A milp bi-objective model for static portfolio selection of R&;D projects with synergies

This paper presents a multi-objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations. Previous works on portfolio selection of R&D projects considered interdependencies mainly at the project level. In a few works considering activity level information the models and solution techniques were restricted to problems with a few projects. We study a generalization of our previous model and show that incorporating interdependencies and activity funding information is useful for obtaining portfolios with better quality. Numerical results are presented to demonstrate the efficiency of the proposed approach for large models.     

Fuzzy R&D portfolio selection of interdependent projects

Global competition of markets has forced firms to invest in targeted R&D projects so that resources can be focused on successful outcomes. A number of options are encountered to select the most appropriate projects in an R&D project portfolio selection problem. The selection is complicated by many factors, such as uncertainty, interdependences between projects, risk and long lead time, that are difficult to measure. Our main concern is how to deal with the uncertainty and interdependences in project portfolio selection when evaluating or estimating future cash flows. This paper presents a fuzzy multi-objective programming approach to facilitate decision making in the selection of R&D projects. Here, we present a fuzzy tri-objective R&D portfolio selection problem which maximizes the outcome and minimizes the cost and risk involved in the problem under the constraints on resources, budget, interdependences, outcome, projects occurring only once, and discuss how our methodology can be used to make decision support tools for optimal R&D project selection in a corporate environment. A case study is provided to illustrate the proposed method where the solution is done by genetic algorithm (GA) as well as by multiple objective genetic algorithm (MOGA).     

资源受限多项目调度问题的两阶段算法

在资源受限项目调度问题中,将可更新资源进一步拓展为具有胜任力差异的人力资源,建立考虑胜任力差异的人力资源受限多目标项目调度问题模型.该模型是对传统多模式资源约束项目调度问题更接近研发项目群实际的扩展.针对模型提出两阶段优化算法,第1阶段是项目时序约束优化阶段,采用蚁群算法(ACO)进行任务列表的优化求解,通过对信息素增量规则的改进、串联进度生成机制(SSGS)及资源冲突消解策略的使用,使蚁群算法的求解效率和质量得以提高;第2阶段是资源约束优化阶段,以第1阶段求得的优化任务列表为输入,逐项对人力资源约束进行核查与调整,最终生成项目调度的优化方案.数值实验表明,考虑胜任力差异的数学优化模型更符合研发项目群管理实践,同时两阶段算法在求解质量方面具有良好性能.     

Developing an research and development (R&D) process improvement system to simulate the performance of R&D activities

Because of a shorter R&D cycle time and the increasing complexity of technology development, efficient decision support systems for R&D activities are necessary to facilitate R&D processes. In particular, small and medium enterprises (SMEs) require efficiency of R&D projects due to a lack of R&D budgets and resources. Therefore, this paper aims to develop a system which can assess current levels of R&D processes of companies and improve problematic processes. To this end, the proposed system applies a standard R&D process to evaluate the R&D level of companies, including four types of database and three modules: performance level analysis, comparison analysis, and performance simulation analysis. First, the module of performance level analysis aims to draw strong and weak R&D processes of companies, using three factors: importance, performance goal and current performance on each process. The second module provides the function of comparison analysis that compares the performance level of a company with that of others. Finally, the performance simulation analysis investigates influences of critical processes on R&D outcomes and predicts how much the processes can enhance technical, economic and process outcomes. In particular, a case study is presented to illustrate the application of the proposed system to an IT company. This system can help managers enhance their R&D performance by presenting necessary improvements on critical processes.     

Large scale portfolio selection with synergies

The paper presents a multi-objective mixed integer linear programming model for portfolio selection of research and development (R and D) projects with synergies. Partial assignment of funds is allowed to different tasks of the project, while synergies are considered at the task and the project level. A synergy is active if a number of its elements sufficiently supported is within certain bounds. The risk associated with insufficient support is introduced. Numerical results are presented to demonstrate the efficiency of the proposed approach for large scale instances.     

