Hamed Kalantari

Bio: Hamed Kalantari is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Computer science & Profit (economics). The author has an hindex of 3, co-authored 5 publications receiving 51 citations.

Fuzzy transfer point location problem: a possibilistic unconstrained nonlinear programming approach

Abstract: Recently, the transfer point location problem with weighted demand points and uniformly distributed coordinates has been introduced. In the real world problems, such as disaster situations, different points of an area might be demand point locations with different possibility degrees. So, it is necessary to develop more applicable models for these kinds of problems. In this paper, a new transfer point location problem with weighted demand points and fuzzy coordinates is developed. The proposed model is formulated as a fuzzy unconstrained nonlinear programming in which decision variables are obtained as fuzzy numbers. Due to the complexity of the developed model, a new fuzzy logic controller is designed based on the derived fuzzy decision variables to infer the optimum or near-optimum values for decision variables. Finally, a numerical example is presented and its results are compared with the optimum solutions of the problem in order to illustrate the efficiency of the proposed model.

Introducing drivers' collaboration network: A two-layers social network perspective in road transportation system analysis

Abstract: Drivers can make significant impacts on transportation systems. They can leave important information due to their social behaviors. But, the role of drivers has been overlooked yet. In this paper, for the first time, drivers' collaboration network is introduced. The network is considered in a heterogenous form, because of existence multiple relationships between drivers in the real-world situation. Since drivers do not belong to only one community, the overlapping of communities is considered and a new overlapping community detection algorithm is developed to discover the hidden structure of the network. Also, we present a new overlapping score to improve the community detection algorithm, using the adjacencies among non-memeber neighbor nodes and communities. Solving the algorithm will lead to discovering dense communities of drivers that have meaningful relationships with each other. This will result in a better understanding of the transportation network and also improving the overall performance of the system. A comparison of the developed algorithm with the others demonstrates the effectiveness of the algorithm. To evaluate the applicability of the algorithm, a real drivers' collaboration network is presented and the developed algorithm is applied to derive insights.

Centrality Measures in Networks

Abstract: We show that although the prominent centrality measures in network analysis make use of different information about nodes' positions, they all process that information in an identical way: they all spring from a common family that are characterized by the same simple axioms. In particular, they are all based on a monotonic and additively separable treatment of a statistic that captures a node's position in the network.

Ambulance routing in disaster response considering variable patient condition: NSGA-II and MOPSO algorithms

Abstract: The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions.

A bi-objective stochastic model for emergency medical services network design with backup services for disasters under disruptions: An earthquake case study

Abstract: In the event of a disaster such as natural and human-made, high demand for medical supplies and a large number of injured people will emerge at the affected areas in a short time period. Rapid distribution of medical supplies and the speed of transferring injured to the hospital play an important role in assuring the effectiveness and efficiency of the emergency medical service (EMS). In this paper, a bi-objective stochastic optimization model is developed for location of transfer points and medical supplies distribution centers (MSDCs). When a disaster happens, resources are usually insufficient for all to be treated immediately. For this reason, determining the priority of injured treatments based on the severity of their condition is one the most important issue on EMS, called as triage system. Therefore, for approaching the model to the real world, triage system and failure probabilities of MSDCs and routes are considered. To enhance efficiency of services during such event, backup MSDCs are also utilized. Furthermore, the proposed bi-objective model is converted to a single-objective mixed-integer programming model applying the e-constraint method. Finally, to show applicability of the model, a case study of earthquake disaster in an urban district in Iran is examined. The proposed model can help interdisciplinary agencies both to prepare and respond to disaster considering the disruption in an efficient manner.

Ambulance routing in disaster response considering variable patient condition: NSGA-II and MOPSO algorithms

Abstract: <p style='text-indent:20px;'>The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions.</p>

Improving the Mechanical Properties of Poly Methyl Methacrylate Nanocomposites for Dentistry Applications Reinforced with Different Nanoparticles

Abstract: Properties of poly methyl methacrylate are improved using different nanoparticles for denture applications and the best combination is selected using multi-criteria decision-making methods. For these purposes, poly methyl methacrylate is melt compounded with TiO2, SiO2, and Al2O3 nanoparticles and then injection molded. The results of mechanical tests revealed that by addition of TiO2 and SiO2, the impact strengths of poly methyl methacrylate were increased 229 and 62%, respectively. Also, the results indicated a significant improvement in Young’s modulus and hardness. The implementation of multi-criteria decision-making methods illustrated that TiO2 nanoparticles are the best candidate for improving the properties of poly methyl methacrylate for dental applications.