Design of multi-warehouse inventory model for an optimal replenishment policy using a Rain Optimization Algorithm

Kumar, Sunil; Mahapatra, Rajendra Prasad

doi:10.1016/j.knosys.2021.107406

Cited by 17 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, Chinelloren et al [22] employed simulation techniques to optimize multi-tier supply chains; Jiang et al [23] established a cost model for ordering and supplying distributed material warehouses, employing an improved Genetic Algorithm-Simulated Annealing (IGA-SA) to address the optimization problem of distributed material warehouses; Hammler et al [24] assessed various optimization algorithms to determine the applicability of deep reinforcement learning in multi-level inventory optimization frameworks, leading to the development of fully dynamic reordering strategies. Kumar et al [25] considered a multi-warehouse model, utilizing the Rain Optimization Algorithm to design optimal replenishment strategies and evaluating the model using data from footwear inventory management. Pirhooshyaran et al [26] proposed a framework that utilizes deep neural networks to optimize inventory decisions in complex multi-tier supply chains.…”

Section: Supply Chain Structurementioning

confidence: 99%

Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach

Tan,

Gu,

et al. 2024

Mathematics

View full text Add to dashboard Cite

This study systematically investigates the pivotal role of inventory management within the framework of “cloud supply chain” operations, emphasizing the efficacy of leveraging machine learning methodologies for inventory allocation with the dual objectives of cost reduction and heightened customer satisfaction. Employing a rigorous data-driven approach, the research endeavors to address inventory allocation challenges inherent in the complex dynamics of a “cloud supply chain” through the implementation of a two-stage model. Initially, machine learning is harnessed for demand forecasting, subsequently refined through the empirical distribution of forecast errors, culminating in the optimization of inventory allocation across various service levels.The empirical evaluation draws upon data derived from a reputable home appliance logistics company in China, revealing that, under conditions of ample data, the application of data-driven methods for inventory allocation surpasses the performance of traditional methods across diverse supply chain structures. Specifically, there is an improvement in accuracy by approximately 13% in an independent structure and about 16% in a dependent structure. This study transcends the constraints associated with examining a singular node, adopting an innovative research perspective that intricately explores the interplay among multiple nodes while elucidating the nuanced considerations germane to supply chain structure. Furthermore, it underscores the methodological significance of relying on extensive, large-scale data. The investigation brings to light the substantial impact of supply chain structure on safety stock allocation. In the context of a market characterized by highly uncertain demand, the strategic adaptation of the supply chain structure emerges as a proactive measure to avert potential disruptions in the supply chain.

show abstract

Section: Supply Chain Structurementioning

confidence: 99%

Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach

Tan,

Gu,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…To do this, it must be determined whether the activities being carried out are necessary and whether the appropriate resources are used. [11].…”

Section: Do Not Removementioning

confidence: 99%

“…The motivation arises because there is a divided approach to food losses and waste in distribution companies [11]. Therefore, the main objective of this study is to design a model based on the Lean Warehousing methodology as a test in order to reduce shrinkage.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Lean Warehousing to reduce food waste of a Distribution Company

Orosco,

Ramos

2023

Proceedings of the 3rd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

View full text Add to dashboard Cite

According to recent research, the application of engineering approaches in distributors in the commercial sector needs to be improved. Likewise, there is evidence of a growing need for the analysis of processes in warehouses in order to reduce activities that do not generate value to increase profitability in companies. In the current study, a food distribution company was examined and a distribution system model focused on solving problems in the warehouse due to waste generated in each process was suggested. The main problem was identified as food losses in the storage area and a suitable solution was suggested to address it. For this, cross contamination must be reduced in the storage process and thus reduce the amount of products lost due to contamination of moisture products and reduce products lost due to improper location of products. Likewise, the amount of expired products in the picking process must be reduced. Finally, errors in the packing process must be reduced with standardized packing and stacking methods. Using the Lean Warehousing methodology, an improvement model was presented based on the integration of tools such as Slotting for Warehouse Control, Kanban, FEFO for perishables control, PokaYoke and Standard work for method error control. Currently, the company proposes a 6.65% reduction in losses in warehouse management and 3% is considered a limitation according to the literature.

show abstract

“…e results indicate the proper performance of the proposed model. Kumar and Mahapatra [27] presented a multiwarehouse inventory model for an optimal replenishment policy. ey solved their model by the rain optimization algorithm (ROA) considering different parameters such as total cost, total delivery time, and total investment on each item.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Mathematical and Decision-Making Model to Determine the Amount of Economic Order considering the Discount

Hazrati

Barzegarinegad²,

Siaby-Serajehlo

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Suppliers are one of the most important parts of the supply chain, whose performance indirectly has a significant impact on customer satisfaction. Because customer demands are different from organizations, organizations have to consider different criteria for selecting their suppliers. In recent years, many studies in this field have been conducted using various criteria and methods. The main purpose defined in this research is to develop a model for simultaneous item ordering systems in real business conditions. In this research, a model is developed by considering the two objectives of minimizing overall costs and maximizing the amount of products ordered from different suppliers based on their weight value. Weights are calculated based on different criteria using the fuzzy analytic hierarchy process method for each supplier in different periods. Then, due to the multiobjective nature of the model, the proposed model has been solved by using the epsilon constraint in GAMS and nondominated sorting genetic algorithm II in MATLAB software. Considering the simultaneous order of inventory of multiproduct with several suppliers in several periods of time in discrete space with discount is one of the contributions of this research. To validate the proposed model, the results of the exact solution are compared with the meta-heuristic solution. Comparison results and assessment metrics indicate that the results of the proposed solution approach with an error of less than 1% had good performance. The results show that the system cost increases, by increasing the amount of discount, because of the increase in the amount of demand. Therefore, with a 30% increase in the discount, the system costs will increase to 36,496 units. Also, with a 20% reduction, the cost reduction will be reduced to 14,170 units.

show abstract

Design of multi-warehouse inventory model for an optimal replenishment policy using a Rain Optimization Algorithm

Cited by 17 publications

References 20 publications

Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach

Supply Chain Inventory Management from the Perspective of “Cloud Supply Chain”—A Data Driven Approach

Implementation of Lean Warehousing to reduce food waste of a Distribution Company

A Hybrid Mathematical and Decision-Making Model to Determine the Amount of Economic Order considering the Discount

Contact Info

Product

Resources

About