A New Neighborhood Function for Discrete Manufacturing Process Design Optimization Using Generalized Hill Climbing Algorithms

2000 ◽  
Vol 122 (2) ◽  
pp. 164-171 ◽  
Author(s):  
Diane E. Vaughan ◽  
Sheldon H. Jacobson ◽  
Derek E. Armstrong
Keyword(s):  
Design Optimization ◽  
Air Force ◽  
Process Design ◽  
Manufacturing Process ◽  
Input Parameter ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Neighborhood Function ◽  
Discrete Manufacturing ◽  
Hill Climbing Algorithms

Discrete manufacturing process design optimization can be difficult, due to the large number of manufacturing process design sequences and associated input parameter setting combinations that exist. Generalized hill climbing algorithms have been introduced to address such manufacturing design problems. Initial results with generalized hill climbing algorithms required the manufacturing process design sequence to be fixed, with the generalized hill climbing algorithm used to identify optimal input parameter settings. This paper introduces a new neighborhood function that allows generalized hill climbing algorithms to be used to also identify the optimal discrete manufacturing process design sequence among a set of valid design sequences. The neighborhood function uses a switch function for all the input parameters, hence allows the generalized hill climbing algorithm to simultaneously optimize over both the design sequences and the inputs parameters. Computational results are reported with an integrated blade rotor discrete manufacturing process design problem under study at the Materials Process Design Branch of the Air Force Research Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA). [S1050-0472(00)01002-3]

scholarly journals Development of a Land Transportation Recommendation System Using the Hill Climbing Algorithm

2020 ◽  
Vol 1 (1) ◽  
pp. 7
Author(s):  
Eka Surya Aditya ◽  
Wikan Danar Sunindyo
Keyword(s):  
Public Transportation ◽  
Recommendation System ◽  
Hill Climbing ◽  
The Public ◽  
Hill Climbing Algorithm ◽  
Time Space ◽  
The Right ◽  
Available Information ◽  
Hill Climbing Algorithms ◽  
The Hill

Communities in big cities often encounter problems in using public transportation due to difficulties in accessing available information. The information is not well integrated and scattered in various places. For this reason, an information and recommendation system is needed to facilitate the public in choosing the right mode of land transportation. The recommendation system can be built using the Hill Climbing algorithm. In this paper, I explain the development of a public land transportation recommendation system using three types of Hill Climbing Algorithms. The results of the recommendations are analyzed based on the complexity of asymptotic time, space complexity, and the quality of the results.

scholarly journals Courses timetabling based on hill climbing algorithm

2020 ◽  
Vol 10 (6) ◽  
pp. 6558
Author(s):  
Abdoul Rjoub
Keyword(s):  
Optimal Solution ◽  
Initial Solution ◽  
Hill Climbing ◽  
Soft Constraints ◽  
Hill Climbing Algorithm ◽  
Hard Constraints ◽  
Side Constraints ◽  
Time Requirements ◽  
Hard And Soft Constraints ◽  
Hill Climbing Algorithms

In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.

scholarly journals A Hybrid Flower Pollination with β-Hill Climbing Algorithm for Global Optimization

2021 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah ◽  
Sharif Naser Makhadmeh ◽  
Ammar Kamal Abasi ◽  
...  
Keyword(s):  
Global Optimization ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Flower Pollination

Study on a Green Manufacturing Process Design System

2013 ◽  
Vol 397-400 ◽  
pp. 57-61
Author(s):  
Dong Jie Zhong
Keyword(s):  
Process Design ◽  
Manufacturing Process ◽  
Design Principle ◽  
Green Manufacturing ◽  
Design System ◽  
Operation Flow

Green manufacturing process is a key segment to guarantee green degree of products manufacturing course. In this paper, a kind of design system is presented by analyzing the function demand of the design system for green manufacturing process and then the modules which constituent the design system are analyzed in detail. Moreover, according to the design principle and the estimate indexes of green manufacturing process, the operation flow of the design system is introduced.

Integrating Product Design With Manufacturing Process Design Using the Robust Concept Exploration Method

1996 ◽  
Author(s):  
Jesse D. Peplinski ◽  
Janet K. Allen ◽  
Farrokh Mistree
Keyword(s):  
Product Design ◽  
Process Design ◽  
Manufacturing Process ◽  
Response Surfaces ◽  
Production Costs ◽  
Early Stages ◽  
Trade Offs ◽  
Development Costs ◽  
Concept Exploration ◽  
Product And Process

Abstract How can the manufacturability of different product design alternatives be evaluated efficiently during the early stages of concept exploration? The benefits of such integrated product and manufacturing process design are widely recognized and include faster time to market, reduced development costs and production costs, and increased product quality. To reap these benefits fully, however, one must examine product/process trade-offs and cost/schedule/performance trade-offs in the early stages of design. Evaluating production cost and lead time requires detailed simulation or other analysis packages which 1) would be computationally expensive to run for every alternative, and 2) require detailed information that may or may not be available in these early design stages. Our approach is to generate response surfaces that serve as approximations to the analyses packages and use these approximations to identify robust regions of the design space for further exploration. In this paper we present a method for robust product and process exploration and illustrate this method using a simplified example of a machining center processing a single component. We close by discussing the implications of this work for manufacturing outsourcing, designing robust supplier chains, and ultimately designing the manufacturing enterprise itself.

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