An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached/detached setup, machine release date and lag-time

Defersha, Fantahun M.; Rooyani, Danial

doi:10.1016/j.cie.2020.106605

Cited by 60 publications

(26 citation statements)

References 70 publications

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“…Lin et al [60] developed a GA that contained a different chromosome representation for the joint decision of process planning and scheduling. Defersha and Rooyani [57] developed a two-stage GA that comprised a solution encoding in the first stage and a common GA approach in the second stage. Wu et al [52] found the optimal solution to minimize makespan in an assembly scheduling problem via the comparison of dynamic differential evolution (DDE), simulated annealing (SA), and cloud-theory-based simulated annealing (CSA).…”

Section: Methodsmentioning

confidence: 99%

“…The job sequence represents the job allocation inside the machines. Allocation problems that combine resource and job sequences are typical in JS-FMS planning and scheduling [20,21,24,31,32,36,40,42,46,48,50,53,56,57,59,62]; this is because the workflow of a job shop is unidirectional or recursive, as there are no constraints on the machines that perform only the first operation of a job or the last operation of the job [8]. Meanwhile, the product sequence concentrates on the sequence of products when a specific product enters a machine.…”

Section: Sequence Typementioning

confidence: 99%

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A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

Meilanitasari

Shin

2021

Processes

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This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.

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Section: Methodsmentioning

confidence: 99%

Section: Sequence Typementioning

confidence: 99%

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

Meilanitasari

Shin

2021

Processes

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“…To make use of continues based algorithms in discrete approaches several suggestions have been contributed. The premise behind most of these suggestions is to project continuous variable parameters as a logical or a crossover method [17,42]. Through that, the related evolution pattern appeared as a multiple-to-one pattern, mostly two-to-one.…”

Section: Mating Proceduresmentioning

confidence: 99%

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

Awad

Abd-Elaziz

2021

Processes

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In order to attain high manufacturing productivity, industry 4.0 merges all the available system and environment data that can empower the enabled-intelligent techniques. The use of data provokes the manufacturing self-awareness, reconfiguring the traditional manufacturing challenges. The current piece of research renders attention to new consideration in the Job Shop Scheduling (JSSP) based problems as a case study. In that field, a great number of previous research papers provided optimization solutions for JSSP, relying on heuristics based algorithms. The current study investigates the main elements of such algorithms to provide a concise anatomy and a review on the previous research papers. Going through the study, a new optimization scope is introduced relying on additional available data of a machine, by which the Flexible Job-Shop Scheduling Problem (FJSP) is converted to a dynamic machine state assignation problem. Deploying two-stages, the study utilizes a combination of discrete Particle Swarm Optimization (PSO) and a selection based algorithm followed by a modified local search algorithm to attain an optimized case solution. The selection based algorithm is imported to beat the ever-growing randomness combined with the increasing number of data-types.

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“…The OSJSPS involves two sequencing problems for main jobs and auxiliary jobs, respectively. We connect the two sequencing problems and use a two-stage algorithm from Defersha et al [66] and Tsai and Li [67]. The first stage searches the candidate sequences among the main jobs, and second stage searches the best sequence including auxiliary jobs, given the main job sequence and considering the precedence relationships.…”

Section: A Two-stage Genetic Algorithm For Osjspsmentioning

confidence: 99%

A Job Sequencing Problem of an Overhead Shuttle Crane in a Rail-Based Automated Container Terminal

Fibrianto¹,

Kang

Hong

2020

IEEE Access

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This study proposes a job scheduling model and its heuristics for an automated container terminal with an overhead shuttle crane (OS) to reduce the total tardiness time of flatcars and external trucks by considering the separation of each job into a main job, and a premarshaling or remarshaling job. The OS is busy or idle according to the fluctuations in the processing times of different pieces of equipment. We identify the OS job sequencing problem considering job separation (OSJSPS) as a mixed-integer programming (MIP) model, which simultaneously sequences a set of jobs and searches for their possible separation into premarshaling and remarshaling jobs. We present a two-stage genetic algorithm (TGA) based on two local improvement procedures: an iterative local search procedure and an opportunistic job separation procedure. We conclude that the two-stage genetic algorithm reduces the total tardiness time of the container terminal's flatcars and external trucks as the number of OS jobs increases.

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An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached/detached setup, machine release date and lag-time

Cited by 60 publications

References 70 publications

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

A New Perspective for Solving Manufacturing Scheduling Based Problems Respecting New Data Considerations

A Job Sequencing Problem of an Overhead Shuttle Crane in a Rail-Based Automated Container Terminal

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