1) make a cover page for the final report, write specifically your team members name and surname and student IDs,

2) after next page, write one page and half paragraph an EXECUTIVE SUMMARY, in this paragraph summary what things you did in your project based on the problem and 1-2 sentences report your main results in this project

3) insert an Table of Contents page to show contents of your report, note that use automatic contents creation of MS Word by assigning different text styles in your project body and headlines, then you will not spend much time to create such a table, it could be created automatically

4) divide your project report into 3 Main Parts according to each 3 Steps you followed,

4.1: revise your Step 1 analysis according to feedback and if possible improve your descriptions, flowcharts or explanations, add necessary analyses graphs in appendices

remember, this part will have,mainly, System Analysis, Problem Definition and Conceptual Model

4.2: revise your Step 2 analysis, (feedback will be given on major issues if seen), and insert them into this part,

remember, this part will have 2 main subparts, the first subpart will have:

- descriptive statistics results, correlation analyses, histograms, goodness of fit tests and results, then your final comments on selection of probability distributions,

the second subpart will have:

- description of the simulation model and its elements roughly and how the statistics with a few snaphots from the model

4.3: this part will have the following analysis: (This part is supposed to be finished after covering the lectures in Week 8 and 9)

(1) Verification: use at least 2 verification methods and explain at least 3-4 sentences how you verified your simulation model,

(2) Construct BLACK-BOX model of your simulation model, clearly show your input and output variables (note: later you can observe them also in Arena Process Analyzer, and get a snaphot of them during Comparison of alternative scenarios)

(3) Validation: run your model initially with 5 or 10 replications with an initially selected simulation length and apply CI approach or t-test, you are free to choose them, after report results how you validated your model, show your calculations clearly and your decisions

(4) Output Analysis of a validated model: your purpose is to analyze a long-run performance of the system, so assume a non-terminating simulation analysis (This part is supposed to be finished in 2 weeks)

- Calculate Ensemble Averages and show the plot of it, after show how you obtained the WARM-UP period on it and write 2-3 sentences explanations on it, answer such questions:

* What do you think for initial bias?

* What are your initial conditions in the simulation model?

* What do you think of steady-state conditions in your real system and what are steady-state performance level in your simulation model?

- Performed required calculations for

* number of replications

* warm up periof and determine ideal simulation length

* since you are using Trial or Student version of the model, you will get a 150 entity error if you increase simulation length, therefore arrange your simulation length carefully, and considering the ideal simulation length, determine to obtain the same precision or estimate level, what the increased number of replications should be (see simulation length vs replication number tradeoff in your book and lecture notes, you can apply it, also remember you can decrease the arrival rate to create less number of inputs and entities so that you can increase the simulation length however, this should not break your input analysis results)

* based on replication number and chosen simulation length run your model and report Confidence Interval plots obtained from Output Analyzer, take snapshots and comment on them

(5) Comparison of Alternative Scenarios:

- Create alternative scenarios by changing some input variables, describe in one pragraph how and why you created such alternative scenarios,

- Apply Arena Process Analyzer, select your scenario as default and main scenario and show alternative scenario results on the same graph based on your performance measures,

- Run alternative scenarios separately, get the output files under each scenario and obtain CI based on mean differences, and take snapshots from at least 2 different scenarios and report/comment them.

5) At the end, write a Conclusions and Discussions part, and in one paragraph evaluate or comment your results, do you think your model results could be implemented in real system? do you think simulation modelling will be helpful on improving system performances?

Also, write one more small parapraph and comment on how you managed your project with team members, if any team members worked a lot more than the others mention, vice versa if anyone did not cooparate or support the project mention very briefly, this could help you think how you manage the project

6) Insert if you have any reference papers, documents apart from lecture book and textbook,

OUR PROBLEM

1. Introduction

This report aims to address a real-life problem and propose a conceptual model to solve it. The report is divided into several sections, starting with a description of the problem, followed by the conceptual model development, assumptions, flowchart description, and finally, the stochastic and deterministic elements. The report concludes with a summary of the proposed solution.

2. Real-Life Problem Description

The real-life problem that our group has chosen to address is the issue of food waste in the restaurant industry. The amount of food waste generated by restaurants has become a significant environmental concern, and it is estimated that 11.4 million tons of food waste are produced in the US alone each year.

To tackle this problem, we have decided to focus on a restaurant chain with multiple locations. Our proposed system will aim to reduce food waste by optimizing food inventory management and minimizing overproduction. We believe that this system will not only reduce food waste but also improve the restaurant chain's profitability by reducing food costs.

Our system boundary includes the restaurant chain's inventory management system, the kitchen, and the dining area. The system domain includes the restaurant chain's operations, such as food ordering, inventory management, food preparation, and serving.

3. Conceptual Model Development

We have developed a conceptual model for our proposed system, which includes the following elements:

Entities: Food items, ingredients, inventory, kitchen staff, servers, customers

Attributes: Quantity, expiration date, cost, recipe, cooking time, serving time, customer preference

Activities: Food ordering, food preparation, food serving, inventory management, waste disposal

Events: Customer arrival, food delivery, food consumption, food expiration

State Variables: Inventory level, food quality, customer satisfaction, waste level

Our model assumes that the restaurant chain has access to real-time inventory data and customer preferences. The system will use this data to optimize inventory management and minimize overproduction. We assume that the kitchen staff and servers are trained to follow the system's guidelines to reduce food waste.

4. Assumptions

Our assumptions include the availability of real-time inventory data and the willingness of the restaurant chain's staff to follow the proposed system's guidelines. We also assume that customers will not significantly change their behavior due to the proposed system's implementation.

5. Flowchart Description

Our proposed system's flowchart includes the following steps:

Receive food orders from customers

Update inventory data

Check inventory level and food expiration dates

Prioritize food items based on inventory data and expiration dates

Prepare food based on prioritized food items

Serve food to customers

Monitor food consumption and update inventory data

Dispose of waste properly

The flowchart aims to optimize food inventory management and minimize overproduction while ensuring customer satisfaction.

6. Stochastic and Deterministic Elements

Our proposed system has both stochastic and deterministic elements. The stochastic elements include customer arrival patterns and food expiration rates, which may vary over time. The deterministic elements include inventory data and customer preferences, which will be used to optimize food inventory management and minimize overproduction.

7. Conclusion

In conclusion, our proposed system aims to address the issue of food waste in the restaurant industry by optimizing food inventory management and minimizing overproduction. The conceptual model we have developed includes entities, attributes, activities, events, and state variables. Our assumptions include the availability of real-time inventory data and the ability to predict customer demand accurately.