This review aims to gain insight into the current research and application of operational management in the area of intelligent agriculture based on the Internet of Things (IoT), and consequently, identify existing shortcomings and potential issues. First, we use the Java application CiteSpace to analyze co-citation networks in the literature related to the operational management of IoT-based intelligent agriculture. From the literature analysis results, we identify three major fields: (1) the development of agricultural IoT (Agri-IoT) technology, (2) the precision management of agricultural production, and (3) the traceability management of agricultural products. Second, we review research in the three fields separately in detail. Third, on the basis of the research gaps identified in the review and from the perspective of integrating and upgrading the entire agricultural industry chain, additional research directions are recommended from the following aspects: The operational management of agricultural production, product processing, and product sale and after-sale service based on Agri-IoT. The theoretical research and practical application of combining operational management theories and IoT-based intelligent agriculture will provide informed decision support for stakeholders and drive the further development of the entire agriculture industry chain.

Condition-based maintenance (CBM) detects early signs of failure and dictates when maintenance should be performed based on the actual condition of a system. In this paper, we first review some of the recent research on CBM under various physical structures and signal data. Then, we summarize several kinds of prognostic models that use monitoring information to estimate the reliability of complex systems or products. Monitoring information also facilitates operational decisions in production planning, spare parts management, reliability improvement, and prognostics and health management. Finally, we suggest some research opportunities for the reliability and operations management communities to fill the research gap between these two fields.

System reliability optimization problems have been widely discussed to maximize system reliability with resource constraints. Birnbaum importance is a well-known method for evaluating the effect of component reliability on system reliability. Many importance measures (IMs) are extended for binary, multistate, and continuous systems from different aspects based on the Birnbaum importance. Recently, these IMs have been applied in allocating limited resources to the component to maximize system performance. Therefore, the significance of Birnbaum importance is illustrated from the perspective of probability principle and gradient geometrical sense. Furthermore, the equations of various extended IMs are provided subsequently. The rules for simple optimization problems are summarized to enhance system reliability by using ranking or heuristic methods based on IMs. The importance-based optimization algorithms for complex or large-scale systems are generalized to obtain remarkable solutions by using IM-based local search or simplification methods. Furthermore, a general framework driven by IM is developed to solve optimization problems. Finally, some challenges in system reliability optimization that need to be solved in the future are presented.

This study provides a critical review of the concepts of Agile, Lean, Scrum, and Last Planner^® System (LPS). A comparative analysis is conducted between LPS and Scrum to expand LPS by considering Scrum’s best practices. Eight dimensions, namely, 1) origins, 2) main purpose, 3) overall system/framework process, 4) tools or artifacts maintained by the team, 5) team composition and main roles, 6) regular events or team meetings, 7) metrics/dashboards, and 8) approach to learning, are evaluated. After analyzing side by side the eight dimensions, it was found that many aspects from Scrum already exist in LPS in the same or similar form. However, the authors identify four main elements from Scrum that can be leveraged to improve the LPS benchmark, such as considering the Scrum “Increment” concept into LPS, having a clear definition of roles and responsibilities, or adding an equivalent to a Scrum Master to have a designated “rule keeper” in LPS. These opportunities to be considered in new LPS benchmarks need to be tested and validated with real applications. To the best of the authors’ knowledge, this work is the first to comprehensively compare Scrum (Agile) and LPS (Lean) and could be seen as a contribution toward the evolution of the Last Planner System for the academic and industrial environments.

Production planning and scheduling are becoming the core of production management, which support the decision of a petrochemical company. The optimization of production planning and scheduling is attempted by every refinery because it gains additional profit and stabilizes the daily production. The optimization problem considered in industry and academic research is of different levels of realism and complexity, thus increasing the gap. Operation research with mathematical programming is a conventional approach used to address the planning and scheduling problem. Additionally, modeling the processes, objectives, and constraints and developing the optimization algorithms are significant for industry and research. This paper introduces the perspective of production planning and scheduling from the development viewpoint.

For many years, project management maturity models (PMMM) derived from the software industry have brought immeasurable benefits. However, the adoption and investigation of PMMM in the construction field have been weak and insufficient, particularly in construction consulting services (CCS). Moreover, professionals have gradually realized the importance of non-process factors (e.g., teamwork, culture, leadership) in the evaluation of PMMM. This study describes the construction of PMMM for CCS that considers non-process factors and combines them with CCS-specific process factors. To verify the effectiveness of the proposed model, we conduct a case study on the overall project management consultancy for China’s 2010 Winter Expo. The results would fill in the PMMM research gap in CCS and provide practitioners with ideas to improve their performance.

