On single item time weighted mixture inventory models with independent stochastic lead times

Srivastav, Achin; Agrawal, Sunil

doi:10.1504/ijsom.2015.070885

Cited by 4 publications

(4 citation statements)

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“…In the past, Montgomery et al 24) , Kim & Park 25) ,Padmanabhan & Vrat 26) , Kumar et al 27) etc., developed mixture inventory models ignoring considering order crossover. Few studies that included crossover of orders in mixture inventory systems are Srivastav and Agrawal 23,28) .…”

Section: Literature Reviewmentioning

confidence: 99%

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Development of Regression Equations for Stochastic Inventory System

Srivastav,

Kumar,

Singh

et al. 2023

Evergreen

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This paper addresses the intractability in stochastic inventory systems due to frequent crossover of shipments. The present scenario due to volatile markets, presence of manifold ways to place orders globally, vacillating requirements of customers, climate disruptions, surge in demand of military equipments and medical devices lead to numerous orders are getting placed at small interval of times, due to which shipments are arrived in different order in which they were booked. The fast-moving items are highly susceptible to order crossover. In this paper, based on the ratio of ordering cost to inventory carrying charge, fast moving items are categorized as fast movers not ordered enough and fast movers frequently ordered. The datasets are used for developing regression equations for optimal total inventory cost, order quantity and safety stock factor from the results of factorial experiments. The first group of regression equations are handy for decision systems lead to essentials items and second one is applicable in decision systems for discretionary products. The regression equations will help the practitioners to compute the right optimal quantity, estimate the correct inventory cost and compute safety stock factor, that will help their industry sustainability in the global world and provide a competitive edge over their competitors.

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Section: Literature Reviewmentioning

confidence: 99%

“…Total approximate cost equation for part of backorders and remaining lost sales is formulated by modifying the cost equation of Montgomery et al 24) and Srivastav & Agrawal 28) for considering order crossover as below.…”

Section: Cost Expression For Determining Inventory Costmentioning

confidence: 99%

Development of Regression Equations for Stochastic Inventory System

Srivastav,

Kumar,

Singh

et al. 2023

Evergreen

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“…Mixture inventory systems experience a fraction of shortage demand as backorder and the remaining fraction as lost sales. Motivated by the works of Montgomery et al (1973), Park (1982), and Srivastav and Agrawal (2015b) on single objective (cost minimization) mixture inventory models, a multi-objective inventory optimization model is formulated. Another motivation stems from the works of Agrell (1995), Tsou (2008), and Moslemi and Zandieh (2011), who formulated the backorder inventory model by considering three objectives.…”

Section: Multi-objective Mixture Inventory Modelmentioning

confidence: 99%

“…In inventory literature, these are referred to as mixture inventory models. Prominent works on single-objective mixture inventory models include: Leung (2008), Montgomery et al (1973), Park (1982), Pentico and Drake (2009), Rosenberg (1979), San José et al (2005) and Srivastav and Agrawal (2015b). It can be observed that none of the studies has developed a multi-objective mixture inventory model that has been solved using evolutionary techniques.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of a mixture inventory system using a MOPSO–TOPSIS hybrid approach

Srivastav

Agrawal

2015

Transactions of the Institute of Measurement and Control

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This paper studies a multi-objective mixture inventory problem for a pharmaceutical distributor. The work starts with a discussion of a mixture inventory model and three objectives, namely the minimization of: 1) ordering and holding costs, 2) number of units that stockout and 3) frequency of stockout occasions. Multi-objective particle swarm optimization (MOPSO) is used to determine the non-dominated solutions and generate Pareto curves for the inventory system. Two variants of MOPSO are proposed, based on the selection of inertia weight. The performance of the proposed MOPSO algorithms is evaluated in comparison with two robust algorithms like non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective cuckoo search (MOCS). The metrics that are used for the performance measurement of the algorithms are error ratio, spacing and maximum spread. Furthermore, the technique of order preference by similarity to ideal solution (TOPSIS) is used to rank the non-dominated solutions and determine the best compromise solution among them. A factorial analysis develops the linear regression expressions of optimal cost, service level measures, lot size and safety stock factor for practitioners. Lastly, the results of the regression equations are compared using a MOPSO–TOPSIS approach and the validity of the developed equations are checked.

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Multi-objective optimization of mixture inventory system experiencing order crossover

Srivastav

Agrawal

2018

Ann Oper Res

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On single item time weighted mixture inventory models with independent stochastic lead times

Cited by 4 publications

References 0 publications

Development of Regression Equations for Stochastic Inventory System

Development of Regression Equations for Stochastic Inventory System

Multi-objective optimization of a mixture inventory system using a MOPSO–TOPSIS hybrid approach

Multi-objective optimization of mixture inventory system experiencing order crossover

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