CSP-Driven Schedule Optimization in NC Equipments Grid Considering Complex Correlated Process Flows

2010 ◽  
Vol 97-101 ◽  
pp. 2650-2653
Author(s):  
Xin Lin ◽  
Ya Bo Luo
Keyword(s):  
Boundary Conditions ◽  
Numerical Control ◽  
Just In Time ◽  
Grid Technology ◽  
Np Hard ◽  
Potential Approach ◽  
Schedule Optimization ◽  
Grid Workflow ◽  
Optimum Solution

Numerical Control (NC) equipments sharing over Internet is a potential approach for improving the utilization of capacity, which is still a complex NP hard difficulty due to the complexity of schedule. This research takes the Constraints Satisfied Problem (CSP) thinking to solve the above problem employing grid technology. First, regarding the NC equipments sharing as CSP, the CSP model for NC equipments grid is developed taking the flexible constraints as the optimum objective and the rigid constraints as the boundary conditions. Second, the detailed algorithm and steps for optimization of NC equipments grid workflow based on Just-In-Time (JIT) thinking are proposed. Finally the digital experiment demonstrates the advantages of the CSP-driven methodology that has the higher efficiency for searching optimum solution.

Similarity-Based Integrated Scheduling of Machining Processes in the NC Resource Grid

2010 ◽  
Vol 97-101 ◽  
pp. 2403-2406
Author(s):  
Ya Bo Luo ◽  
Ming Chun Tang
Keyword(s):  
Numerical Control ◽  
Grid System ◽  
Machining Processes ◽  
Integrated Scheduling ◽  
Similarity Principle ◽  
Manufacturing Efficiency ◽  
Scheduling Model ◽  
Delivery Date ◽  
Complex Constraints ◽  
Optimum Solution

Grouping the similar processes is a good approach to improve the manufacturing efficiency, however, which is facing with two difficulties of the group automation and the constraints coupling. Regarding the numerical control (NC) machines and tasks as a grid system, this paper proposes a similarity-based tactic to solve the above difficulties. First, the methodology for analyzing the similarity among NC tasks is proposed to implement group automation taking the similarity principle as theory foundation. Second, based on the results drawn from the first step, the complex constraints including similarity constraints, delivery date constraints, and serial constraints are coupled to develop an integrated scheduling model. Finally, the integrated model is solved and the optimum solution is gotten using a specialized ants algorithm.

Electronic Circuit Simulation and Digital PC-Based Implementation of Dynamic MRR Optimization

2006 ◽  
Vol 505-507 ◽  
pp. 493-498
Author(s):  
Tian Syung Lan ◽  
Kuei Shu Hsu ◽  
Tung Te Chu ◽  
Long Jyi Yeh ◽  
Ming Guo Her
Keyword(s):  
Electronic Circuit ◽  
Removal Rate ◽  
Circuit Simulation ◽  
Numerical Control ◽  
Numerical Control Machining ◽  
Virtual Machining ◽  
Cnc Lathe ◽  
Linear Regression Technique ◽  
Optimum Solution ◽  
Electronic Circuit Simulation

Dynamic MRR (material removal rate) modeling is constructed and optimum solution through Calculus of Variations in maximize the machining profit of an individual cutting tool under fixed tool life is introduced. The mathematical model is formulated by reverse experiments on an ECOCA PC-3807 CNC lathe, and the electronic circuit is developed using linear regression technique for virtual machining. The inaccuracy between actual and simulated voltage is assured to be within 2%. By introducing a real-world CNC (computerized numerical control) machining case from AirTAC into the virtual system, the simulated cutting forces are shown to promise the feasible applicability of the optimum MRR control. Additionally, the implementation of dynamic solution is experimentally performed on a proposed digital PC-based lathe system. The surface roughness of all machined work-pieces is found to not only stabilize as the tool consumed, but also accomplish the recognized standard for finish turning.

scholarly journals Integrated Vendor–Buyer Lot-Sizing Model with Transportation and Quality Improvement Consideration under Just-in-Time Problem

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 944 ◽  
Author(s):  
Leuveano ◽  
Ab Rahman ◽  
Mahmood ◽  
Saleh
Keyword(s):  
Lot Sizing ◽  
Just In Time ◽  
Quality Improvements ◽  
New Approach ◽  
Numerical Examples ◽  
Batch Number ◽  
Inventory Replenishment ◽  
Proposed Model ◽  
Time Problem ◽  
Optimum Solution

