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7+ " # Assignments in this Course\n "
8+ " \n "
9+ " Management Science\n "
10+ " \n "
11+ " ## Welcome\n "
12+ " \n "
13+ " This course uses two practical assignments to help you learn how to\n "
14+ " think like an optimization-minded analyst. Your focus throughout should\n "
15+ " be: understand the problem, design a sensible algorithmic approach,\n "
16+ " interpret results, and translate findings into clear business\n "
17+ " recommendations. Perfect code is not the goal, insight is.\n "
18+ " \n "
19+ " > **Tip**\n "
20+ " >\n "
21+ " > You’re encouraged to use AI tools responsibly. Treat them like a smart\n "
22+ " > pair-programmer that helps you learn and debug, but make sure you\n "
23+ " > understand all code you submit and can explain it. You are fully\n "
24+ " > responsible for the correctness and originality of submitted work,\n "
25+ " > regardless of gAI use.\n "
26+ " \n "
27+ " > **Warning**\n "
28+ " >\n "
29+ " > Do not include gAI-generated meta-comments describing what gAI\n "
30+ " > changed. Thus, keep comments focused on explaining the code itself. If\n "
31+ " > I find instances of such gAI meta-comments, you will loose points for\n "
32+ " > the technical implementation. Keep your comments clean and relevant.\n "
33+ " \n "
34+ " > **Warning**\n "
35+ " >\n "
36+ " > gAI may hallucinate additional features that are not required. Be\n "
37+ " > careful here!\n "
38+ " \n "
39+ " ## The Two Assignments\n "
40+ " \n "
41+ " **Assignment 1: Risk Analysis & Forecasting (Due at the start of Lecture\n "
42+ " 8, weight 30%)**\n "
43+ " \n "
44+ " You’ll simulate investment outcomes to understand risk and then forecast\n "
45+ " demand using simple time-series techniques. Expect to implement Monte\n "
46+ " Carlo simulations, moving averages, and exponential smoothing, then\n "
47+ " compare methods and justify a recommendation.\n "
48+ " \n "
49+ " **Assignment 2: Optimization in Practice (Due at the start of Lecture\n "
50+ " 10, weight 30%)**\n "
51+ " \n "
52+ " You’ll build and improve delivery routes and construct valid staff\n "
53+ " schedules under constraints, then iterate to improve quality and\n "
54+ " satisfaction. Expect to implement heuristics, evaluate improvements,\n "
55+ " visualize routes/schedules, and discuss trade-offs.\n "
56+ " \n "
57+ " Work is in groups for both assignments. Typical time commitment is 4–6\n "
58+ " hours for Assignment 1 and 5–7 hours for Assignment 2.\n "
59+ " \n "
60+ " ## How You’ll Be Graded\n "
61+ " \n "
62+ " ### Technical Implementation (60% of assignment grade)\n "
63+ " \n "
64+ " - **Excellent (90-100%):** Code runs correctly, handles edge cases,\n "
65+ " well-structured\n "
66+ " - **Good (80-89%):** Code runs with minor issues, mostly\n "
67+ " well-structured\n "
68+ " - **Satisfactory (70-79%):** Code runs but has some problems, basic\n "
69+ " structure\n "
70+ " - **Needs Improvement (60-69%):** Code has significant issues but\n "
71+ " shows understanding\n "
72+ " - **Inadequate (\\ <60%):** Code doesn’t run or shows fundamental\n "
73+ " misunderstanding\n "
74+ " \n "
75+ " ### Business Insight (20% of assignment grade)\n "
76+ " \n "
77+ " - **Excellent:** Clear actionable recommendations with quantified\n "
78+ " impact\n "
79+ " - **Good:** Solid insights with some quantification\n "
80+ " - **Satisfactory:** Basic insights communicated clearly\n "
81+ " - **Needs Improvement:** Insights unclear or not business-relevant\n "
82+ " - **Inadequate:** No meaningful insights provided\n "
83+ " \n "
84+ " ### Presentation Quality (20% of assignment grade)\n "
85+ " \n "
86+ " - **Excellent:** Compelling visualizations, clear narrative\n "
87+ " - **Good:** Good visuals, mostly clear communication\n "
88+ " - **Satisfactory:** Basic visualizations, understandable presentation\n "
89+ " - **Needs Improvement:** Poor visuals or unclear communication\n "
90+ " - **Inadequate:** No effective communication of results\n "
91+ " \n "
92+ " > **Important**\n "
93+ " >\n "
94+ " > Use AI to clarify concepts, explore alternatives, and debug. Please,\n "
95+ " > do not submit code you don’t understand. You’re responsible for every\n "
96+ " > line. If you draw from external sources, cite them briefly in your\n "
97+ " > notebook.\n "
98+ " \n "
99+ " ## Tips for Success\n "
100+ " \n "
101+ " - Start simple, then iterate. Get a working baseline first.\n "
102+ " - Check your logic with small tests and intermediate prints.\n "
103+ " - Keep business questions in view—your job is to inform a decision.\n "
104+ " \n "
105+ " > **Tip**\n "
106+ " >\n "
107+ " > Common pitfalls to watch for: forgetting return-to-depot in routing,\n "
108+ " > violating constraints after a change, optimizing forever instead of\n "
109+ " > delivering “good enough,” and failing to actually answer the business\n "
110+ " > questions.\n "
111+ " \n "
112+ " ## Submission Requirements\n "
113+ " \n "
114+ " Submit one Jupyter notebook per assignment that includes:\n "
115+ " \n "
116+ " - Your commented code with outputs and clear visualizations.\n "
117+ " - Short, direct answers to the business questions.\n "
118+ " \n "
119+ " File naming:\n "
120+ " \n "
121+ " - Assignment 1: Assignment_01\\ _\\ [GroupNames\\ ].ipynb\n "
122+ " - Assignment 2: Assignment_02\\ _\\ [GroupNames\\ ].ipynb\n "
123+ " \n "
124+ " ## Late Policy\n "
125+ " \n "
126+ " Submissions lose 10% per day late, up to a maximum of three days.\n "
127+ " Exceptions are granted only for documented emergencies."
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