Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Finite element method. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Incorporating risk measures in closed-loop supply chain network design

Abstract: This paper considers a location-allocation problem in a closed-loop supply chain (CLSC) with two extensions: first, demand and prices of new and return products are regarded as non-deterministic parameters and second, the objective function is developed from expected profit to three types of mean-risk ones. Indeed, design and planning an integrated CLSC in real-world volatile markets is an important and necessary issue. Further, risk-neutral approaches, which are considered expected values, are not efficient for such uncertain conditions. Hence, this paper, copes with the design and planning problem of a CLSC in a two-stage stochastic structure. Besides, risk criteria are considered through using three types of popular and well-behaved risk measures: mean absolute deviation, value at risk and conditional value at risk (CVaR). Consequently, three types of mean-risk models are developed as objective functions and decision-making procedures are undertaken based on the expected values and risk adversity crite...

A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization

Abstract: Optimization in dynamic environment is considered among prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization approach, in which several mechanisms were employed to face the challenges in this domain. In this algorithm, an improved multi-swarm approach has been used for finding peaks in the problem space and tracking them after an environment change in an appropriate time. Moreover, a novel method based on change in velocity vector and particle positions was proposed to increase the diversity of swarms. For improving the efficiency of the algorithm, a local search based on adaptive exploiter particle around the best found position as well as a novel awakening-sleeping mechanism were utilized. The experiments were conducted on Moving Peak Benchmark which is the most well-known benchmark in this domain and results have been compared with those of the state-of-the art methods. The results show the superiority of the proposed method.