A zero-one model for project portfolio selection and scheduling

A zero-one integer linear programming model is proposed for selecting and scheduling an optimal project portfolio, based on the organisation's objectives and constraints such as resource limitations and interdependence among projects. The model handles some of the issues that frequently arise in real world applications but are not addressed by previously suggested models, such as situations in which the amount of available and consumed resources varies in different periods. It also allows for interactive adjustment following the optimisation process, to provide decision makers a method for controlling portfolio selection, based on criteria that may be difficult to elicit directly. It is critical for such a system to provide fast evaluation of alternatives the decision makers may want to examine, and this requirement is addressed. The proposed model not only suggests projects that should be incorporated in the optimal portfolio, but it also determines the starting period for each project. Scheduling considerations can have a major impact on the combination of projects that can be incorporated in the portfolio, and may allow the addition of certain projects to the portfolio that could not have been selected otherwise. An example problem is described and solved with the proposed model, and some areas for future research are discussed.     

On the incorporation of adversarial reasoning in course of action analysis and resource allocation

Course of Action analysis and Resource Management are concerned with the allocation of resources over time to effect desired actions as a result of the perceived situation awareness. Decision Support Systems provide automated recommended courses of action to decision makers, considering relevant resource capabilities and constraints. Incorporating potential adversary actions and reactions to the current course of action decision (and the resources effecting the actions) in the decision making process will make the decision support system more robust and increase confidence that the recommended decisions are appropriate responses to the unfolding situations. We discuss research results from the inclusion of possible adversary actions and reactions into the course of action/resource allocation decision making framework. The overall decision problem is formulated as a multi-stage mathematical program. As the problem is NP-hard, an heuristic is developed through a natural problem decomposition. Simulated results show the effectiveness of the heuristic in producing good-quality solutions in an efficient manner.     

An interactive desirability function method to multiresponse optimization

Multiresponse optimization problems often involve incommensurate and conflicting responses. To obtain a satisfactory compromise in such a case, a decision maker (DM)’s preference information on the tradeoffs among the responses should be incorporated into the problem. This paper proposes an interactive method based on the desirability function approach to facilitate the preference articulation process. The proposed method allows the DM to adjust any of the preference parameters, namely, the shape, bound, and target of a desirability function in a single, integrated framework. The proposed method would be highly effective in generating a compromise solution that is faithful to the DM’s preference structure.     

基于GMCR的双渠道供应链价格冲突动态交互模型

在冲突分析图模型中,稳定性分析的均衡结果和决策者偏好信息之间存在一个动态反复的交互过程。为此,本文提出了基于GMCR的冲突分析动态交互理论,在偏好信息和均衡结果之间引入决策者的风险认知系统,以动态模拟冲突中决策者的风险认知、偏好信息与均衡解之间的交互关系。然后,构建了基于GMCR的双渠道供应链价格冲突动态交互模型,从冲突分析动态交互视角解释了价格战背后制造商和零售商的博弈行为,并有效预测了风险规避情况下双渠道供应链价格冲突的均衡结果,为供应链管理者提供了一套系统有效的冲突分析工具。     

Improving the computational efficiency in a global formulation (GLIDE) for interactive multiobjective optimization

In this paper, we present a new general formulation for multiobjective optimization that can accommodate several interactive methods of different types (regarding various types of preference information required from the decision maker). This formulation provides a comfortable implementation framework for a general interactive system and allows the decision maker to conveniently apply several interactive methods in one solution process. In other words, the decision maker can at each iteration of the solution process choose how to give preference information to direct the interactive solution process, and the formulation enables changing the type of preferences, that is, the method used, whenever desired. The first general formulation, GLIDE, included eight interactive methods utilizing four types of preferences. Here we present an improved version where we pay special attention to the computational efficiency (especially significant for large and complex problems), by eliminating some constraints and parameters of the original formulation. To be more specific, we propose two new formulations, depending on whether the multiobjective optimization problem to be considered is differentiable or not. Some computational tests are reported showing improvements in all cases. The generality of the new improved formulations is supported by the fact that they can accommodate six interactive methods more, that is, a total of fourteen interactive methods, just by adjusting parameter values.     

An integrated decision support tool for airlines schedule recovery during irregular operations

This paper presents a decision support tool for airlines schedule recovery during irregular operations. The tool provides airlines control centers with the capability to develop a proactive schedule recovery plan that integrates all flight resources. A rolling horizon modeling framework, which integrates a schedule simulation model and a resource assignment optimization model, is adopted for this tool. The schedule simulation model projects the list of disrupted flights in the system as function of the severity of anticipated disruptions. The optimization model examines possible resource swapping and flight re-quoting to generate an efficient schedule recovery plan that minimizes flight delays and cancellations. A detailed example that illustrates the application of the tool to recover the schedule of a major US air-carrier during a hypothetical ground delay program scenario is presented. The results of several experiments that illustrates overall model performance in terms of solution quality and computation experience are also given.     

