Job Shop Scheduling
21 papers with code • 0 benchmarks • 0 datasets
Scheduling Task
Benchmarks
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Libraries
Use these libraries to find Job Shop Scheduling models and implementationsMost implemented papers
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
zcajiayin/L2D
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NeurIPS 2020
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
A Reinforcement Learning Environment For Job-Shop Scheduling
Scheduling is a fundamental task occurring in various automated systems applications, e. g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste.
Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling
Many optimization techniques evaluate solutions consecutively, where the next candidate for evaluation is determined by the results of previous evaluations.
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems
thomasWeise/jsspInstancesAndResults
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Bio-Inspired computing is the subset of Nature-Inspired computing.
An ant colony optimization algorithm for job shop scheduling problem
thomasWeise/jsspInstancesAndResults
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The nature has inspired several metaheuristics, outstanding among these is Ant Colony Optimization (ACO), which have proved to be very effective and efficient in problems of high complexity (NP-hard) in combinatorial optimization.
Google vs IBM: A Constraint Solving Challenge on the Job-Shop Scheduling Problem
The job-shop scheduling is one of the most studied optimization problems from the dawn of computer era to the present day.
Metaheuristics for the Online Printing Shop Scheduling Problem
This challenging real scheduling problem, that emerged in the nowadays printing industry, corresponds to a flexible job shop scheduling problem with sequencing flexibility; and it presents several complicating specificities such as resumable operations, periods of unavailability of the machines, sequence-dependent setup times, partial overlapping between operations with precedence constraints, and fixed operations, among others.
An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents
giorgiograni/jssp_actor-critic_agasucci_monaci_grani
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The aim is to build up a greedy-like heuristic able to learn on some distribution of JSSP instances, different in the number of jobs and machines.
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism
The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realistic smart manufacturing settings.
Learning to generalize Dispatching rules on the Job Shop Scheduling
optimization-and-machine-learning-lab/job-shop
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Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem.
