Restructuring industrial districts, scaling up regional development: a study of the Wenzhou Model, China

Working PaperThe Wenzhou Municipality in Zhejiang Province is spearheading China's marketization and development of private enterprises. Its successful development trajectory, centered on family-owned small businesses embedded in thick local institutions, resembles Marshallian industrial districts (MIDs). However, with China's changing institutional environment and intensifying competition, Wenzhou has been facing challenges. Since the late 1980s, Wenzhou has gone through two major rounds of restructuring (from family enterprises to shareholding cooperatives to shareholding enterprises), that have included four major types of strategic response: institutional change, technological upgrading, industrial diversification, and spatial restructuring. Firms in Wenzhou have gone through localization and delocalization, and locational choices reflect the dual destinations of globalizing cities and interior cities. The formation of new firms and clusters has been accompanied by mergers, acquisitions, and the emergence of multiregional enterprises (MREs), some of which have relocated their headquarters and specialized functions to metropolitan areas, especially Shanghai and Hangzhou. More recently, Wenzhou's growth has slowed, leading some to question the sustainability of the Wenzhou model. We argue that Wenzhou's development is in danger of regional lock-ins--relational, intergenerational, and structural. Wenzhou's experience challenges the orthodox concept of MIDs and calls for "scaling up" regional development

Worst-Case Analysis of Process Flexibility Designs

Theoretical studies of process flexibility designs have mostly focused on expected sales. In this paper, we take a different approach by studying process flexibility designs from the worst-case point of view. To study the worst-case performances, we introduce the plant cover indices (PCIs), defined by bottlenecks in flexibility designs containing a fixed number of products. We prove that given a flexibility design, a general class of worst-case performance measures can be expressed as functions of the design’s PCIs and the given uncertainty set. This result has several major implications. First, it suggests a method to compare the worst-case performances of different flexibility designs without the need to know the specifics of the uncertainty sets. Second, we prove that under symmetric uncertainty sets and a large class of worst-case performance measures, the long chain, a celebrated sparse design, is superior to a large class of sparse flexibility designs, including any design that has a degree of two on each of its product nodes. Third, we show that under stochastic demand, the classical Jordan and Graves (JG) index can be expressed as a function of the PCIs. Furthermore, the PCIs motivate a modified JG index that is shown to be more effective in our numerical study. Finally, the PCIs lead to a heuristic for finding sparse flexibility designs that perform well under expected sales and have lower risk measures in our computational study.National Science Foundation (U.S.) (Grant CMMI-0758069)Masdar Institute of Science and TechnologyFord-MIT AllianceNatural Sciences and Engineering Research Council of Canada (Postgraduate Scholarship

