The furniture company: deductive databases and the scheduling problem

Pons, Alexander P.

doi:10.1016/j.ijinfomgt.2003.09.005

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Query responses are then deduced from the stored facts and rules by an inference mechanism. Such a deductive database can be utilized for an effective solution to a scheduling problem at a furniture company to handle order processing, inventory control, customer and manufacturer communication history, and financial analysis (Ponis 2003). Build-to-order (BTO) and lean manufacturing processes are changing the paradigms under which business-to-business (B2B) marketers operate in multiple industries such as office furniture, personal computers and windows.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy scheduling of a build-to-order supply chain

Demirli

Yimer

2008

International Journal of Production Research

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The overwhelming majority of the literature in the area of supply chain planning and scheduling considers the traditional make-to-stock (MTS) environment. However, manufacturers of assembled products such as cars, computers, furniture, etc. adopt the build-to-order supply chain (BOSC) to become agile in a mass customization process in order to meet diversified customer requirements. In this paper we propose an integrated production-distribution planning model for a multi-echelon, multi-plant and multi-product supply chain operating in a build-to-order (BTO) environment. The uncertainties associated with estimation of the various operational cost parameters are represented by fuzzy numbers. The BOSC scheduling model is thus constructed as a mixed-integer fuzzy programming (MIFP) problem with the goal of reducing the overall operating costs related to component fabrication, procurement, assembling, inspection, logistics and inventory, while improving customer satisfaction by allowing product customization and meeting delivery promise dates at each market outlet. An efficient compromise solution approach by transforming the problem into an auxiliary multi-objective linear programming model is also suggested.

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Section: Literature Reviewmentioning

confidence: 99%