知识共享/转移背景下的动态群体决策绩效研究

现有的动态群体决策实践经常存在决策速度慢、观点不收敛的问题。本研究融入知识共享/转移的因素,从决策动态交互的过程视角,探讨决策绩效(一致性及决策时间)的改善机理、影响因素,从而提高相应绩效指标。研究通过构建决策过程模型,证明了知识共享/转移考虑下一致性序列在交互过程中的收敛性。同时,通过定性模拟判断知识共享/转移对决策时间的影响,以最小化决策时间为优化目标,搜索知识共享/转移水平,给出了个体知识差异构成的多维空间下最优的行动方案。     

A simulation optimization method that considers uncertainty and multiple performance measures

Simulation optimization provides a structured approach to system design and configuration when analytical expressions for input/output relationships are unavailable. This research focuses on the development of a new simulation optimization technique applicable to systems having multiple performance measures. The aim of this research is to incorporate a simulation end user’s preference towards risk and uncertainty into the search process for the best decision alternative. Automation of the optimization procedure is a necessity. Therefore, this paper proposes a simulation optimization method that involves a preference model, specifically adapted for decision making with simulation models.     

考虑风险偏好的动态生产库存问题的鲁棒优化模型

不同阶段需求不确定情况下,决策者的风险偏好和生产过程中的废品处理影响着供应链生产库存管理和供应链整体效益。本文考虑决策者风险偏好下,构建了包含I个生产者企业,一个库存点和一个废物处理基地的T阶段动态供应链生产库存框架,建立了椭球型需求不确定集下,以追求整体收益最大化为目标的不确定优化模型,并应用鲁棒优化理论得到了数据确定性线性鲁棒对应模型,讨论了模型解的可靠性和有效性。最后的算例表明,只有当决策者风险偏好参数在一定范围内时,才会存在满足条件且具有较高可靠性的鲁棒决策,验证了该鲁棒优化模型的合理性。     

Aspiration level approach in stochastic MCDM problems

The paper considers a discrete stochastic multiple criteria decision making problem. This problem is defined by a finite set of actions A, a set of attributes X and a set of evaluations of actions with respect to attributes E. In stochastic case the evaluation of each action with respect to each attribute takes form of a probability distribution. Thus, the comparison of two actions leads to the comparison of two vectors of probability distributions. In the paper a new procedure for solving this problem is proposed. It is based on three concepts: stochastic dominance, interactive approach, and preference threshold. The idea of the procedure comes from the interactive multiple objective goal programming approach. The set of actions is progressively reduced as the decision maker specifies additional requirements. At the beginning the decision maker is asked to define preference threshold for each attribute. Next, at each iteration the decision maker is confronted with the set of considered actions. If the decision maker is able to make a final choice then the procedure ends, otherwise he/she is asked to specify aspiration level. A didactical example is presented to illustrate the proposed technique.     

Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection

A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer programming problems.     

Resource-based strategies and problem structuring: using resource maps to manage resource systems

The resource-based view of strategy seeks to explain why some firms consistently outperform rivals in the same industry by acquiring a unique set of strategic assets (or resources). However, differences between dominant managerial mental models in management teams lead to disagreement at the moment of implementing distinct resource-building strategies. This managerial and cognitive view of strategic decision making and competition lends itself to investigation through problem structuring methods. We suggest that resource maps, as a problem structuring method, can be used to interpret managerial mental models for strategic decision making in terms of resource-building processes. Through resource maps, we represent the system of asset stocks believed to be most important for driving business performance. We illustrate the framework by comparing and contrasting maps of the system of resources (asset stocks) that best characterize two leading firms in the UK commercial radio broadcasting industry.     

Scheduling and control,performance measures and discrete event simulation

There are a growing number of finite capacity scheduling tools which are in use and available to industry. To be fully effective, a scheduling system needs to be incorporated within a control methodology which enables shop-floor performance to be analysed and, when necessary, corrective actions to be formulated. Manufacturing system performance, however, is not only dependent upon short-term planning decisions, but is also constrained by the capability for which it is designed. Hence, any control methodology should be based on a consistent set of performance measures and well defined procedures which help to integrate decision making at all levels in the order fulfilment process. The paper examines the distinctive roles of process optimisation and process control in the decision hierarchy; it classifies a set of measures of performance and other system variables according to the functions they serve in manufacturing control, and suggests a hierarchical process control methodology. The role of discrete event simulation techniques in linking decisions on manufacturing system design and production planning/control is also discussed.     

Using artificial intelligence and visual techniques into preference disaggregation analysis: The MUDAS system

Multicriteria Disaggregation-Aggregation (D-A) approaches include two major issues: The first one is to assess the decision maker's preference model from his global preferences on a reference set of alternative actions. The second issue concerns the interactive character of the approach in order to aid the DM to understand his/her own way of preferring. This paper aims to present the Multicriteria Interactive Intelligence Decision Aiding System (MIIDAS), an implementation of the extended UTA II method. Also, the contribution of the Artificial Intelligence and Visual techniques, embedded into the procedures of the MIIDAS system, is presented. Finally, a case study coming from a portfolio management selection problem is used to illustrate the system.     

