Optimal tool selection for pocket machining in process planning

In process planning for pocket machining, selection of tool size, tool path, cutting width at each tool path, and calculation of machining time are very important factors for optimal process planning. The tool size is the most important factor because the other factors depend on tool size. Therefore, the optimal selection of tool size is the most essential task in pocket machining process planning. This paper presents a method for selecting optimal tools for pocket machining for the components of injection mold. The branch and bound method is applied to select the optimal tools which minimize the machining time by using the range of feasible tools and the breadth-first search.     

Supplier selection model with contingency planning for supplier failures

We consider the optimal allocation of demand across a set of suppliers given the risk of supplier failures. We assume items sourced are used in multiple facilities and can be purchased from multiple suppliers with different cost and reliability characteristics. Suppliers have production flexibility that allows them to deliver a contingency quantity in case other suppliers fail. Costs considered include supplier fixed costs and variable costs per unit, while failure to deliver to a demand point results in a particular financial loss. The model utilizes the decision tree approach to consider all the possible states of nature when one or more suppliers fail, as well as expand the traditional transportation problem. Unlike other supplier selection models, this model considers contingency planning in the decision process, minimizing the total network costs. This results in a base allocation to one or more of the available suppliers and a state of nature specific delivery contingency plan from the suppliers to each demand point. A numerical example, as well as sensitivity analysis, is presented to illustrate the model and provide insights.     

Adaptive selection of reactive/deliberate planning for a dynamic environment

This paper proposes and evaluates a new real-time reactive planning approach for a dynamic environment. In addition to having the features of conventional real-time reactive planning, which can react in a dynamic environment, our planning can perform deliberate planning appropriately. The proposed planning uses three kinds of agents: behavior agents that control simple behavior, planning agents that make plans to achieve their own goals, and behavior-selection agents that intermediate between behavior agents and planning agents. They coordinate a plan in an emergent way for the planning system as a whole. We confirmed the effectiveness of our planning by means of a simulation. Furthermore, we implemented an active-vision system and used it to verify the real-world efficiency of our planning.     

Modeling of process parameter selection with mathematical logic for process planning

Process planning is the systematic determination of detailed methods by which workpieces or parts can be manufactured economically and competitively from initial stages to finished stages. One of the key problems of computer-aided process planning (CAPP), however, is the complexity of process knowledge representation of process planning and the diversity of manufacturing background. Process knowledge representation and inference mechanism of process parameter selection is one of the most important issues in the research on CAPP. A proper methodology for modeling inference mechanism of process parameter selection, hence, is essential for selection of process parameters in process planning. The paper presents an atomic inference engine model of process parameter selection in process planning using mathematical logic. The methodology of modeling the inference mechanism of process parameter selection is proposed with backward chaining of mathematical logic that is a form of goal-directed reasoning. An illustrative case has been analyzed using the proposed approach to demonstrate its potential application in the real manufacturing environment, by combining with a practical application of a hole-making in a industrially relevant workpiece. The outcomes of this work provide a process reasoning mechanism for process parameter selection in process planning and thus alleviate automated process reasoning problems in process planning.     

An integrated approach for requirement selection and scheduling in software release planning

It is essential for product software companies to decide which requirements should be included in the next release and to make an appropriate time plan of the development project. Compared to the extensive research done on requirement selection, very little research has been performed on time scheduling. In this paper, we introduce two integer linear programming models that integrate time scheduling into software release planning. Given the resource and precedence constraints, our first model provides a schedule for developing the requirements such that the project duration is minimized. Our second model combines requirement selection and scheduling, so that it not only maximizes revenues but also simultaneously calculates an on-time-delivery project schedule. Since requirement dependencies are essential for scheduling the development process, we present a more detailed analysis of these dependencies. Furthermore, we present two mechanisms that facilitate dynamic adaptation for over-estimation or under-estimation of revenues or processing time, one of which includes the Scrum methodology. Finally, several simulations based on real-life data are performed. The results of these simulations indicate that requirement dependency can significantly influence the requirement selection and the corresponding project plan. Moreover, the model for combined requirement selection and scheduling outperforms the sequential selection and scheduling approach in terms of efficiency and on-time delivery.     