As building practices change, procedures that seemed indispensable at one point can be abandoned for others, one example of which is the bill of quantities (B/Q). Research into the extant literature attributes the declining use of B/Qs to a multitude of reasons, such as its complexity, the potentially long time required to produce it, the growth in popularity of non-traditional procurement systems, and the challenge of using the information within the document in a construction schedule. With these issues in mind, building information modeling (BIM) and virtual reality (VR) are combined and proposed as a potential solution that allows inclusion of the client into the design process. Following a literature review and precedent study, an experiment was carried out using this new process to simulate a client’s design decisions on window and interior furnishings. The choices made by the client using VR automatically updated a B/Q schedule built in Revit and allowed them to have a firm understanding of project costs. Besides giving the client more confidence in a pleasing final outcome, the technology also ensured an up-to-date, accurate, and easily understandable B/Q. The proposed method features great potential savings in cost and time and gives the B/Q a newfound importance in future construction processes. The research case presented in this paper is a stepping stone in exploring new opportunities offered by VR and BIM and how they could improve the reliability and accuracy of traditional procurement within construction, specifically within the B/Q document.

To obtain a sustainable competitive advantage and achieve global innovation leadership, firms in China and other emerging economies must develop strong indigenous innovation capabilities through the coevolution of strategy, organization, resources, and culture. Drawing on current innovation management theories and practices, this study proposes four systematic paths for improving firm innovation systems (FISs), namely, the R&D-based internal collaborative FIS, the strategic vision-driven FIS, the open and user-driven FIS, and the holistic ecosystem-driven FIS. This study contributes to the systematic approach for enhancing corporate indigenous innovation capability based on FISs. Moreover, this study provides theoretical and practical insights for China as well as other developing countries to cultivate world-class enterprises and build an innovative nation.

Financial incentives that stimulate energy investments under public–private partnerships are considered scarce public resources, which require deliberate allocation to the most economically justified projects to maximize the social benefits. This study aims to solve the financial incentive allocation problem through a real option-based nonlinear integer programming approach. Real option theory is leveraged to determine the optimal timing and the corresponding option value of providing financial incentives. The ambiguity in the evolution of social benefits, the decision-maker’s attitude toward ambiguity, and the uncertainty in social benefits and incentive costs are all considered. Incentives are offered to the project portfolio that generates the maximum total option value. The project portfolio selection is formulated as a stochastic knapsack problem with random option values in the objective function and random incentive costs in the probabilistic budget constraint. The linear probabilistic budget constraint is subsequently transformed into a nonlinear deterministic one. Finally, the integer non-linear programming problem is solved, and the optimality gap is computed to assess the quality of the optimal solution. A case study is presented to illustrate how the limited financial incentives can be optimally allocated under uncertainty and ambiguity, which demonstrates the efficacy of the proposed method.

In this study, we considered a bi-objective, multi-project, multi-mode resource-constrained project scheduling problem. We adopted three objective pairs as combinations of the net present value (NPV) as a financial performance measure with one of the time-based performance measures, namely, makespan (C_max), mean completion time (MCT), and mean flow time (MFT) (i.e., minC_max/maxNPV, minMCT/maxNPV, and minMFT/maxNPV). We developed a hybrid non-dominated sorting genetic algorithm II (hybrid-NSGA-II) as a solution method by introducing a backward–forward pass (BFP) procedure and an injection procedure into NSGA-II. The BFP was proposed for new population generation and post-processing. Then, an injection procedure was introduced to increase diversity. The BFP and injection procedures led to improved objective functional values. The injection procedure generated a significantly high number of non-dominated solutions, thereby resulting in great diversity. An extensive computational study was performed. Results showed that hybrid-NSGA-II surpassed NSGA-II in terms of the performance metrics hypervolume, maximum spread, and the number of non-dominated solutions. Solutions were obtained for the objective pairs using hybrid-NSGA-II and three different test problem sets with specific properties. Further analysis was performed by employing cash balance, which was another financial performance measure of practical importance. Several managerial insights and extensions for further research were presented.

This research investigates the role of local governments in stimulating an emerging industry and focuses on the specific growth of the new energy vehicle industry in Hangzhou, China. This research confirms that enabling firms to access emerging technology, acquire financial support, and touch customers and/or suppliers are critical to foster the emergence and development of industries. Moreover, the primary contribution of this study is to emphasize the support of the local government in the development of emerging industries on the perspective of the creation of a large-scale market demand. The creation of large-scale market demand may inspire actors to be proactive in responding to these incentives; thus, public and private actions may help increase the accessibility to technology, infrastructure, and finances. Hence, a market-oriented policy that incentivizes the creation and expansion of market demand among diverse public and private actors should be seen as the key issue for the emergence and growth of emerging industries. Policies should also be adopted promptly with the development of the market.