Study of stochastic job shop dynamic scheduling

2005 ◽  
Author(s):  
Bi-Xi Zhang ◽  
Lu-Xia Yi ◽  
Shi Xiao
Keyword(s):  
Job Shop ◽  
Dynamic Scheduling

Dual Resource Constrained Job Shop Dynamic Scheduling with Local Time Window

2014 ◽  
Vol 933 ◽  
pp. 708-713 ◽  
Author(s):  
Yuan Huang ◽  
Xiu Tian Yan ◽  
Jing Yao Li
Keyword(s):  
Local Time ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Time Window ◽  
Scheduling Algorithm ◽  
Resource Constrained ◽  
Dynamic Disturbance ◽  
Scheduling System ◽  
Scheduling Method ◽  
Disturbance Analysis

To solve the Dual Resource Constrained Job Shop Dynamic Scheduling Problem with heterogeneous workers (DRCJDSP-HW),a three-tier dynamic scheduling system including dynamic disturbance analysis, dynamic disturbance evaluation and dynamic scheduling method was constructed based on the idea of scheduling with local time window. To minimize the revision of old scheduling scheme in DRCJDSP-HW, a hybrid dynamic scheduling algorithm was proposed based on the chromosome reduction mechanism and the scheduling optimization of local job in stages. The outstanding application effect of the dynamic scheduling system and the hybrid dynamic scheduling algorithm were validated reducing the influence of dynamic disturbances effectively by simulation experiment at last.

Dynamic Scheduling Model and Algorithm for Dispatching Trains in High-Speed Railway Passenger Station

2010 ◽  
Author(s):  
Yixiang Yue ◽  
Leishan Zhou
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Time Window ◽  
Real Life ◽  
Greedy Heuristic ◽  
Railway Station ◽  
High Speed Railway ◽  
Railway Passenger

Regarding the railway station tracks and train running routes as machines, all trains in this railway station as jobs, dispatching trains in high-speed railway passenger stations can be considered as a special type of Job-Shop Problem (JSP). In this paper, we proposed a multi-machines, multi-jobs JSP model with special constraints for Operation Plan Scheduling Problem (OPSP) in high-speed railway passenger stations, and presented a fast heuristic algorithm based on greedy heuristic. This algorithm first divided all operations into several layers according to the yards attributes and the operation’s urgency level. Then every operation was allotted a feasible time window, each operation was assigned to a specified “machine” sequenced or backward sequenced within the time slot, layer by layer according to its priority. As we recorded and modified the time slots dynamically, the searching space was decreased dramatically. And we take the South Beijing High-speed Railway Station as example and give extensive numerical experiment. Computational results based on real-life instance show that the algorithm has significant merits for large scale problems; can both reduce tardiness and shorten cycle times. The empirical evidence also proved that this algorithm is industrial practicable.

scholarly journals Energy-Efficient Job-Shop Dynamic Scheduling System Based on the Cyber-Physical Energy-Monitoring System

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 52238-52247 ◽  
Author(s):  
Yixiong Feng ◽  
Qirui Wang ◽  
Yicong Gao ◽  
Jin Cheng ◽  
Jianrong Tan
Keyword(s):  
Monitoring System ◽  
Energy Efficient ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Energy Monitoring ◽  
Scheduling System ◽  
Physical Energy

Modeling and Scheme Generation of Dynamic Flexible Job-Shop Scheduling

2013 ◽  
Vol 423-426 ◽  
pp. 2232-2236
Author(s):  
Ying Pan ◽  
Dong Juan Xue ◽  
Tian Yi Gao ◽  
Li Bin Zhou ◽  
Xiao Yu Xie
Keyword(s):  
Dynamic Simulation ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Job Shop Scheduling ◽  
Adaptive Genetic Algorithm ◽  
Flexible Job Shop ◽  
Uncertain Information ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Scheduling Method

Aiming at uncertain information and dynamic characteristic during flexible job-shop scheduling process, some kind of dynamic scheduling method for flexible job-shop scheduling problem (FJSP) is put forward based on real-time adjustment. A dynamic simulation solution mode framework is presented for FJSP. This framework is inspired by adaptive control, combined with the robust scheduling and foreseeing scheduling. It has both advantages of such two scheduling methods, and its stable and highly efficient. Preliminary scheme generation method based on foreseeing dynamics scheduling is introduced then. Foreseeing function is realized by fault-handling algorithm and dynamic simulation solver on the basis of Adaptive Genetic Algorithm (AGA).

scholarly journals Off-Line and Dynamic Production Scheduling – A Comparative Case Study

2016 ◽  
Vol 7 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Andrzej Bożek ◽  
Marian Wysocki
Keyword(s):  
Petri Net ◽  
Production Scheduling ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Graph Representation ◽  
Comparative Case Study ◽  
Reactive Scheduling ◽  
Multi Agent System ◽  
Multi Agent

Abstract A comprehensive case study of manufacturing scheduling solutions development is given. It includes highly generalized scheduling problem as well as a few scheduling modes, methods and problem models. The considered problem combines flexible job shop structure, lot streaming with variable sublots, transport times, setup times, and machine calendars. Tabu search metaheuristic and constraint programming methods have been used for the off-line scheduling. Two dynamic scheduling methods have also been implemented, i.e., dispatching rules for the completely reactive scheduling and a multi-agent system for the predictivereactive scheduling. In these implementations three distinct models of the problem have been used, based on: graph representation, optimal constraint satisfaction, and Petri net formalism. Each of these solutions has been verified in computational experiments. The results are compared and some findings about advantages, disadvantages, and suggestions on using the solutions are formulated.

scholarly journals Dynamic scheduling in a job-shop production system with reinforcement learning

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 104-109
Author(s):  
Csaba Kardos ◽  
Catherine Laflamme ◽  
Viola Gallina ◽  
Wilfried Sihn
Keyword(s):  
Reinforcement Learning ◽  
Production System ◽  
Job Shop ◽  
Dynamic Scheduling
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