the makespan. The model gives an exact solution by solving both the assignment and the sequencing problems concurrently. The model was implemented in Docplex and was run on three instances of varying sizes. The model solved the three introduced examples, including a large instance involving 20 operations with 4 workers and 4 machines, using only 1662 variables and 5217 constraints in 156.43 seconds, indicating that the proposed model is adequate. The model can be used to label training examples for machine learning-based techniques as well as help track and compare models developed using heuristics.

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Section: Literature Reviewmentioning

confidence: 99%

Mixed-Integer Linear Programming Model for the Dual-Resource Flexible Job-Shop Scheduling Problem

Agrawal,

Vital-Soto,

Olivares-Aguila

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…Before year 2000, various issues on dual resource-constrained (DRC) systems have been studied, such as resource flexibility in job shops (Bobrowski & Park, 1993;Fry et al, 1995), workforce flexibility and learning effect (Malhotra et al, 1993), workforce flexibility in cellular manufacturing systems (CMS) (Morris & Tersine, 1994), and workforce assignment in job shop scheduling problems (Liao & Lin, 1998;ElMaraghy et al, 1999). These publications were already analyzed in the survey (Xu et al, 2011).…”

Section: Dual Resource Constrained (Drc) Systemmentioning

confidence: 99%

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

Hashemi-Petroodi

Thevenin

Kovalev

et al. 2020

Annual Reviews in Control

103

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A manufacturing system able to perform a high variety of tasks requires different types of resources. Fully automated systems using robots possess high speed, accuracy, tirelessness, and force, but they are expensive. On the other hand, human workers are intelligent, creative, flexible, and able to work with different tools in different situations. A combination of these resources forms a human-machine/robot (hybrid) system, where humans and robots perform a variety of tasks (manual, automated, and hybrid tasks) in a shared workspace. Contrarily to the existing surveys, this study is dedicated to operations management problems (focusing on the applications and features) for human and machine/robot collaborative systems in manufacturing. This research is divided into two types of interactions between human and automated components in manufacturing and assembly systems: dual resource constrained (DRC) and human-robot collaboration (HRC) optimization problems. Moreover, different characteristics of the workforce and machines/robots such as heterogeneity, homogeneity, and ergonomics are introduced. Finally, this paper identifies the optimization challenges and problems for hybrid systems. The existing literature on HRC focuses mainly on the robotic point of view and not on the operations management and optimization aspects. Therefore, the future research directions include the design of models and methods to optimize HRC systems in terms of ergonomics, safety, and throughput. In addition, studying flexibility and reconfigurability in hybrid systems is one of the main research avenues for future research.

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“…The method included random input turbulences, multiple criteria and multiple job routes. A scheduling approach was developed in [11] using a GA, to determine the efficient assignment of machines and workers in order to optimize performance criteria such as the mean flow time. The problem of scheduling of a Flexible Manufacturing System using a GA with knowledge based genetic operators is included in [12].…”

Section: State Of the Artmentioning

confidence: 99%

A Knowledge-Based Decision-Making Framework for the Design of Manufacturing Networks for Custom-Made Products

Mourtzis

Doukas

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Part 2: Knowledge Discovery and SharingInternational audienceEfficient design of manufacturing networks is paramount for a sustainable growth. The establishment of mass customization and the transition to personalization complicates design activities and leads to vast amounts of unexploited data. This research work aims to exploit existing knowledge for enhancing decision-making during the initial manufacturing networks design, which carry out custom orders of industrial equipment. A method developed into software is proposed, comprising a Genetic Algorithm with knowledge-enriched operators and an intelligent initialization algorithm that exploits existing planning knowledge. The validation of the method is performed using data from a high-precision mold-making manufacturer and its network of first-tier suppliers

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A Genetic Algorithm Based Approach for Scheduling of Dual-Resource Constrainded Manufacturing Systems

Cited by 25 publications

References 9 publications

Mixed-Integer Linear Programming Model for the Dual-Resource Flexible Job-Shop Scheduling Problem

Mixed-Integer Linear Programming Model for the Dual-Resource Flexible Job-Shop Scheduling Problem

Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey

A Knowledge-Based Decision-Making Framework for the Design of Manufacturing Networks for Custom-Made Products

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