A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times

In this paper, we propose a model for Flexible Job Shop Scheduling Problem (FJSSP) with transportation constraints and bounded processing times. This is a NP hard problem. Objectives are to minimize the makespan and the storage of solutions. A genetic algorithm with tabu search procedure is proposed to solve both assignment of resources and sequencing problems on each resource. In order to evaluate the proposed algorithm's efficiency, five types of instances are tested. Three of them consider sequencing problems with or without assignment of processing or/and transport resources. The fourth and fifth ones introduce bounded processing times which mainly characterize Surface Treatment Facilities (STFs). Computational results show that our model and method are efficient for solving both assignment and scheduling problems in various kinds of systems.     

Valuation of R&;D Sequential Exchange Options Using Monte Carlo Approach

This article describes a methodology for evaluating R&D investment projects using Monte Carlo method. R&D projects generally involve multiple phases with or without overlapping. R&D investments are made often in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as sequential investment. Moreover, each stage creates an opportunity (option) for subsequent investment. Therefore, R&D projects can be considered as ‘Compound Options’ in which investments present uncertainty both in the gross project value and in their costs. It is possible to use exchange options to value the R&D investment opportunities. In this paper, we propose to evaluate the European and American Real Compound exchange options through Monte Carlo simulations. We also provide a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.      

Multi-objective simulation optimization for uncertain resource assignment and job sequence in automated flexible job shop

In this study a multi-objective problem considering uncertainty and flexibility of job sequence in an automated flexible job shop (AFJS) is considered using manufacturing simulation. The AFJS production system is considered as a complex problem due to automatic elements requiring planning and optimization. Several solution approaches are proposed lately in different categories of meta-heuristics, combinatorial optimization and mathematically originated methods. This paper provides the metamodel using simulation optimization approach based on multi-objective efficiency. The proposed metamodel includes different general techniques and swarm intelligent technique to reach the optimum solution of uncertain resource assignment and job sequences in an AFJS. In order to show the efficiency and productivity of the proposed approach, various experimental scenarios are considered. Results show the optimal resources assignment and optimal job sequence which cause efficiency and productivity maximization. The makespan, number of late jobs, total flow time and total weighted flow time minimization have been resulted in an automated flexible job shop too.     

A new continuous berth allocation and quay crane assignment model in container terminal

Over the past decades, Chinese ports throughput grew rapidly, and more and more concerns were shown on the operational efficiency and effectiveness. Many studies have been made for scheduling berth and quay cranes, which are the critical resources in container terminals. In this paper, a two-phase model for berth allocation and quay crane assignment is proposed. In the first phase, according to the relationships of time and space between vessels, a new continuous berth allocation model is established, in which not only the common restricts but the coverage area of quay crane are considered. Then in the quay crane assignment phase, a multi-objective programming model is proposed, in which the first objective is to minimize the range of maximum and minimum quay cranes used for resources saving, and the second one is to minimize the movements of quay cranes so as to improve the efficiency. A particle swarm optimization algorithm for BAP was developed. The results of numerical experiments show that the proposed approach can improve the essential operations in container terminal.     

A Petri Net Approach to Support Resource Assignment in Project Management

Petri nets have long been used in modeling and simulating project execution because of their great capability to describe concurrent activities and simulate the evolvement of processes. Although a number of extended Petri net models have been proposed to model and simulate resource sharing and activity dependence in projects, none of them has ever included a resource assignment mechanism into their models. Because resource assignments influence how limited resources are allocated among conflicting activities, they may heavily affect the availability of resources and the execution of projects. Therefore, a model without considering resource-sharing and resource assignment strategies may lead to a misunderstanding about project scheduling, resource consumption behaviors, and estimated project time. Accordingly, this paper proposes a new extended Petri net model that can describe how resources are shared and assigned among concurrent activities of multiple projects. The proposed model is named as resource assignment Petri net (RAPN), which extends an object composition Petri net with new places, transitions, attributes, and firing rules to model resource-sharing and resource assignment strategies. Finally, we prove that RAPN can correctly model the resource consumption behaviors of projects and can correctly compute the total elapsed time of projects.     

