Strategies for packing

Packing problems are a class of optimisation problems in algorithms which involve attempting to pack objects together into containers.
The main goal of these problems is to either pack a single container as densely as possible or pack all objects using as few containers as possible.
Real-world applications include packing items in cargo, loading data in memory blocks and time scheduling.

First Fit algorithm places the incoming items into the bin where it will fit first. If there is no existing bin that can accommodate the item, a new bin is created.
Next Fit algorithm is similar to the first fit but instead of starting with the first bin for every new object, it places each new item in the same bin as the last item if it fits, if not, it places the new object into a new bin.
The Best Fit algorithm scans all bins and finds the best one that fits. This approach uses the most memory but can find the most optimal packing solution.
Worst Fit algorithm places each item in the bin with the most remaining space.

Computing time is a key aspect when analysing packing algorithms. Some algorithms strive for good approximations in feasibly computable time.
Space required by algorithms is also another important aspect in analysis. Some algorithms are memory intensive.
Performance of packing algorithms is often analysed using Big O notation which describes the maximum amount of time the algorithm would take to run.

An optimised packing algorithm should balance between storing as many items as possible and minimising the amount of wasted space.
Heuristic methods can be applied in packing algorithms to improve efficiency in real-world operations, algorithms can utilise heuristics to make decisions.
It’s important to consider if a packing problem is a bin packing problem (one-dimensional) or a knapsack problem (multi-dimensional) as it affects the algorithm’s approach.

When implementing packing algorithms, one should consider the fitness function (how to evaluate a good packing), the type of objects to be packed (homogeneous or heterogeneous) and constraints such as pack order and item rotation.
In a more advanced strategy, metaheuristic algorithms such as Genetic Algorithm or Simulated Annealing could be used to tackle complex packing problems.