Given the following definitions, how would this linear program be set up in Microsoft Excel to solve for two problem instances of: 5 patients and 10 patients? The problems instances are defined in the below tables

sij	1	2	3	4	5
1	0	20	15	8	6
2	15	0	18	9	28
3	24	23	0	13	13
4	15	27	8	0	14
5	8	17	24	15	0

sij	1	2	3	4	5	6	7	8	9	10
1	0	9	12	26	11	24	12	13	17	15
2	24	0	28	23	22	5	7	18	9	23
3	19	30	0	30	15	22	25	15	28	15
4	18	10	27	0	28	12	16	19	22	7
5	5	16	11	7	0	25	27	30	23	15
6	7	26	6	17	6	0	28	10	13	28
7	23	26	20	20	24	30	0	16	18	27
8	23	20	22	8	18	10	14	0	14	12
9	7	13	9	19	29	27	18	23	0	30
10	16	10	11	11	28	26	6	11	12	0

As previously stated, the FOSP for MASCAL events entails finding an optimal OR surgical schedule for the FST given modeling framework, let N- 1,2,n represent the set of unique surgeries to be sched- uled, where n is the total number of unique CATA surgical patients. Let X = {zjii,je N, i?j) represent the set of decision variables, and let S= {sij :?E N, j) represent the matrix of surgical setup times. t OR setup times. Considering an assignment We assume that each unique surgery can only be performed once, and we assume a cyclic (or closed) surgical schedule because each patient must see the surgeon an additional time prior to discharge. This means that the optimal surgical schedule will start and end at the same unique surgery. We now proceed with the formulation of the ILP by describing the decision variables, parameters, objective function, and constraints. Tij the decision variable, equals 1 if surgery i is performed immediately before surgery j or 0 otherwise. siy -the setup time for surgery j when it immediately follows surgery i Since the sy are sequence-dependent, the setup time matrix S is asymmetric (sy sj) min ijij -1 j-1 subject to Lay 1 VjeN Ty-1 Vi E N The objective function in(1) will minimize the total surgical setup time, thereby minimiz- ing the total length (makespan) of the OR surgical schedule. Constraints (2) and (3) are the standard assignment constraints. In (2), for every surgery j only one unique surgery i can immediately proceed j. In (3), for every surgery i only one unique surgery j can immediately follow i. The constraints in (4) ensure that the decision variables are binary. Although we formulated the assignment version of the ILP, this problem can also be formulated using a directed network representation where each surgery corresponds toa node and a pair of nodes are connected via an are (or directed edge). The formulation in (1)-(4) assumes a complete network, where every node is connected to every other node. For a complete network, the only arcs that do not exist are those along the diagonal ofX because s-0 ViE N.Therefore, we use the following constraints in (5) to exclude the self-loop decision variables () by fixing the diagonal elements of X to equal zero. Another crucial aspect for solving this problem is to ensure that the surgical schedule is continuous by preventing sub-schedules. Specifically, we add sub-schedule elimination constraints in (6) to prevent obtaining solutions containing degenerate sequences between intermediate surgeries (9], where P represents a subeet of unique surgeries and |Pl is the cardinality of P iePjEP As previously stated, the FOSP for MASCAL events entails finding an optimal OR surgical schedule for the FST given modeling framework, let N- 1,2,n represent the set of unique surgeries to be sched- uled, where n is the total number of unique CATA surgical patients. Let X = {zjii,je N, i?j) represent the set of decision variables, and let S= {sij :?E N, j) represent the matrix of surgical setup times. t OR setup times. Considering an assignment We assume that each unique surgery can only be performed once, and we assume a cyclic (or closed) surgical schedule because each patient must see the surgeon an additional time prior to discharge. This means that the optimal surgical schedule will start and end at the same unique surgery. We now proceed with the formulation of the ILP by describing the decision variables, parameters, objective function, and constraints. Tij the decision variable, equals 1 if surgery i is performed immediately before surgery j or 0 otherwise. siy -the setup time for surgery j when it immediately follows surgery i Since the sy are sequence-dependent, the setup time matrix S is asymmetric (sy sj) min ijij -1 j-1 subject to Lay 1 VjeN Ty-1 Vi E N The objective function in(1) will minimize the total surgical setup time, thereby minimiz- ing the total length (makespan) of the OR surgical schedule. Constraints (2) and (3) are the standard assignment constraints. In (2), for every surgery j only one unique surgery i can immediately proceed j. In (3), for every surgery i only one unique surgery j can immediately follow i. The constraints in (4) ensure that the decision variables are binary. Although we formulated the assignment version of the ILP, this problem can also be formulated using a directed network representation where each surgery corresponds toa node and a pair of nodes are connected via an are (or directed edge). The formulation in (1)-(4) assumes a complete network, where every node is connected to every other node. For a complete network, the only arcs that do not exist are those along the diagonal ofX because s-0 ViE N.Therefore, we use the following constraints in (5) to exclude the self-loop decision variables () by fixing the diagonal elements of X to equal zero. Another crucial aspect for solving this problem is to ensure that the surgical schedule is continuous by preventing sub-schedules. Specifically, we add sub-schedule elimination constraints in (6) to prevent obtaining solutions containing degenerate sequences between intermediate surgeries (9], where P represents a subeet of unique surgeries and |Pl is the cardinality of P iePjEP