During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, espe...
详细信息
During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially geneticalgorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed geneticalgorithm-robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust geneticalgorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust geneticalgorithm indicate significant cost improvements compared to the conventional geneticalgorithm methods and a selective number of other heuristic and meta-heuristic techniques.
The burgeoning technology of optical intelligent reflecting surfaces (OIRSs) offers significant potential within the realm of visible light communication (VLC) systems. This is primarily attributed to the capability o...
详细信息
The burgeoning technology of optical intelligent reflecting surfaces (OIRSs) offers significant potential within the realm of visible light communication (VLC) systems. This is primarily attributed to the capability of OIRS to exploit reflected propagation paths, which in turn mitigates the inherent signal blockage challenges encountered in VLC. In this article, a nonorthogonal multiple access (NOMA)-based multiple-input-single-output (MISO) VLC system with the aid of OIRS is investigated, in which multiple light-emitting diodes (LEDs) serve multiple users equipped with a photodetector (PD) simultaneously. To this end, an analysis of the VLC channel gains is undertaken, encompassing the evaluation of both the Line of Sight (LoS) and the OIRS-reflected paths. Subsequently, the problem is formulated with the objective of jointly optimizing the active beamforming at LEDs and the passive beamforming at the OIRS to maximize the achievable sum data rate, while considering the constraints of OIRS and the successive interference cancelation (SIC) process. However, the formulated problem is nonconvex. To address this challenge, a block coordinate descent (BCD) algorithm is introduced to decompose the original joint beamforming problem into two subproblems. Specifically, relaxed iterative algorithms based on semi-positive definite relaxation and Taylor expansion are employed to solve these subproblems. Additionally, a permutation-based genetic algorithm is proposed to tackle the decoding order problem. Meanwhile, the simulation results illustrate the improvement in the sum data rate achieved by the proposed algorithm, providing valuable insights for future research on OIRS-aided VLC systems.
This comprehensive study develops advantageous optimization methods to solve a nascent problem, namely multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling. MTSSDRC is a complex problem t...
详细信息
This comprehensive study develops advantageous optimization methods to solve a nascent problem, namely multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling. MTSSDRC is a complex problem that deals with machine assignment, job sequencing, operator allocation, and task sequencing. Setup and unloading must be scheduled to operators, and they are allowed to leave machines while processing jobs. Earlier research on MTSSDRC developed a permutation-based genetic algorithm (PGA) with a specific decoding scheme, namely DSE, to solve the problem. Many previous studies succeed in solving scheduling problems by modifying well-known metaheuristic techniques. Therefore, we are inspired by this to explore further modifications to particular metaheuristics. The first contribution of the present study lies in the development of new decoding schemes that can perform better than the existing option. Five new decoding schemes are considered. Two of those schemes, namely DS2 and DS4, perform significantly better than DSE, reaching 6% relative deviation. DS4 is superior in terms of solution quality, but DS2 can run eight times faster. Another contribution is the development of six modified metaheuristics that are implemented for the MTSSDRC problem: tabu search, simulated annealing, particle swarm optimization, bees algorithm (BA), artificial bee colony, and grey wolf optimization. The performance of these metaheuristics is compared with that of the PGA. The results show that the PGA and BA are consistently superior for medium- and large-sized problems. The BA is more promising in terms of solution quality, but the PGA is faster.
暂无评论