scholarly journals A Scheduling Algorithm for Multi-Workshop Production Based on BOM and Process Route

2021 ◽  
Vol 11 (11) ◽  
pp. 5078
Author(s):  
Lihong Qiao ◽  
Zhenwei Zhang ◽  
Zhicheng Huang
Keyword(s):  
Scheduling Algorithm ◽  
Particle Swarm Algorithm ◽  
Scheduling Problem ◽  
Complex Products ◽  
Bill Of Materials ◽  
Search Path ◽  
Process Route ◽  
Path Search ◽  
Level Process

For the scheduling problem of complex products in multi-workshop production, this paper studied the BOM (Bill of Materials) structure of complex products and the characteristics of the process route and developed the construction method of a multi-level process network diagram. Based on this, a comprehensive mathematical model for scheduling on multi-workshop production was proposed. An improved particle swarm algorithm (PSO) was proposed to solve the problem. By constructing the network subgraph, the invalid search path of the algorithm was avoided, and the efficiency of the algorithm was improved. In addition, for the scheduling problem with product time constraints, this paper presented a path search rescheduling strategy to ensure that the algorithm could obtain an effective search path. Finally, the model and algorithm were verified through a case study. This paper optimized the parameters of the algorithm by different tests and obtained the optimal range of the parameters. At the same time, through the analysis of the scheduling of complex products in a multi-workshop environment, the effectiveness and practicability of the above methods were verified.

A Hybrid Differential Evolution Scheduling Algorithm to Heterogeneous Distributed System

2014 ◽  
Vol 631-632 ◽  
pp. 271-275
Author(s):  
Yan Kang ◽  
Zhong Min Wang ◽  
Ying Lin ◽  
Xiang Yun Guo
Keyword(s):  
Differential Evolution ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Greedy Heuristic ◽  
Particle Swarm Algorithm ◽  
Scheduling Problem ◽  
Heuristic Strategy

This paper presents a differential evolution algorithm with designed greedy heuristic strategy to solve the task scheduling problem. The static task scheduling problem is NP-complete and is a critic issue in parallel and distributed computing environment. A vector consists of a task permutation assigned to each individual in the target population by using DE mutation and crossover operators. A heuristic strategy is used to generate the feasible solutions as there a lot of infeasible solutions in the solution space as the size of the problem increase. And the strategies of the particle swarm algorithm are employed to modify the DE crossover operator for speeding up the search to optimal solution. And then, the individual is replaced with the corresponding target individual if it is global best or local best in terms of fitness. The performance of the algorithm is illustrated by comparing with the existing effectively scheduling algorithms. The performances of the proposed algorithms are tested on the benchmark and compared to the best-known solutions available. The computational results demonstrate that effectively and efficiency of the presented algorithm.

Integrated Scheduling Algorithm with Setup Time

2011 ◽  
Vol 213 ◽  
pp. 226-230
Author(s):  
Zhi Qiang Xie ◽  
Jing Yang ◽  
Yu Zheng Teng ◽  
Lan Lan
Keyword(s):  
Time Complexity ◽  
Scheduling Algorithm ◽  
Research Result ◽  
Setup Time ◽  
Scheduling Problem ◽  
Start Time ◽  
Complex Products ◽  
Integrated Scheduling

Aiming at the problem that there is no research result in the complex products processing and assemble integrated scheduling problem with setup time, this paper proposes strategy to resolve this problem. That is to determine scheduling sequence of procedures according to layer priority strategy, shorten time strategy and long path strategy. Then adopt algorithm of inserting setup time dynamically to determine the start time of procedures by scheduling sequence. As this algorithm avoids to move scheduled procedures many times after inserting setup time, the time complexity is only secondary. So this algorithm is simple and has high scheduling efficiency.

A multi-shop integrated scheduling algorithm with fixed output constraint

2021 ◽  
pp. 1-9
Author(s):  
Yingchun Xia ◽  
Zhiqiang Xie ◽  
Yu Xin ◽  
Xiaowei Zhang
Keyword(s):  
Constraint Programming ◽  
Programming Model ◽  
Scheduling Algorithm ◽  
Processing Parameters ◽  
Scheduling Problem ◽  
Integrated Scheduling ◽  
Scheduling Scheme ◽  
Wide Range ◽  
Constraint Programming Model ◽  
Output Constraint

The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible.

