Recipes on Skedulo Documentation

Recipes overview

Mon, 01 Jan 0001 00:00:00 +0000

Introduction

Efficient resource scheduling is critical for any organization, enabling teams to maximize productivity, streamline workflows, and meet customer demands. A one-size-fits-all approach often falls short of addressing unique business needs.

Skedulo addresses this challenge with recipes, which allow organizations to tune objectives and tailor scheduling outcomes to their specific priorities. By weighing and prioritizing different objectives, teams can generate optimized schedules and suggestions that enhance operational efficiency and align with strategic goals.

Objective: Minimize disruption

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The concept of minimizing disruption revolves around jobs that already have a specific resource assigned to them. These assignments could be due to various factors, such as prior knowledge, expertize, or operational requirements.

Maintaining the assigned resources is a significant factor when working with pre-existing/tentative schedules. For example, if a rough schedule is created early and optimization fine-tunes the schedule with updated data.

Objective: Balanced workload

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The concept of balancing workload refers to the distribution of jobs among available resources in a way that ensures equitable allocation and efficient utilization of resources. It aims to prevent resource overloads or under utilization, promoting a harmonious and productive workflow.

Objective: Resource rating

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The concept of Resource Rating allows organizations to influence which resources are preferred during optimization, based on their relative performance, experience, or suitability. Each resource can be assigned a score between 1 and 100, where 100 represents the most highly rated resource.

Objective: Travel time

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Travel time refers to the duration required for resources to travel between different locations to fulfill assigned jobs. It accounts for the time spent driving between locations, impacting overall scheduling efficiency and resource utilization.

The travel time objective aims to keep the time resources spend traveling between locations to a minimum. This helps optimize scheduling outcomes by reducing the time spent on the road, allowing resources to spend more time on the job.

Objective: Minimize resources

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Minimizing resources refers to the objective of optimizing resource allocation by minimizing the number of resources required to complete a schedule of jobs. It focuses on achieving efficient resource utilization and cost-effectiveness by reducing the total number of resources involved in the scheduling process.

Objective: Work priority

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Work priority refers to the urgency or importance assigned to specific jobs or tasks, indicating that they need to be given precedence over other jobs. This prioritization could stem from factors such as emergency work or critical deadlines that require immediate attention and completion.

Objective: Soft skills

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Soft skills encompass the skill tags assigned to specific jobs or tasks, indicating the required skills that resources need to possess in order to undertake those jobs. These skills can be categorized with priorities such as low, medium, or high, indicating their relative importance or proficiency level.

Create recipes

Mon, 01 Jan 0001 00:00:00 +0000

Overview

When schedulers optimize, they consider the unique goals of their organization and the specific scheduling scenario they are trying to optimize. By giving schedulers granular control over optimization settings, they can tailor schedules to their organization’s needs, improving overall efficiency.

Optimization recipes are pre-defined combinations of objectives, constraints, and rules that can be applied to specific scenarios. Recipes let users shape how optimization behaves by configuring the weights of relevant objectives and selecting rules that influence scheduling outcomes.