07-c1-flow optimization in a closed loop green supply chain network-bektas_ozceylan_paksoy

8/12/2019 07-C1-Flow Optimization in a Closed Loop Green Supply Chain Network-Bektas_Ozceylan_Paksoy

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Flow Optimization in a Closed-Loop Green

Supply Chain Network

Turan PAKSOY

Selcuk University, Department ofIndustrial Engineering, Campus,

42031, Konya, Turkey

[email protected]

Tolga BEKTA

School of Management, Universityof Southampton, Highfield,

Southampton, SO17 1BJ, UK

[email protected]

Eren ZCEYLAN

Selcuk University, Department ofIndustrial Engineering, Campus,

42031, Konya, Turkey

[email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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Agenda

Introduction with definitions

Literature Review

Proposed Model Formulation

A Numerical Example and Scenario Analysis

Conclusion and Suggestions

Flow Optimization in a Closed-Loop Green Supply Chain Network


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Supply Chain

Supply Chain is a set of activities:

purchasing,

manufacturing,

logistics,

distribution,

marketing,

that perform the function of

delivering value to end customer.



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Supply Chain Network Design

is determining positions and count of actors;

amount of product flow between and

decreasing transportation costs arehandled as network design problem

in supply chain management.



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Green Supply ChainThe green supply chain (GrSC ) designextends this definition by

including:

(i) Waste of all processes,

(ii) Using efficient energy resources,

(iii) Greenhouse gas emissions,

(iv) Using capacities and resources efficiently,

(v) Considering legal environmental factors.



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Closed-Loop Supply Chain

To further develop supply chains for recyclable and recycled materials, it will benecessary to improve recycling technologies, to allow recyclable materials to bereprocessed into recycled materials of sufficient quality that they can compete withvirgin materials.

Combining the forward and

reverse supply chain as a

whole network.



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Summary

We study a GrSC network optimization problem, where

CO2 gas emissions according to trucks options and

recyclable products

are considered to become a mirrorof greenness.

Penaltycost to prevent more CO2 gas emissions.

Small profit to encourage the customers to use recyclable products.

Trucks rental fees and purchasing costs of recyclable products can

influence the environmental indicators in the model because of a trade-off.

We optimize the network also under

transportation costs andcapacity allocation constraints.



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Literature Review Beamon 1999, described the current state of the natural environments,investigated the environmental factors, presented performance measures for theGrSCs and developed a general procedure towards achieving the GrSC. Sarkis 2003, aimed to focus on the components and elements of GrSN

management and how they serve as a foundation for the decision framework. Thedecision support models for design of global supply chains, and assess the fitbetween the research literature in this area and the practical issues are handled inMeixell and Gargeyas (2005) study. Sheu et al. 2005 presented an optimization-based model to deal with integratedlogistics operational problems of GrSC management. In the proposed methodology,

a linear multi-objective programming model is formulated that systematicallyoptimizes the operations of both integrated logistics and corresponding used productreverse logistics in a given GrSC. Beamon 2008 described the challenges and opportunities facing the supply chainof the future and described sustainability and effects on supply chain design,management and integration.



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Problem Definition

From the aforementioned concepts we described above, it is known that a

CLSC network structure is necessary to design a GrSC. Regarding the traditional

supply chains, CLSC and GrSC problems involved more complex, and need more

efforts to control forward and reverse logistics simultaneously considering the

environmental impacts. Mostly the cost is considered via enterprises to measure

the effectiveness of the network. Besides the cost factor, the following conditions

are handled in our model;

CO2 gas emission,

Products which consist of different recyclable ratio raw materials,

Opportunity prices to encourage customers using recyclable products.



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Assumptions

To specify the scope and facilitate model formulation,our assumptions are:

The demand of each customer is certain and must be satisfied.

The flowis only allowed to be transferredbetween two sequential echelons (except warehouse-customers).

The capacitiesof all actors are limited and certain.

The transportation, purchasing, penalty and opportunity costs are given.

The CO2emissions and all reverse part rates are given.

Minimizing the total costs(transportation, purchasing, and penalty)is aimed at while maximizing the amount of productwhich is recycled.



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Forward PartThe network is structured as a typical 5-layerforward supply chain:

(i) raw material supply,

(ii) plants,(iii) warehouses,

(iv) distribution centers and

(v) customers (end-users).

Let S denote the index set of suppliers, Q denote the index set of plants,V denote the index of warehouses, K denote the index set of

distribution centers (DCs ), and L denote the index set of customers.



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Reverse PartSimilarly, a 5-layerstructure is considered for the

used-product reverse supply chain:

(i) collecting centers,(ii) repairing centers,(iii) dismantlers,(iv) decomposition centers and(v) final disposal locations of waste material.

Let M denote the index set of collection centers,U denote the index set of repairing center,P denote the index set of dismantlers,O denote the index set of disposalsandD denote the index set of decomposition centers.



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Supply chain network which consists of

forwardpart G = (N, A),

where N is the set of forwardnodesA is the set of forward arcs,

reversepart G= (N , A ),

whereN is the set of reversenodes

A is she set of reverse arcs.

Here, N = S Q V K L, N = M U P O D .

Forward and Reverse Parts



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T index set of available transportation options (03, 47, 811 years old trucks).

Each transportation option tT between two levels i andjof the forward-chain incurs a certain operation cost denoted by

For instance, where i S andj Q, denotes theunit transportation costfrom supplier i to plantj.

Each transportation option also has an estimated amount of CO2emissionsand this is denoted by , where i S andj Q correspond to the indicesof two different layers of the forward chain.

ijtH

2ijt

CO

ijtH




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Expected minimum collection rate: rminExpected maximum collection rate: rmaxfrom customers to collection centers for product of type rR .

