A Mathematical Model for Integrated Green Healthcare Supply Network Design

2021 ◽  
Vol 8 (1) ◽  
pp. 58-86
Author(s):  
Meisam Nasrollahi ◽  
Mehdi Safaei ◽  
Nooshin Mahmoodi
Keyword(s):  
Mathematical Model ◽  
Performance Evaluation ◽  
Network Design ◽  
Pareto Front ◽  
Performance Metrics ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Solution Algorithm ◽  
Supply Network ◽  
And Performance

This research presents a novel integrated mathematical programming model for green healthcare supply network design. A multi-graph integrated supply network was designed to meet real-world situations. Minimizing total cost and minimizing delivery time are considered as the objective functions. Since the considered problem is np-hard and the presented mathematical model is very complex and highly constrained, an innovative non-dominated ranked genetic algorithm (NRGA) called M-NRGA is developed to solve the real word problems. Three different selection procedures were implemented to improve the quality and diversity of solutions in the Pareto-front resulted from M-NRGA. Several numerical examples and a case study are solved to validate the model and performance evaluation of the solution algorithm. Four different performance metrics are implemented for performance evaluation of the solution algorithm. The quality of resulted solutions, the diversity of the solutions in the Pareto front are calculated for evaluation. The results are compared with two other meta-heuristic algorithms.

Cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management

2021 ◽  
pp. 0734242X2110039
Author(s):  
Elham Shadkam
Keyword(s):  
Waste Management ◽  
Network Design ◽  
Optimization Algorithm ◽  
Reverse Logistics ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Fixed Costs ◽  
Logistics System ◽  
Cuckoo Optimization Algorithm ◽  
Proposed Model

Today, reverse logistics (RL) is one of the main activities of supply chain management that covers all physical activities associated with return products (such as collection, recovery, recycling and destruction). In this regard, the designing and proper implementation of RL, in addition to increasing the level of customer satisfaction, reduces inventory and transportation costs. In this paper, in order to minimize the costs associated with fixed costs, material flow costs, and the costs of building potential centres, a complex integer linear programming model for an integrated direct logistics and RL network design is presented. Due to the outbreak of the ongoing global coronavirus pandemic (COVID-19) at the beginning of 2020 and the consequent increase in medical waste, the need for an inverse logistics system to manage waste is strongly felt. Also, due to the worldwide vaccination in the near future, this waste will increase even more and careful management must be done in this regard. For this purpose, the proposed RL model in the field of COVID-19 waste management and especially vaccine waste has been designed. The network consists of three parts – factory, consumers’ and recycling centres – each of which has different sub-parts. Finally, the proposed model is solved using the cuckoo optimization algorithm, which is one of the newest and most powerful meta-heuristic algorithms, and the computational results are presented along with its sensitivity analysis.

scholarly journals Efficient Traffic Engineering in an NFV Enabled IoT System

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3198 ◽  
Author(s):  
Thi-Thuy-Lien Nguyen ◽  
Tuan-Minh Pham
Keyword(s):  
Traffic Engineering ◽  
Performance Metrics ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Smart Cities ◽  
Network Function Virtualization ◽  
Smart Manufacturing ◽  
Weight System ◽  
Link Weight ◽  
Multiple Paths

The Internet of Things (IoT) is increasingly creating new market possibilities in several industries’ sectors such as smart homes, smart manufacturing, and smart cities, to link the digital and physical worlds. A key challenge in an IoT system is to ensure network performance and cost-efficiency when a plethora of data is generated and proliferated. The adoption of Network Function Virtualization (NFV) technologies within an IoT environment enables a new approach of providing services in a more agile and cost-efficient way. We address the problem of traffic engineering with multiple paths for an NFV enabled IoT system (vIoT), taking into account the fluctuation of traffic volume in various time periods. We first formulate the problem as a mixed linear integer programming model for finding the optimal solution of link-weight configuration and traffic engineering. We then develop heuristic algorithms for a vIoT system with a large number of devices. Our solution enables a controller to adjust a link weight system and update a flow table at an NFV switch for directing IoT traffic through a service function chain in a vIoT system. The evaluation results under both synthetic and real-world datasets of network traffic and topologies show that our approach to traffic engineering with multiple paths remarkably improves several performance metrics for a vIoT system.

Optimization and Performance Evaluation of an Integrated Production-Distribution Plan in Supply Chains

2009 ◽  
Author(s):  
Behnam Fahimnia ◽  
Lee Luong ◽  
Romeo Marian
Keyword(s):  
Supply Chain ◽  
Performance Evaluation ◽  
End Users ◽  
Mixed Integer ◽  
Multiple Time ◽  
Supply Network ◽  
Distribution Centers ◽  
Production Distribution ◽  
And Performance ◽  
The Right

Supply Chain Management is the process of integrating and utilizing suppliers, manufacturers, distribution centers, and retailers; so that products are produced and delivered to the end-users at the right quantities and at the right time, while minimizing costs and satisfying customer requirements. From this definition, a supply chain includes three sub-systems: procurement, production, and distribution. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production-distribution plan. A production-distribution plan excludes the procurement activities and integrates the decisions in production, transport and warehousing as well as inventory management. Hence, one key issue in the performance evaluation of a supply network is the modeling and optimization of production-distribution plan considering its actual complexity. This paper develops a mixed integer formulation for a two-echelon supply network that expands the previously reported production-distribution models through the integration of Aggregate Production Plan and Distribution Plan as well as considering the real-world variables and constraints. A Genetic Algorithm is designed for the optimization of the developed model. The methodology will be then implemented to solve a real-life problem incorporating multiple time periods, multiple products, multiple manufacturing plants, multiple warehouses and multiple end-users. To demonstrate the capability of the approach, the validation and performance evaluation of this model will be finally studied for the presented case study.

scholarly journals Multi-Period Multi-Product Supply Chain Network Design in the Competitive Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jian Wang ◽  
Xueyan Wang ◽  
Mingzhu Yu
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Price Competition ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Mixed Integer ◽  
Competitive Environment ◽  
Market Area ◽  
Supply Chain Network Design ◽  
Supply Chain Network

This paper studies a supply chain network design model with price competition. The supply chain provides multiple products for a market area in multiple periods. The model considers the location of manufacturers and retailers and assumes a probabilistic customer behavior based on an attraction function depending on both the location and the quality of the retailers. We aim to design the supply chain under the capacity constraint and maximize the supply chain profit in the competitive environment. The problem is formulated as a mixed integer nonlinear programming model. To solve the problem, we propose two heuristic algorithms—Simulated Annealing Search (SA) and Particle Swarm Optimization (PSO)—and numerically demonstrate the effectiveness of the proposed algorithms. Through the sensitivity analysis, we give some management insights.

Mathematical Model and Performance Evaluation of AMI Applied to Mobile Environment

2016 ◽  
pp. 127-142 ◽  
Author(s):  
Shunsuke Matsuzawa ◽  
Satoru Harada ◽  
Kazuya Monden ◽  
Yukihiro Takatani ◽  
Yutaka Takahashi
Keyword(s):  
Mathematical Model ◽  
Performance Evaluation ◽  
Mobile Environment ◽  
And Performance
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