develop a quantitative model (linear programming) using the data, analyse and interpret the model using excel solver and report your findings. we need to identify an objective function clearly stating the purpose of the quantitative model. we should be able to define the variables under consideration for the quantitative model development. Further, we should be to collect relevant information from stakeholders for developing the constraints. The report should outline the stakeholder's analysis for data collection and developing constraints. The developed model should be analysed using the Excel solver function to generate an optimal solution. The report should outline the step-by-step procedure of the solver function. The final output should be presented to management for decision analysis. You are required to set up the MS Excel dashboard to assist any user to make decisions. we are to provide quantitative model for managerial decision making with optimal outcomes for the organisation by using Excel solver tools and validate the importance of specific analysis and interpretation for the management decisions

The managerial decisions you make in relation to this assessment must be made with regards to any one of the following 6. Multiperiod Cashflow Problems 

	Let Xij(i=1,2j=1,2,...5)be the optimized number of the shoes send by distribution center Ai to store Bj:
	Store Distrubition center	Melbourne	Sydney	Sunshine Coast	Adelaide	Perth	Capacity
	Melbourne	0.4	1	1.3	1	1.5	6000
	Sydney	0.8	0.5	0.6	1.2	1.4	3500
	order number	3000	4000	500	600	650
Objective function
		min=0.4*x(1,1)+1*x(1,2)+1.3*x(1,3)+1*x(1,4)+1.5*x(1,5)+0.8*x(2,1)+0.5*x(2,2)+0.6*x(2,3)+1.2*x(2,4)+1.4*x(2,5)
Constrains
C1	X(1,1)+X(1,2)+X(1,3)+X(1,4)+X(1,5)<=6000
C2	X(2,1)+X(2,2)+X(2,3)+X(2,4)+X(2,5)<=3500
C3	X(1,1)+X(2,1)>=3000
C4	X(1,2)+X(2,2)>=4000
C5	X(1,3)+X(2,3)>=500
C6	X(1,4)+X(2,4)>=600
C7	X(1,5)+X(2,5)>=650
C8	Xij>=0
	Objective function
	Min	z=	5575
	Decision variable	X(1,1)	X(1,2)	X(1,3)	X(1,4)	X(1,5)	X(2,1)	X(2,2)	X(2,3)	X(2,4)	X(2,5)
	Coefficients	0.4	1	1.3	1	1.5	0.8	0.5	0.6	1.2	1.4
	Value	3000	1000	0	600	650	0	3000	500	0	0
	Subject to												LHS		RHS
	C1	1	1	1	1	1	0	0	0	0	0		5250	<=	6000
	C2	0	0	0	0	0	1	1	1	1	1		3500	<=	3500
	C3	1	0	0	0	0	1	0	0	0	0		3000	>=	3000
	C4	0	1	0	0	0	0	1	0	0	0		4000	>=	4000
	C5	0	0	1	0	0	0	0	1	0	0		500	>=	500
	C6	0	0	0	1	0	0	0	0	1	0		600	>=	600
	C7	0	0	0	0	1	0	0	0	0	1		650	>=	650