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    Designing an efficient humanitarian supply chain network during an emergency: A scenario-based multi-objective model
    (Elsevier Science Inc, 2023) Jafarzadeh-Ghoushchi, Saeid; Asghari, Mohammad; Mardani, Abbas; Simic, Vladimir; Tirkolaee, Erfan Babaee
    Efficient humanitarian supply chain (HSC) management plays an underlying role in saving lives, reducing human torment, and contributing to sustainable development during a disaster. Accordingly, the issue of locating and allocating relief facilities in the first hours after the occurrence of a disaster has a great impact on providing timely service. This study addresses a sustainable location-allocation-inventory problem (LAIP) to design an efficient HSC through concurrently optimizing four objectives of fairness, timeliness, economic productivity, and social justice. To do so, a novel scenario-based multi-objective mixed-integer linear programming (MILP) model is developed to formulate the problem under uncertainty. According to this model, the process of taking care of injured people is carried out in three stages of decision-making. Maximum facilities for sending relief supplies are used to supply the demand at each stage. In addition, the three factors of supply, demand, and communication routes between the centers and the affected areas are defined as fuzzy random parameters. Since the proposed model contains multiple objectives, goal programming (GP) is applied to provide a single-objective model. The validation of the developed methodology is made with the help of an illustrative example in the literature, and the results are obtained and evaluated using sensitivity analysis of the objective functions' weights. As one of the main findings, sending the maximum available supplies in MDCs to the affected areas in three stages using surplus vehicles is the best solution to cover the shortage of products. Finally, it is revealed that the proposed methodology can be utilized by managers to tackle the complexity of the problem during natural disasters.
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    Evaluating Holistic Privacy Risk Posed by Smart Home Ecosystem: A Capability-Oriented Model Accommodating Epistemic Uncertainty and Wisdom of Crowds
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Chang, Jian-Peng; Zheng, Hong-Liang; Mardani, Abbas; Pedrycz, Witold; Chen, Zhen-Song
    Evaluating the holistic privacy risk (HPR) presented by a smart home ecosystem (SHE), encompassing both internal and external entities that may be targeted by different adversaries seeking to compromise users' privacy, can enhance the comprehensive understanding of the privacy risk landscape within the SHE. This matter is influenced by the complexity of risk surroundings, the diverse perspectives of users toward privacy, and the lack of historical data. Unfortunately, existing literature falls short in addressing these factors. To fill the gap, this article develops an innovative capability-oriented model that accommodates epistemic uncertainty and wisdom of crowds (WoC), designed to assist smart home device manufacturers in accurately assessing HPR posed by their SHEs. The model presents a method for representing subjective judgments that captures epistemic uncertainty and a technique for weighting individual judgments to mitigate overconfidence bias, thus effectively harnessing WoC. In addition, this model features two specialized methods: one for quantifying HPR and another for prioritizing associated single risks, both tailored to operate effectively within uncertain context. These innovative methods are versatile and can be applied to various risk assessment scenarios, especially where historical data are not available. The practicality and effectiveness of our model are demonstrated through a detailed case study.
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    A novel two-echelon hierarchical location-allocation-routing optimization for green energy-efficient logistics systems
    (Springer, 2021) Babaee Tirkolaee, Erfan; Goli, Alireza; Mardani, Abbas
    The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids.