Simplifying Bin Packing Using OR-Tools for a Single Bin Scenario

What will you learn?

In this comprehensive tutorial, you will dive into the realm of bin packing problems using Google’s OR-Tools. However, with a unique twist – focusing on a scenario with only one bin. You’ll grasp the fundamental concepts required to set up and solve this specific optimization problem efficiently.

Introduction to the Problem and Solution

Bin packing is a classic optimization challenge where the goal is to pack objects of varying sizes into bins in a way that minimizes the number of bins used. In our context today, we simplify this task by concentrating on fitting items into just one bin while ensuring minimal wasted space and determining if all items can be accommodated.

To tackle this simplified yet intriguing problem, we turn to Google’s Operations Research tools (OR-Tools), equipped with a range of solvers for diverse optimization problems including bin packing. By treating our single-bin constraint as a special case, we harness these robust algorithms to effectively derive solutions. Through Python integration and leveraging OR-Tools’ linear solver module, we will define our problem parameters and constraints to achieve an optimal fit or ascertain feasibility based on item sizes and bin capacity.


from ortools.linear_solver import pywraplp

def solve_bin_packing_one_bin(item_sizes, bin_capacity):
    # Create solver instance.
    solver = pywraplp.Solver.CreateSolver('SCIP')

    # Variable creation: Indicates whether an item is placed in the bin.
    x = [solver.IntVar(0, 1, f'x{i}') for i in range(len(item_sizes))]

    # Constraint: Sum of size*variable must be less than or equal to bin capacity.
    constraint_expr = sum(item * var for item,var in zip(item_sizes,x))
    solver.Add(constraint_expr <= bin_capacity)

    # Objective: Maximize summed weights - try to fill the bin as much as possible.

    status = solver.Solve()

    if status == pywraplp.Solver.OPTIMAL:
        print("Objective value =", solver.Objective().Value())
        packed_items = [i for i in range(len(x)) if x[i].solution_value() > 0]
        return packed_items
        print("The problem does not have an optimal solution.")

# Copyright PHD


The provided code snippet illustrates how to construct and solve a single-bin packing problem using OR-Tools. Here are key components explained:

  • Creating Solver Instance: Initialization of a Solver object from OR-Tools enables us to define variables, constraints, and objectives crucial for solving our optimization puzzle.

  • Variable Creation: Binary variables (x) are generated for each item that could potentially go into the bin (item_sizes). These variables signify whether an item is included (1) or not (0) in the final packing list.

  • Adding Constraints: The primary constraint ensures that the total volume of selected items does not exceed bin_capacity. This is achieved by creating an expression that sums up each item’s size multiplied by its corresponding variable while ensuring it stays within our limit.

  • Defining Objective: As maximizing space utilization without surpassing capacity is ideal; hence, maximizing the total size of packed items becomes our objective function.

By executing solve_bin_packing_one_bin with input sizes of available items and details about your ‘single’ bin�s capacity � you obtain insights into which items should ideally be included (if feasible) to maximize usage without overfilling.

    1. What Is Bin Packing?

      • Bin Packing involves allocating objects into containers (‘bins’) aiming to minimize either used containers or unused space within them based on criteria like object sizes vs container limitations; often applied in logistics/resource allocation tasks among other fields!
    2. How Does Google’s OR-Tools Help?

      • Google�s Operations Research Tools offer diverse algorithms designed to handle complex optimization problems efficiently�providing necessary functionalities to model real-world dilemmas effectively resolve them via programming techniques!
    3. Can I Use Multiple Bins Instead?

      • While our example focuses on ‘single-bin’ scenarios; adjustments can certainly be made to accommodate more complex versions involving multiple bins by adding additional variables/constraints accordingly tailoring underlying logic better suit varying needs!
    4. Is It Possible To Solve Without Fitting All Items?

      • Yes! If unable fit all given pieces due restrictions at hand; algorithm picks out best possible combination under circumstances sometimes leaving out certain parts depending overall strategy/goal aimed towards achieving initially anyway…
    5. Do I Need Any Special Software Installations?

      • To run provided examples, Python installation alongside ortools package accessibility through pip installation command suffice found online respective official documentation pages further assistance needed during setup process beforehand starting off coding journey here together us today!
    6. How Accurate Are The Solutions Provided By OR-Tools?

      • Solutions derived are highly accurate so long proper formulations input correctly since rely on mathematical models solvers optimize results based upon inputs received thus ensuring reliability outcomes obtained afterwards always meet high professional standards expected!

Harnessing Google’s OR-tools specifically tailored for handling unique cases such as single-bin versions amidst traditional broader scope challenges presents an efficient way manage resources optimize outputs effectively�even when faced limited options restrictions place key understanding lies grasping fundamentals behind modeling process applying correct methodologies derive desired outcomes consistently reliably future projects alike remember practice makes perfect continue experimenting different scenarios increase familiarity confidence handling similar issues arise down line wishing nothing success endeavors ahead until then take care happy coding everyone thank joining lesson today goodbye now!

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