Variation of Bin Packing Problem with Constraints

Question

Problem Definition
You have N items to be placed in M bins. Each item n has weight Ni and each bin 'm' has capacity of Mi items and Mj weight.

Solve with First Fit.

Data Input Types:

N<itemID, weight> = [(1, 10), (2, 12), (3, 6), (4, 15), (5, 2), (6, 10), (7, 15)]
M<binID, maxItems, maxWeight> = [('a', 2, 39), ('b', 4, 10), ('c', 5, 5), ('d', 4, 20)]

Although Python or C code is more preferable, you can answer with psuedo-code

Thank you

Answer 1

items = [(1, 10), (2, 12), (3, 6), (4, 15), (5, 2), (6, 10), (7, 15)]
bins = [('a', 2, 39), ('b', 4, 10), ('c', 5, 5), ('d', 4, 20)]

bin_capacities = {bin_id: {'maxItems': max_items, 'maxWeight': max_weight, 'currentItems': 0, 'currentWeight': 0} for bin_id, max_items, max_weight in bins}
assignments = {bin_id: [] for bin_id, _, _ in bins}

def first_fit(items, bin_capacities, assignments):
    for item_id, item_weight in items:
        placed = False
        for bin_id, capacity in bin_capacities.items():
            if capacity['currentItems'] < capacity['maxItems'] and capacity['currentWeight'] + item_weight <= capacity['maxWeight']:
                assignments[bin_id].append((item_id, item_weight))
                capacity['currentItems'] += 1
                capacity['currentWeight'] += item_weight
                placed = True
                break
        if not placed:
            print(f"Item {item_id} with weight {item_weight} could not be placed in any bin.")
    return assignments

assignments = first_fit(items, bin_capacities, assignments)

for bin_id, items in assignments.items():
    print(f"Bin {bin_id} contains items: {[i[0] for i in items]} \t {sum([i[1] for i in items])}")

Output:

Item 6 with weight 10 could not be placed in any bin.
Item 7 with weight 15 could not be placed in any bin.
Bin a contains items: [1, 2]     22
Bin b contains items: [3, 5]     8
Bin c contains items: []         0
Bin d contains items: [4]        15