A fuzzy approach for the reviewer assignment problem

Assignment of experts to project proposals is a significant task for funding agencies which have to assess the potential value of the research and development (R&D) projects through peer review. The problem is known as reviewer assignment problem and has real-world applications in funding agencies, conferences and research journals. Given a set of experts and a set of proposals; the problem can be defined as assigning the most suitable experts to the proposals under some constraints, which are generally encountered by funding agencies. In this study, a fuzzy model is offered to solve the reviewer assignment problem. The objective of the model is to maximize the total matching degree of assigned experts under some constraints such as cost of forming a panel and the size of a panel. The matching degrees are defined using linguistic variables to denote the expertise of each expert with respect to each proposal. The fuzzy mathematical model, which also takes into account different constraints related to the problem, is solved via the selected fuzzy ranking methods namely; the signed distance method and the method of ranking fuzzy numbers with integral value. The solution of an example problem – inspired from a real-life situation – with both of the mentioned methods revealed the effectiveness of the solution approach. It is believed that the use of the offered fuzzy approach could improve the accuracy of the decisions made by funding agencies.     

Optimization of resource allocation for underlay device-to-device communications in cellular networks

Underlay device-to-device (D2D) communication in cellular networks has been considered as a promising technique that can improve the spectral efficiency of cellular systems and meet the growing demand for wireless local services. In underlay D2D, it is of primary importance to manage the mutual interference between cellular links and D2D links through effective resource allocation. While most of previous works on D2D resource allocation are developed based on the knowledge of the channel state information (CSI) on the interference channels as well as the desired channels, it is hard to obtain full CSI in practice. Accordingly, we consider D2D resource allocation schemes based on distance between nodes. In particular, we formulate two optimization problems for D2D resource allocation using the outage probability computed based on the distance information as cost functions. One is a linear sum assignment problem (LSAP) and the other is a linear bottleneck assignment problem (LBAP). By applying the graph theory, we provide efficient algorithms for solving the optimization problems. Numerical results are provided to show the effectiveness of the proposed optimization as compared to previously proposed distance-based resource allocation algorithms.     

Random assignment method based on genetic algorithms and its application in resource allocation

Assignment problem is considered a well-known optimization problem in manufacturing and management processes in which a decision maker’s point of view is merged into a decision process and a valid solution is established. In this study, taking the complementary relations between expected value and variance in decision making and the synthesizing effect of random variables into consideration, a new model for random assignment problems is proposed; in which the characteristic of assignment problems are considered to present a concrete scheme based on genetic algorithms (denoted by SE ⊕ GA-SAF, for short). We study the model’s convergence using the Markov chain theory, and analyze its performance through simulation. All of these indicate that this solution model can effectively aid decision making in the assignment process, and that it possesses the desirable features such as interpretability and computational efficiency, as such it can be widely used in many aspects including manufacturing, operations, logistics, etc.     

Finding research trend of convergence technology based on Korean R&D network

Traditionally, collaboration network or citation network is used to answer the old question how scientists or engineers interact with each other. This paper introduces a R&D network to make up the missing aspect of the traditional approaches about using multi-sources and to find out the trend of convergence technology R&D in Korea. We collect data about human resources and national R&D projects from Korean national R&D databases, and then construct a weighted network between experts by using meta-data mapping and the network folding technique. And we apply Newman’s grouping algorithm that is generalized to a weighted network for detecting the community structure of the network. Gathering data from multi-sources is useful to reveal the structure of network rather than to use only one database. Lastly, we perform a network analysis to examine important experts. The result shows significant information about research trend and core experts in Korea. We expect this study will be helpful in three ways: (1) how to make a network from heterogeneous multi-sources, (2) how to figure out the current situation of convergence technology R&D, (3) how to discover who are important people in Korean convergence technology R&D network. And this paper is just a cornerstone of the work to investigate the current situation of national R&D projects in Korea.     

