Integrated process planning and scheduling using genetic algorithms

Chaudhry, Imran Ali; Usman, Muhammad

doi:10.17559/tv-20151121212910

Cited by 9 publications

(6 citation statements)

References 46 publications

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“…I. A. Chaudhry and M. Usman used a genetic algorithm and independent spreadsheets to simultaneously solve scheduling problems and process planning in a job shop environment [11]. R. Meolic and Z. Brezočnik proposed a new approach to solving job shop scheduling problems with an emphasis on identifying feasible solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation framework for determining the order and size of the product batches in the flow shop: A case study

Ištoković¹,

Perinić²,

Doboviček³

et al. 2019

Adv produc engineer manag

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The problems of determining the order and size of the product batches in the flow shop with multiple processors (FSMP) and sequence-dependent setup times are among the most difficult manufacturing planning tasks. In today's environment, where necessity for survival in the market is to deliver the goods in time, it is crucial to optimize production plans. Inspired by real sector manufacturing system, this paper demonstrates the discrete event simulation (DES) supported by the genetic algorithm (GA) optimization tool. The main aim is to develop the simulation framework as a support for the daily planning of manufacturing with emphasis on determining the size and entry order of the product batches within specific requirements. Procedures are developed within the genetic algorithm, which are implemented in Tecnomatix Plant Simulation software package. A genetic algorithm was used to optimize mean flow time (MFT) and total setup time (TST) performance measures. Primary constraint for on-time delivery was imposed on the model. The research results show that solutions are industrially applicable and provide accurate information on the batch size of the defined products, as well as a detailed schedule and timing of entry into the observed system. Display of the solution, in a simple and concise manner, serves as a tool for manufacturing operations planning.

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Section: Literature Reviewmentioning

confidence: 99%

Simulation framework for determining the order and size of the product batches in the flow shop: A case study

Ištoković¹,

Perinić²,

Doboviček³

et al. 2019

Adv produc engineer manag

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm was compared with NSGA-II, SPEA2 and VEGA, and the excellent performance of the proposed algorithm was verified. Chaudhry [17] proposed a genetic algorithm for the IPPS problem which can select the best process planning and job scheduling method in the job shop at the same time. Jin et al [18] formulated a new MILP model for IPPS in flexible shop floor systems which introduced network diagrams to constrain different operation sequences.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimization of Process Planning and Scheduling for Aerospace Complex Component Based on Honey-Bee Mating Algorithm

et al. 2023

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To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop scheduling for aerospace manufacturing enterprises is proposed. This paper considers the process flexibility of aerospace complex components comprehensively, and an integrated optimization model for the process planning and production scheduling of aerospace complex components is established with the optimization objectives of achieving a minimum makespan, machining time and machining cost. A honey-bee mating optimization algorithm (HBMO) combined with the greedy algorithm was proposed to solve the model. Then, it formulated a four-layer encoding method based on a feature-processing sequence, processing method, and machine tool, a tool was designed, and five worker bee cultivation strategies were designed to effectively solve the problems of infeasible solutions and local optimization when a queen bee mated to a drone. Finally, taking the complex component parts of an aerospace enterprise as an example, the integrated optimization of process planning and workshop scheduling is carried out. The results demonstrate that the proposed model and algorithm can effectively shorten the makespan and machining time, and reduce the machining cost.

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“…One of the applications of GA is the resourceconstrained project scheduling problems. Many researchers studied on this subject [8][9][10][11][12][13][14][15]. Davis and Patterson [16] solved a project consisting of 2 dummy activities and a total of 27 activities with GA and they compared the solution with the results of other heuristic methods.…”

Section: Introductionmentioning

confidence: 99%

Scheduling of Construction Projects under Resource-Constrained Conditions with a Specifically Developed Software using Genetic Algorithms

Erdal

Kanit

2021

Teh. vjesn.

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The purpose of this study is to develop a genetic algorithm (GA) based software that can perform resource allocation close to optimum and that can determine the critical path by minimizing the project duration according to the resource profile for a present work schedule and resource pool using a programmable objective function. In this context, the methodology of GAs was presented, the software was developed and the performance of this software was tested with a sample project. With the developed software, by minimizing the activity durations in both constrained and unconstrained resource conditions, projects can be scheduled, total duration and the critical path of the projects can be determined. With this software, any construction company will be able to determine how much time would be required to complete a project at the bidding stage by considering its resources and constraints and can take the required precautions. The main difference of this present study is that the developed code performs minimization of schedule duration integrated with resource allocation and levelling. It also determines the critical path of the final solutions. Both renewable and non-renewable resources are included in the code which is not often considered in the literature. By minimizing project duration and optimizing resource allocation, construction projects can become more sustainable, and the environmental impact of the construction process could be minimized.

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Integrated process planning and scheduling using genetic algorithms

Cited by 9 publications

References 46 publications

Simulation framework for determining the order and size of the product batches in the flow shop: A case study

Simulation framework for determining the order and size of the product batches in the flow shop: A case study

Integrated Optimization of Process Planning and Scheduling for Aerospace Complex Component Based on Honey-Bee Mating Algorithm

Scheduling of Construction Projects under Resource-Constrained Conditions with a Specifically Developed Software using Genetic Algorithms

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