Butterfly Algorithm for Sustainable Lot Size Optimization

Abstract

The challenges faced by classical supply chain management affect efficiency with regard to business. Classical supply chain management is associated with high risks due to a lack of accountability and transparency. The use of optimization algorithms is considered decision-making support to improve the operations and processes in green manufacturing. This paper suggests a solution to the green lot size optimization problem using bio-inspired algorithms, specifically, the butterfly algorithm. For this, our methodology consisted of first collecting the real data, then the data were expressed with a simple function with several constraints to optimize the total costs while reducing the CO2 emission, serving as input for the butterfly algorithm BA model. The BA model was then used to find the optimal lot size that balances cost-effectiveness and sustainability. Through extensive experiments, we compared the results of BA with those of other bio-inspired algorithms, showing that BA consistently outperformed the alternatives. The contribution of this work is to provide an efficient solution to the sustainable lot-size optimization problem, thereby reducing the environmental impact and optimizing the supply chain well. Conclusions: BA has shown that it can achieve the best results compared to other existing optimization methods. It is also a valuable chainsaw tool.

WOA: Wombat Optimization Algorithm for Solving Supply Chain Optimization Problems

“…FA is developed inspired by the optical communication and information exchange between fireflies. The butterfly optimization algorithm (BA) is an advanced metaheuristic for optimization created by Arora that draws inspiration from biology, which is centered on the behavior of butterflies when they are seeking food [28]. ABC is designed based on modeling the hierarchical cooperation and activities of colony bees to obtain food resources.…”

Two Lot-Sizing Algorithms for Minimizing Inventory Cost and Their Software Implementation

“…In recent years, the concept of backorders and "goodwill loss costs" [18] has been introduced to the models by focusing on scenarios involving partial backorders and re-workable products, offering an outer approximation method for determining optimal lot sizes. Additionally, models for "yield uncertainty" [19], stochastic [20,21], uncertain [22], and non-stationary demand with heuristic solving methods [17] have been presented. The problem exists and has been tried to be solved even for the production phase [23].…”

Optimizing Manufacturing Cycles to Improve Production: Application in the Traditional Shipyard Industry

“…Several studies have been conducted to identify and address the challenges associated with enhancing the manufacturing cycle efficiency in the TSI. For instance, [21,22] conducted studies that shed light on various challenges faced by the industry, including prolonged lead times, excessive material wastage, inefficient production layouts, and suboptimal workforce management. These findings underline the significance of implementing appropriate strategies and approaches to overcome these challenges and enhance the performance of the production processes.…”

“…Meanwhile, research shows how the application of lean principles can result in significant increases in lead times and productivity in shipyards. In this case, emphasizing value mapping and eliminating waste are important in achieving increased efficiency [22][23][24][25][26][27][28].…”