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Supply planning model for remanufacturing system in reverse logistics environment Authors: Kibum Kim, Iksoo Song, Juyong Kim, Bongju Jeong Journal of Computers.

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Presentation on theme: "Supply planning model for remanufacturing system in reverse logistics environment Authors: Kibum Kim, Iksoo Song, Juyong Kim, Bongju Jeong Journal of Computers."— Presentation transcript:

1 Supply planning model for remanufacturing system in reverse logistics environment Authors: Kibum Kim, Iksoo Song, Juyong Kim, Bongju Jeong Journal of Computers and Industrial Engineering Presentation: Jirawan Niemsakul (January 28, 2011)

2 Conclusions Contents Introduction Supply planning model An illustrative example 1 1 2 2 3 3 4 4

3 1. Introduction Reverse logistics can be defined as the logistics activities all the way from used products no longer required by the customer to products again usable in the market.

4 1. Introduction Increased environmental concerns Stringent environmental laws. Motivation

5 1. Introduction Reuse Recycling Remanufacturing Reverse logistics categories

6 1. Introduction Remanufacturing, Remanufacturing, is an industrial process in which worn-out products are restored to like-new condition, such as electronic machine, toner cartridge, and automobile part. Discarded products are completely disassembled through a series of industrial process in factory environment. Usable parts are cleaned, refurbished, and put into part inventory. Then the new product is reassembled from the old and, new parts to produce a fully equivalent and superior in performance and expected lifetime to the original new product

7 2. Supply planning model 2.1 Framework for remanufacturing system

8 2. Supply planning model 2.2 Formulation of model (Mixed-integer Programming) Objective: Minimize total remanufacturing cost Decision: 1. How many returned products should be thrown into the remanufacturing process such as refurbishing and disassembling for ‘as new’ condition ? 2. How many new parts need to be purchased from the external supplier ?

9 2. Supply planning model Indices: p = Product index, p = { 1,…..,P } i = Part index, i = { 1,…..,I } t = Time period, t = { 1,…..,T }

10 2. Supply planning model Parameters:

11 2. Supply planning model Decision Variables:

12 2. Supply planning model Objective Function:

13 2. Supply planning model Subject To:

14 2. Supply planning model Subject To:

15 3. An illustrative example There are three types of products and five types of parts from those products, and 10 time-periods for planning horizon.

16 3. An illustrative example The quantity of parts requirement is generated as many as 1.5 times of the quantity of collected products so that external supplier may supply the shortage of remanufacturing quantity.

17 3. An illustrative example The capacity of remanufacturing facility for each individual product or part type is set to be 150 and 900 units of product for collection and disassembly site, respectively, and 1000 units of part for refurbishing site. The unit purchasing cost is set to be 40, 30, 20, 30, and 20 dollars for each part type and the idle cost of the disassembly facility and refurbishing facility are set to be 2000 and 2500 dollars, respectively. The upper bound for disposal rate of disassembled part is set to be 0.15 for all parts by which usually company wants to reduce remanufacturing inefficiency. Other relevant cost data are also generated but not shown here. Initial inventory for each part is set as 500 units.

18 3. An illustrative example Example Results:

19 3. An illustrative example Example Results:

20 3. An illustrative example

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23 4. Conclusions The research results can be guidelines on the relevant research. The proposed remanufacturing framework and model can be applicable to the various industries. A proposed model is introduced in terms of a general framework, many future works are accordingly needed. The proposed model is formulated as mixed integer programming, the computational burden for optimal solution increases exponentially as the size of problem increases. An efficient heuristic algorithm needs to be developed in order to solve the large-scale problems in real business environment.

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