I have provided all of the information that was given to me. there is no excel sheet provided, as the question requires me to create an excel model.

please help me and let me know where I can help or add any information. thank you.

questions:

1. what is the total cost Minitoys.com incurs in 2019 with the current warehouse arrangement?

2. what is the cost mini toys incurs under the following scenarios in 2020,2021 and 2022?

a) keeping the existing large warehouse in st Kilda with additional warehouse rented all in Melbourne;

b) keeping the existing large warehouse in St.Kilda with additional warehouses rented in different cities (note: some of the additional warehouses can be in Melbourne if necessary)

c) not keeping the existing large warehouse or using any warehouse in Melbourne but renting new warehouses in other cities.

CASE:

in December 2019, Mini Monroe was busy evaluating the performance of minitoys.com over the previous year. total demand had grown by 80% in one year. this growth, however, was a mixed blessing. the venture capitalist supporting the company, basically her parents and relatives, were very pleased with the growth in sales and the resulting increase in revenue. Mini, however, could clearly see that costs would grow faster than revenues if demand continued to grow and the supply chain network was not redesigned. She decided to analyse the performance of the current network to see how it could be redesigned to best cope with the rapid growth anticipated over the next three years.

Mini Monroe founded Minitoys.com in 2017 with a mission of supplying kids with more affordable second hand limited edition figurines and other mini toys. parents complained about having to spend a lot of time and money to look for limited edition figurines and mini toys for their kids, given that this kind of toys were very popular among primary students in Australia. Mini's initial plan was for the company to set up a website to purchase used minitoys from families and sell them to other families via the internet. the idea was well received in the marketplace, demand grew rapidly, and by the end of 2018, the company had sales of over $800,000. by this time a variety of figurines and mini toys were being sold, and the company received significant venture capital support.

In September 2017, mani leased part of a large warehouse in St.Kilda of Melbourne to process the large amount of used toys being brought and sold. sellers sent their used toys to the warehouse. they were cleaned, sorted, shelved, photographed and advertised on the company website. customer ordered via the internet and their orders were picked and then shipped by Australia post from there. many of works had to be done by hand and the process was slow. as demand grew, minitoys.com leased more space within the warehouse. by 2019, minitoys.com leased the entire warehouse and orders were being shipped to customers all over Australia. mini divided Australia into eight customer zones based on state boundary for planning purposes. demand from each customer zone in 2019 was as shown in table 1. mini estimated that the demand will continue to grow in the next three years before it stabilises. the expected annual growth rates are 80%,50% and 20% for 2020,2021 and 2022. it can be assumed that the growth rate applies to all customer zones consistently. from 2023 onward, the demand would remain basically unchanged.

Table 1: demand for minitoys.com for 2019

customer zone	demand in 2019
NSW	5670
QLD	3440
NT	176
WA	1863
SA	1216
VIC	4583
TAS	370
ACT	282

Network options

Mini could see that she needed more warehouse space to cope with the anticipated growth. one option was to lease more warehouse space in Melbourne itself. other options included leasing warehouses all over the country. leasing a warehouse involved fixed costs based on the size of the warehouse and variable costs that varied with the others handled at the warehouse. four potential locations for warehouses were identified in Sydney, Brisbane, Adelaide, and Perth. warehouses leased could be either small (about 5,000 sq.ft) or large (10,000sq.ft) small warehouses could handle a flow of up to 10,000 orders per year, whereas large warehouses could handle a flow of up to 20,000 orders per year. the warehouse in St.Kilda being leased was a large one. the fixed and variable costs of small and large warehouses in different locations are shown in table .2 they are related to shelving the inventory items coming into the warehouse and retrieving them for fulfilling customer orders.

table 2: annual fixed and variable costs of potential warehouses

Mini estimated that the annual processing cost at a city where the warehouses were leased was about $50,000y + 6.5F where Y is the number of warehouses leased in the city and F is the total number of orders progressed in those warehouses in a year. te annual processing cost includes the wages of the temporary staff, the superannuation, and the associated expenses.

