Intensity of Interaction in Supply of Business Advice and Client Impact: A Comparison of Consultancy,Business Associations and Government Support Initiatives for SMEs

This paper assesses the supply of business advice using new empirical evidence from a large-scale survey of SMEs. The chief focus of the paper is on a comparison of suppliers that operate in different environments of regulation, contract and reputation. The paper argues that interaction intensity varies with the level of information asymmetry of these different environments, between different types of service supplier and their clients. Interaction intensity between suppliers also varies as a result of the level of trust they enjoy: for example, the low trust enjoyed by consultants appears to encourage higher intensity of interaction which improves the tailoring of the service to the client's needs and enhances impact. The paper assesses interaction intensity using the existence of site visits and/or a written brief/contract as indicators. Although these measures have limitations, the paper demonstrates clear and significant differences between suppliers in terms of interaction intensity, use of contracts and impact in three broad categories: private-sector consultancy (low trust, high intensity, high impact), business associations (high trust, low intensity, moderate impact) and government support agencies (moderate trust, moderate to high intensity, moderate or low impact). Multivariate estimation methods demonstrate that significant differences in interaction intensity, use of contracts and impact by client type are much less important than differences in supplier type. This indicates that suppliers generally develop more into niche service fields or groups of services rather than niches related to types of firm.     

基于时间满意的最大覆盖选址问题

传统的选址问题过于简单地考量时间这一对企业竞争力影响重大的因素,结合这一特点,本文对时间满意度函数进行了定义并提出了基于时间满意的最大覆盖选址问题。给定的网络G(V,A)中,在总的顾客对服务站响应速度的满意程度最大的目标下建立了最大覆盖选址问题模型,我们在讨论了问题的特点之后给出了基于拉格朗日松驰的启发式算法,并通过MATLAB进行了编程计算实验,分析了实验结果。     

Some further results on minimum distribution cost flow problems

Manufacturing network flow (MNF) is a generalized network model that can model more complicated manufacturing scenarios, such as the synthesis of different materials to one product and/or the distilling of one material to many different products. Minimum distribution cost flow problem (MDCF) is a simplified version of MNF optimization problems, in which a general supplier wants to proportionally distribute certain amount of a particular product from a source node to several retailers at different destinations through a distribution network. A network simplex algorithm has been outlined in recent years for solving a special case of MDCF. In this paper, we characterize the network structure of the bases of the MDCF problem and develop a primal simplex algorithm that exploits the network structure of the problem. These results are extensions of those of the ordinary network flow problems. In conclusion, some related interesting problems are proposed for future research. This research is partially supported by the National Natural Science Foundation of China (No. 10371028) and a grant from Southern Yangtze University (No. 0003182).     

A Hybrid Genetic—GRASP Algorithm Using Lagrangean Relaxation for the Traveling Salesman Problem

Hybridization techniques are very effective for the solution of combinatorial optimization problems. This paper presents a genetic algorithm based on Expanding Neighborhood Search technique (Marinakis, Migdalas, and Pardalos, Computational Optimization and Applications, 2004) for the solution of the traveling salesman problem: The initial population of the algorithm is created not entirely at random but rather using a modified version of the Greedy Randomized Adaptive Search Procedure. Farther more a stopping criterion based on Lagrangean Relaxation is proposed. The combination of these different techniques produces high quality solutions. The proposed algorithm was tested on numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the algorithms of the DIMACS Implementation Challenge are also presented.     

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.     

A simulated annealing algorithm for designing cellular manufacturing systems with productivity consideration

In this paper, we propose a productivity model for solving the machine-part grouping problem in cellular manufacturing (CM) systems. First, a non-linear 0-1 integer programming model is developed to identify machine groups and part families simultaneously. This model aims to maximize the system productivity defined as the ratio of total output to the total material handling cost. Second, an efficient simulated annealing (SA) algorithm is developed to solve large-scale problems. This algorithm provides several advantages over the existing algorithms. It forms part families and machine cells simultaneously. It also considers production volume, sales price, and maximum number of machines in each cell and total material handling cost. The proposed SA also has the ability to determine the optimum number of manufacturing cells. The performance of the developed models is tested on eight problems of different size and complexity selected from the literature. The results show the superiority of the SA algorithm over the mathematical programming model in both productivity and computational time.     

