An enhanced PSO algorithm to configure a responsive-resilient supply chain network considering environmental issues: a case study of the oxygen concentrator device

Abstract

In recent years, the hyper-competitive marketplace has led to a drastic enhancement in the importance of the supply chain problem. Hence, the attention of managers and researchers has been attracted to one of the most crucial problems in the supply chain management area called the supply chain network design problem. In this regard, this research attempts to design an integrated forward and backward logistics network by proposing a multi-objective mathematical model. The suggested model aims at minimizing the environmental impacts and the costs while maximizing the resilience and responsiveness of the supply chain. Since uncertainty is a major issue in the supply chain problem, the present paper studies the research problem under the mixed uncertainty and utilizes the robust possibilistic stochastic method to cope with the uncertainty. On the other side, since configuring a supply chain is known as an NP-Hard problem, this research develops an enhanced particle swarm optimization algorithm to obtain optimal/near-optimal solutions in a reasonable time. Based on the achieved results, the developed algorithm can obtain high-quality solutions (i.e. solutions with zero or a very small gap from the optimal solution) in a reasonable amount of time. The achieved results demonstrate the negative impact of the enhancement of the demand on environmental damages and the total cost. Also, according to the outputs, by increasing the service level, the total cost and environmental impacts have increased by 41% and 10%, respectively. On the other hand, the results show that increasing the disrupted capacity parameters has led to a 17% increase in the total costs and a 7% increase in carbon emissions. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-022-07739-8.

Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm

“…More importantly, the problem-solving process of our proposed artificial system is greatly simplified, where the complex biological instruments and professional biological operations [14,15,42,43] have all disappeared in our method, such as the Physarum cultivation, environmental control, experimental design, biosafety and data analysis. Now, the related artificial intelligence algorithms are listed here for a comparison to solve the traffic planning problem of Mexican highways, including the ant colony optimization (ACO) [36], artificial neural network (ANN) [37,38], particle swarm optimization (PSO) [13,39,40], genetic algorithm (GA) [39] and deep learning (DL) approach [41]. The related results are shown in Figure 7a-d, where the number of network nodes is extended from 30, 60, 90 to 120.…”

“…A robust logistics network may diminish the loss of disruptions or the number of disruption nodes [33]. This kind of fast recovery performance is often called resilience [3,7,9,13,19,22]. Furthermore, Evangelos et al [34] made a literature review on the quantitative modeling of resilient 3PL supply chain network designs, and Maryam et al [35] surveyed the strategic alliance for resilience in the supply chain.…”

“…Due to the difficulty of solving the robustness problem of the logistics network, many researchers applied a lot of artificial intelligence algorithms to improve the network's robustness, such as the fuzzy set [11,12], ant colony optimization (ACO) [36], artificial neural network (ANN) [37,38], particle swarm optimization (PSO) [13,39,40], genetic algorithm (GA) [39] and deep learning (DL) approach [41]. In 2010, a novel Physarum method was used for the optimization of a traffic network [14].…”

“…Additionally, the robust optimization and sustainable scheduling of the logistics networks are challenging and costly. Now, many artificial intelligence methods are applied in this area, i.e., fuzzy AHP and fuzzy VIKOR methods [11,12] and particle swarm optimization (PSO) [13]. In the past ten years, a strange living thing has extended our minds to solve the network robustness problems.…”

Measuring the Oxygen Flow Rate and Purity in an Optimal Portable Oxygen Concentrator Performance with an Air Pressure Sensor

“…[4] Nanozeolites replaced molecular zeolites in oxygen concentrators to improve oxygen delivery e ciency. [5] This article examined green-resilient-responsive elements, including economic, environmental, and resilience aspects, to construct an oxygen concentrator supply chain network. [6] This work investigated a medical oxygen concentrator device using rapid pressure swing adsorption for continuous oxygen supply.…”