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Process models specific to the supply chain domain are an important tool for the analysis of interorganizational interfaces and requirements of information technology (IT) systems supporting supply chain decision-making. The purpose of this study is to examine the effectiveness of supply chain process models for novice analysts in conveying domain semantics compared to alternative textual representations.

A laboratory experiment with graduate students as proxies for novice analysts was conducted. Participants were randomly assigned to either the diagram group, which worked with “thread diagrams” created from the modeling grammar “Supply Chain Operation Reference (SCOR) model”, or the text group, which worked with semantically equivalent textual representations. Domain understanding was measured using cognitively demanding information acquisition for two different domains.

Diagram users were more accurate in identifying product-related information and organizing this information in a graph compared to those using the textual representation. The authors found considerable improvements in domain understanding, and using the diagrams was perceived as easy as using the texts.

The study's findings are unique in providing empirical evidence for supply chain process models being an effective representation for novice analysts. Such evidence is lacking in prior research because of the evaluation methods used, which are limited to scenario, case study and informed argument. This study adds the diagram user's perspective to that literature and provides a rigorous empirical evaluation by contrasting diagrammatic and textual representations.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

For prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.

This paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.

This paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the proposed model and the improved BBO.

With respect to limitations, only linear weighted transformations are used for objective optimization. In regards to research implications, this paper considers the production of prefabricated components in an environment where all parties in the supply chain of prefabricated components participate to solve the production scheduling problem. In addition, this paper creatively applies the improved BBO to the production scheduling problem of prefabricated components. Compared to other algorithms, the results show that the improved BBO show optimized result.

The proposed approach helps prefabricated component manufacturers consider complex requirements which could be used to formulate a more scientific and reasonable production plan. The proposed plan could ensure the construction project schedule and balance the reasonable requirements of all parties. In addition, improving the ability of prefabricated component production enterprises to deal with uncertain events. According to actual production conditions (such as the occupation of mold resources and storage resources of completed components), prefabricated component manufacturers could adjust production plans to reduce the cost and improve the efficiency of the whole prefabricated construction project.

The value of this article is to provide details of the procedures and resource constraints from the perspective of the precast components supply chain, which is closer to the actual production process of prefabricated components. In addition, developing the production scheduling for lean production will be in line with the concept of sustainable development. The proposed lean production scheduling could establish relationships between prefabricated component factory manufacturers, transportation companies, on-site contractors and production workers to reduce the adverse effects of emergencies on the prefabricated component production process, and promote the smooth and efficient operation of construction projects.

The purpose of this paper is to develop a multi-agent-based model for quantitatively measuring how the design change management strategies improve project performance.

Based on questionnaires and interviews, this paper investigates the coordination mechanism of risks due to design changes in prefabricated construction (PC) projects. Combined with all the variables related with design change risks, a multi-agent-based simulation model is proposed to evaluate the design change management effect.

The coordination mechanism between design change factors, design change events, risk consequence and management strategy in PC projects is described and then the simulation-based design change management mechanism in PC projects is used to assess the effect of management strategies under dynamic scenarios.

PC projects have rapidly increased in recent years due to the advantages of fast construction, high quality and labor savings. Different from traditional on-site construction, the impact and risk from design changes are likely to be greater due to the prefabricated project being multi-stage, highly interactive and complex. The simulations presented in this paper make it possible to test different design change management strategies in order to study their effectiveness and support managerial decision making.

Evaluates the increase in inter‐ and intra‐organizational knowledge sharing capabilities brought about by the Internet‐driven “new economy” technologies and the resulting managerial implications. Presents a framework for evaluating and deploying such technologies. Firms employing knowledge networks can also use e‐knowledge to improve organizational decision making, react more quickly to changes in the economic landscape, and create dynamic custom content and consumer intimacy. Builds on the extensive literature in knowledge management and inter‐organizational systems by identifying the opportunities of each in creating “e‐knowledge networks” to support organizational collaboration. This framework is applied to four industry case studies – supply chain management networks, adserver networks, content syndication networks, and business‐to‐business exchange networks. Analysis suggests that in the new economy, characterized by ubiquitous and often automated information sharing capabilities, the ability to create knowledge‐based networks of partners will be critical to maintaining competitive advantage.

From the point of view of computing environments, the paper aims to present a goal‐based contextual problem detection and management (GCPDM) method whose core benefit in its extendability of detection capability to deal with unpredicted problems.

Approaches the subject by designing a goal graph, designing actions, designing achievable relations between actions and goals, defining contextual factors of each action, defining CCGs and implementing the GCPD engine.

That self‐managed, as opposed to traditional, software is designed to avoid runtime failures by adapting to unpredictable situations.

This paper presents a GCPDM method.

As systems get complex, requirements elicitation and analysis are becoming increasingly difficult and important in software development. Even though various analysis methods have been proposed, including scenario‐based analysis, goal‐based analysis, combining goal with scenario and use case‐driven analysis – each method has its own strengths and weaknesses and do not support requirements elicitation and analysis efficiently. This paper proposes a multi‐view approach to analyze the requirements of complex software systems. The multi‐view approach comprises four views, which incorporate many factors that are part of existing methods. This paper discusses the need for these four views, the activities that are part of each view and how they are carried out. As a proof of concept, we apply the multi‐view approach to an automatic teller machine system development.

A software product line (SPL) captures commonalities and variations (C&V) within a family of systems. Although, feature‐oriented approaches have been proposed for building product lines, none of them provide a systematic approach for identifying features. This paper proposes a domain analysis method for creating SPL based on scenarios, goals and features. In particular, the paper presents a domain requirements model (DRM) that integrates features with goals and scenarios, and a domain requirements modeling method that uses the DRM. This approach has been applied to the home integration system (HIS) domain to demonstrate its feasibility. This approach makes it possible to systematically identify features and provide the rationale for both features and C&V.

There is a tremendous explosion in the amount of data that organizations generate, collect and store. Managers are beginning to recognize the value of this asset, and are increasingly relying on intelligent systems to access, analyze, summarize, and interpret information from large and multiple data sources. These systems help them make critical business decisions faster or with a greater degree of confidence. Data mining is a promising new technology that helps bring business intelligence into these systems. While there is a plethora of data mining techniques and tools available, they present inherent problems for end‐users such as complexity, required technical expertise, lack of flexibility and interoperability, etc. These problems can be mitigated by deploying software agents to assist end‐users in their problem solving endeavors. This paper presents the design and development of an intelligent software agent based data analysis and mining environment called IDM, which is utilized in decision making activities.