基于证据推理的不确定多属性决策方法

针对基本属性权重的不确定性,以及基本属性与广义属性评价集的不一致性等问题,提出一种基于证据推理的不确定多属性决策方法,将证据推理算法推广到更一般的决策环境中.根据决策矩阵的信息熵客观地获得属性的权系数;而对于基本属性与广义属性评价集不一致的情况,则通过对基本属性分布评价的模糊化及模糊变换,合理地实现到广义分布评价的统一形式;最后应用证据推理算法得到整个方案集的排序.实例结果表明,该方法是可行的、有效的.     

A Cross-Cultural Comparison of Managers' Whistleblowing Tendencies*

In a global marketplace, managers often face major difficulties when it comes to handling ethical problems within a different cultural context. Whistleblowing is one possible response to ethical problems. This study considers the differences in culture between a United States sample of managers and a Jamaica, West Indies sample of managers using Hofstede's (1991) theory of International Cultures. The study explores how those cultural differences may help in our understanding of the differences in reported whistleblowing tendencies. This revised version was published online in July 2006 with corrections to the Cover Date.     

FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms

Various methods and algorithms have been developed for multiclass classification problems in recent years. How to select an effective algorithm for a multiclass classification task is an important yet difficult issue. Since the multiclass algorithm selection normally involves more than one criterion, such as accuracy and computation time, the selection process can be modeled as a multiple criteria decision making (MCDM) problem. While the evaluations of algorithms provided by different MCDM methods are in agreement sometimes, there are situations where MCDM methods generate very different results. To resolve this disagreement and help decision makers pick the most suitable classifier(s), this paper proposes a fusion approach to produce a weighted compatible MCDM ranking of multiclass classification algorithms. Several multiclass datasets from different domains are used in the experimental study to test the proposed fusion approach. The results prove that MCDM methods are useful tools for evaluating multiclass classification algorithms and the fusion approach is capable of identifying a compromised solution when different MCDM methods generate conflicting rankings.     

双旋转货架拣货作业优化设计

本文讨论了由一个拣货员负责的双旋转货架系统,目标是寻找一种最优拣货顺序使得总拣货时间。该问题被证明为强NP-难问题,文章提出了启发式算法寻找最优拣货顺序,并对相关模型及算法进行模拟测试以证明其有效性,为方法选择与应用提供了依据。     

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

     

分支蚁群动态扰动算法求解TSP问题

蚁群优化算法是一种求解组合优化难题的强启发式算法,它利用正反馈和并行计算原理,具备很强的搜索能力。近年来,蚁群优化算法广泛应用于TSP问题的研究。本文提出分支蚁群动态扰动(DPBAC)算法,该算法主要从5个方面对基本蚁群算法做出改进:引入分支策略选取出发城市;改进状态转移规则;引入变异策略改进蚂蚁路径;改进信息素更新规则;引入条件动态扰动策略。实验表明,该算法可以有效改善基本蚁群算法搜索时间较长、容易陷入局部极小等缺点。     

A note on pre-emptive scheduling with earliness and tardiness costs

The option of pre-emption seems to have escaped the attention of existing literature on scheduling problems with earliness and tardiness costs. This note presents an attempt to accommodate pre-emption in such settings. The focus is on models with a common due date for all jobs under different cost structures ( job dependent versus job independent), and different objectives ( minsum and minmax). It appears that only one of these cases is not polynomially solvable: the case of minsum objective and job-dependent ( linear) cost. An exact dynamic programming algorithm is introduced for this problem, as well as an efficient heuristic, which is shown to perform extremely well in our numerical tests.     

Genetic algorithm to production planning and scheduling problems for manufacturing systems

Fundamental and extended multi-objective (MO) models are designed to address earliness/tardiness production scheduling planning (ETPSP) problems with multi-process capacity balance, multi-product production and lot-size consideration. A canonical genetic algorithm (GA) approach and a prospective multi-objective GA (MOGA) approach are proposed as solutions for different practical problems. Simulation results as well as comparisons with other techniques demonstrate the effectiveness of the MOGA approach, which is a noted improvement to any of the existing techniques, and also in practice provides a new trend of integrating manufacturing resource planning (MRPII) with just-in-time (JIT) in the production planning procedure.     