Dispatching rule selection using artificial neural networks for dynamic planning and scheduling

To schedule a job shop, the first task is to select an appropriate scheduling algorithm or rule. Because of the complexity of scheduling problems, no general algorithm sufficient for solving all scheduling problems has yet been developed. Most job-shop scheduling systems offer alternative algorithms for different situations, and experienced human schedulers are needed to select the best dispatching rule in these systems. This paper proposes a new algorithm for job-shop scheduling problems. This algorithm consists of three stages. First, computer simulation techniques are used to evaluate the efficiency of heuristic rules in different scheduling situations. Second, the simulation results are used to train a neural network in order to capture the knowledge which can be used to select the most efficient heuristic rule for each scheduling situation. Finally, the trained neural network is used as a dispatching rule selector in the real-time scheduling process. Research results have shown great potential in using a neural network to replace human schedulers in selecting an appropriate approach for real-time scheduling. This research is part of an ongoing project of developing a real-time planning and scheduling system.     

An approaching genetic algorithm for automatic beam angle selection in IMRT planning

A method named approaching genetic algorithm (AGA) is introduced to automatically select the beam angles for intensity-modulated radiotherapy (IMRT) planning. In AGA, the best individual of the current population is found at first, and the rest of the normal individuals approach the current best one according to some specially designed rules. In the course of approaching, some better individuals may be obtained. Then, the current best individual is updated to try to approach the real best one. The approaching and updating operations of AGA replace the selection, crossover and mutation operations of the genetic algorithm (GA) completely. Using the specially designed updating strategies, AGA can recover the varieties of the population to a certain extent and retain the powerful ability of evolution, compared to GA. The beam angles are selected using AGA, followed by a beam intensity map optimization using conjugate gradient (CG). A simulated case and a clinical case with nasopharynx cancer are employed to demonstrate the feasibility of AGA. For the case investigated, AGA was feasible for the beam angle optimization (BAO) problem in IMRT planning and converged faster than GA.     

Computationally efficient ontology selection in software requirement planning

Understanding the needs of stakeholders and prioritizing requirements are the vital steps in the development of any software application. Enabling tools to support these steps have a critical role in the success of the corresponding software application. Based on such a critical role, this paper presents a computationally efficient ontology selection in software requirement planning. The key point guiding the underlying design is that, once gathered, requirements need to be processed by decomposition towards the generation of a specified systems design. A representational framework allows for the expression of high level abstract conceptions under a single schema, which may then be made explicit in terms of axiomatic relations and expressed in a suitable ontology. The initial experimental results indicate that our framework for filtered selection of a suitable ontology operates in a computationally efficient manner.     

Multi-objective optimisation in inventory planning with supplier selection

Supplier selection and inventory planning are critical and challenging tasks in Supply Chain Management. There are many studies on both topics and many solution techniques have been proposed dealing with each problem separately. In this study, we present a two-stage integrated approach to the supplier selection and inventory planning. In the first stage, suppliers are ranked based on various criteria, including cost, delivery, service and product quality using Interval Type-2 Fuzzy Sets (IT2FS)s. In the following stage, an inventory model is created. Then, an Multi-objective Evolutionary Algorithm (MOEA) is utilised simultaneously minimising the conflicting objectives of supply chain operation cost and supplier risk. We evaluated the performance of three MOEAs with tuned parameter settings, namely NSGA-II, SPEA2 and IBEA on a total of twenty four synthetic and real world problem instances. The empirical results show that in the overall, NSGA-II is the best performing MOEA producing high quality trade-off solutions to the integrated problem of supplier selection and inventory planning.     