This paper deals with the problem of transportation and quality within a Just-in-Time (JIT) inventory replenishment system. Formerly, transportation and quality problem are often modelled separately in most integrated inventory lot-sizing models. Hence, this paper develops an integrated vendor-buyer lot-sizing model by considering transportation and quality improvements into a JIT environment. The model is developed for minimising a total vendor–buyer system cost by optimising decisions such as delivery quantity, production batch, number of shipments, and process quality. Numerical examples and sensitivity analysis are provided to illustrate the proposed model. The developed model was also compared with an enumeration method to analyse the effectiveness of the proposed model to find the optimum solution. The results emphasise that the proposed model contributes to a new approach and obtains a near optimum solution for inventory replenishment decisions. The results are also beneficial to JIT practices as the model can improve the transport payload and reduce the chance of defective products and improving quality-related costs.

scholarly journals Inverse Generalized Maximum Flow Problems

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 899 ◽  
Author(s):  
Javad Tayyebi ◽  
Adrian Deaconu
Keyword(s):  
Flow Problem ◽  
Natural Extension ◽  
Maximum Flow ◽  
Fast Method ◽  
Np Hard ◽  
Flow Problems ◽  
Maximum Flow Problem ◽  
Hard Problems ◽  
Optimum Solution ◽  
Np Hard Problems

A natural extension of maximum flow problems is called the generalized maximum flow problem taking into account the gain and loss factors for arcs. This paper investigates an inverse problem corresponding to this problem. It is to increase arc capacities as less cost as possible in a way that a prescribed flow becomes a maximum flow with respect to the modified capacities. The problem is referred to as the generalized maximum flow problem (IGMF). At first, we present a fast method that determines whether the problem is feasible or not. Then, we develop an algorithm to solve the problem under the max-type distances in O ( m n · log n ) time. Furthermore, we prove that the problem is strongly NP-hard under sum-type distances and propose a heuristic algorithm to find a near-optimum solution to these NP-hard problems. The computational experiments show the accuracy and the efficiency of the algorithm.

Just-In-Time Scheduling with Generalized Due Dates and Identical Due Date Intervals

2018 ◽  
Vol 35 (06) ◽  
pp. 1850046 ◽  
Author(s):  
Byung-Cheon Choi ◽  
Myoung-Ju Park
Keyword(s):  
Single Machine Scheduling ◽  
Just In Time ◽  
Scheduling Problem ◽  
Np Hard ◽  
Due Dates ◽  
Due Date ◽  
Time Scheduling ◽  
Weighted Number ◽  
Polynomially Solvable ◽  
Tardy Jobs

We consider a single-machine scheduling problem such that the due dates are assigned not to the jobs but to the position at which the job is processed. We focus on the case with identical due date intervals. The objective is to minimize the weighted number of early and tardy jobs. First, we show that the problem is strongly NP-hard and has no [Formula: see text]-approximation algorithm for any fixed value [Formula: see text]. Then, we investigate polynomially solvable cases. Finally, we show that the preemption version is weakly NP-hard through its equivalence to the problem of minimizing the weighted number of tardy jobs.

scholarly journals A Systematic Study of Deep Generative Models for Rapid Topology Optimization

2020 ◽  
Author(s):  
Manoj Malviya
Keyword(s):  
Topology Optimization ◽  
Boundary Conditions ◽  
Optimization Problems ◽  
Generative Models ◽  
The Novel ◽  
Design Constraints ◽  
Diverse Range ◽  
Computational Sciences ◽  
Optimum Solution ◽  
Novel Design

With the advent in Additive Manufacturing (AM) technologies and Computational Sciences, design algorithms such as Topology Optimization (TO) have garnered the interest of academia and industry. TO aims to generate optimum structures by maximizing the stiffness of the structure, given a set of geometric, loading and boundary conditions. However, these approaches are computationally expensive as it requires many iterations to converge to an optimum solution. The purpose of this work is to explore the effectiveness of deep generative models on a diverse range of topology optimization problems with varying design constraints, loading and boundary conditions. Specifically, four distinctive models were successfully developed, trained, and evaluated to generate rapid designs with comparable results to that of conventional algorithms. Our findings highlight the effectiveness of the novel design problem representation and proposed generative models in rapid topology optimization.

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