A HEURISTIC ALGORITHM FOR THE FLOWSHOP PROBLEM WITH A COMMON JOB SEQUENCE ON ALL MACHINES*

This paper presents a scheduling algorithm to solve flowshop problems with a common job sequence on all machines. This algorithm uses makespan as its criterion. Initially, it chooses a preferred sequence by scanning the processing times matrix and making a few calculations. The makespan time of the preferred job-sequence is further reduced by using an improvement routine that allows interchanges between adjacent jobs. Solutions of 1200 problems are compared with the best solutions previously reported for corresponding size problems in the Campbell-Dudek-Smith (C-D-S) paper [1]. This algorithm offers up to 1% average improvement in reducing the makespan of nearly 50% of the problem sets over the results of the existing algorithms, and its computational time requirements are about one-fifth of that of the C-D-S algorithm.     

自动化立体仓库中的储位分配及存取路径优化

储位分配和存取作业路径优化是仓储管理中的两个重要决策问题。本文研究如何在自动化立体仓库中对这两个问题进行同时决策。提出了一个混合整数规划模型对该问题进行优化建模,设计开发了一个基于有向连接图的两阶段优化算法对问题求初始解,并利用禁忌搜索算法对所求得的解进行改进。算法第一阶段解决储位分配问题,在此基础上第二阶段利用Hungarian算法对堆垛机的存取作业路径优化问题进行求解。最后利用实例对算法效率和精度进行分析评价,计算结果验证了算法的有效性。     

An extension of the relaxation algorithm for solving a special case of capacitated arc routing problems

In this paper, we propose an exact method for solving a special integer program associated with the classical capacitated arc routing problems (CARPs) called split demand arc routing problems (SDARP). This method is developed in the context of monotropic programming theory and bases a promising foundation for developing specialized algorithms in order to solve general integer programming problems. In particular, the proposed algorithm generalizes the relaxation algorithm developed by Tseng and Bertsekas (Math. Oper. Res. 12(4):569–596, 1987) for solving linear programming problems. This method can also be viewed as an alternative for the subgradient method for solving Lagrangian relaxed problems. Computational experiments show its high potential in terms of efficiency and goodness of solutions on standard test problems.     

EMPIRICAL TESTS OF CONSTRAINED NONLINEAR OPTIMIZATION ALGORITHMS

A major difficulty in practicing constrained nonlinear optimization is that different optimization algorithms are effective for different classes of problems. The search algorithms tested were selected because they seemingly had the capability of solving ill-defined problems. The emphasis of this research was on problems that are realistic and do not necessarily satisfy the classical assumptions of convexity, continuity, unimodality, etc. All algorithms were tested on a variety of problems. A problem was described by three types of attributes: the environmental attributes, the objective function attributes, and the constraint set attributes. The results of the study provide selective logic for choosing an effective search algorithm.     

A hybrid genetic algorithm for production and distribution

This paper develops a hybrid genetic algorithm for production and distribution problems in multi-factory supply chain models. Supply chain problems usually may involve multi-criterion decision-making, for example operating cost, service level, resources utilization, etc. These criteria are numerous and interrelated. To organize them, analytic hierarchy process (AHP) will be utilized. It provides a systematic approach for decision makers to assign weightings and relate them. Meanwhile, genetic algorithms (GAs) will be utilized to determine jobs allocation into suitable production plants. Genetic operators adopted to improve the genetic search algorithm will be introduced and discussed. Finally, a hypothetical production–distribution problem will be solved by the proposed algorithm. The optimization results show that it is reliable and robust.     

An iterated local search with multiple perturbation operators and time varying perturbation strength for the aircraft landing problem

Landing aircraft safely is an important operation that air traffic controllers have to deal with on a daily basis. For each arriving aircraft a runway and a landing time must be allocated. If these allocations can be done in an efficient way, it could give the airport a competitive advantage. The Aircraft Landing Problem (ALP) aims to minimize the deviation from a preferred target time of each aircraft. It is an NP-hard problem, meaning that we may have to resort to heuristic methods as exact methods may not be suitable, especially as the problem size increases. This paper proposes an iterated local search (ILS) algorithm for the ALP. ILS is a single solution based search methodology that successively invokes a local search procedure to find a local optimum solution. A perturbation operator is used to modify the current solution in order to escape from the local optimum and to provide a new solution for the local search procedure. As different problems and/or instances have different characteristics, the success of the ILS is highly dependent on the local search, the perturbation operator(s) and the perturbation strength. To address these issues, we utilize four perturbation operators and a time varying perturbation strength which changes as the algorithm progresses. A variable neighborhood descent algorithm is used as our local search. The proposed ILS generates high quality solutions for the ALP benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both solution quality and computational time. Moreover, the proposed ILS produces new best results for some instances.