Mini toys.com charged a flat fee of %15 per shipment sent to a customer. the company, in turn, contracted with the Australian post to handle all of its outbound shipments. Australia Post charges were based on both the origin and the destination of the shipment. Australia Post charges were based on both the origin and the destination of the shipment and are shown in table 3. there is no inbound transportation cost for shipments from sellers as they arranged their own transports. therefore, it has no impact, not the warehouse configurations

- please let me know if there is any more information missing that will assist you in helping me answer the question, thanks

. Location Melbourne Sydney Brisbane Adelaide Perth Small Warehouse Fixed Cost ($ / year) Variable Cost ($ / Order) 30,000 2.0 25,000 3.0 22,000 2.5 22,000 1.5 24,000 1.0 Large Warehouse Fixed Cost ($ / year) Variable Cost ($ / Order) 50,000 2.0 42,000 3.0 37,500 2.5 37,500 1.5 40,000 1.0 Table 3: Australia Post Charges per Shipment (in $) NSW QLD NT WA Melbourne 11 28 35 37 Sydney 4 25 37 43 Brisbane 11 19 34 46 Adelaide 17 24 27 27 Perth 49 44 32 11 SA 18 22 25 9 29 VIC 4 11 20 9 41 TAS 6 14 25 15 44 ACT 7 4 15 14 46 Here are some tips to help you complete the quantitative assignment of the course. The assignment is about network optimization and will require the use of Solver to find the optimal solutions. For this assignment, you will need to first develop a generic model on a spreadsheet and validate it. Then, with proper modifications in terms of model input, e.g., demand figures, unit transportation costs, and constraints in Solver, e.g., condition that reflects the requirement of a particular option, the model can be used as a tool to help find the optimal warehouse configuration for MiniToys - the case company. You can set up the model using the SunOil example as a reference. Both problems are identical in nature and are categorized as Capacitated Facility Location Problems. It means that their mathematical formulations are very similar, although some minor modification is needed in the case of MiniToys as order handling cost and transportation cost are separated. In addition, there is order processing cost. Basically, the objective function is a cost functional comprising fixed cost, order handling cost, order processing cost and shipment cost. In the MiniToys case, fixed costs are the annualized costs for leasing the warehouses. Variable costs are order handling (i.e., Shelving and retrieving) and order processing (i.e., picking and packaging) which vary with the number of orders processed in the year. There is also shipment cost which is also variable as it varies with the number of orders distributed to customers from various warehouses. The constraints in the MiniToys case include demand constraint (.e., all demand must be met), capacity constraint (i.e., available capacity cannot be exceeded), non-negativity constraint (.e., values of all variables cannot be negative) and integer constraint (i.e., warehouses must be in whole number). The best way to go is to first create a model representing the current or the as-is situation, i.e., Year 2019. This will be needed to answer Case Question 1 (Q1). To work out the total operating cost for 2019, you do not need Solver at all because everything is fixed and there is no alternative course of action, hence no need for optimization. However, it will still be good to use Solver to find the solution because this will allow you to (1) cross-check if your cost figure is correct and (ii) have a generic model that can be copied and modified for option or scenario testing. As the layout of all the other models will be the same as that of the generic model, and the Solver setups will be quite similar, you can simply copy the generic model and rename it to become another model for a particular scenario. There are three scenarios to investigate in the MiniToys case. For each scenario, the optimized warehouse configurations for 2020, 2021 and 2022 have to be determined (C2). The three scenarios are as follows: a) Keeping the existing large warehouse in St. Kilda with additional warehouses rented all in Melbourne; b) Keeping the existing large warehouse in St. Kilda with additional warehouses rented in different cities (Note: some of the additional warehouses can be in Melbourne if necessary); and c) Not keeping the existing large warehouse or using any warehouse in Melbourne but renting new warehouses in other cities. Upon analysing the outcome of the three scenarios, you will need to make a recommendation on which option to take and prepare a year-by-year action plan (ie., to do what in 2020, 2021 and 2022 respectively) (Q3). For each scenario, you will need three models to find the optimal network configurations for each year from 2020 to 2022. As mentioned above, this can be done easily by copying the validated generic model to a new spreadsheet and rename it. Next, alter the input parameters of the model or revise the constraints in the Solver dialogue box where appropriate to reflect the condition of the scenario. Then, run Solver to get the optimal solution for that scenario. Basically, the workbook should comprise 11 worksheets. One for the as-is situation (i.e., 2019), nine for the three scenarios (i.e., a, b and c), each with three years (i.e., 2020, 2021 and 2020), and one worksheet to summarize the model outputs to enable you to make the final