SAGA Algorithm Research Articles

Enhancing supply chain management in the physical internet: a hybrid SAGA approach

This paper introduces an advanced inventory replenishment optimization approach tailored for the Physical Internet (PI), addressing the dynamic and complex nature of this environment. We propose a hybrid Simulated Annealing–Genetic Algorithm (SA–GA), engineered to optimize the balance between exploration and exploitation, ensuring adaptability and efficiency in a variety of PI contexts. The study also presents an enriched mathematical model integrating dynamic demand, and multi-objective optimization. The SA–GA algorithm emerges as a novel contribution, characterized by its computational efficiency and adaptability, marking an advancement in PI inventory management. The incorporation of real-time data analytics in our dynamic inventory replenishment strategy enhances adaptability and responsiveness, while the robust mathematical model offers a versatile tool for both theoretical analysis and practical application. Collectively, these innovations help bridge existing gaps in PI inventory management and serve as a reference for other similar studies.

Can Commercial Low-Cost Drones and Open-Source GIS Technologies Be Suitable for Semi-Automatic Weed Mapping for Smart Farming? A Case Study in NE Italy

Weed management is a crucial issue in agriculture, resulting in environmental in-field and off-field impacts. Within Agriculture 4.0, adoption of UASs combined with spatially explicit approaches may drastically reduce doses of herbicides, increasing sustainability in weed management. However, Agriculture 4.0 technologies are barely adopted in small-medium size farms. Recently, small and low-cost UASs, together with open-source software packages, may represent a low-cost spatially explicit system to map weed distribution in crop fields. The general aim is to map weed distribution by a low-cost UASs and a replicable workflow, completely based on open GIS software and algorithms: OpenDroneMap, QGIS, SAGA and OpenCV classification algorithms. Specific objectives are: (i) testing a low-cost UAS for weed mapping; (ii) assessing open-source packages for semi-automatic weed classification; (iii) performing a sustainable management scenario by prescription maps. Results showed high performances along the whole process: in orthomosaic generation at very high spatial resolution (0.01 m/pixel), in testing weed detection (Matthews Correlation Coefficient: 0.67–0.74), and in the production of prescription maps, reducing herbicide treatment to only 3.47% of the entire field. This study reveals the feasibility of low-cost UASs combined with open-source software, enabling a spatially explicit approach for weed management in small-medium size farmlands.

Open-Source Software Application for Hydrogeological Delineation of Potential Groundwater Recharge Zones in the Singida Semi-Arid, Fractured Aquifer, Central Tanzania

This study attempted to delineate and map potential groundwater recharge zones of the Singida, semi-arid, fractured crystalline basement aquifer using open source remote sensing and GIS software. Various thematic maps such as lithology/hydrogeology, soil, land-cover/use, slope, lineament density, drainage density and rainfall distribution were integrated in QGIS software. Vector input layers were rasterized and resampled using QGIS wrap projection function to make sure that the grid cells are of the same size. Reclassification using SAGA and GRASS reclass algorithms in QGIS was carried out to realign the factor classes in a consistent scale, and reclassification to a scale of 1 to 5 was carried out to harmonize the results. The study identified a number of potential areas for groundwater recharge, groundwater exploration, groundwater development and potential areas for artificial groundwater recharge. Potential groundwater recharge zones for the Singida semi-arid fractured aquifer are restricted to areas with high lineament density, cultivated areas, grassland and flat to gentle slopes. The potential of groundwater recharge is also observed in areas with low drainage density. The delineated zones provide a good understanding of the potential recharge zones, which are a starting point for recharge zone protection. This blended approach can be utilized for carrying out suitability analysis using the weighted overlay analysis approach. Areas designated good and very good are recommended for artificial recharging structures as an alternative technique for enhancing groundwater recharge through rainwater harvesting. This will help to augment groundwater storage in this semi-arid environment.

A hybrid framework based on genetic algorithm and simulated annealing for RNA structure prediction with pseudoknots

RNA structure prediction with pseudoknots is an NP-complete problem, in which an optimal RNA structure with minimum energy is to be computed. In past decades, several methods have been developed to predict RNA structure with pseudoknots. Among them, metaheuristic approaches have proven to be beneficial for predicting long RNA structure in a very short time. In this paper, we have used two metaheuristic algorithms; Genetic Algorithm (GA) and Simulated Annealing (SA) for predicting RNA secondary structure with pseudoknots. We have also applied a combination of these two algorithms as GA-SA where GA is used for a global search and SA is used for a local search, and conversely SA-GA, where SA is used for a global search and GA is used for a local search. Four different energy models have been applied to calculate the energy of RNA structure. Five datasets, constructed from the RNA STRAND and Pseudobase++ database, have been used in the algorithms. The performances of the algorithms have been compared with several existing metaheuristic algorithms. Here we have obtained that the combination of GA and SA (GA-SA) gives better results than GA, SA and SA-GA algorithms and all other four state-of-art algorithms on all datasets.

