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Development of a Dynamic Information Fractal Framework to Monitor and Optimise Sustainability in Food Distribution Network

  • Sameh M. Saad EMAIL logo and Ramin Bahadori
Published/Copyright: July 25, 2019
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International Journal of Food Engineering
From the journal International Journal of Food Engineering Volume 16 Issue 5-6

Abstract

The aim of this research paper is to develop a new framework for an information fractal to improve the food distribution network sustainability through two variables; Greenfield service constraints and minimum vehicle weight fill level on board. This paper applies the proposed framework to a hypothetical distribution network. Further, Supply Chain GURU Software is adapted to implement Greenfield analysis to identify the optimal number and location for setting up the new facilities through different Greenfield service constraints. A new Green Split Delivery-Vehicle Routing Problem also is developed to minimise CO2 emission and implemented using the simulated annealing algorithm. The results revealed that the proposed dynamic control system has led to an enhancement in both collaboration and integration to decide upon the optimal number and location of distribution facilities as well as optimal vehicle weight fill levels to improve the sustainability throughout the food distribution chain.

Keywords: food supply chain; sustainable distribution network; information fractal; Greenfield analysis; Green Vehicle Routing Problem; split delivery

Appendix

A MATLAB codes

I Create the Distribution Centre model

function model=CreateDCModel(I,J) % I= number of Retailers, J= number of Vehicle E=[];                % CO2 Emission rate of vehicle TW =[];            % Tare Weight of vehicle r=[];                % Retailer Demands c=[];                % Vehicle Capacity x=[];                % Longitudinal coordinates of retailers x0=[];            % Latitude coordinates of distribution centre y=[];                % Longitudinal coordinates of retailers y0=[];            % Latitude coordinates of distribution centre d=zeros(I,I); d0=zeros(1,I);                     for i=1:I         %%% Distance among retailers                                     for i2= i+1:I                                             d(i,i2)=distdim(distance(x(i),y(i),x(i2),y(i2)),'deg','kilometers');                                             d(i2,i)=d(i,i2);                                     end                         %%%Distance from depot to each retailers                                     d0(i)=distdim(distance(x0,y0,x(i),y(i)),'deg','kilometers');                     end end

II Create Random solution

 function q=CreateRandomSolution(model)          q=randperm(I+J-1);          DelPos=find(q>I); %DelPos= Delimiter Position          From=[0 DelPos]+1;          To=[DelPos I+J]-1;          L=cell(J,1); %L= List of retailers who received the service from the vehicle j                      for j=1:J                          L{j}=q(From(j):To(j))                      end end

III Generate the objective function. In the below code only CO2 emission calculation is presented

function sol=CO2C(q,model)                CH=0;                ucap=zeros(J,1);                C=zeros(J,1);                DC=0;                sh=0;                %%% Vehicle load moments            for j=1:J                                if ~isempty(L{j})                                            last_costm=L{j}(end);                                                %%% Output loading weight from the depot                                                s(j)=0;                                                for ii=1:length(L{j})                                                                s(j)=s(j)+r(L{j}(ii));                                                                    ucap(j)=sum(r(L{j}));                                                                    CH=CH+max(ucap(j)-c,0);    %%%Vehicle capacity constraint                                                end                                                        sh=s(j);                                                    %%%CO2 emission from depot to first retailer                                                C(j)=((TW+sh)*E)*d0(L{j}(1));                                                sh=sh-r(L{j}(1));                                                r(L{j}(1))=0;                                                                                                %%% CO2 emission among retailers                                            for k=2:numel(L{j})                                            %%% Apply constraint to guarantee that vehicle cannot continue to serve more customers in length of each route if the weight of its shipment on board, coming down is from a specified minimum shipment weight.                                                            DC=DC+max(Ms-sh,0);                                                                                        if sh>=r(L{j}(k))                                                                                                    sh=sh-r(L{j}(k));                                                                                                    r(L{j}(k))=0;                                                                                    else                                                                                                    r(L{j}(k))=r(L{j}(k))-sh;                                                                                                    sh=0;                                                                                                        last_costm=L{j}(k);                                                                                    end                                            C(j)=C(j)+((vw+sh)*E)*d(L{j}(k-1),L{j}(k));                                            end                                                %%% CO2 emission from last retailer to depot                                                C(j)=C(j)+(TW*E)*d0(last_costm)                                end                                    %%% Identify retailers which their demand is not fully fulfilled                                    rn=nonzeros(r);                                    rr=find(r==0);                                    A=d;                                    A(rr,:)=[];                                    A(:,rr)=[];                                    A0=d0;                                    A0(:,rr)=[];                                    In=numel(rn);                                    Jn=numel(rn);                                    rb=zeros(In,1);                                    rb=rn;                end end

IV Generate CO2 emission function

 function [z sol]=MyCO2(q,model)           global    NFE;           NFE=NFE+1;           sol=CO2C(q,model);           eta=[];           beta=[];           z=sol.TotalC;           z=z+ beta*sol.CH+ eta*sol.DC;                        end

V Create neighbourhood Solution (xnew)

function qnew=CreateNeighbor(q)                m=randi([1 3]);            switch m                            case 1                                            % Do Swap                                            qnew=Swap(q);                            case    2                                            % Do Reversion                                            qnew=Reversion(q);                            case    3                                            % Do Insertion                                            qnew=Insertion(q);            end end      function    qnew=Swap(q)            n=numel(q);            i=randsample(n,2);            i1=i(1);            i2=i(2);            qnew=q;            qnew([i1 i2])=q([i2 i1]); end function qnew=Reversion(q)            n=numel(q);            i=randsample(n,2);            i1=min(i(1),i(2));            i2=max(i(1),i(2));            qnew=q;            qnew(i1:i2)=q(i2:-1:i1); end function    qnew=Insertion(q)            n=numel(q);            i=randsample(n,2);            i1=i(1);            i2=i(2);            if    i1<i2                            qnew=[q(1:i1-1) q(i1+1:i2) q(i1) q(i2+1:end)];            else                            qnew=[q(1:i2) q(i1) q(i2+1:i1-1) q(i1+1:end)];            end end

