The Disaster-Oriented Assessment of Urban Clusters for Locating Production Systems in China

The Disaster-Oriented Assessment of Urban Clusters for Locating Production Systems in China

Zhen Chen (Liverpool John Moores University, UK), Heng Li (The Hong Kong Polytechnic University, China), Qian Xu (Liverpool John Moores University, UK) and Szu-Li Sun (The University of Reading, UK)
Copyright: © 2008 |Pages: 19
DOI: 10.4018/978-1-59904-843-7.ch030
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The choice of location is one of the most important decisions usually taken in the procedure of building any production system (Pavic & Babic, 1991). In order to solve the problem of location choice, Pavic and Babic indentified a group of location indicators, including basic location factors such as transportation costs, production costs, and duration of transport, and additional factors such as bottleneck time, building costs, infrastructure costs, labour costs, weather conditions, expansion possibility, and transportation possibilities. Based on these criteria, Pavic and Babic used the preference ranking organisation method for enrichment evaluation (PROMETHEE) method (Mattioli, 2005) to support decision making in location choice. However, there are two concerns about their study. The first concern is that whether those indicators are enough and appropriate in the location choice of production systems. In fact, they have lost some relevant important factors. For example, geographic and geological conditions; environmental pollution; climate change; industrial and technology policies; disaster containment; and emergency services are all necessary considerations before locating production systems. The second concern is that whether the PROMETHEE method is an appropriate approach to effectively and efficiently deal with problems in which structured hierarchies of indicators are used in modelling. In fact, researchers have begun to explore alternatives to overcome the weaknesses of the PROMETHEE method in multi-criteria decision making. For example, Macharis, Springael, De Brucker, and Verbeke (2004) discussed the strengths and weaknesses of the PROMETHEE method and recommended the integration of a number of useful features of the analytic hierarchy process (AHP) method (Saaty, 1980) into the PROMETHEE process; especially in regards to the design of the decision-making hierarchy (ordering of goals, sub-goals, dimensions, criteria, projects, etc.) and the determination of weights. Based on these two concerns, the authors think there are potentials in conducting operations research into the location choice problem by modelling the hierarchies or network of indicators.

Key Terms in this Chapter

Natural Disaster: According to NASA (2007), there are a number of natural disasters, including wildfires, eruptions, avalanches, tsunamis, earthquakes, landslides, flooding, hurricanes, tornadoes, cyclones, storm surge, lahars, drought, typhoons, diseases, and so forth.

PROMETHEE: PROMETHEE is a multi-criteria decision-making method developed by Jean-Pierre Brans.

Analytic Network Process (ANP): According to Saaty (2005), the ANP is a general theory of relative measurement used to derive composite priority ratio scales from individual ratio scales that represent relative measurements of the influence of elements that interact with respect to control criteria. It is the most comprehensive framework for the analysis of societal, governmental, and corporate decisions that is available today to the decision maker.

Production System: Production system is an industrial system that supports manufacturing and its logistics.

Climate Change: According to Defra (2007), climate refers to the average weather experienced over a long period. This includes temperature, wind, and rainfall patterns. The climate of the Earth is not static and has changed many times in response to a variety of natural causes.

Urban Cluster: According to (SPS, 2007), urban cluster is a new statistical geographic entity designated by the Census Bureau for the 2000 Census, consisting of a central core and adjacent densely settled territory that together contains between 2,500 and 49,999 people. Typically, the overall population density is at least 1,000 people per square mile. Urban clusters are based on census block and block group density and do not coincide with official municipal boundaries.

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