Multi-mode resource-constrained project scheduling problem with material ordering under bonus–penalty policies

This study emphasizes that project scheduling and material ordering (time and quantity of an order) must be considered simultaneously to minimize the total cost, as setting the material ordering decisions after the project scheduling phase leads to non-optimal solutions. Hence, this paper mathematically formulates the model for the multi-mode resource-constrained project scheduling with material ordering (MRCPSMO) problem. In order to be more realistic, bonus and penalty policies are included for the project. The objective function of the model consists of four elements: the material holding cost, the material ordering cost, the bonus paid by the client and the cost of delay in the project completion. Since MRCPSMO is NP-hard, the paper proposes three hybrid meta-heuristic algorithms called PSO-GA, GA-GA and SA-GA to obtain near-optimal solutions. In addition, the design of experiments and Taguchi method is used to tune the algorithms’ parameters. The proposed algorithms consist of two components: an outside search, in which the algorithm searches for the best schedule and mode assignment, and the inside search, which determines the time and quantity of orders of the nonrenewable resources. First, a comparison is made for each individual component with the exact or best solutions available in the literature. Then, a set of standard PROGEN test problems is solved by the proposed hybrid algorithms under fixed CPU time. The results reveal that the PSO-GA algorithm outperforms both GA-GA and SA-GA algorithms and provides good solutions in a reasonable time.

RETRACTED ARTICLE: OSGA: genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises

The problem of open-shop scheduling includes a set of activities which must be performed on a limited set of machines. The goal of scheduling in open-shop is the presentation of a scheduled program for performance of the whole operation, so that the ending performance time of all job operations will be minimised. The open-shop scheduling problem can be solved in polynomial time when all nonzero processing times are equal, becoming equivalent to edge coloring that has the jobs and workstations as its vertices and that has an edge for every job-workstation pair with a nonzero processing time. For three or more workstations, or three or more jobs, with varying processing times, open-shop scheduling is NP-hard. Different algorithms have been presented for open-shop scheduling so far. However, most of these algorithms have not considered the machine maintenance problem. Whilst in production level, each machine needs maintenance, and this directly influences the assurance reliability of the system. In this paper, a new genetic-based algorithm to solve the open-shop scheduling problem, namely OSGA, is developed. OSGA considers machine maintenance. To confirm the performance of OSGA, it is compared with DGA, SAGA and TSGA algorithms. It is observed that OSGA performs quite well in terms of solution quality and efficiency in small and medium enterprises (SMEs). The results support the efficiency of the proposed method for solving the open-shop scheduling problem, particularly considering machine maintenance especially in SMEs’.

Risk Reserve Constrained Economic Dispatch of Wind Power Penetrated Power System Based on UPSMC and SAGA Algorithms

A short-term Economic Dispatch (ED) model with risk constraint for wind penetrated power systems was built to face the challenge of scheduling spinning reserves brought from wind energies. The proposed model utilizes the probability of spinning reserve shortage as measurement of system risk and evaluates the risk by an Unequal Probabilities Sampling based Monte Carlo (UPSMC) method. A Genetic Algorithm (GA) improved with Simulated Annealing (SA) strategy is presented as SAGA to solve the problem. By comparing simulation results under different wind penetrations and risk constraints, coal consumptions will not always decrease with wind penetration and risk constraint but for most times. In addition, unit risk benefit has a trend to increase with wind penetration and decrease with risk constraint while contribution of unit wind generation has the contrary character. Simulation results also show that the proposed sampling method could improve the sampling efficiency and the SAGA method had better performance than traditional GA.

An Improved Simulated Annealing Algorithm Based on Genetic Algorithm

An effective hybrid Simulated Annealing Algorithm based on Genetic Algorithm is proposed to apply to reservoir operation. Compared with other optimal methods, it is proved that SA-GA algorithm is a quite effective optimization method to solve reservoir operation problem. The simulated annealing algorithm is introduced to Genetic Algorithm, which is feasibility and validity. As a result of stronger ability of global search and better convergence property of SA-GA, and compared with other algorithms, the approximate global optimal solution would be obtained in little time. The operation speed is more quickness and the results are more stabilization by SA-GA, than Genetic Algorithm and the traditional Dynamic Programming and POA.

Feature subset selection in large dimensionality domains

Searching for an optimal feature subset from a high dimensional feature space is known to be an NP-complete problem. We present a hybrid algorithm, SAGA, for this task. SAGA combines the ability to avoid being trapped in a local minimum of simulated annealing with the very high rate of convergence of the crossover operator of genetic algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks. We compare the performance over time of SAGA and well-known algorithms on synthetic and real datasets. The results show that SAGA outperforms existing algorithms.