Best practices: Optimizing work with multiple resources (Beta)

Overview

Optimizing work allocation and resource utilization is essential for maximizing productivity and efficiency in multi-person work scenarios. However, the complexities involved in such scenarios require a thoughtful approach and the implementation of best practices.

This article explores the best practices for optimizing work and resources in multi-person work settings in order to help you achieve optimal outcomes and streamline your operations.

The problem space

“Problem space” refers to the set of all possible combinations and permutations that need to be considered when optimizing work and resources in multi-person work scenarios. When there are scenarios that require work with more resources, the “problem space” expands significantly for the optimizer, which presents challenges in finding an optimal solution.

For example, suppose there are three tasks (Task A, Task B, and Task C) that need to be completed by a team of four individuals (Person 1, Person 2, Person 3, and Person 4). Each task can be assigned to one or more individuals, depending on the requirements and dependencies.

In this case, the problem space starts small with only a few possibilities. For instance, Task A could be assigned to Person 1, Task B to Person 2, and Task C to Person 3, leaving Person 4 unassigned. However, as the number of individuals required for each task increases, the problem space expands rapidly.

Let’s say Task A now requires the involvement of either Person 1 or Person 2, Task B requires either Person 2 or Person 3, and Task C requires either Person 3 or Person 4. Now, there are several combinations to consider:

Task A Task B Task C
Person 1 Person 2 Person 3
Person 1 Person 2 Person 4
Person 1 Person 3 Person 4
Person 2 Person 3 Person 4
…and so on.

As more tasks and individuals are added, the problem space expands exponentially. The number of possible combinations and permutations increases and this makes it increasingly difficult to find the optimal allocation of resources and schedule the tasks efficiently.

Moreover, the problem space also grows larger when considering other factors, such as job dependencies and matching skills. Each additional constraint or consideration adds more dimensions to the problem space and makes the optimization process even more complex.

When the problem space becomes too large, traditional optimization techniques can become impractical or computationally unfeasible. It is then essential to employ alternative strategies, such as prioritization frameworks, or human expertise, to navigate the expanded problem space effectively and find satisfactory solutions within reasonable timeframes.

Key limitations

Lack of time constraints

One of the key hurdles in optimizing work and resources is the absence of clear time constraints. When there aren’t specific indications or guidance on when work should start or be completed, the problem space expands exponentially. Without well-defined time constraints, it becomes increasingly difficult to determine the most efficient order of tasks or allocate resources optimally. As a result, suboptimal outcomes and inefficiencies may arise.

Unspecified job times

Another challenge arises when the duration of individual work items or tasks is unknown beforehand. Without the ability to estimate the time required for each job, resource allocation and scheduling become more complex. The lack of work time information can impede the optimization process, leading to inefficient resource allocation. It becomes essential to find alternative approaches to make informed decisions and manage expectations effectively.

Absence of snapping to the nearest minute

Optimization algorithms often rely on precise measurements and time calculations to find the best possible solutions. However, when it is not possible to snap work durations to the nearest minute or to specific time increments, the problem space expands significantly. The inability to fine-tune time constraints restricts our ability to efficiently allocate resources and schedule work. This limitation demands alternative strategies to navigate through the complexities and find satisfactory solutions.

Overcoming the limitations

While these limitations pose challenges, it’s important to approach the optimization process with realistic expectations and explore alternative strategies. Here are a few approaches that can help overcome these limitations:

  • Job urgency: In the absence of clear time constraints, developing prioritization frameworks becomes crucial. By assigning priority levels to work based on its strategic importance, criticality, or dependencies, we can ensure that the most important and time-sensitive tasks are addressed first. This allows for more efficient resource allocation and keeps the workflow on track.

  • Iterative planning: Adopting an iterative planning approach can be beneficial when job times are unspecified. By breaking down complex tasks into smaller, more manageable units and planning in shorter intervals, we can adjust and optimize resource allocation as we gain more insights and information about work durations. Regular reviews and adjustments ensure that resources are allocated effectively throughout the business schedule.

  • Leveraging human expertise: In situations where optimization algorithms face limitations, tapping into the collective expertise of the team becomes crucial. The experience and insights of team members can provide valuable input in determining the best resource allocation strategies. Human intuition and judgment can complement the limitations of algorithmic optimization, leading to more practical and efficient outcomes.