Mojtaba Afsharian; Bijan Baghbani; Masoumeh Lajevardi; Amin Saeidi khasraghi; Mohammad Reza Sasouli
Abstract
In today's era, organizations recognize the challenges of meeting the evolving needs and preferences of customers. Simply improving products and individual performance is insufficient to satisfy customer requirements. Instead, organizations have embraced a collaborative strategy, utilized efficient supply ...
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In today's era, organizations recognize the challenges of meeting the evolving needs and preferences of customers. Simply improving products and individual performance is insufficient to satisfy customer requirements. Instead, organizations have embraced a collaborative strategy, utilized efficient supply chains and leveraged each other's expertise and resources to enhance customer satisfaction. This approach has been made possible by technological advancements. The literature review identifies two research gaps: insufficient consideration of inherent uncertainty in construction projects and inade-quate attention to the multi-objective and multimodal nature of construction project models. To address uncertainties in construction projects, this study employed the Chance-Constrained Programming approach. Uncertainty-related parame-ters were identified and integrated into an optimization model. The primary objective of this study is to minimize project implementation delays. To achieve this, we employ exact algorithms for small and medium-scale problems and utilize NSGAII for large-scale scenarios. Our research emphasizes the critical importance of efficient project timing, cost optimi-zation, and proactive delay management for achieving successful project outcomes. The study reveals critical insights into the impact of resource allocation on the first objective function. The findings show 20% increase in resources for the first activity (i) raises the objective function to 310 units, while a 30% reduction in activity i's completion time lowers it to 188 units. These findings offer valuable benchmarks for decision-making and project optimization. Managers can use these insights to enhance decision-making, optimize resource allocation, and ensure timely project completion while maintaining quality and cost control.
Ali Bozorgi Amiri; Mostafa Akbari; Iman Dadashpour
Abstract
Quick response to the relief needs right after disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas. In this paper, by focusing on the distribution of relief commodities after disaster, the best possible allocation for the affected ...
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Quick response to the relief needs right after disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas. In this paper, by focusing on the distribution of relief commodities after disaster, the best possible allocation for the affected areas is specified and shortest path to vehicle transporting is determined. The objective of the proposed model is the minimization of the maximum distance traveled by each vehicle in order to achieve fairness in response to the wounded. In our proposed model, the location of demand is uncertain and determined by the simulation approach. The proposed approach solves the proposed model and determines appropriate allocation and best route for vehicles according to the allocation, simultaneously. Consequently, using genetic algorithm with two-part chromosome structure in routing and allocation problems. Computational results show the efficiency and effectiveness of the proposed model and algorithm for solving real decision-making problems.
Maryam Shams; Ahmad Jafarzadeh Afshari; Amir Khakbaz
Abstract
Cloud computing is considered to be a new service provider technology for users and businesses. However, the cloud environment is facing a number of challenges. Resource allocation in a way that is optimum for users and cloud providers is difficult because of lack of data sharing between them. On the ...
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Cloud computing is considered to be a new service provider technology for users and businesses. However, the cloud environment is facing a number of challenges. Resource allocation in a way that is optimum for users and cloud providers is difficult because of lack of data sharing between them. On the other hand, job scheduling is a basic issue and at the same time a big challenge in reaching high efficiency in the cloud computing environment. In this paper, “the cloud resources management problem” is investigated that includes allocation and scheduling of computing resources, such that providers achieve the high efficiency of resources and users receive their needed applications in an efficient manner and with minimum cost. For this purpose, a group technology based non-linear mathematical model is presented with an aim at minimization of load difference of servers, number of transfers between servers, number of active virtual machines, maximum construction time, the cost of performing jobs and active servers energy consumption. To solve the model, a meta-heuristic multi-objective hybrid Genetic and Particle Swarm Optimization algorithm is proposed for resource allocation and scheduling. In order to demonstrate the validity and efficiency of the algorithm, a number of problems with different dimensions are randomly created and accordingly the efficiency and convergence capability of the suggested algorithm is investigated. The results indicated that the proposed hybrid method has had an acceptable performance in generating high quality, diverse and sparse solutions.