VI Simulated Annealing algorithm

clc; clear; close all; global NFE; NFE=0;       Problem Definition model=SelectModel();                                                                            % Select Model of the Problem CO2Function=@(q) MyCO2(q,model);                            %    Objective Function, CO2 emission                              function      SA Parameters MaxIt=1000;                        % Maximum Number of Iterations by default MaxIt2=80;                        % Maximum Number of Inner Iterations by default T0=100;                                    % Initial Temperature by default alpha=0.99;                    % Temperature Damping Rate by default         Initialisation %    Create Initial Solution x.Position=CreateRandomSolution(model); [x.CO2 x.Sol]=CO2Function(x.Position); %    Update Best Solution Ever Found BestSol=x; %    Array to Hold Best CO2 Values BestCO2=zeros(MaxIt,1); %    Array to Hold NFEs nfe=zeros(MaxIt,1); %    Set Initial Temperature T=T0;       SA Main Loop for it=1:MaxIt                 for it2=1:MaxIt2                                 % Create Neighbor                                 xnew.Position=CreateNeighbor(x.Position);                                 [xnew.CO2 xnew.Sol]=CO2Function(xnew.Position);                                 if xnew.CO2<=x.CO2                                                 % xnew is better, so it is accepted                                                 x=xnew;                                         else                                                 % xnew is not better, so it is accepted conditionally                                                 delta=xnew.CO2-x.CO2;                                                 p=exp(-delta/T);                                                 if    rand<=p                                                                 x=xnew;                                                 end                                 end                 end                 %    Update Best Solution                                 if    x.CO2<=BestSol.CO2                                                 BestSol=x;                                 end                 %    Store Best CO2                 BestCO2(it)=BestSol.CO2;                 if    BestSol.Sol.IsFeasible                                 FLAG='    *';                 else                                 FLAG='';                 end                 % Store  NFE                 nfe(it)=NFE;                 % Display Iteration Information                 disp(['Iteration ' num2str(it) ': Best CO2 = ' num2str(BestCO2(it)) FLAG ]);                 % Reduce Temperature                 T=alpha*T;                 %Plot Solution %                    figure(1); %                    PlotSolution(BestSol.Sol,model); %                    pause(0.01); end     Results figure; plot(nfe,BestCO2,'LineWidth',2); xlabel('NFE'); ylabel('Best CO2');

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Received: 2018-02-15
Revised: 2019-04-30
Accepted: 2019-06-14
Published Online: 2019-07-25

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Editorial
  2. Special issue “Selected papers from the International Food Operations & Processing Simulation Workshop”
  3. Articles
  4. Economic Assessment of Pig Meat Processing and Cutting Production by Simulation
  5. A Simulation-Based Tool to Support Decision-Making in Logistics Design of a Can Packaging Line
  6. Word of Mouth, Viral Marketing and Open Data: A Large-Scale Simulation for Predicting Opinion Diffusion on Ethical Food Consumption
  7. Development of a Dynamic Information Fractal Framework to Monitor and Optimise Sustainability in Food Distribution Network
  8. Estimating the Impact of Blockchain Adoption in the Food Processing Industry and Supply Chain
  9. Developing a Linearization Method to Determine Optimum Blending for Surimi with Varied Moisture Contents Using Linear Programming
  10. Developing an Accurate Heat Transfer Simulation Model of Alaska Pollock Surimi Paste by Estimating the Thermal Diffusivities at Various Moisture and Salt Contents
  11. Utilisation of the REA-method to a Convective Drying of Apple Rings at Ambient Temperature
  12. Shelf life analysis of a ricotta packaged using Modified Atmosphere Packaging or High Pressure Processing
https://doi.org/10.1515/ijfe-2018-0061
Keywords for this article
food supply chain; sustainable distribution network; information fractal; Greenfield analysis; Green Vehicle Routing Problem; split delivery

Articles in the same Issue

  1. Editorial
  2. Special issue “Selected papers from the International Food Operations & Processing Simulation Workshop”
  3. Articles
  4. Economic Assessment of Pig Meat Processing and Cutting Production by Simulation
  5. A Simulation-Based Tool to Support Decision-Making in Logistics Design of a Can Packaging Line
  6. Word of Mouth, Viral Marketing and Open Data: A Large-Scale Simulation for Predicting Opinion Diffusion on Ethical Food Consumption
  7. Development of a Dynamic Information Fractal Framework to Monitor and Optimise Sustainability in Food Distribution Network
  8. Estimating the Impact of Blockchain Adoption in the Food Processing Industry and Supply Chain
  9. Developing a Linearization Method to Determine Optimum Blending for Surimi with Varied Moisture Contents Using Linear Programming
  10. Developing an Accurate Heat Transfer Simulation Model of Alaska Pollock Surimi Paste by Estimating the Thermal Diffusivities at Various Moisture and Salt Contents
  11. Utilisation of the REA-method to a Convective Drying of Apple Rings at Ambient Temperature
  12. Shelf life analysis of a ricotta packaged using Modified Atmosphere Packaging or High Pressure Processing
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