Managing readiness-relevant activities for the organizational dimension of ERP implementation

An organization requires performing readiness-relevant activities to ensure successful implementation of an enterprise resource planning (ERP) system. This paper develops a novel approach to managing these interrelated activities to get ready for implementing an ERP system. The approach enables an organization to evaluate its ERP implementation readiness by assessing the degree to which it can achieve the interrelated readiness relevant activities using fuzzy cognitive maps. Based on the interrelationship degrees among the activities, the approach clusters the activities into manageable groups and prioritizes them. To help work out a readiness improvement plan, scenario analysis is conducted.     

Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

The paper presents a spatial analysis of points especially suited to estimate a preference map for new consumers, which is then used as an analytical tool in spatial electric load forecasting. This approach is an exploratory spatial data analysis used to discover useful point patterns in the spatial location of distribution transformers to calculate a preference value for each area, rating it with respect to a hypothetical load change that may occur. We consider the locations of distribution transformers occupied land. Random points are generated in the study area where the new loads are expected; these points are referred to as unoccupied land. The method uses a generalized additive model (GAM) to estimate the probability of unoccupied land becoming occupied land. We test the approach with data from a real distribution system in a mid-size city in Brazil; the result is a preference map that shows the areas where new consumers are most likely to be allocated. The main advantage of this method is the ability work with a small-scale resolution, which enables the use of a resolution suitable for spatial load forecasting method chosen. We test the calculated probabilities in a spatial load forecasting simulation, yielding results with lower spatial error when compared with the heuristic technique.     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

A two level metaheuristic for the operating room scheduling and assignment problem

Given a surgery department comprising several specialties that share a fixed number of operating rooms and post-surgery beds, we study the joint operating room (OR) planning and advanced scheduling problem. More specifically, we consider the problem of determining, over a one week planning horizon, the allocation of OR time blocks to specialties together with the subsets of patients to be scheduled within each time block. The aim of this paper is to extend and generalize existing approaches for the joint OR planning and scheduling problem. First, by allowing schedules that include patients requiring weekend stay beds which was not the case previously. Second, by tackling simultaneously both the OR planning and patient scheduling decision levels, instead of taking them into account in successive phases. To achieve this, we exploit the inherent hierarchy between the two decision levels, i.e., the fact that the assignment decisions of OR time blocks to surgical specialties directly affect those regarding the scheduling of patients, but not the reverse. The objective function used in this study is an extension of an existing one. It seeks to optimize both patient utility (by reducing waiting time costs) and hospital utility (by reducing production costs measured in terms of the number of weekend stay beds required by the surgery planning). 0–1 linear programming formulations exploiting the stated hierarchy are proposed and used to derive a formal proof that the problem is NP-hard. A two level metaheuristic is then developed for solving the problem and its effectiveness is demonstrated through extensive numerical experiments carried out on a large set of instances based on real data.     

Design guidelines for titania-silica-alumina ceramics with tuned anatase to rutile transformation

Titania-based ceramics with adjustable anatase-rutile fractions were obtained by milling of anatase, quartz and corundum precursors, uniaxial pressing and firing at 1100?°C. The influence of silica and alumina, combined with milling time and compaction pressure, was studied by design of experiments. The L9 orthogonal array with a three-level noise factor was employed. Firing of pure titania at 1100?°C yielded complete anatase to rutile transformation (ART), whereas stabilized samples show that an optimum amount of 9% silica and 33% alumina reduces phase transformation to only about 5?wt% rutile. An extended correlation matrix combined with analysis of variance (ANOVA) was applied to assess the combined effects of quartz, alumina, milling time and uniaxial compressing pressure on relative density, and anatase to rutile transformation. Results show absence of ART after milling, and controlled partial conversion of anatase to rutile after firing. Very good fitting was obtained by multivariate analysis on considering first and second order terms for dependence on silica contents and interactions between silica and each of the remaining factors, including milling time. This empirical dependence could be interpreted on a sound physicochemical basis, allowing the prediction of suitable compositions and processing conditions to obtain rutile-free samples by conventional ceramic processing, and to design ceramic samples with controlled fractions of anatase and rutile.     

