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Decision Support Methodology for Scheduling Orders in Additive Manufacturing

https://doi.org/10.1002/9781119798798.ch2

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Creating a Multi-iterative-Priority-Rule for the Job Shop Scheduling Problem with Focus on Tardy Jobs via Genetic Programming

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A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

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An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

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How Additive Manufacturing Technology Changes Business Models? – Review of Literature

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Nesting and scheduling problems for additive manufacturing: A taxonomy and review

inferredcites →https://doi.org/10.1016/j.asoc.2020.106208openalex-snapshot

Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning

inferredcites →https://doi.org/10.1016/j.cie.2018.09.048openalex-snapshot

Production scheduling and nesting in additive manufacturing

inferredcites →https://doi.org/10.1016/j.cie.2020.106428openalex-snapshot

Integration of hybrid additive/subtractive manufacturing planning and scheduling by metaheuristics

inferredcites →https://doi.org/10.1016/j.cie.2020.106778openalex-snapshot

A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem

inferredcites →https://doi.org/10.1016/j.cie.2020.106863openalex-snapshot

An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption

inferredcites →https://doi.org/10.1016/j.cor.2017.01.013openalex-snapshot

Production planning in additive manufacturing and 3D printing

inferredcites →https://doi.org/10.1016/j.cor.2019.01.006openalex-snapshot

MILP models to minimise makespan in additive manufacturing machine scheduling problems

inferredcites →https://doi.org/10.1016/j.cor.2021.105245openalex-snapshot

Machine scheduling with orientation selection and two-dimensional packing for additive manufacturing

inferredcites →https://doi.org/10.1016/j.cor.2021.105272openalex-snapshot

A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems

inferredcites →https://doi.org/10.1016/j.jclepro.2015.09.097openalex-snapshot

Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption

inferredcites →https://doi.org/10.1016/j.jclepro.2020.123183openalex-snapshot

Energy-aware production scheduling for additive manufacturing

inferredcites →https://doi.org/10.1016/j.jmapro.2013.06.006openalex-snapshot

The development of a novel process planning algorithm for an unconstrained hybrid manufacturing process

inferredcites →https://doi.org/10.1016/j.jmsy.2020.04.008openalex-snapshot

Digital Twin Enhanced Dynamic Job-Shop Scheduling

inferredcites →https://doi.org/10.1016/j.jmsy.2021.01.014openalex-snapshot

A cost benefit analysis for industry 4.0 in a job shop environment using a mixed integer linear programming model

inferredcites →https://doi.org/10.1016/j.promfg.2015.09.045openalex-snapshot

Optimizing Material Deposition Direction for Functional Internal Architecture in Additive Manufacturing Processes

inferredcites →https://doi.org/10.1016/j.promfg.2018.06.035openalex-snapshot

Optimization Model to Extend Existing Production Planning and Control Systems for the Use of Additive Manufacturing Technologies in the Industrial Production

inferredcites →https://doi.org/10.1016/j.promfg.2020.01.388openalex-snapshot

Impact of Scheduling Policies on the Performance of an Additive Manufacturing Production System

inferredcites →https://doi.org/10.1016/j.tws.2020.107228openalex-snapshot

Methods and materials for additive manufacturing: A critical review on advancements and challenges

inferredcites →https://doi.org/10.1080/00207543.2017.1401240openalex-snapshot

Job shop scheduling with a combination of four buffering constraints

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An interactive algorithm for portfolio bi-criteria optimization of R&D projects in public organizations

inferredcites →https://doi.org/10.1134/s1064230711060165openalex-snapshot

A milp bi-objective model for static portfolio selection of R&D projects with synergies

inferredcites →https://doi.org/10.1287/moor.1.2.117openalex-snapshot

The Complexity of Flowshop and Jobshop Scheduling

inferredcites →https://doi.org/10.1504/ijmor.2009.024291openalex-snapshot

An integrated mathematical-computer approach for R&D project selection in large public organisations

inferredcites →https://doi.org/10.3390/app10155159openalex-snapshot

A Decision-Support Model for Additive Manufacturing Scheduling Using an Integrative Analytic Hierarchy Process and Multi-Objective Optimization

inferredcites →https://doi.org/10.3390/app11052186openalex-snapshot

Smart Manufacturing Scheduling Approaches—Systematic Review and Future Directions

inferredcites →https://doi.org/10.3390/su13147684openalex-snapshot

A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0

inferredcites →https://doi.org/10.4114/intartif.vol21iss62pp40-52openalex-snapshot

Planning and Scheduling in Additive Manufacturing

inferredcites →https://doi.org/10.5267/j.ijiec.2020.1.001openalex-snapshot

A modified tabu search algorithm for the single-machine scheduling problem using additive manufacturing technology

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inferred← citeshttps://doi.org/10.2139/ssrn.4680266openalex-extracted

A Real-Time Digital Twin to Inform In-Process Decision Support in Additive Manufacturing

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