Product selection in Internet business: a fuzzy approach

In this paper, we propose a methodology which helps customers buy products through the Internet. This procedure takes into account the customer's level of desire in the product attributes, which are normally fuzzy, or in linguistically defined terms. The concept of fuzzy number will be used to measure the degree of similarities of the available products to that of the customer's requirements. The degrees of similarities so obtained over all the attributes give rise to the fuzzy probabilities and hence the fuzzy expected values of availing a product on the Internet as per the customer's requirement. Attribute‐wise the fuzzy expected values are compared with those of the available products on the Internet and the product that is closest to the customer's preference is selected as the best product. The multi‐attribute weighted average method is used here to evaluate and hence to select the best product.     

Formulation of radiometric feasibility measures for feature selection and planning in visual servoing

Feature selection and planning are integral parts of visual servoing systems. Because many irrelevant and nonreliable image features usually exist, higher accuracy and robustness can be expected by selecting and planning good features. Assumption of perfect radiometric conditions is common in visual servoing. The following paper discusses the issue of radiometric constraints for feature selection in the context of visual servoing. Here, radiometric constraints are presented and measures are formulated to select the optimal features (in a radiometric sense) from a set of candidate features. Simulation and experimental results verify the effectiveness of the proposed measures.     

The location selection of ground-based promotion in city using human mobility data

Multimedia Tools and Applications - With the rapid development of urbane-centered economy, today swelling cities is being filled with massive socioeconomic activities, and urban area have gone...     

多项目多任务选择计划模型及其智能决策

针对多项目多任务选择计划及项目流程调度问题,综合考虑项目的权重、承继约束、各项目内部任务的时序约束及各种资源在使用上存在着先后顺序等特点,将项目层、任务层及资源利用统筹考虑,结合多层决策的思想,建立项目、任务、资源统筹兼顾的混合规划模型。依据免疫系统中克隆选择、细胞克隆、记忆细胞获取、亲和突变等机制,设计求解此模型的免疫克隆选择算法。数值实验结果说明了该模型设计的合理性及算法的有效性。     

A comprehensive study of the relationship between enterprise resource planning selection criteria and enterprise resource planning system success

We developed a conceptual framework for investigating how ERP selection criteria are linked to system quality and the service provided by suppliers and consultants, and thus how these influenced ERP implementation success. Through a cross-sectional survey of the top 5000 largest companies in Taiwan, using a balanced scorecard concept and path analysis, we showed that four system selection criteria (consultant's suggestion, a certified high-stability system, compatibility between the system and the business process, and the provision of best practices) were positively related to system quality. Three supplier selection criteria (international market position, training support by the supplier and supplier technical support and experience) had a significant influence on supplier service quality, and two consultant selection criteria (consultant's ERP implementation experience in a similar industry and consultant's support after going live) were related to consultant service quality. However, we found that most organizations did not consider all these criteria when implementing ERP systems. Our study also suggested that enhanced system quality and service quality could increase user perspective and ERP success.     

Simple heuristics-based selection of guides for multi-objective PSO with an application to electrical distribution system planning

In multi-objective particle swarm optimization (MOPSO), a proper selection of local guides significantly influences detection of non-dominated solutions in the objective/solution space and, hence, the convergence characteristics towards the Pareto-optimal set. This paper presents an algorithm based on simple heuristics for selection of local guides in MOPSO, named as HSG-MOPSO (Heuristics-based-Selection-of-Guides in MOPSO). In the HSG-MOPSO, the set of potential guides (in a PSO iteration) consists of the non-dominated solutions (which are normally stored in an elite archive) and some specifically chosen dominated solutions. Thus, there are two types of local guides in the HSG-MOPSO, i.e., non-dominated and dominated guides; they are named so as to signify whether the chosen guide is a non-dominated or a dominated solution. In any iteration, a guide, from the set of available guides, is suitably selected for each population member. Some specified proportion of the current population members follow their respective nearest non-dominated guides and the rest follow their respective nearest dominated guides. The proposed HSG-MOPSO is firstly evaluated on a number of multi-objective benchmark problems along with investigations on the controlling parameters of the guide selection algorithm. The performance of the proposed method is compared with those of two well-known guide selection methods for evolutionary multi-objective optimization, namely the Sigma method and the Strength Pareto Evolutionary Algorithm-2 (SPEA2) implemented in PSO framework. Finally, the HSG-MOPSO is evaluated on a more involved real world problem, i.e., multi-objective planning of electrical distribution system. Simulation results are reported and analyzed to illustrate the viability of the proposed guide selection method for MOPSO.     

