地方政府竞争与区域对外开放——基于我国省级面板数据的实证研究

本文将政府与空间两重维度进行结合,从地方政府竞争的视角来分析区域对外开放问题.对地方政府进行适当的分类,分为独立型、合谋型与互助型的地方政府,尝试性地将地方政府竞争行为从纵向竞争和横向竞争扩展到时期竞争,同时以省级面板数据为基础进行实证分析,解释地方政府竞争对区域对外开放进程产生的不同效应.实证结果表明,税收优惠对区域对外开放的整体效果已经减弱.多数经济发达的地区已经不再把税收优惠作为主要的经济竞争手段,而转向改善地方公共物品的供给竞争.但在硬件和软件公共物品之间,地方政府更倾向于硬件基础设施投资,这也引发了政府对软件供给物品的供给不足.此外,制度优化竞争涉及到的产权保护、法律制度和中介组织发育等一系列制度环境因素,也会对区域的对外开放产生重要的影响意义.     

A simulation-based multi-objective optimization framework: A case study on inventory management

We propose a simulation-based solution framework for tackling the multi-objective inventory optimization problem. The goal is to find appropriate settings of reorder point and order quantity to minimize three objective functions simultaneously, which are the expected values of the total inventory cost, the average inventory level, and the frequency of inventory shortage. We develop new algorithms that can exploit statistically valid ranking and selection (R&S) procedures and the desirable mechanics of conventional multi-objective optimization techniques. Two simulation algorithms are proposed to be applied in different scenarios depending on the preference information that is revealed either during or after the actual optimization process. Experimental results are provided to evaluate the efficiency of the developed algorithms and other existing solution frameworks.     

京津冀城市群人口空间结构演变及优化路径研究

人口的流动和分布直接影响着城市群城镇体系的质量,进而影响着城市群经济社会的可持续协调发展。文章从人口分布的空间差异、动态变化以及空间格局三个层面探讨了京津冀城市群人口分布演化特征;基于中心地理论和新经济地理理论,从行政、经济、交通三个角度构建了城市人口规模分布演化影响因素分析框架,利用Zipf法则对2003～2013年京津冀城市群人口演化规律进行研究。结果表明,城市行政级别、人力资源水平、经济结构、交通建设等因素对京津冀城市群人口规模分布影响显著。基于此,提出应当通过公共服务均等化、城市技能互补、产业布局优化、交通网络建设完善等对策引导京津冀地区人口合理布局的建议,助推京津冀协同发展重大战略的实施。     

农业工程类专业本科人才培养模式的研究与实践

针对面向21世纪人才培养的要求,从如何转变教育思想观念、构建和优化农业工程人才培养目标和培养方案、优化课程体系、更新教学内容、改进教学方法和教学手段等方面出发,介绍了在教学管理实践中所取得的经验和效果。     

土地资源管理专业课程体系优化的理论与实践研究——以广西师范学院土地资源管理专业为例

土地资源管理专业课程体系在设置上存在着专业设置的标准不够规范,主干学科和主要课程设置差异较大;课程体系不够成熟,没有体现出培养不同人才的特点,主干课程的教学大纲没有统一;在人才培养上重理论、轻技术和能力等问题。研究从土地资源管理专业课程体系设置的外部环境和内部结构入手,结合新的教育理论、专业特点和社会需要等,对本专业的课程体系进行优化,分为资源、经济、管理、技术、应用和选修等六个模块,并已在广西师范学院土地资源管理专业课程设置中实践。     

On the use of local optimizations within Metropolis–Hastings updates

Summary.  We propose new Metropolis–Hastings algorithms for sampling from multimodal dis- tributions on ℜ ⁿ . Tjelmeland and Hegstad have obtained direct mode jumping proposals by optimization within Metropolis–Hastings updates and different proposals for 'forward' and 'backward' steps. We generalize their scheme by allowing the probability distribution for forward and backward kernels to depend on the current state. We use the new setting to combine mode jumping proposals and proposals from a prior approximation. We obtain that the frequency of proposals from the different proposal kernels is automatically adjusted to their quality. Mode jumping proposals include local optimizations. When combining this with a prior approximation it is tempting to use local optimization results not only for mode jumping proposals but also to improve the prior approximation. We show how this idea can be implemented. The resulting algorithm is adaptive but has a Markov structure. We evaluate the effectiveness of the proposed algorithms in two simulation examples.     

Exact methods in optimum disassembly sequence search for problems subject to sequence dependent costs

Disassembling complex products is formally approached via network representation and subsequent mathematical modeling, aimed at selecting a good or optimum sequence of disassembly operations. This is done via heuristics, metaheuristics or mathematical programming. In contrast with heuristics and metaheuristics, which select a near-optimum solution, mathematical programming guarantees the selection of the global optimum. This problem is relatively simple if the disassembly costs can be assumed sequence independent. In practice, however, sequence dependent disassembly costs are frequently encountered, which causes NP-completeness of the problem. Although methods, e.g., based on the two-commodity network flow approach, are available to solve this constrained asymmetric Traveling Salesperson problem rigorously, this requires the introduction of integer variables. In this paper, a modification of the two-commodity network flow approach is proposed, which reduces the number of integer variables. This is applied to product structures that can be represented by a disassembly precedence graph. It is demonstrated that use of integer variables is completely avoided by iteratively solving a binary integer linear programming problem. This appears to be more efficient than solving the corresponding integer linear programming problem. It is demonstrated, on the basis of some cases, that this method might provide the exact solution of problems with increased complexity compared to those discussed so far in the literature. This appears particularly useful for evaluating heuristic and metaheuristic approaches.     

A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem

We develop a new genetic algorithm to solve an integrated Equipment-Workforce-Service Planning problem, which features extremely large scales and complex constraints. Compared with the canonical genetic algorithm, the new algorithm is innovative in four respects: (1) The new algorithm addresses epistasis of genes by decomposing the problem variables into evolutionary variables, which evolve with the genetic operators, and the optimization variables, which are derived by solving corresponding optimization problems. (2) The new algorithm introduces the concept of Capacity Threshold and calculates the Set of Efficient and Valid Equipment Assignments to preclude unpromising solution spaces, which allows the algorithm to search much narrowed but promising solution spaces in a more efficient way. (3) The new algorithm modifies the traditional genetic crossover and mutation operators to incorporate the gene dependency in the evolutionary procedure. (4) The new algorithm proposes a new genetic operator, self-evolution, to simulate the growth procedure of an individual in nature and use it for guided improvements of individuals. The new genetic algorithm design is proven very effective and robust in various numerical tests, compared to the integer programming algorithm and the canonical genetic algorithm. When the integer programming algorithm is unable to solve the large-scale problem instances or cannot provide good solutions in acceptable times, and the canonical genetic algorithm is incapable of handling the complex constraints of these instances, the new genetic algorithm obtains the optimal or close-to-optimal solutions within seconds for instances as large as 84 million integer variables and 82 thousand constraints.     

Impact of Number of Interactions,Different Interaction Patterns,and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.