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Showing 3 results for Multi-Objective Optimization
Discovering groups of key potential customers in social networks: A multi-objective optimization model
Aghil Hamidihesarsorkh, Ali Papi, Ali Bonyadi Naeini, Armin Jabarzadeh,
Volume 28, Issue 1 (3-2017)
Abstract

Nowadays, the popularity of social networks as marketing tools has brought a deal of attention to social networks analysis (SNA). One of the well-known Problems in this field is influence maximization problems which related to flow of information within networks. Although, the problem have been considered by many researchers, the concept behind of this problem has been used less in business context. In this paper, by using a cost-benefits analysis, we propose a multi-objective optimization model which helps to identify the key nodes location, which are a symbol of potential influential customers in real social networks. The main novelty of this model is that it determines the best nodes by combining two essential and realistic elements simultaneously: diffusion speed and dispersion cost. Also, the performance of the proposed model is validated by detecting key nodes on a real social network


A Robust Optimization Approach for a Discrete Time-Cost-Environment Trade-off Project Scheduling Problem Under Uncertainty
Ali Salmasnia, Elahe Heydarnezhad, Hadi Mokhtari,
Volume 35, Issue 2 (6-2024)
Abstract
Abstract. One of the important problems in managing construction projects is selecting the best alternative for activities' execution to minimize the project's total cost and time. However, uncertain factors often have negative effects on activity duration and cost. Therefore, it is crucial to develop robust approaches for construction project scheduling to minimize sensitivity to disruptive noise factors. Additionally, existing methods in the literature rarely focus on environmentally conscious construction management. Achieving these goals requires incorporating the project scheduling problem with multiple objectives. This study proposes a robust optimization approach to determine the optimal construction operations in a project scheduling problem, considering time, cost, and environmental impacts (TCE) as objectives. An analytical algorithm based on Benders decomposition is suggested to address the robust problem, taking into account the inherent uncertainty in activity time and cost. To evaluate the performance of the proposed solution approach, a computational study is conducted using real construction project data. The case study is based on the wall of the east coast of Amirabad port in Iran. The results obtained using the suggested solution approach are compared to those of the CPLEX solver, demonstrating the appropriate performance of the proposed approach in optimizing the time, cost, and environment trade-off problem.

Addressing Disruption Risks in Location-Inventory Problems for Perishable Products through Lateral Transshipment
Ahmad Mohammadpour Larimi, Babak Shirazi, Iraj Mahdavi,
Volume 36, Issue 2 (6-2025)
Abstract
location-inventory problem (LIP) is a significant issue in supply chain management (SCM), aiming to reduce and integrate the costs of inventory and location. Perishable-LIP (PLIP) includes products, particularly those with a short expiration date, also known as perishable items. This feature necessitates the supply chain to maintain high reliability and resilience to minimize costs faced with disruption risks. Implementing reliability and resilience in PLIP (R2-PLIP) requires methods such as lateral transshipment. These methods not only enhance the reliability and resiliency of the SC but also mitigate the risks associated with supply disruptions and demand fluctuations. Demand for perishable products is influenced by their expiration dates. By incorporating lateral transshipment, companies can ensure a more balanced inventory distribution. This study investigates the role of lateral transshipment in enhancing supply chain robustness. A multi-objective optimization model is developed, focusing on minimizing costs while maximizing resilience and service levels. The project aims to optimize the overall system efficiency. Additionally, the sensitivity analysis conducted in the research indicates that the shortage cost and the DC capacity each had the greatest variations in one of the objective functions. This research provides practical insights for designing resilient perishable supply chains.
 
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