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    A two-stage stochastic programming model for comprehensive risk response action selection: A case study in Industry 4.0
    (Elsevier Ltd, 2024) Hajipour, Vahid; Di Caprio, Debora; Santos-Arteaga, Francisco J.; Amirsahami, Amirali; Vazifeh Noshafagh, Samira
    Effective project risk management is critical in environments where both micro-level and macro-level risks are present. Traditional models often focus on micro-level risks, neglecting broader macroeconomic uncertainties such as geopolitical instability and supply chain disruptions. This research introduces a two-stage stochastic programming model designed to optimize the selection of Risk Response Actions (RRAs) under uncertainty while addressing both types of risk. The model incorporates “here-and-now” decisions at the planning stage and “wait-and-see” decisions as uncertainties unfold, enabling adaptive risk management throughout the project lifecycle. To solve the model efficiently, we employ an evolutionary algorithm combined with Sample Average Approximation (SAA) to handle the computational complexity of multiple scenarios. The model is applied to a real-world case study involving the integration of IoT and ERP systems in a smart factory in Iran, a project characterized by significant macroeconomic and geopolitical risks. Our key contribution lies in providing a comprehensive risk response strategy selection model that simultaneously addresses micro- and macro-level risks while incorporating strategic flexibility through outsourcing decisions. The results demonstrate that our model outperforms traditional deterministic models, offering enhanced resilience against macro-level risks and improved project performance under uncertainty. These findings provide valuable insights for project managers aiming to increase resilience and adaptability in volatile environments. By integrating both internal and external risk factors, our model offers a robust tool for managing complex projects, enhancing decision-making and project outcomes in uncertain conditions.
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    A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems
    (Elsevier, 2023) Tavana, Madjid; Hajipour, Vahid; Alaghebandha, Mohammad; Di Caprio, Debora
    Flow shop scheduling deals with the determination of the optimal sequence of jobs processing on machines in a fixed order with the main objective consisting of minimizing the completion time of all jobs (makespan). This type of scheduling problem appears in many industrial and production planning applications. This study proposes a new bi-objective mixed -integer programming model for solving the synchronous flow shop scheduling problems with completion time. The objective functions are the total makespan and the sum of tardiness and earliness cost of blocks. At the same time, jobs are moved among machines through a synchronous transportation system with synchronized processing cycles. In each cycle, the existing jobs begin simultaneously, each on one of the machines, and after completion, wait until the last job is completed. Subsequently, all the jobs are moved concurrently to the next machine. Four algorithms, including nondominated sorting genetic algorithm (NSGA II), multi -objective simulated annealing (MOSA), multi -objective particle swarm optimization (MOPSO), and multi -objective hybrid vibration -damping optimization (MOHVDO), are used to find a near -optimal solution for this NP -hard problem. In particular, the proposed hybrid VDO algorithm is based on the imperialist competitive algorithm (ICA) and the integration of a neighborhood creation technique. MOHVDO and MOSA show the best performance among the other algorithms regarding objective functions and CPU Time, respectively. Thus, the results from running small-scale and mediumscale problems in MOHVDO and MOSA are compared with the solutions obtained from the epsilon -constraint method. In particular, the error percentage of MOHVDO's objective functions is less than 2% compared to the epsilon -constraint method for all solved problems. Besides the specific results obtained in terms of performance and, hence, practical applicability, the proposed approach fills a considerable gap in the literature. Indeed, even though variants of the aforementioned meta -heuristic algorithms have been largely introduced in multiobjective environments, a simultaneous implementation of these algorithms as well as a compared study of their performance when solving flow shop scheduling problems has been so far overlooked.
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    Maturing the scrum framework for software projects portfolio management: a case study-oriented methodology
    (Institute of Electrical and Electronics Engineers Inc., 2022) Vazifeh-Noshafagh, Samira; Hajipour, Vahid; Jalali, Sajjad; Di Caprio, Debora; Santos-Arteaga, Francisco Javier
    In the modern era, information technology-based solution providers are encountering a growing request for satisfying the versatile requirements of their customers in terms of software applications. To this end, specific approaches have been designed to streamline the way of accomplishing software projects in an efficient manner, i.e., agile-oriented frameworks. Even though previous studies have highlighted variations of such a framework, the literature has not addressed the adaptations required in response to the gradual maturity of a wide-ranging case study dealing with software applications. Following a case study-oriented methodology, this paper focuses on elaborating a set of workable maneuvers to mature the Scrum framework when applied to portfolio management. Particularly, we highlight how Scrum should be adapted from its basic setting to a vision and goal-oriented configuration or Scrumban under certain conditions. As a maturing practice, we propose a heuristic scoring technique to determine the sprint length of subprojects with different characteristics in the context of a portfolio. The study also introduces a multi-level refinement structure to enhance the monitoring of the teams' performance under the proposed mature framework. The results obtained display a considerable spike in the realization rate of release planning in light of the actual performance.