Multi-objective Joint Optimization Research Articles

Performance Optimization of a Multi-Source, Multi-Sensor Beamforming Wireless Powered Communication Network With Backscatter

This paper formulates and solves a multi-objective joint optimization problem where both the sum-throughput and fairness of a radio frequency (RF) energy harvesting wireless powered communication network (WPCN) are maximized using multiple beamforming hybrid access points (H-APs) and backscatter communication-enabled combination sensors. The paper proposes the multi-source, multi-sensor blind adaptive beamforming with combination sensors (MS2-BABF/combo) protocol. The protocol is analyzed to determine its performance with metrics, including WPCN sum-throughput, fairness in the achievable rates by sensors, sum-throughput and fairness tradeoff, and sensor dropout rate. Numerical results show that the MS2-BABF/combo protocol delivered up to approximately 296% increase in sum-throughput compared to the reference time-switching (TS) RF energy harvesting protocol in a dynamic environment, achieved a dropout rate of 0% instead of 28.5% by the reference TS protocol at 10mW H-AP transmission power, and increased Jain’s fairness index from J = 0.76 to up to 0.90 and 0.80 with and without sensors operating in backscatter mode, respectively. The findings are significant for future widespread adoption of WPCN systems by increasing its performance thus the versatility of applicable scenarios in the real world.

Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem

In order to solve the joint optimization of production scheduling and maintenance planning problem in the flexible job-shop, a multiobjective joint optimization model considering the maximum completion time and maintenance costs per unit time is established based on the concept of flexible job-shop and preventive maintenance. A weighted sum method is adopted to eliminate the index dimension. In addition, a double-coded genetic algorithm is designed according to the problem characteristics. The best result under the circumstances of joint decision-making is obtained through multiple simulation experiments, which proves the validity of the algorithm. We can prove the superiority of joint optimization model by comparing the result of joint decision-making project with the result of independent decision-making project under fixed preventive maintenance period. This study will enrich and expand the theoretical framework and analytical methods of this problem; it provides a scientific decision analysis method for enterprise to make production plan and maintenance plan.

A joint optimization strategy for scale-based product family positioning

AbstractWith the development of modern technologies and global manufacturing, it becomes more difficult for companies to distinguish themselves from their competitors. In order to keep their competitive advantages, companies must properly position their product families by offering a right product portfolio to each target market. To evaluate competitive advantages for a scale-based product family, this paper takes product family competitive advantage (PFCA) as a measure metric which is consisted of customer choice probability, sales, and profit. Meanwhile, to keep lower manufacturing costs, a commonality index of scale-based product family is proposed based on product design technology parameters in a product family. A multi-objective joint optimization model that balances the competitive advantages and the commonality is proposed. Based on a case study of motor product family positioning, Pareto frontier solutions are generated by genetic algorithm, and the results show that the joint optimization model ...