社区居家养老服务设施的规划配置研究

社区和居家养老是我国当前和未来的主要养老模式，然而，与机构养老设施相比较而言，我国社区居家养老服务设施的发展明显滞后，具体表现为设施的建成基础薄弱、空间资源的配置要素模糊、规范和管控手段缺位等。在概述现状与问题的基础上，系统地探讨了我国社区层面养老服务资源的配置主体、配置对象和配置思路。针对养老服务需求和供给主体日益多元化的挑战，提出结合社区实际情况灵活配置服务模块的建议，采用总量控制和复合设置的规划手段落实配建，从而实现服务设施综合效益最大化的建设目标。     

城市低碳群体建筑外部边缘空间规划仿真研究

针对当前空间规划方法存在的计算时间长、规划成本高等问题,提出一种基于蚁群算法的低碳群体建筑外部边缘空间规划方法。依据城市低碳群体建筑外部边缘空间的模糊特性,引入模糊集合理论对建筑外部边缘空间进行理解和建筑外部边缘空间模糊矩阵的构建,对边缘空间进行划分和限定。结合建筑外部边缘空间的特性和具体关系模式进行空间层次划分,计算空间属性权重,以此确立空间规划目标,根据空间规划目标函数构建空间规划模型,设定求解空间规划模型的蚁群算法信息素更新方式,依据个体信息素转移概率指导个体进行模型最优解搜索。实验结果表明,所提方法与传统的空间规划方法相比,有效减少了计算时间,降低了规划成本。     

新型树启发式搜索算法的机器人路径规划

面对三维空间移动机器人从起始点到终止点的最短路径问题,提出一种新型的边缘点树启发式搜索(TreeEP)算法,该方法将地图空间进行密度可调的三维离散化处理,根据障碍安全距离筛选出障碍物的可靠边缘点信息,再利用树扩散架构选出最能引导搜索方向的潜力点进行扩散搜索,最终得出最短路径。提出局部调整策略,得到改进的Tree-EP算法。实验结果表明,在带障碍复杂地形最短路径搜索应用中,提出的Tree-EP算法与已有方法相比,能找到更短的移动路径。     

基于混合蚁群算法的异质车队低碳VRP研究

针对货运车辆在配送调度过程中产生大量碳排放的问题，建立模型将多种影响碳排放量的因素协同优化。模型中考虑了不同载重量的异质车队，两个节点之间有多条道路的柔性路径，以及车辆重量随卸货而减少的动态负载等因素，以碳排放量、行驶时间和行驶路程为优化目标，并加入了节点需求时间窗、根据速度变化划分路段、交接和卸货时间的约束。提出了一种混合蚁群算法，利用蚁群算法信息素强度更新方式保持群体记忆性，利用粒子群算法的快速收敛特性增加计算效率。通过随机数值算例的仿真优化与对比分析，验证了算法和模型的有效性。     

Rolling Kanban: a new visual tool to schedule family batch manufacturing processes with kanban

Rolling Kanban is the name of a kanban management methodology for batch processes manufacturing environment. Proposed in the early 2000s by FESTO Consulting, for two decades we have lost knowledge both from operative and scientific point of view. Basically, Rolling Kanban means a visual planning methodology based on the production of product-families and variants where: (i) set-up times are reduced between the products of the same family, and (ii) relevant times (dozens of minutes if not even hours) must be considered for changeover between products of different families. In addition, the cyclic production sequence between different product families cannot be maintained. Considering that very few technical information and documents are available about this approach, the main objective of this paper is to retrieve and present, for the first time to the scientific community, the Rolling Kanban methodology. Besides, a real industrial implementation concerning a manufacturer of domestic fittings is discussed as a case study. More specifically, two novel versions of the original Rolling Kanban technique are fully presented to effectively overcome certain limits and criticalities found during its operative use, such as the difficulty to realise a pull production, considering increased set-up time for changeover between products of different families.