Linear Rail Space Dynamic Scheduling Technology for Multi-Tasking Hybrid Assembly Sections

2012 ◽  
Vol 271-272 ◽  
pp. 650-656
Author(s):  
Zhi Bing Lu ◽  
Ai Min Wang ◽  
Cheng Tong Tang ◽  
Jing Sheng Li
Keyword(s):  
Production Scheduling ◽  
Dynamic Scheduling ◽  
Scheduling Algorithm ◽  
Real Data ◽  
Utilization Rate ◽  
Filter Method ◽  
Scheduling Problem ◽  
Rule Based ◽  
Comprehensive Utilization ◽  
Space Production

For the rapid response to production scheduling problem driven by high-density production tasks, a dynamic scheduling technology for the large precision strip products assembly with a mixture of task time nodes and line-rail space is proposed. A scheduling constrained model containing coverage, proximity, timeliness and resource is established. A linear rail space production scheduling technology using heuristic automatic scheduling and event-driven method is put forward. The time rule based on delivery and single completion assembly is formed, at the same time the space rule based on the adjacent rail and comprehensive utilization is researched. Supposing the privilege of single product assembling as the core, the scheduling parts filter method based on multiple constraints and former rules. For the space layout problem, a clingy forward and backward algorithms is proposed to judge the assemble position regarding the space comprehensive utilization rate. The classification of the various disturbances in the actual production is summarized. Three basic algorithms are proposed, including insertion, moving and re-scheduling algorithm, in order to solve the assembly dynamic scheduling problem driven by production disturbance events. Finally, take rocket as the example， the rocket assembly space production scheduling system is developed, combining with the proposed algorithm. The practicability of the system is validated using real data.

Scheduling Algorithms With Application to Parallel Computing of Aeroacoustics

2000 ◽  
Author(s):  
Alex Povitsky
Keyword(s):  
Energy Transfer ◽  
Sound Propagation ◽  
Scheduling Algorithm ◽  
Multiprocessor Systems ◽  
Scheduling Problem ◽  
Sound Waves ◽  
Mean Flow ◽  
Shop Scheduling ◽  
Special Case ◽  
Efficient Parallelization

Abstract In this study we consider one method of parallelization of implicit numerical schemes on multiprocessor systems. Then, the parallel high-order compact numerical algorithm is applied to physics of amplification of sound waves in a non-uniform mean flow. Due to the pipelined nature of this algorithm, its efficient parallelization is based on scheduling of processors for other computational tasks while otherwise the processors stay idle. In turn, the proposed scheduling algorithm is taken as a special case of the general shop scheduling problem and possible extentions and generalizations of the proposed scheduling methodology are discussed. Numerical results are discussed in terms of baroclinic generation of wave-associated vorticity that appear to be a key process in energy transfer between a non-uniform mean flow and a propagating disturbance. The discovered phenomenon leads to significant amplification of sound waves and controls the direction of sound propagation.

scholarly journals Optimisation Based on Simulation: A Patient Asmission Scheduling Problem In A Radiology Department Case Study

2015 ◽  
Vol 4 (2) ◽  
pp. 241-249
Author(s):  
Manupati V.K ◽  
Mourya Teja G ◽  
Zakir Hussain S.K ◽  
Sandeep Y ◽  
Akhil Varma A
Keyword(s):  
Radiology Department ◽  
Scheduling Problem

ONLINE SCHEDULING OF DYNAMIC TREES

1995 ◽  
Vol 05 (04) ◽  
pp. 635-646 ◽  
Author(s):  
MICHAEL A. PALIS ◽  
JING-CHIOU LIOU ◽  
SANGUTHEVAR RAJASEKARAN ◽  
SUNIL SHENDE ◽  
DAVID S.L. WEI
Keyword(s):  
Lower Bound ◽  
Competitive Ratio ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Online Scheduling ◽  
The Other ◽  
Static Case ◽  
Scheduling Problem ◽  
Dynamic Trees ◽  
Dynamic Tree

The scheduling problem for dynamic tree-structured task graphs is studied and is shown to be inherently more difficult than the static case. It is shown that any online scheduling algorithm, deterministic or randomized, has competitive ratio Ω((1/g)/ log d(1/g)) for trees with granularity g and degree at most d. On the other hand, it is known that static trees with arbitrary granularity can be scheduled to within twice the optimal schedule. It is also shown that the lower bound is tight: there is a deterministic online tree scheduling algorithm that has competitive ratio O((1/g)/ log d(1/g)). Thus, randomization does not help.

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