Product is recoverable through repairingor refurbishing, or to be dismantled.

Repair/recycle rate r of product of type rR in each collection center .Repair rate r of product of type rRat each repairing centerand transported to DCs.

r : Expected fraction of the product that is to be disposed of.

r : Rate of reusable parts of product of typer R,directly sent to suppliers

The rest are sent to production plants.




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Reverse Part Rates



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Parameters



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Parameters and Variables



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Objective Functions

Ctioij

tH

Ctioij

rC

Ctio

Ctio)2ijtCO

Ctio



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Objective Functions

j

rP



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Constraints



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Constraints



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Constraints



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Constraints



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Numerical Example



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This network contains two main parts:

First part: Forward logistic(3 suppliers, 3 plants, 1 warehouse, 2 DCs , 5 customers)

Three kinds of raw materials provided by suppliers

100% recyclable, 50% recyclable, non-recyclable raw materials.

100% recyclable products have to contain re-useable materials.

But recyclable raw materials are more expensive than normal products

Numerical Example

So the decision maker faces a trade-off:

purchasing costs versus the recyclable rate.



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We consider the greenhouse gas emissions in forward logistic.

Outsourcing is used for transportationonly.Three kinds of trucks: 0-3 years, 4-7 years, 8-11 years old.

Greenness of the modelTrucks are aging,

their rental fees will be cheaper choose oldest trucks,

their CO2emissions also increase

2

c

COP

Numerical Example

The added deterrent penalty cost( = 0.05 $ / (gr than more 2000 kg CO2))

puts the decision maker into another trade-off situation:

penalty cost versusCO2 emissions.



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Costs and Emissions



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Capacities



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Other Data



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Reverse Part

Second part: Reversing logistic(2 collection centers, 2 dismantlers, 1 repairing center,

1 disposal, 2 decomposition centers)

Collection centers: collecting the used-products from customers.



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Results

In this example, with I=3, J=5, K=2, L=5, M=2, P=2, D=2, T=3

and R=3, there are 445 variables, and 602 constraints.

Using LINDO 6.1with the most 1 (s) elapsed time, we obtained the

optimal solution as shown in the following table.

Calculated objective values are given in the table.

All the experiments are conducted on a notebookwith the

Intel Core2 Duo 1.66 GHz and2 GB RAM.



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Results

Total transportation costin forward logistic: 453530.00 $.

for transporting 90000 units product

28000 units 100% recyclable,

32500 units 50% recyclable,

29500 units non-recyclable.



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Results

Trucks emissions in forward logistic : 317800 gr CO2

Lower limit for emissions: 2000 kg CO2

Total penalty cost: 5890 $

90000 units of raw materialsare purchased from three suppliers via paying 333600.00 $.

27000 units of re-used products are collected and sent to the collection centers.

Because of preferring the recyclable raw materials,

the decision maker gained 57382.80 $ via saving the re-purchasing costs.



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Scenario Analysis

When the results are examined, it is seen that some capacity

restrictions for all facilities or reverse rate parameters affect the modeldirectly.

By extracting some parts of the objective functions or by changing

the capacity limitations of facilities and reverse parameters, different

scenarios can be applied to the model to examine the relations and trade

offs among model variables.



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Scenario Analysis

By analyzing different scenarios of the model, we try to give a decision map

to management of the supply chain. The scenarios are listed below:

Scenario 1:Increasing five times the capacities of suppliers, plants,warehouse, and DCs while the other parameters are constant.

Scenario 2:Increasing five times the recycling parameters of collectioncenters, repairing center, dismantlers, and decomposition centers while theother parameters are constant.

Scenario 3:Applying the scenario 1 and scenario 2 at the same time while

the other parameters are constant.



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Scenario 1

Scenario 1 shows that increasing the capacity limits of forward facilities do notdirectly affect the sub-objectives. The main reason of this situation is that becauseof no changes about the demand, all sub-objectives costs are stabled includingthe total cost. This scenario shows that if the decision maker wishes to decreaseOBJF1, OBJF2, OBJF3, and OBJF4 costs, he/she does not have to try to increaseforward facility capacities.



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Scenario 2

Scenario 2 shows that increasing the recycling parameters also increased the totalprofit which is gained returned products and reverse transportation costs. Thisanalysis shows that the recycling parameters do not directly affect the forwardtransportation costs (OBJF1). The changes on recycling parameters do not have anyinfluence on CO2 emissions amount (about 0, 71 %). While the purchasing costs areconstant, increasing the gained profit also decreased OBJF4 costs.



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Scenario 3

Scenario 3 show that increasing the recycling parameters also increased the totalprofit which is gained returned products. Because of no changes about thedemand, total purchasing costs are stabled between 39, 99 % and 41, 2 % of thetotal cost. The total reverse transportation is also increasing by increasing therecycling parameters. Total reverse transportation costs started from 4, 5 % andfinished 12, 62 % of the total cost. According to scenario, OBJF4 is linearlydecreased from 39, 2 to 32, 84 % of the total cost.



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For Researches

For further researches, the model could be extended in a few directions.

For example, the uncertainty embedded in demand, capacity and recovery rates

should be handled to facilitate practical applications.

Another extension is associating the reverse part of this model with

plants or other facilities in other supply chains.

And, as a last suggestion, the modelsenvironmental and

greenness factors can be enlarged via adding noise pollution, accident risk

andtime assessment factors, etc..



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Teekkrler

Danke

Merci

Shoukran

Thank You

Grazie

Gracias

Salamat

07-c1-flow optimization in a closed loop green supply chain network-bektas_ozceylan_paksoy

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