recommendation with a year-by-year action plan. fixed cost, variable cost and the shipment cost. The values of all decision variables should be determined by Solver and not manually set. 4. The constraints in this case are exactly the same as those in the Sun Oil example, i.e., capacity and demand constraints. First, total shipments from all warehouses should not exceed total available capacity. Excess capacity is permitted as it is unlikely that the total demand would exactly match the available capacity. However, total shipment to a customer zone should be exactly equal to the expected demand as excess order imply errors which can be costly. Unmet demand is also undesirable as again it means errors and translates to cost of lost sales. Just refer to the Sun Oil example and you should see how to set up these two constraints on the spreadsheet and in the Solver dialogue box. There are other constraints such as non- negative value and integer constraints to be set up. Again, they are very similar to those in the Sun Oil example. 5. Once the model is properly set up, you can use it to work out the optimal network configuration and the annual total cost under the three scenarios for three years. To help you check if your model is correct, the total annual cost of the as-is situation (i.e., Q1) should be $263,795. This is the sum of the annual fixed cost (i.e., leasing of the warehouse), order handling cost (i.e., Shelving and retrieving), order processing cost (i.e., manual picking and packaging), and shipment cost (.e., Australian Post shipment charges) in 2019. 6. Once you have obtained the total cost of the as-is situation, you can use the model (with Solver properly set up and duly revised to reflect the requirement of a scenario) to work out the optimal configurations of the three scenarios in three years, i.e., 2020 2021, and 2022. Again, the annual total cost will be the sum of the annual fixed, order handling, order processing, and shipment cost. This figure will then be used for comparison with that of other scenarios to determine the optimal arrangement to be adopted, entirely from a cost perspective. Just to serve as benchmarks, the total annual costs for 2020, 2021, and 2022 under the first scenario (i.e., leasing all warehouses in Melbourne only) should be $494.831, $722.247. and $830,696 respectively 7. To answer Q4, asking the customer to bear the shipment cost basically means we do not need to consider it in the model because the Australian Post shipment charges will be paid by the customer as part of the order cost. So, all we need to do is to rerun the models without considering shipment cost in the objective function. 8. To show how you come to the recommendation, it would be necessary to use a worksheet to summarise the findings of the scenario tests for easy comparison. Then, a year-by-year plan can be made based on the recommended option (see Figure 2). This applies to both Q3 and Q4. 9. By comparing the 3-year total costs and the long-run optimal warehouse network configurations obtained in Questions 3 and 4, we can easily answer Q5 and tell whether the current shipping arrangement (i.e., charging customer a flat fee for shipping the order and pay the Australian Post based on distance) or the proposed arrangement (i.e., letting customer bear the shipping cost charged by the Australian Post) and why. Finally, you may wish to note that the assessment of this assignment is not based entirely on the Excel model or the correctness of the total cost figures. I will also look at the model logic, the model structure, the layout design (e.g., use of colour to clearly demarcate the various sections of the model for easy tracking), the depth of the analysis, and the organization and presentation of the report, among other things, to give the final marks. The weight of the model and the report is 50%-50% although on Canvas a whole number must be used, i.e., submitting a report without the model or vice versa will have a maximum of 15 marks only. Therefore, both the report - a Word file (no PDF file) and the model an Excel file (no PDF file) - must be submitted. NOTE: It is unacceptable to submit a model or solution downloaded from the publisher or the Internet without adequate understanding of the model logic and appropriate modifications to the model design to incorporate the scenarios of the case questions. Even though there might be published solutions, you still need to put in adequate effort to create a base model of your own with variations for scenario analyses. Otherwise, there will be significant deduction in marks and possible hearing for suspected plagiarism in severe cases. Furthermore, contract cheating is unethical and also a violation of the RMIT assessment regulation. Students must do the assignment by themselves to achieve the set learning objectives. Severe academic disciplinary action will be taken against those who outsourced their work to third parties, be it commercial entities, peers, friends or relatives. So, please try your best to complete the assignment with your own effort. If you need help, please feel free to contact me as soon as possible and I will try my best to assist. NOTE: DO NOT use only one single spreadsheet or a summary report in the Excel Workbook for everything and expect the assessor to create the models for the different options or scenarios from scratch to check your answers. Marks will be deducted if the required nine