Applications of genetic algorithms in process planning: tool sequence selection for 2.5-axis pocket machining

Tool sequence selection is an important activity in process-planning for milling and has great bearing on the cost of machining. Currently, it is accomplished manually without consideration of cost factors a priori. Typically, a large tool is selected to quickly generate the rough shape and a smaller clearing tool is used to generate the net-shape. In this paper, we present a new systematic method to select the optimal sequence of tool(s), to machine a 2.5-axis pocket given pocket geometry, a database of cutting tools, cutting parameters, and tool holder geometry. Algorithms have been developed to calculate the geometric constructs such as accessible areas, and pocket decomposition, while considering tool holders. A Genetic Algorithm (GA) formulation is used to find the optimal tool sequence. Two types of selection mechanisms namely “Elitist selection” and “Roulette method” are tested. It is found that the Elitist method converges much faster than the Roulette method. The proposed method is compared to a shortest-path graph formulation that was developed previously by the authors. It is found that the GA formulation generates near optimal solutions while reducing computation by up to 30% as compared to the graph formulation.     

Formalizing planning knowledge for hierarchical planning

A hierarchical planning system achieves efficiency by planning with the most important conditions first, and considering details later in the planning process. Few attempts have been made to formalize the structure of the planning knowledge for hierarchical planning. For a given domain, there is usually more than one way to define its planning knowledge. Some of the definitions can lead to efficient planning, while others may not. In this paper, we provide a set of restrictions which defines the relationships between a non-primitive action and its set of subactions. When satisfied, these restrictions guarantee improved efficiency for hierarchical planning. One important feature of these restrictions is that they are syntactic and therefore do not depend on the particular structure of any plan. Along with these restrictions, we also provide algorithms for preprocessing the planning knowledge of a hierarchical planner. When used during planning, the preprocessed operator hierarchies can enable a planner to significantly reduce its search space.     

利用自适应选择算子结合遗传算法的机器人路径规划方法

在二维复杂环境中,为了避免机器人运动规划中可能出现的局部陷阱和过早收敛问题,提出一种改进的元启发式算法——自适应遗传算法。首先,利用随机Dijkstra算法创建初始种群;然后,在遗传算法的每一代中,改进所创建的路径,并用自适应算子替代常规选择算子;最后,通过搜索过程中的反馈信息,可以令自适应选择算子在整个算法运行中恰当地控制选择压力。为了验证所提方法的有效性,在MATLAB中进行了仿真实验,并将所提方法与另外两种典型方法进行了对比。实验结果表明,提出的方法可以有效避免路径规划中的局部收敛问题,且在复杂环境中也可以产生可行路径。     

Product concept generation and selection using sorting technique and fuzzy c-means algorithm

Product conceptualization is regarded as a key activity in new product development (NPD). In this stage, product concept generation and selection plays a crucial role. This paper presents a product concept generation and selection (PCGS) approach, which was proposed to assist product designers in generating and selecting design alternatives during the product conceptualization stage. In the PCGS, general sorting was adapted for initial requirements acquisition and platform definition; while a fuzzy c-means (FCM) algorithm was integrated with a design alternatives generation strategy for clustering design options and selecting preferred product concepts. The PCGS deliberates and embeds a psychology-originated method, i.e., sorting technique, to widen domain coverage and improve the effectiveness in initial platform formation. Furthermore, it successfully improves the FCM algorithm in such a way that more accurate clustering results can be obtained. A case study on a wood golf club design was used for illustrating the proposed approach. The results were promising and revealed the potential of the PCGS method.     

A formulation of the integrated facility layout,product selection and process planning problem

A mathematical formulation for integrated production system planning is proposed. The economic decision model integrates product selection, capacity planning, process planning, and facility layout. The network structure of the material flow constraints facilitates the solution.