Project performance measurement offers a wide range of methods to help project managers monitor projects. However, several critical issues remain in project performance measurement, such as an unbalanced use of indicators due to a lack of prediction-based (leading) indicators with regard to outcome-based (lagging) indicators. For its part, systems engineering measurement, although a more recent discipline, offers a wide variety of indicators, including a set of leading indicators. As our objective is to increase project performance and success rates, this involves improving project performance measurement on which project management decisions are based. This purpose, the proposal put forward in this paper is to extend the number and type of indicators used in project performance measurement by adapting the leading indicators defined in systems engineering measurement. The methodology involves first mapping the systems engineering indicators with the project management activities, resulting in identification of a set of potentially useful indicators for measuring the different activities, then tailoring a selection of these indicators with project-specific data to define a set of the most relevant indicators for a given project. This methodology is illustrated by means of a case study in a manufacturing company.

A simulation model enables manufacturers to accurately evaluate the performance of their manufacturing systems. A vital input to build simulation models of manufacturing systems is the system behaviour, which is modelled based on extensive data on manufacturing systems and their elements. However, for outsourced manufacturing systems, data provided from external suppliers to manufacturers are generally limited and insufficient. Therefore, a solution is required to identify and secure system behaviour from available data. We propose herein a behaviour extraction process along with detailed activities and diagrams to extract system behaviour from limited data. By using the systems engineering standard ANSI/EIA-632, the proposed process systematically restructures available data on a manufacturing system and its elements and then provides complete system behaviour to build a simulation model. A case study on a cooling water control system in a hot strip mill convincingly demonstrates the applicability and effectiveness of the proposed process.

Sensitivity analyses based on an algebraic representation in the graph model for conflict resolution (GMCR) are generalized for ascertaining the robustness of stability results by varying decision makers’ preference ranking. The ordinal preferences in GMCR are advantageous to carry out sensitivity analyses with respect to systematically identifying the influence of preference alterations upon the four basic stabilities consisting of Nash stability, general metarationality, symmetric metarationality and sequential stability. The proposed algebraic representation of the four basic stabilities is not only effective and convenient for computer implementation of sensitivity analysis, but also makes it easier to understand the meaning of the four stabilities when compared with the existing matrix representation. Further, these sensitivity analyses results are embedded into the latest version of the decision support system NUAAGMCR, which can be used to study real-world conflicts. To illustrate how these contributions to sensitivity analyses can be applied in practice and provide valuable strategic insights, they are used to investigate the civil war conflict in South Sudan.

Rational drug use requires that patients receive medications for an adequate period of time. The adequate duration time of medications not only improve the therapeutic effect of medicines, but also reduce the side effects and adverse reactions of medicines. This paper proposes a data-driven method to mine typical treatment duration patterns for rational drug use from electronic medical records (EMRs). Firstly, a quintuple is defined to describe drug use duration statistics (DUDS) for each drug and treatment record is further represented with DUDS vector (DUDSV). Next a similarity measure method is adopted to compute the similarity between treatment records. Meanwhile, a clustering algorithm is used to cluster all patient treatment records to extract typical treatment duration patterns including typical drug sets, effective drug use day sets, and the DUDSs of each typical drug. Then the extracted typical treatment duration patterns are evaluated and annotated based on patients’ demographic information, disease severity scores, treatment outcome and diagnostic information. Finally, a real-world EMR dataset is performed to indicate that the approachwe proposed can effectively mine typical treatment duration patterns from EMRs and recommend the appropriate treatment regimens for patients based on their admission information.

As a high-energy-consumption and high-CO₂-emission industry in China, the transportation sector has been under increasing pressure to improve its performance. This paper develops a novel parallel DEA approach to measure Chinese transportation sector’s energy and environmental performance (EEP) over all possible weights, which is to avoid the risk of using the extreme or the most favorable weights in performance evaluation. In our method, the transportation sector is consisted of two parallel subsystems (passenger transportation and freight transportation) with shared inputs and undesirable shared outputs. All possible weights are considered in the EEP evaluation, then the EEP of a transportation sector is represented by a ranking interval. Finally, the proposed approach is applied to the transportation sectors in 30 Chinese provinces. Results show that the best and the worst ranking of most provinces vary greatly. Besides, the EEP of most provinces is hard to dominate others strictly, but the general tendency is the EEP of eastern provinces better than western provinces. Furthermore, the EEP difference of some adjacent provinces with similar features is distinct. These findings are not all the same as previous studies, which verifies the necessity of considering all possible weights in performance evaluation. Therefore, our method provides a new point of view in performance evaluation and can give more robust results for decision makers.

This paper investigates the effect of money-back guarantee (MBG) and customer value on dual-channel supply chain. Manufacturer could produce and sell old generation product by direct channel to compete with new generation product by retail channel. Both retailer and manufacturer could choose whether implementing the MBG strategy or not. This paper discusses whether manufacturer should open the direct channel, how to make MBG strategy to realize maximum profits, and how customer value influences the supply chain. We give the boundary conditions of four situations about the decisions of retailer and manufacturer: GG situation, GN situation, NG situation and NN situation. We find that MBG strategy by retailer brings higher retail unit price, MBG strategy by manufacturer brings higher direct unit price and influences the wholesale price by the net salvage value of new generation product. This paper shows that if the demand was positive, opening the direct channel always benefits the manufacturer. Besides, customer value affects demands of two channels and the threshold value of parameter is given in this paper.