Integrating customer responsiveness and distribution cost in designing a two-echelon supply chain network with considering capacity level and piecewise linear cost

Azad, Nader; Ameli, Mohammad Saeed Jabal

doi:10.1080/17509653.2008.10671049

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…Annual fixed cost for DC operation of product; U 2 k : Annual fixed cost for DC operation; U 3 ki : Unit producing cost of product for DC; U 2 kji : Unit shipment cost of product for customer through DC; U 5 ki : Unit holding cost of product for DC; U 3 kji : Unit transportation cost of product for customer through DC; D ji :…”

Section: Parameters: U 1 Kimentioning

confidence: 99%

“…Constraint (4) presents that a distribution center is used when and only if there is a demand for any product. Constraint (5) guarantees that each customer is allocated to exactly one distribution center.…”

Section:  mentioning

confidence: 99%

“…Present study includes combination of two, or more of these areas. Also take into account, Azad et al developed a two-stage distribution model [5]. In addition, Jakor and Seifbarghy explained a two-stage inventory system, where they consider the independent Poisson demand with constant transportation [6].…”

Section: Introductionmentioning

confidence: 99%

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Mathematical Modeling and Distribution Design for Agricultural Products in Bangladesh

Islam¹,

Alam

Uddin

et al. 2020

AJPAS

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In this paper, we have formulated a mixed integer linear programming (MILP) model for the distribution design of Agricultural products in Bangladesh. The scheme of distribution is very important for the supply chain network (SCN), which is choosing the suitable distribution center (DC) for the distribution of the products. This study is a real life distribution problem. To developed this model, we have collected data from various market players who are directly or indirectly involved in Agriculture sector. We have to solve this model, by using a mathematical programming language (AMPL). We have verified a multi-stage SCN, which includes producer, DC and customer. Also this model is to optimize profit, allocations of the products and most useable DC which satisfied most of the customer demands. Finally, we can analyze the profit for the uncertainty parameters.

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Section: Parameters: U 1 Kimentioning

confidence: 99%

Section:  mentioning

confidence: 99%

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Mathematical Modeling and Distribution Design for Agricultural Products in Bangladesh

Islam¹,

Alam

Uddin

et al. 2020

AJPAS

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Vendor-buyer coordination and supply chain optimization with deterministic demand function

Uddin

Mondal

Hussain

2016

YUGOSLAV J OPERATION

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This paper presents a model that deals with a vendor-buyer multi-product, multi-facility and multi-customer location selection problem, which subsume a set of manufacturer with limited production capacities situated within a geographical area. We assume that the vendor and the buyer are coordinated by mutually sharing information. We formulate Mixed Integer Linear Fractional Programming (MILFP) model that maximize the ratio of return on investment of the distribution network, and a Mixed Integer Program (MIP), used for the comparison. The performance of the model is illustrated by a numerical example. In addition, product distribution and allocation of different customers along with the sensitivity of the key parameters are analyzed. It can be observed that the increment of the opening cost decreases the profit in both MILFP and MIP models. If the opening cost of a location decreases or increases, the demand and the capacity of that location changes accordingly.

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Integrating customer responsiveness and distribution cost in designing a two-echelon supply chain network with considering capacity level and piecewise linear cost

Cited by 2 publications

References 14 publications

Mathematical Modeling and Distribution Design for Agricultural Products in Bangladesh

Mathematical Modeling and Distribution Design for Agricultural Products in Bangladesh

Vendor-buyer coordination and supply chain optimization with deterministic demand function

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