A Markovian queueing model for ambulance offload delays

Ambulance offload delays are a growing concern for health care providers in many countries. Offload delays occur when ambulance paramedics arriving at a hospital Emergency Department (ED) cannot transfer patient care to staff in the ED immediately. This is typically caused by overcrowding in the ED. Using queueing theory, we model the interface between a regional Emergency Medical Services (EMS) provider and multiple EDs that serve both ambulance and walk-in patients. We introduce Markov chain models for the system and solve for the steady state probability distributions of queue lengths and waiting times using matrix-analytic methods. We develop several algorithms for computing performance measures for the system, particularly the offload delays for ambulance patients. Using these algorithms, we analyze several three-hospital systems and assess the impact of system resources on offload delays. In addition, simulation is used to validate model assumptions.     

Multicriteria decision support using rules that represent rough-graded preference relations

The approach described in this paper aims to support multicriteria choice and ranking of actions when the input preference information acquired from the decision maker is a graded comprehensive pairwise comparison (or ranking) of reference actions. It is based on decision-rule preference model induced from a rough approximation of the graded comprehensive preference relation among the reference actions. The set of decision rules applied to a new set of actions provides a graded fuzzy preference relation, which can be exploited by weighted-fuzzy net flow score or lexicographic-fuzzy net flow score procedure to obtain a final recommendation in terms of the best choice or of the ranking.     

A posterior preference articulation approach to multiresponse surface optimization

In multiresponse surface optimization (MRSO), responses are often in conflict. To obtain a satisfactory compromise, the preference information of a decision maker (DM) on the tradeoffs among the responses should be incorporated into the problem. In most existing work, the DM expresses a subjective judgment on the responses through a preference parameter before the problem-solving process, after which a single solution is obtained. In this study, we propose a posterior preference articulation approach to MRSO. The approach initially finds a set of nondominated solutions without the DM’s preference information, and then allows the DM to select the best solution from among the nondominated solutions. An interactive selection method based on pairwise comparisons made by the DM is adopted in our method to facilitate the DM’s selection process. The proposed method does not require that the preference information be specified in advance. It is easy and effective in that a satisfactory compromise can be obtained through a series of pairwise comparisons, regardless of the type of the DM’s utility function.     

A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction

Two main concepts are established in the literature for the Parameter Setting Problem of metaheuristics: Parameter Tuning Strategies (PTS) and Parameter Control Strategies (PCS). While PTS result in a fixed parameter setting for a set of problem instances, PCS are incorporated into the metaheuristic and adapt parameter values according to instance-specific performance feedback. The idea of Instance-specific Parameter Tuning Strategies (IPTS) is aiming to combine advantages of both tuning and control strategies by enabling the adoption of parameter values tailored to instance-specific characteristics a priori to running the metaheuristic. This requires, however, a significant knowledge about the impact of instance characteristics on heuristic performance. This paper presents an approach that semi-automatically designs the fuzzy logic rule base to obtain instance-specific parameter values by means of decision trees. This enables the user to automate the process of converting insights about instance-specific information and its impact on heuristic performance into a fuzzy rule base IPTS system. The system incorporates the decision maker’s preference about the trade-off between computational time and solution quality.     

Resource,service quality and performance triad: a framework for measuring efficiency of banking services

In this paper, we present the development of a theoretical framework for measuring the efficiency of banking services taking into account physical and human resources, service quality and performance. Expenditures on quality improvement efforts and the impact of service quality on financial outcomes have long intrigued researchers. Banks have traditionally focused on how to transform their physical resources to generate financial performance, and they inadvertently ignored the mediating intangible factor of service quality. A theoretical framework on the optimization triad of resource, service quality and performance is proposed here, thereby linking the marketing variables to the financial metrics. A measure for the return on quality is developed as the ratio of the potential improvements in financial performance by enhancement of service quality to the observed performance figures. Empirical results obtained from a study of 27 Indian public sector banks and their customers allow us to measure the impact of service quality on financial performance, optimal level of service quality that can be generated using existing resources and the opportunity cost for sub-optimal service delivery. Banks delivering better service are shown to have better transformation of resource to performance using superior service delivery as the medium. Our results confirm the linkage between resource, service quality and performance for services.     

An integrative evaluation framework for intelligent decision support systems

Evaluation of the overall effectiveness of decision support systems (DSS) has been a research topic since the early 1980s. As artificial intelligence methods have been incorporated into systems to create intelligent decision support systems (IDSS), researchers have attempted to quantify the value of the additional capabilities. Despite the useful and relevant insights generated by previous research, existing evaluation methodologies offer only a fragmented and incomplete view of IDSS value and the contribution of its technical infrastructure. This paper proposes an integrative, multiple criteria IDSS evaluation framework through a model that links the decision value of an IDSS to both the outcome from, and process of, decision making and down to specific components of the IDSS. The proposed methodology provides the designer and developer specific guidance on the intelligent tools most useful for a specific user with a particular decision problem. The proposed framework is illustrated by evaluating an actual IDSS that coordinates management of urban infrastructures.