Fuzzy inventory model with two warehouses under possibility constraints

Maiti, Manas Kumar; Maiti, Manoranjan

doi:10.1016/j.fss.2005.06.021

Cited by 107 publications

(34 citation statements)

References 23 publications

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“…Some examples where heuristic algorithms have been used to solve fuzzy inventory models are the work done by Wee et al [60], Maiti and Maiti [34], and Gen et al [14].…”

Section: Fuzzy Programming Problemsmentioning

confidence: 99%

Inventory management under uncertainty: A military application

Bean

Joubert

Luhandjula

2016

Computers & Industrial Engineering

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“…Some examples where heuristic algorithms have been used to solve fuzzy inventory models are the work done by Wee et al [60], Maiti and Maiti [34], and Gen et al [14].…”

Section: Fuzzy Programming Problemsmentioning

confidence: 99%

Inventory management under uncertainty: A military application

Bean

Joubert

Luhandjula

2016

Computers & Industrial Engineering

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“…Due to the difficulty of simultaneously taking into account many parameter variables in the analytical models, more and more papers suggest identifying uncertainty through the introduction of a fuzzy environment. Some articles (Mandal, Roy, 2006), (Roy, 2007), (Taleizadeh, 2009), (Hsieh, 2002), (Maiti, 2006), present an approach that assumes that demand, lead time, stock-carrying costs, customer service, etc. are fuzzy values.…”

Section: Overview Of Inventory Control Solutionsmentioning

confidence: 99%

Intelligent Approach to Inventory Control in Logistics under Uncertainty Conditions

Więcek

2016

Libro De Actas CIT2016. XII Congreso De Ingeniería Del Transporte

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The article presents a proposal for a combined application of fuzzy logic and genetic algorithms to control the procurement process in the enterprise. The approach presented in this paper draws particular attention to the impact of external random factors in the form of demand and lead time uncertainty. The model uses time-variable membership function parameters in a dynamic fashion to describe the modelled output fuzzy (sets) values. An additional element is the use of genetic algorithms for optimisation of fuzzy rule base in the proposed method. The approach presented in this paper was veryfied according to four criteria based on a computer simulation performed on the basis of the actual data from an enterprise. OVERVIEW OF INVENTORY MANAGEMENT ISSUESAs a result of the on-going globalisation and mass consumption, the demand on the goods market is characterised by intense dynamics and a certain level of uncertainty, especially in large agglomerations and urban areas. The logistical processes that occur there as part of supply networks focus primarily on the flow of the streams of material goods, but also take into account the flows of necessary information and financial resources. The volatility of these processes and certain level of uncertainty cause all sorts of inventory to amass at various levels of the logistic network in order to ensure the continuity of production and the uninterrupted availability of the finished products to customers. The goods amassed in the nodal points of the logistic network act as buffers that mitigate the differences in customer demand for the products. In practice, despite the use of modern systems, such as JIT (Just In Time), ERP (Enterprise Resource Planning), MRP (Material resource Planning), it is not possible to entirely eliminate the inventory. In fact, economic processes are stochastic in nature (which results from both the operating environment of these processes and the impact of their surroundings), so it is possible to identify them only to a certain extent, with a greater or smaller error (Wolski, 2010). Due to the impact of random factors on the nodal elements of the supply network (manufacturing plants, distribution centres, warehouses, etc.) through the volatility of demand for semi-finished products or finished products, lead time changeability, vendors' limited capabilities, etc., the optimal policy for the supply and inventory control logistics is of utmost importance to the effectiveness of the entire logistic network. As a result of the above-mentioned factors and the ever-increasing competition among entities, logistics companies are often forced to keep a high inventory level in order to maintain the desired service level. This behaviour makes it possible to dynamically respond

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“…Pal et al (2009) developed an EPQ model incorporating price discounted promotional demand in a fuzzy planning horizon. They assume that producer offers price discount in every production cycle for some time, but ignore the effect of product availability in stimulating demand, though this effect is well established (Chung et al, 2000;Maiti and Maiti, 2006). Liang and Zhou (2011) published a two-warehouse inventory model for deteriorating items under conditionally permissible delay in payment.…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy lifetime-based particle swarm optimisation with varying swarm size to solve a production inventory model

Guchhait

Maiti²

2016

IJCCIA

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Here, a modified particle swarm optimisation (MPSO) algorithm with varying swarm size for constrained optimisation problem is proposed. In this MPSO, a life time is assigned to each particle at the time of generation depending on its fitness. After completion of a generation, if no movement is made by the particle, its age is increased by unity. When age of a particle exceeds the lifetime, it is discarded from the swarm. Diversity in the swarm is maintained using information entropy theory. A fuzzy possibility/necessity-based fitness evolution is proposed to deal with fuzzy optimisation problems using this MPSO. Efficiency of the algorithm is tested against a list of crisp valued standard benchmark nonlinear test functions. This algorithm is used to solve a production inventory model with fuzzy costs, where lifetime of the product is random in nature. At the beginning of planning horizon price discount is offered to the customers for few cycles to boost the demand. Demand also depends on stock and selling price. The model is illustrated with numerical examples and some sensitivity analyses have been made. Keywords: modified PSO algorithm; fuzzy lifetime; varying swarm size; possibility/necessity measure; EPQ model; price discounted promotional demand; random planning horizon.Reference to this paper should be made as follows: Guchhait, P. and Maiti, M.K. (2016)

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Fuzzy inventory model with two warehouses under possibility constraints

Cited by 107 publications

References 23 publications

Inventory management under uncertainty: A military application

Inventory management under uncertainty: A military application

Intelligent Approach to Inventory Control in Logistics under Uncertainty Conditions

A fuzzy lifetime-based particle swarm optimisation with varying swarm size to solve a production inventory model

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