Spatial determinants of urban growth in Chinese Cities: a case study of dongguan

posterBy 2012, 51.3% of the population in China lived in the urban areas

Effectiveness and design of sparse process flexibilities

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 119-121).The long chain has been an important concept in the design of flexible processes. This design concept, as well as other sparse flexibility structures, have been applied by the automotive and other industries as a way to increase flexibility in order to better match available capacities with variable demands. Numerous empirical studies have validated the effectiveness of these structures. However, there is little theory that explains the effectiveness of the long chain, except when the system size is large, i.e., by applying an asymptotic analysis. Our attempt in this thesis is to develop a theory that explains the effectiveness of long chain and other sparse flexibility structures for finite size systems. We study the sales of sparse flexibility structures under both stochastic and worst-case demands. From our analysis, we not only provide rigorous explanation to the effectiveness of the long chain, but also refine guidelines in designing other sparse flexibility structures. Under stochastic demand, we first develop two deterministic properties, supermodularity and decomposition of the long chain, that serve as important building blocks in our analysis. Applying the supermodularity property, we show that the marginal benefit, i.e., the increase in expected sales, increases as the long chain is constructed, and the largest benefit is always achieved when the chain is closed by adding the last arc to the system. Then, applying the decomposition property, we develop four important results for the long chain under IID demands: (i) an effective algorithm to compute the performance of long chain using only matrix multiplications; (ii) a proof on the optimality of the long chain among all 2-flexibility structures; (iii) a result that the gap between the fill rate of full flexibility and that of the long chain increases with system size, thus implying that the effectiveness of the long chain relative to full flexibility increases as the number of products decreases; (iv) a risk-pooling result implying that the fill rate of a long chain increases with the number of products, but this increase converges to zero exponentially fast. Under worst-case demand, we propose the plant cover index, an index defined by a constrained bipartite vertex cover problem associated with a given flexibility structure. We show that the plant cover index allows for a comparison between the worst-case performances of two flexibility structures based only on their structures and is independent of the choice of the uncertainty set or the choice of the performance measure. More precisely, we show that if all of the plant cover indices of one structure are greater than or equal to the plant cover indices of the other structure, then the first structure is more robust than the second one, i.e. performs better in worst-case under any symmetric uncertainty set and a large class of performance measures. Applying this relation, we demonstrate the effectiveness of the long chain in worst-case performances, and derive a general heuristic that generates sparse flexibility structures which are tested to be effective under both stochastic and worst-case demands. Finally, to understand the effect of process flexibility in reducing logistics cost, we study a model where the manufacturer is required to satisfy deterministic product demand at different distribution centers. Under this model, we prove that if the cost of satisfying product demands at distribution centers is independent of production plants or distribution centers, then there always exists a long chain that is optimal among 2-flexibility structures. Moreover, when all plants and distribution centers are located on a line, we provide a characterization for the optimal long chain that minimizes the total transportation cost. The characterization gives rise to a heuristic that finds effective sparse flexibility structures when plants and distribution centers are located on a 2-dimensional plane.by Yehua Wei.Ph.D

Economic transition and urban land expansion in Provincial China

a b s t r a c t China has undergone economic transition characterized by marketization, globalization and decentralization, which has resulted in profound change in land use and urban space. This paper integrates globalization, institutional change, and China's economic transition to better understand urban land expansion in China. We use land use survey data in Jiangsu province at the county level to shed the light on the impact of economic transition on land use change and urban land expansion in China. We have found that a dramatic land use change in Jiangsu characterized by rapid urban land expansion, particularly Sunan (Southern Jiangsu) and municipal districts. This can be well explained by government policies including tax reform and intergovernmental competition, the participation in the global economy, and the development of a market economy. We have also found that urban land expansion has a temporal dimension, and was driven mainly by local governments in the early stage of the reform, followed by marketization, and more recently globalization after China's entry into the World Trade Organization (WTO)

Belief Propagation for Min-Cost Network Flow: Convergence and Correctness

Distributed, iterative algorithms operating with minimal data structure while performing little computation per iteration are popularly known as message passing in the recent literature. Belief propagation (BP), a prototypical message-passing algorithm, has gained a lot of attention across disciplines, including communications, statistics, signal processing, and machine learning as an attractive, scalable, general-purpose heuristic for a wide class of optimization and statistical inference problems. Despite its empirical success, the theoretical understanding of BP is far from complete. With the goal of advancing the state of art of our understanding of BP, we study the performance of BP in the context of the capacitated minimum-cost network flow problem—a cornerstone in the development of the theory of polynomial-time algorithms for optimization problems and widely used in the practice of operations research. As the main result of this paper, we prove that BP converges to the optimal solution in pseudopolynomial time, provided that the optimal solution of the underlying network flow problem instance is unique and the problem parameters are integral. We further provide a simple modification of the BP to obtain a fully polynomial-time randomized approximation scheme (FPRAS) without requiring uniqueness of the optimal solution. This is the first instance where BP is proved to have fully polynomial running time. Our results thus provide a theoretical justification for the viability of BP as an attractive method to solve an important class of optimization problems.National Science Foundation (U.S.). Career Project (CNS 0546590)Natural Sciences and Engineering Research Council of Canada (NSERC). Postdoctoral FellowshipNational Science Foundation (U.S.). EMT Project (CCF 0829893)National Science Foundation (U.S.). (CMMI-0726733