models (each in one sheet) and one summary sheet are not provided. To answer Q4, basically we just take out the shipment cost from the models and rerun them again to see if the outcomes are significantly different or not. The findings will enable us to decide if we can recommend the same as in Q3 or we have to revise our recommendation. Together with the findings from Q3, we can also decide if the new shipping arrangement should be adopted and why? Here are some suggestions for you to set up the model: 1. There are many ways to build the model. You are encouraged to use your own design keeping in mind that the model logic needs to be easy to follow and understand while the model layout is simple and clear. Proper colour scheme and legend should be used where appropriate to make the model self-explanatory. As an example, the tables for the input parameters, such as warehouse fixed and variable costs, capacity, annual demand (can be 2019, 2020, 2021 or 2022) from the various customer zones, and current unit shipment costs, together with the decision variables, constraints and the cost items, could be set up as shown in Figure 1. To fill in the input parameter tables, you would need to study the case carefully and make full use of the data and information provided there he setting up of constraints and the cost items are basically the same as that of the Sun Oil example. Ansuts. Conte De Capacite Astan Poat Charges per Ordier Shaped NT WA SA VIC Sea WH Lage WH Find Cest Fred Unt Order Harding Carl NSW OLD TAS ACT Sugely chy Melbourne sydney Prane delaide Perth Am Dund Decision Variables Onders Shop WA SA Toral Orders Shaped Largu WOW NSW OLD NT VIC TAS ACT laboume Sydney Pro Melaide Per Total Ordinace Constraints Exces CACE Melboume sydney Pri Adelaide NSW OLD NT WA SA VIC TAS ACT Dew Fred, Operating & Shipping Casta Order Handing Cool Order Processing Cost Shipment Cost Colective Fiction Total rut Parameters Decision Varios Cans Outive function Figure 1 - Possible design of input parameter tables for the MiniToys case 2. While we introduced the concept of binary variables in the SunOil example, we should use integer variables in the MiniToys case because there could be situations with more than one small or large warehouse in a city. Using binary variables will limit the number of warehouse to one or zero which is not appropriate in this case. 3. The other decision variables are of course the number of orders to be shipped from the warehouses to the various customer zones. Getting these numbers correctly would help you work out the minimum total annual Year (A) All in Melbourne (B) One Large in Melbourne (C) None in Melbourne Total Cost W/H Configuration Total Cost W/H Configuration Total Cost W/H Configuration 2020 MEL SYD BRI ADE PER 2021 MEL SYD BRI ADE PER 2022 MEL SYD BRI ADE PER 3-year Total $0 $0 $0 Recommendation Year-by-Year Action Plan Action Year 2020 2021 2022 Figure 2 - Possible layout of the summary sheet with recommendation and year-by-year action plan I hope the above tips are useful. Regards, Charles . Location Melbourne Sydney Brisbane Adelaide Perth Small Warehouse Fixed Cost ($ / year) Variable Cost ($ / Order) 30,000 2.0 25,000 3.0 22,000 2.5 22,000 1.5 24,000 1.0 Large Warehouse Fixed Cost ($ / year) Variable Cost ($ / Order) 50,000 2.0 42,000 3.0 37,500 2.5 37,500 1.5 40,000 1.0 Table 3: Australia Post Charges per Shipment (in $) NSW QLD NT WA Melbourne 11 28 35 37 Sydney 4 25 37 43 Brisbane 11 19 34 46 Adelaide 17 24 27 27 Perth 49 44 32 11 SA 18 22 25 9 29 VIC 4 11 20 9 41 TAS 6 14 25 15 44 ACT 7 4 15 14 46 Here are some tips to help you complete the quantitative assignment of the course. The assignment is about network optimization and will require the use of Solver to find the optimal solutions. For this assignment, you will need to first develop a generic model on a spreadsheet and validate it. Then, with proper modifications in terms of model input, e.g., demand figures, unit transportation costs, and constraints in Solver, e.g., condition that reflects the requirement of a particular option, the model can be used as a tool to help find the optimal warehouse configuration for MiniToys - the case company. You can set up the model using the SunOil example as a reference. Both problems are identical in nature and are categorized as Capacitated Facility Location Problems. It means that their mathematical formulations are very similar, although some minor modification is needed in the case of MiniToys as order handling cost and transportation cost are separated. In addition, there is order processing cost. Basically, the objective function is a cost functional comprising fixed cost, order handling cost, order processing cost and shipment cost. In the MiniToys case, fixed costs are the annualized costs for leasing the warehouses. Variable costs are order handling (i.e., Shelving and retrieving) and order processing (i.e., picking and packaging) which vary with the number of orders processed in the year. There is also shipment cost which is also variable as it varies with the number of orders distributed to customers from various warehouses. The constraints in the MiniToys case include demand constraint (.e., all demand must be met), capacity constraint (i.e., available capacity cannot be exceeded), non-negativity constraint (.e., values of all variables cannot be negative) and integer constraint (i.e., warehouses must be in whole number). The