Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when mode...Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm （GA）, non-dominated sorting in genetic algorithm-Ⅱ （NSGA-Ⅱ） also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ （GNSGA-Ⅱ） is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.展开更多
In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location...In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.展开更多
We incorporate large losses risks into the DeM arzo et al.(2012) model of dynamic agency and the q theory of investment.The large losses risks induce losses costs and losses arising from agency conflicts during the la...We incorporate large losses risks into the DeM arzo et al.(2012) model of dynamic agency and the q theory of investment.The large losses risks induce losses costs and losses arising from agency conflicts during the large losses prevention process.Both of them reduce firm’s value,distort investment policy and generate a deeper wedge between the marginal and average q.In addition,we study the implementation of the contract to enhance the practical utility of our model.The agent optimally manages the firm’s cash flow and treats the cash reservation and credit line as the firm’s financial slack,and hedges the productivity shocks and large losses shocks via futures and insurance contracts,respectively.展开更多
The aim of this study is to examine the effects of family involvement and altruism on agency costs of equity and debt, as well on the performance of small family businesses. To achieve this objective, the authors revi...The aim of this study is to examine the effects of family involvement and altruism on agency costs of equity and debt, as well on the performance of small family businesses. To achieve this objective, the authors reviewed the literature on family business. Drawing from agency theory and stewardship theory, the authors also proposed a research model that highlights the links among the variables. In so doing, this paper makes some contributions to the literature in three ways. Firstly, it takes an integrative framework that may help to explain behaviors oriented towards maximizing potential performance within a context in which pro-organizational attitudes co-exist with self-serving motivations. Secondly, it advances the understanding of corporate governance mechanisms in small family businesses, and finally, it deepens the discussion of prior research by advancing a set of propositions based on two theoretical approaches. Thus, the authors believe that a systematic comparison of different contexts provides new insights into small family business governance. The implications and directions for future research are in the last section.展开更多
基金supported by National Natural Science Foundation of China (No.60474059)Hi-tech Research and Development Program of China (863 Program,No.2006AA04Z160).
文摘Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm （GA）, non-dominated sorting in genetic algorithm-Ⅱ （NSGA-Ⅱ） also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ （GNSGA-Ⅱ） is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.
基金Natural Science Foundation of Shanghai,China(No.15ZR1401600)the Fundamental Research Funds for the Central Universities,China(No.CUSF-DH-D-2015096)
文摘In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.
基金Supported by the National Natural Science Foundation of China(11571310 and 71371168)
文摘We incorporate large losses risks into the DeM arzo et al.(2012) model of dynamic agency and the q theory of investment.The large losses risks induce losses costs and losses arising from agency conflicts during the large losses prevention process.Both of them reduce firm’s value,distort investment policy and generate a deeper wedge between the marginal and average q.In addition,we study the implementation of the contract to enhance the practical utility of our model.The agent optimally manages the firm’s cash flow and treats the cash reservation and credit line as the firm’s financial slack,and hedges the productivity shocks and large losses shocks via futures and insurance contracts,respectively.
文摘The aim of this study is to examine the effects of family involvement and altruism on agency costs of equity and debt, as well on the performance of small family businesses. To achieve this objective, the authors reviewed the literature on family business. Drawing from agency theory and stewardship theory, the authors also proposed a research model that highlights the links among the variables. In so doing, this paper makes some contributions to the literature in three ways. Firstly, it takes an integrative framework that may help to explain behaviors oriented towards maximizing potential performance within a context in which pro-organizational attitudes co-exist with self-serving motivations. Secondly, it advances the understanding of corporate governance mechanisms in small family businesses, and finally, it deepens the discussion of prior research by advancing a set of propositions based on two theoretical approaches. Thus, the authors believe that a systematic comparison of different contexts provides new insights into small family business governance. The implications and directions for future research are in the last section.
