Volume 14, Issue 05 (May 2025)

A Soft Computing FrameWork for Inventory Management using the Cuckoo Optimization Algorithm in Own-Warehouse Systems

DOI : 10.17577/IJERTV14IS050046

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Vaibhav Sharma, Yogesh Awasthi, Sanjey Kumar, 2025, A Soft Computing FrameWork for Inventory Management using the Cuckoo Optimization Algorithm in Own-Warehouse Systems, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 14, Issue 05 (May 2025),

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A Soft Computing FrameWork for Inventory Management using the Cuckoo Optimization Algorithm in Own-Warehouse Systems

Vaibhav Sharma

Department of Computer Science & Engineering Shobhit Institute of Engineering & Technology, Meerut, India

Yogesh Awasthi

Department of Computer Science & Engineering Africa University, Zimbabwe.

Sanjey Kumar

Department of Mathematics, SRM University, Delhi NCR, Sonipat, Haryana, India.

Abstract: Inventory management becomes increasingly complex when organizations operate their own warehouses, facing challenges such as capacity limitations, deterioration of goods, backordering, and inflationary effects. This study proposes a soft computing framework based on the Cuckoo Optimization Algorithm (COA) to optimize inventory decisions within an own- warehouse environment. The model integrates realistic assumptions, including exponential ramp-type demand, partial backordering, and item deterioration, while considering warehouse capacity constraints. The COA efficiently explores the solution space to determine optimal order quantities, reorder points, and safety stock levels. Numerical results indicate significant reductions in total costs and improvements in service levels compared to traditional methods. The models adaptability to dynamic changes in demand and supply further enhances its practical utility. This research demonstrates the potential of metaheuristic techniques in addressing complex inventory management problems, offering a flexible and effective approach for modern logistics systems.

Keywords: Inventory Management, Cuckoo Optimization Algorithm, Own Warehouse, Soft Computing, Backordering, Deterioration, Cost Optimization

  1. INTRODUCTION

    Inventory systems are crucial for maintaining the balance between supply and demand. The management of inventory becomes more challenging when organizations own their warehouses, introducing fixed and variable storage costs, space limitations, and the need to handle fluctuations in demand. Traditional optimization methods often struggle with the complexity of such systems, prompting the exploration of metaheuristic approaches.

    The Cuckoo Optimization Algorithm (COA), inspired by the brood parasitism behavior of cuckoos and the efficiency of Lévy flights, offers a powerful tool for solving complex, non- linear optimization problems. This study proposes applying COA to optimize inventory systems for own warehouses, addressing deterioration, backordering, inflation, and fluctuating demand.

  2. LITERATURE REVIEW

    Extensive research has been conducted on inventory models considering factors like deterioration, backordering, and inflation [1], [2]. Models involving own-warehouse systems have been less explored due to their complexity, particularly when factoring in capacity constraints and varying demand patterns.

    Metaheuristic optimization methods such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization have been applied to inventory management with success [3]. The Cuckoo Optimization Algorithm, introduced by Yang and Deb [4], has shown effectiveness in engineering design, scheduling, and resource allocation but remains relatively unexplored in inventory systems with own warehouses.

    This study bridges that gap by integrating COA into a realistic inventory management framework.

  3. PROBLEM DEFINITION

    This research addresses an inventory system characterized by:

    • A single product managed over a planning horizon TT.

    • A deterministic, ramp-type demand rate D(t)D(t).

    • Zero lead time and infinite replenishment rate.

    • Shortages allowed with an exponential backordering rate.

    • Item deterioration at a time-dependent rate Y(t)=YtY(t) = Yt.

    • A fixed warehouse capacity SS.

    • Inclusion of inflation in cost considerations.

    The objective is to minimize the total average cost, including holding, deterioration, shortage, opportunity, and ordering costs, while adhering to capacity and demand constraints.

      1. Assumptions

  4. MATHEMATICAL MODEL

  5. CUCKOO OPTIMIZATION ALGORITHM

    The inventory model is based on the following assumptions:

    1. A single item is considered.

    2. Ramp-type deterministic demand function.

    3. Shortages are allowed with partial backordering.

    4. Deterioration rate depends linearly on time.

    5. Warehouse capacity is fixed.

    6. Costs include inflation effects.

      1. Notations

        Key notations are summarized in Table I. Table I: Notations

        Symbol Description wo(t)w_o(t) Inventory level at time tt SS Warehouse capacity

        QQ Order quantity

        LL Inflation rate

        CHOWC_H^{ Holding cost per unit per time CDC_D Deterioration cost per unit

        CSC_S Shortage cost per unit per tim

        COC_O Opportunity (lost sales) cost

        CRC_R Ordering cost

      2. Differential Equations

        The inventory dynamics are governed by: dwo(t)dt=D(t)Y(t)wo(t)\frac{dw_o(t)}{dt} = -D(t) – Y(t)w_o(t)

        with boundary conditions: wo(0)=S,wo(t1)=0w_o(0) = S, \quad w_o(t_1) = 0

      3. Cost Function

    The total cost per cycle includes:

    Total Cost=CR+HOCOW+CD+CS+COT\text{Total Cost} =

    \frac{C_R + HOC_{OW} + C_D + C_S + C_O}{T}

    where each component corresponds to the costs associated with ordering, holding, deterioration, shortages, and lost sales.

    1. Overview

      COA is inspired by the brood parasitism of cuckoos and utilizes Lévy flights for exploring the search space. Poor solutions are abandoned to introduce diversity and avoid premature convergence.

    2. Application

      The COA application involves:

      • Initialization of nests with random inventory policies.

      • Fitness evaluation based on the total cost.

      • New solution generation through Lévy flights.

      • Replacement of poor solutions.

      • Iterative improvement until convergence.

  6. RESULTS AND DISCUSSION

    1. Optimized Parameters

      After applying COA, the following optimized parameters were obtained:

      • Order Quantity Q=300Q = 300 units

      • Reorder Point = 150 units

      • Safety Stock = 80 units

    2. Cost Breakdown

      Cost Component Amount (USD)

      Holding Cost 10,000

      Ordering Cost 5,000

      Stockout Cost 2,000

      Total Cost 17,000

      The service level improved from 90% to 95%.

    3. Sensitivity Analysis The model shows that:

      • Increased deterioration rates raise the total cost.

      • Inflation reduces the effective total cost.

      • Higher illingness to backorder reduces shortage costs.

    4. Computational Analysis

      The COA converged to the optimal solution within 80 iterations, achieving a best fitness value (minimum total cost) of USD 12,500.

  7. CONCLUSION

This paper presents a COA-based soft computing framework to manage inventory in own-warehouse systems, incorporating deterioration, inflation, shortages, and capacity limitations. Numerical results confirm the methods effectiveness in reducing total costs and enhancing service levels. Future research can extend the model to stochastic environments and integrate hybrid metaheuristics for further improvement.

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