Two-Stage Genetic Algorithm for Scheduling Stochastic Unrelated Parallel Machines in a Just-in-Time Manufacturing Context

Cao, Zhengcai; Lin, ChengRan; Zhou, MengChu; Zhou, Can; Sedraoui, Khaled

doi:10.1109/tase.2022.3178126

Cited by 11 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chien and Lan integrated the DQN and a hybrid GA (HGA) to solve the R m r j , s lk , incompatible C max [115]. Cao et al studied the R m r j , p j , s lk C max considering stochastic processing time depends on gamma or log-normal distribution [116]. They designed a simulation optimization approach, in which the first stage uses a GA to generate initial solutions with great potential and the second stage uses an optimal computing budget allocation (OCBA)-based simulation to accurately evaluate the solutions.…”

Section: Non-batch Machine Scheduling Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Fang,

Cheang,

Lim

2023

Sustainability

View full text Add to dashboard Cite

Machine scheduling problems associated with semiconductor manufacturing operations (SMOs) are one of the major research topics in the scheduling literature. Lots of papers have dealt with different variants of SMOs’ scheduling problems, which are generally difficult to tackle theoretically and computationally. In this paper, the single machine, parallel machines, flow shops, and job shops scheduling problems from SMOs have been reviewed, based on different processing constraints, e.g., batch processing, auxiliary resources, incompatible job families, and reentrant flow, etc., with the cycle time, flow time, and throughput-related performance measures. Given the vast and diverse nature of the current literature, it is urgently needed to make a systematic survey in order to identify the important research problems, research trends, and the progress of the related solution methods, as well as clarify future research perspectives. We hope the findings and observations could provide some insights to the researchers and practitioners in this domain.

show abstract

Section: Non-batch Machine Scheduling Problemsmentioning

confidence: 99%

“…Schedule repair is commonly used in practice because it reduces computational complexity and preserves schedule stability. Similar to deterministic problems, metaheuristics can be utilized to obtain new schedules [116,199]. Regarding when to reschedule, periodic, event-driven, and hybrid methods can be used.…”

Section: Stochastic/dynamic Scheduling Problemsmentioning

confidence: 99%

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Fang,

Cheang,

Lim

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Meta-heuristic algorithms (e.g., ant colony algorithm, particle swarm optimization, and genetic algorithm) [29][30][31][32] are broadly applicable search techniques. Tey are quite suitable for obtaining accurate or acceptable accuracy within a given time.…”

Section: Improved Genetic Algorithmmentioning

confidence: 99%

An Integrated Approach to Scheduling B-CAVs in Container Terminals considering Battery Management

Wang,

Luo,

et al. 2023

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

With the aim of promoting environmental sustainability and enhancing transport efficiency, battery-powered connected and automated vehicles (B-CAVs) are employed to replace diesel-powered ones in horizontal transport systems (HTSs) of container terminals. The operational efficiency of an HTS can be increased by the cooperation of B-CAVs. However, it is time-consuming to charge them. Therefore, their battery management becomes a critical issue of a transport schedule. To run container terminals more economically and efficiently, this work proposes an integrated scheduling approach to B-CAVs tasks’ dispatch and route planning, where battery management is taken into account. An integer programming model is constructed with the goal of minimizing the total travel distance. Then, a sustainable charging policy is designed to ensure the consistent transport capacity of an HTS. Furthermore, a congestion-free path plan-based improved genetic algorithm is presented to obtain a near-optimal plan for dispatching B-CAVs to perform transporting and charging operations. A series of experiments are carried out to verify the effectiveness and efficiency of our approach.

show abstract

“…Cao et al studied unrelated parallel machines problem in a stochastic JIT manufacturing environment. A modified genetic algorithm and an improved Monte-Carlo policy evaluation were suggested to tackle the problem [18].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Minimizing JIT scheduling of unrelated parallel machine with family setups and soft time window constraints

Shafipour

Rezaeian

Foroutan

2022

Preprint

View full text Add to dashboard Cite

Multi-machine scheduling has been one of the best-known and practical problems in the last decade and its applications are constantly increasing. This study addresses an unrelated parallel machine scheduling problem with family setups and soft time windows, in which machine eligibility and precedence constraints are considered. The objective is to minimize the total of weighted early and tardy costs. The problem is investigated for different sizes of jobs, families and machines. Two different metaheuristic algorithms, a simulated annealing (SA) and an artificial immune system (AIS) are presented. Two strategies called repair and penalty are proposed to implement predecessor constraints. Some computational experiments are performed and one-way analysis of variance (ANOVA) is conducted to compare the performance of the proposed metaheuristics and evaluate the designed combinations in terms of objective values and computational (CPU) times. Results demonstrate that SA with repair strategy generally outperforms other proposed methods.

show abstract

Two-Stage Genetic Algorithm for Scheduling Stochastic Unrelated Parallel Machines in a Just-in-Time Manufacturing Context

Cited by 11 publications

References 43 publications

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey

An Integrated Approach to Scheduling B-CAVs in Container Terminals considering Battery Management

Minimizing JIT scheduling of unrelated parallel machine with family setups and soft time window constraints

Contact Info

Product

Resources

About