best way to go is to first create a model representing the current or the as-is situation, i.e., Year 2019. This will be needed to answer Case Question 1 (Q1). To work out the total operating cost for 2019, you do not need Solver at all because everything is fixed and there is no alternative course of action, hence no need for optimization. However, it will still be good to use Solver to find the solution because this will allow you to (1) cross-check if your cost figure is correct and (ii) have a generic model that can be copied and modified for option or scenario testing. As the layout of all the other models will be the same as that of the generic model, and the Solver setups will be quite similar, you can simply copy the generic model and rename it to become another model for a particular scenario. There are three scenarios to investigate in the MiniToys case. For each scenario, the optimized warehouse configurations for 2020, 2021 and 2022 have to be determined (C2). The three scenarios are as follows: a) Keeping the existing large warehouse in St. Kilda with additional warehouses rented all in Melbourne; b) Keeping the existing large warehouse in St. Kilda with additional warehouses rented in different cities (Note: some of the additional warehouses can be in Melbourne if necessary); and c) Not keeping the existing large warehouse or using any warehouse in Melbourne but renting new warehouses in other cities. Upon analysing the outcome of the three scenarios, you will need to make a recommendation on which option to take and prepare a year-by-year action plan (ie., to do what in 2020, 2021 and 2022 respectively) (Q3). For each scenario, you will need three models to find the optimal network configurations for each year from 2020 to 2022. As mentioned above, this can be done easily by copying the validated generic model to a new spreadsheet and rename it. Next, alter the input parameters of the model or revise the constraints in the Solver dialogue box where appropriate to reflect the condition of the scenario. Then, run Solver to get the optimal solution for that scenario. Basically, the workbook should comprise 11 worksheets. One for the as-is situation (i.e., 2019), nine for the three scenarios (i.e., a, b and c), each with three years (i.e., 2020, 2021 and 2020), and one worksheet to summarize the model outputs to enable you to make the final recommendation with a year-by-year action plan. fixed cost, variable cost and the shipment cost. The values of all decision variables should be determined by Solver and not manually set. 4. The constraints in this case are exactly the same as those in the Sun Oil example, i.e., capacity and demand constraints. First, total shipments from all warehouses should not exceed total available capacity. Excess capacity is permitted as it is unlikely that the total demand would exactly match the available capacity. However, total shipment to a customer zone should be exactly equal to the expected demand as excess order imply errors which can be costly. Unmet demand is also undesirable as again it means errors and translates to cost of lost sales. Just refer to the Sun Oil example and you should see how to set up these two constraints on the spreadsheet and in the Solver dialogue box. There are other constraints such as non- negative value and integer constraints to be set up. Again, they are very similar to those in the Sun Oil example. 5. Once the model is properly set up, you can use it to work out the optimal network configuration and the annual total cost under the three scenarios for three years. To help you check if your model is correct, the total annual cost of the as-is situation (i.e., Q1) should be $263,795. This is the sum of the annual fixed cost (i.e., leasing of the warehouse), order handling cost (i.e., Shelving and retrieving), order processing cost (i.e., manual picking and packaging), and shipment cost (.e., Australian Post shipment charges) in 2019. 6. Once you have obtained the total cost of the as-is situation, you can use the model (with Solver properly set up and duly revised to reflect the requirement of a scenario) to work out the optimal configurations of the three scenarios in three years, i.e., 2020 2021, and 2022. Again, the annual total cost will be the sum of the annual fixed, order handling, order processing, and shipment cost. This figure will then be used for comparison with that of other scenarios to determine the optimal arrangement to be adopted, entirely from a cost perspective. Just to serve as benchmarks, the total annual costs for 2020, 2021, and 2022 under the first scenario (i.e., leasing all warehouses in Melbourne only) should be $494.831, $722.247. and $830,696 respectively 7. To answer Q4, asking the customer to bear the shipment cost basically means we do not need to consider it in the model because the Australian Post shipment charges will be paid by the customer as part of the order cost. So, all we need to do is to rerun the models without considering shipment cost in the objective function. 8. To show how you come to the recommendation, it would be necessary to use a worksheet to summarise the findings of the scenario tests for easy comparison. Then, a year-by-year plan can be made based on the recommended option (see Figure 2). This applies to both Q3 and Q4. 