Optimal Policies for a Multi-Echelon Inventory Problem

Andrew J. Clark; Herbert Scarf

doi:10.1287/mnsc.6.4.475

Optimal Policies for a Multi-Echelon Inventory Problem

Management Science ◽

10.1287/mnsc.6.4.475 ◽

1960 ◽

Vol 6 (4) ◽

pp. 475-490 ◽

Cited By ~ 960

Author(s):

Andrew J. Clark ◽

Herbert Scarf

Keyword(s):

Inventory Problem ◽

Optimal Policies

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Optimal Policies for a Dual-Sourcing Inventory Problem with Endogenous Stochastic Leadtimes

SSRN Electronic Journal ◽

10.2139/ssrn.2769658 ◽

2016 ◽

Author(s):

Jing-Sheng Jeannette Song ◽

Li Xiao ◽

Hanqin Zhang ◽

Paul Zipkin

Keyword(s):

Inventory Problem ◽

Dual Sourcing ◽

Optimal Policies

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Optimal policies for inventory model with shortages, time-varying holding and ordering costs in trapezoidal fuzzy environment

Independent Journal of Management & Production ◽

10.14807/ijmp.v12i2.1212 ◽

2021 ◽

Vol 12 (2) ◽

pp. 557-574

Author(s):

Pavan Kumar

Keyword(s):

Fuzzy Model ◽

Continuous Functions ◽

Inventory Model ◽

Inventory Problem ◽

Distance Method ◽

Fuzzy Environment ◽

Optimal Policies ◽

Demand Rate ◽

Holding Cost ◽

Fuzzy Inventory

This paper proposes the optimal policies for a fuzzy inventory model considering the holding cost and ordering cost as continuous functions of time. Shortages are allowed and partially backlogged. The demand rate is assumed in such to be linearly dependent on time during on-hand inventory, while during the shortage period, it remains constant. The inventory problem is formulated in crisp environment. Considering the demand rate, holding cost and ordering cost as trapezoidal fuzzy numbers, the proposed problem is transformed into fuzzy model. For this fuzzy model, the signed distance method of defuzzification is applied to determine the average total cost (ATC) in fuzzy environment. The objective is to optimize the ATC and the order quantity. One solved example is provided in order to show the applicability of the proposed model. The convexity of the cost function is verified with the help of 3D-graph.

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