Purpose Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and ...
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Purpose Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0. Design/methodology/approach The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies - Lot for Lot, Silver-Meal heuristic and wagner-whitin algorithm - are reviewed and analyzed. The suggested machine learning (ML)-based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production. Findings When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes. Originality/value The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.
Effective inventory management is crucial for businesses to balance minimizing holding costs while optimizing ordering strategies. Monthly or sporadic orders over time may lead to high ordering or holding costs, respe...
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Effective inventory management is crucial for businesses to balance minimizing holding costs while optimizing ordering strategies. Monthly or sporadic orders over time may lead to high ordering or holding costs, respectively. In this study, we introduce two novel algorithms designed to optimize ordering replenishment quantities, minimizing total replenishment, and holding costs over a planning horizon for both partially loaded and fully loaded trucks. The novelty of the first algorithm is that it extends the classical wagner-whitin approach by incorporating various additional cost elements, stock retention considerations, and warehouse capacity constraints, making it more suitable for real-world problems. The second algorithm presented in this study is a variation of the first algorithm, with its contribution being that it incorporates the requirement of several suppliers to receive order quantities that regard only fully loaded trucks. These two algorithms are implemented in Python, creating the software tool called "Inventory Cost Minimizing tool" (ICM). This tool takes relevant data inputs and outputs optimal order timing and quantities, minimizing total costs. This research offers practical and novel solutions for businesses seeking to streamline their inventory management processes and reduce overall expenses.
Efforts to incorporate inventory management (IM) strategies in small and mediumsized enterprises (SMEs) are very limited due to the lack of cost-effective and easyto-use techniques. This research article suggests an e...
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Efforts to incorporate inventory management (IM) strategies in small and mediumsized enterprises (SMEs) are very limited due to the lack of cost-effective and easyto-use techniques. This research article suggests an effective and proven IM strategy that can improve the productivity and competitiveness of SMEs. The purpose of this research article is twofold: first, to develop an integrated IM strategy for SMEs to reduce inventory carrying costs;second, to design individual replenishment policies for each product. This article discusses an integrated approach considering rank order clustering (ROC) technique, a forward version of the wagner-whitin (W-W) lotsizing algorithm, and quantity discounts. First ROC is used to form clusters of different assemblies consisting of common components for aggregating the demand. The W-W algorithm is tested next over 1 year of time horizon followed by quantity discounts. Insights derived from a case study proved that the proposed integrated IM approach could save a substantial amount of total cost compared to the existing purchase policy. In addition, this approach can be a promising approach to determine appropriate replenishment quantities for each planning period. In addition, stock-out situations can be minimized. The novelty of this study is that it proposes a practical, simple-to-implement, and proven IM technique for increasing productivity and achieving sustainable development of SMEs, which are working in growing economies like India.
In Turkey, Pınar Et is one of the leading companies in meat sector since 1985. Within the scope of senior year project, this paper considers the optimization of lot sizes of auxiliary and packaging materials, purchase...
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MRP(Material Requirements Planning) is mainly applied in the stock control of dependence requirement *** requirement which can decrease the stock level *** paper designs a kind of MRP system that can concerns the prod...
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MRP(Material Requirements Planning) is mainly applied in the stock control of dependence requirement *** requirement which can decrease the stock level *** paper designs a kind of MRP system that can concerns the production with guarantee period by using wagner-whitin algorithm.
In most of the medium scale industries, demand is uncertain and difficult to forecast. Hence Ordering in right quantities at right time is always a crucial issue. In this paper; the authors present a model for determi...
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In most of the medium scale industries, demand is uncertain and difficult to forecast. Hence Ordering in right quantities at right time is always a crucial issue. In this paper; the authors present a model for determining the ordering policy which will minimize the total inventory cost. This paper takes into consideration various models such as lot by lot size, economic order quantity, periodic order quantity, least unit cost, least total cost, least period cost, wagner-whitin algorithm etc. Total annual inventory costs for various items are calculated by each method. The results obtained by applying each model for different items are summarized which shows that wagner-whitin algorithm gives optimum cost in each case.
We investigate the dynamic lot-size problem under stochastic and non-stationary demand over the planning horizon. The problem is tackled by using three popular heuristic methods from the fields of evolutionary computa...
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We investigate the dynamic lot-size problem under stochastic and non-stationary demand over the planning horizon. The problem is tackled by using three popular heuristic methods from the fields of evolutionary computation and swarm intelligence, namely particle swarm optimization, differential evolution and harmony search. To the best of the authors' knowledge, this is the first investigation of the specific problem with approaches of this type. The algorithms are properly manipulated to fit the requirements of the problem. Their performance, in terms of run-time and solution accuracy, is investigated on test cases previously used in relevant works. Specifically, the lot-size problem with normally distributed demand is considered for different planning horizons, varying from 12 up to 48 periods. The obtained results are analyzed, providing evidence on the efficiency of the employed approaches as promising alternatives to the established wagner-whitin algorithm, as well as hints on their proper configuration. (C) 2011 Elsevier Ltd. All rights reserved.
In this paper, we present an ant-based algorithm for solving unconstrained multi-level lot-sizing problems called ant system for multi-level lot-sizing algorithm (ASMLLS). We apply a hybrid approach where we use ant c...
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In this paper, we present an ant-based algorithm for solving unconstrained multi-level lot-sizing problems called ant system for multi-level lot-sizing algorithm (ASMLLS). We apply a hybrid approach where we use ant colony optimization in order to find a Mod lot-sizing sequence, i.e. a sequence of the different items in the product structure in which we apply a modified wagner-whitin algorithm for each item separately. Based on the setup costs each ant generates a sequence of items. Afterwards a simple single-stage lot-sizing rule is applied with modified setup costs. This modification of the setup costs depends on the position of the item in the lot-sizing sequence, on the items which have been lot-sized before, and on two further parameters, which are tried to be improved by a systematic search. For small-sized problems ASMLLS is among the best algorithms, but for most medium- and large-sized problems it outperforms all other approaches regarding solution quality as well as computational time. (c) 2005 Elsevier Ltd. All rights reserved.
The goal of this paper is to explore an effective approach for identifying chaotic types of demands and to develop a production control method for the corresponding chaotic demands. Chaos phenomena is a set of unpredi...
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The goal of this paper is to explore an effective approach for identifying chaotic types of demands and to develop a production control method for the corresponding chaotic demands. Chaos phenomena is a set of unpredictable behaviors of nonlinear deterministic models. Being distinct from probabilistic types of demands, chaotic demands have a significant impact on production planning due to its butterfly effect. The experiments conducted in the research show that the maximal Lyapunov exponent is very effective in classifying chaos and non-chaos demands. We develop a modified wagner-whitin algorithm to facilitate production planning for chaotic demands. The proposed approach is capable of making economical production plans in terms of cost for unpredictable, chaotic demands. The algorithm is tested under a variety of scenarios, such as chaotic demand types, initial conditions, set-up costs, holding costs, and entropy types. The conducted experiment indicates that this algorithm is robust to all of the observed situations, and the minimal total production cost of the proposed method approaches to the optimal one which is produced by the naive wagner-whitin algorithm for static demand environments.
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