9. By comparing the 3-year total costs and the long-run optimal warehouse network configurations obtained in Questions 3 and 4, we can easily answer Q5 and tell whether the current shipping arrangement (i.e., charging customer a flat fee for shipping the order and pay the Australian Post based on distance) or the proposed arrangement (i.e., letting customer bear the shipping cost charged by the Australian Post) and why. Finally, you may wish to note that the assessment of this assignment is not based entirely on the Excel model or the correctness of the total cost figures. I will also look at the model logic, the model structure, the layout design (e.g., use of colour to clearly demarcate the various sections of the model for easy tracking), the depth of the analysis, and the organization and presentation of the report, among other things, to give the final marks. The weight of the model and the report is 50%-50% although on Canvas a whole number must be used, i.e., submitting a report without the model or vice versa will have a maximum of 15 marks only. Therefore, both the report - a Word file (no PDF file) and the model an Excel file (no PDF file) - must be submitted. NOTE: It is unacceptable to submit a model or solution downloaded from the publisher or the Internet without adequate understanding of the model logic and appropriate modifications to the model design to incorporate the scenarios of the case questions. Even though there might be published solutions, you still need to put in adequate effort to create a base model of your own with variations for scenario analyses. Otherwise, there will be significant deduction in marks and possible hearing for suspected plagiarism in severe cases. Furthermore, contract cheating is unethical and also a violation of the RMIT assessment regulation. Students must do the assignment by themselves to achieve the set learning objectives. Severe academic disciplinary action will be taken against those who outsourced their work to third parties, be it commercial entities, peers, friends or relatives. So, please try your best to complete the assignment with your own effort. If you need help, please feel free to contact me as soon as possible and I will try my best to assist. NOTE: DO NOT use only one single spreadsheet or a summary report in the Excel Workbook for everything and expect the assessor to create the models for the different options or scenarios from scratch to check your answers. Marks will be deducted if the required nine models (each in one sheet) and one summary sheet are not provided. To answer Q4, basically we just take out the shipment cost from the models and rerun them again to see if the outcomes are significantly different or not. The findings will enable us to decide if we can recommend the same as in Q3 or we have to revise our recommendation. Together with the findings from Q3, we can also decide if the new shipping arrangement should be adopted and why? Here are some suggestions for you to set up the model: 1. There are many ways to build the model. You are encouraged to use your own design keeping in mind that the model logic needs to be easy to follow and understand while the model layout is simple and clear. Proper colour scheme and legend should be used where appropriate to make the model self-explanatory. As an example, the tables for the input parameters, such as warehouse fixed and variable costs, capacity, annual demand (can be 2019, 2020, 2021 or 2022) from the various customer zones, and current unit shipment costs, together with the decision variables, constraints and the cost items, could be set up as shown in Figure 1. To fill in the input parameter tables, you would need to study the case carefully and make full use of the data and information provided there he setting up of constraints and the cost items are basically the same as that of the Sun Oil example. Ansuts. Conte De Capacite Astan Poat Charges per Ordier Shaped NT WA SA VIC Sea WH Lage WH Find Cest Fred Unt Order Harding Carl NSW OLD TAS ACT Sugely chy Melbourne sydney Prane delaide Perth Am Dund Decision Variables Onders Shop WA SA Toral Orders Shaped Largu WOW NSW OLD NT VIC TAS ACT laboume Sydney Pro Melaide Per Total Ordinace Constraints Exces CACE Melboume sydney Pri Adelaide NSW OLD NT WA SA VIC TAS ACT Dew Fred, Operating & Shipping Casta Order Handing Cool Order Processing Cost Shipment Cost Colective Fiction Total rut Parameters Decision Varios Cans Outive function Figure 1 - Possible design of input parameter tables for the MiniToys case 2. While we introduced the concept of binary variables in the SunOil example, we should use integer variables in the MiniToys case because there could be situations with more than one small or large warehouse in a city. Using binary variables will limit the number of warehouse to one or zero which is not appropriate in this case. 3. The other decision variables are of course the number of orders to be shipped from the warehouses to the various customer zones. Getting these numbers correctly would help you work out the minimum total annual Year (A) All in Melbourne (B) One Large in Melbourne (C) None in Melbourne Total Cost W/H Configuration Total Cost W/H Configuration Total Cost W/H Configuration 2020 MEL SYD BRI ADE PER 2021 MEL SYD BRI ADE PER 2022 MEL SYD BRI ADE PER 3-year Total $0 $0 $0 Recommendation Year-by-Year Action Plan Action Year 2020 2021 2022 Figure 2 - Possible layout of the summary sheet with recommendation and year-by-year action plan I hope the above tips are useful. Regards, Charles