In resource-management simulations, identifying the most efficient production sequence is crucial for maximizing output per unit time. This guide demonstrates how to parse semi-structured game economy data, calculate net value added for each item, and map the temporal footprint across distinct manufacturing facilities using Python.

1. Modeling the Production Ecosystem

Consider a tier-10 manufacturing setup featuring five distinct facilities: Raw Materials, Components, Tools, Furniture, and Agriculture. Each facility generates specific goods with predefined market values, production durations, and ingredient requirements. Raw materials serve as base items with zero dependencies, while advanced goods require composite inputs. The analytical objective is to decompose each final product into its fundamental time-cost acrosss all facilities, enabling the identification of the highest-yield production schedule.

2. Data Ingestion and Structuring

Raw manufacturing data is typically distributed across multiple spreadsheet tabs. We will load these into a unified dictionary structure, mapping facility names to their respective product catalogs. Each product entry will store its market price, production duration, and a list of required components. This transformation bridges the gap between flat spreadsheet formats and relational data models.

import pandas as pd
import numpy as np

def load_production_data(filepath):
    # Load all sheets into a dictionary of DataFrames
    raw_data = pd.read_excel(filepath, sheet_name=None, index_col=0)
    manufacturing_db = {}
    product_origin = {}

    for facility_name, df in raw_data.items():
        facility_products = {}
        for item_name in df.columns:
            item_info = {}
            for metric, value in df[item_name].items():
                if metric == 'requirements':
                    # Handle comma-separated dependencies or empty values
                    val_str = str(value)
                    item_info[metric] = val_str.split(',') if pd.notna(value) and ',' in val_str else []
                else:
                    item_info[metric] = value
            facility_products[item_name] = item_info
            product_origin[item_name] = facility_name
        manufacturing_db[facility_name] = facility_products

    return manufacturing_db, product_origin

3. Calculating Net Value and Facility Time Footprint

With the structured database in place, we can compute two critical metrics for every product: net value added and the total time each production step occupies across the facility network. Net value is derived by subtracting the cost of direct components from the final market price. The time footprint requires tracking both direct production time and the indirect time consumed by manufacturing dependencies.

To avoid deeply nested loops, we construct a flat record for each item, aggregating time usage into a dictionary that aligns with facility names. This approach scales efficiently and prepares the dataset for subsequent optimization routines.

def compute_production_metrics(db, origin_map):
    records = []
    for facility, products in db.items():
        for item, specs in products.items():
            # Calculate direct net value (Price - sum of component costs)
            base_cost = sum(
                db[origin_map[comp]][comp]['price']
                for comp in specs['requirements']
                if comp in origin_map and origin_map[comp] in db
            ) if specs['requirements'] else 0
            net_value = specs['price'] - base_cost

            # Initialize time tracking for all facilities
            time_usage = {f: 0 for f in db.keys()}
            time_usage[facility] = specs['time']

            # Account for direct component time
            for comp in specs['requirements']:
                if comp in origin_map:
                    comp_facility = origin_map[comp]
                    time_usage[comp_facility] += db[comp_facility][comp]['time']
                    # Account for sub-components (recursive breakdown)
                    if db[comp_facility][comp]['requirements']:
                        for sub_comp in db[comp_facility][comp]['requirements']:
                            if sub_comp in origin_map:
                                sub_facility = origin_map[sub_comp]
                                time_usage[sub_facility] += db[sub_facility][sub_comp]['time']

            records.append({
                'item': item,
                'facility': facility,
                'net_value': net_value,
                'production_time': specs['time'],
                **time_usage
            })
    return pd.DataFrame(records)

4. Optimization and Conclusion

Executing these functions yields a comprehensive DataFrame containing each product's net value and its precise temporal allocation across all manufacturing departments. This structured output transforms a loose formatted spreadsheet into a rigorous analytical foundation.

With the time-footprint and value metrics quantified, the scheduling problem becomes a constrained optimization task. Given fixed operational hours for each facility, the goal shifts to selecting a product mix that maximizes total net value. While small-scale scenarios can be solved via exhaustive enumeration, real-world industrial applications typically employ linear programming (e.g., PuLP, SciPy), genetic algorithms, or reinforcement learning to navigate the solution space efficiently. This data preprocessing pipeline ensures that downstream models receive clean, normalized inputs ready for algorithmic processing.