A semantic-based platform for R&D project funding management

Innovation is one of the keys to success in the business world, particularly within the current economic climate. R&D projects constitute the building blocks of the innovation process, hence the importance of searching for funding for these projects. As ontologies and semantic technologies mature, they provide a consistent and reliable means to represent and aggregate knowledge from different sources. The present work explores the use of ontologies to model R&D grant funding calls and the application of semantic technologies to the development of an enhanced funding management system. Our experiments confirm the success of the proposed approach, and reveal that it may bring considerable benefits to R&D funding.     

A new approach for selecting portfolio of new product development projects

The most decisive factor that survives enterprises under stiff competition is the development of new product (NPD), and when entering the product development stage after the fuzzy front end, a best project portfolio should be finalized in order to potentially create expected revenue and competitive advantage. However, even it reaches the end of the fuzzy front stage; the NPD project is still significantly involved with uncertainties, complexities and fuzziness. To assist R&D managers making decision in this environment, this study proposes a new approach which combines fuzzy set theory and multi-criteria group decision making method into a NPD project portfolio selection model. This model takes into account project performance, project delivery and project risk, and formulates the selection decision of NPD project portfolio as a fuzzy linear programming problem. The illustrative example shows that the model proposed can generate projects with the highest success rate under limited resources and manpower.     

基于改进火力分配模型的综合防空火力智能优化分配

针对综合防空火力分配中,传统火力分配模型没有全面考虑火力通道射击效能因素,且在火力资源相对充足的情况下火力资源易浪费和易延误战机的问题,将射击有利度、飞临时间与威胁度等因素结合,构建一种新的综合防空火力分配模型.基于此模型,针对来袭目标、火力节点以及制导节点3者之间的火力优化匹配问题,提出一种基于深度优先搜索预处理的多种群并行布谷鸟搜索算法(PMPCSA).该方法采用Prolog智能规划语言构建目标-火力节点-制导节点匹配规则库,在规则库中利用深度优先搜索快速生成可行的目标-火力节点-制导节点的匹配预处理方案;采用多种群并行布谷鸟搜索算法,对得到的可行分配空间进行搜索,通过引入多种群并行优化搜索,不同种群赋予不同控制参数的思想,兼顾算法的全局探索和局部开发能力,可有效提高算法寻优效率.最后,通过仿真结果验证所建综合防空火力分配模型的优势性,同时表明,所提出的PMPCSA算法能有效地平衡全局探索与局部开发,在保证较高收敛速度的同时,提高全局探索能力.     

A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations

A resource investment problem with discounted cash flows (RIPDCF) is a project-scheduling problem in which (a) the availability levels of the resources are considered decision variables and (b) the goal is to find a schedule such that the net present value of the project cash flows optimizes. In this paper, the RIPDCF in which the activities are subject to generalized precedence relations is first modeled. Then, a genetic algorithm (GA) is proposed to solve this model. In addition, design of experiments and response surface methodology are employed to both tune the GA parameters and to evaluate the performance of the proposed method in 240 test problems. The results of the performance analysis show that the efficiency of the proposed GA method is relatively well.     

How Imitations Impact on R&D Behaviors

I. INTRODUCTION Technology innovations are important for firms to compete in the market, whereas spillovers of research knowledge are unavoidable. Arrow (1962) has argued that technologies have some public good characteristics. Moreover, rival firms are able to access and copy new innovations through industrial espionage and reverse engineering, and are thus able to eat into monopoly profits of innovators. While patents are designed to provide protection to inventors, they provide only im…     

Adaptive genetic algorithm for advanced planning in manufacturing supply chain

A main function for supporting global objectives in a manufacturing supply chain is planning and scheduling. This is considered such an important function because it is involved in the assignment of factory resources to production tasks. In this paper, an advanced planning model that simultaneously decides process plans and schedules was proposed for the manufacturing supply chain (MSC). The model was formulated with mixed integer programming, which considered alternative resources and sequences, a sequence-dependent setup and transportation times.The objective of the model was to analyze alternative resources and sequences to determine the schedules and operation sequences that minimize makespan. A new adaptive genetic algorithm approach was developed to solve the model. Numerical experiments were carried out to demonstrate the efficiency of the developed approach. Received: June 2005 / Accepted: December 2005