Canran Zhang, Jianping Dou, Shuai Wang and Pingyuan Wang
The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP…
Abstract
Purpose
The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I.
Design/methodology/approach
Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed.
Findings
A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality.
Originality/value
The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.
Details
Keywords
The purpose of this paper is to provide the historical background of genealogical records and analyze the value of Chinese genealogical research through the study of names and…
Abstract
Purpose
The purpose of this paper is to provide the historical background of genealogical records and analyze the value of Chinese genealogical research through the study of names and genealogical resources.
Design/methodology/approach
The paper examines the historical evolution and value of Chinese genealogical records, with the focus on researching the Islamic Chinese names used by the people living in Guilin. The highlight of this paper includes the analysis and evolution of the Islamic Chinese names commonly adopted by the local people in Guilin. It concludes with the recommendations on emphasizing and making the best use of genealogical records to enhance the research value of Chinese overseas studies.
Findings
The paper covers the history of Islam and describes how the religion was introduced into China, as well as Muslims' ethnicity and identity. It also places focus on the importance of building a research collection in Asian history and Chinese genealogy.
Research limitations/implications
This research study has a strong subject focus on Chinese genealogy, Asian history, and Islamic Chinese surnames. It is a narrow field that few researchers have delved into.
Practical implications
The results of this study will assist students, researchers, and the general public in tracing the origin of their surnames and developing their interest in the social and historical value of Chinese local history and genealogies.
Social implications
The study of Chinese surnames is, by itself, a particular field for researching the social and political implications of contemporary Chinese society during the time the family members lived.
Originality/value
Very little research has been done in the area of Chinese local history and genealogy. The paper would be of value to researchers such as historians, sociologists, ethnologists and archaeologists, as well as students and anyone interested in researching a surname origin, its history and evolution.
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Keywords
Xiaodong Wu, Junfeng Shi, Fujun Chen and Yaru Wang
The purpose of this paper is to present a new approach for selecting the good heavy oil reservoirs to develop preferentially, which can avoid the huge economical loss resulted…
Abstract
Purpose
The purpose of this paper is to present a new approach for selecting the good heavy oil reservoirs to develop preferentially, which can avoid the huge economical loss resulted from wrong decision.
Design/methodology/approach
A new method of ranking the development priority of heavy oil reservoir is present, in which the neural network is applied for the first time to acquire reservoir parameters' weights through training samples and the genetic algorithm is used to optimize the joint weighs of neurons in case that neural network falling into local minimum. Additionally, the paper establishes subordinate function of every parameter. Eventually, comprehensive evaluation values of all heavy oil reservoirs are obtained.
Findings
The method can ensure the veracity and creditability of the parameters' weights, avoid the randomicity brought by experts.
Research limitations/implications
Accessibility of the data of many heavy oil reservoirs is the main limitation.
Practical implications
A very useful and new method for the decision makers of heavy oil reservoirs development.
Originality/value
The new approach of ranking the development priority of heavy oil reservoir based on the neural network and the genetic algorithm. The paper is aimed at the leaders who manage the development of heavy oil reservoirs.