Automatic planning tools are programs that combined artificial intelligence and designed algorithms for planning and Scheru tasks, resources and activities. They are used in a variety of industries, included manufacturing, supply chain management, healthcare and transport where they can help improve efficiency and reduce the cost of automating repeated and time -consuming planning tasks.
Work order distribution is a critical task in many of these industries; It is a type of resource allocation problem where the goal is to plan a set of work orders on a set of resources (machines, staff, etc.) in an efficient and effective way. It involves the allocation of resources, such as labor, equipment and materials, for specific tasks in a way that is different limitations and goals.
At the 2022 meeting of the Association of European Operational Research Sociations (Euro), I presented a paper titled “Automed Planning Tool (APT): A mixed integer non-linear programming problem relays for planning work order” describing a new approach to solving work organization.
My method, called Autometed Planning Tool (APT), uses a Mixed-Heldal Non-Linear Programming (MinLP) solver to deal with the complex and non-linear nature of work-orglocation problems. Minlp is an optimization technique that variable combinations limited to integer values with continuous non -linear functions.
The APT algorithm is based on the branch-and-bottom method, which causes an optimization problem in smaller sub-problems and then uses a bounding feature to estimate the solution for each sub-problem. The bounding feature provides an upper or lower limit on the optimal solution that can be used to crop the search room by eliminating sub -problems known not to contain the optimal solution.
Becuse it involves a large number of restrictions (such as task dependents, deadlines and resource restrictions), work order distribution is NP implementation problem, which means that for even a reasonable number of variables it can beat affected time consumption. It becomes even more complex and challenging when it does not involve –Linear limitations, such as resource utilization and maintenance costs.
Thus, automatic planning tools must find approximate solutions to optimization problems or utilize the problem structure to reduce the calculation complexity. Some techniques used in automated planning tools include
- Pert (Program evaluation and review technique)
- Critical Sti Method (CPM) to Gantt diagrams
- Linear programming (LP)
- Integer Programming (IP)
- Programming of Limitation (CP)
- Artificial-Intelligence Planning (AIP).
However, traditional optimization techniques, such as linear programming, do not follow very well with solving these types of problems.
Complexity
APT’s complexity is closely linked to the limitations to be taken into account when creating a cut. These may include the time window within which a task is to be performed, the technicians’ available and qualifications or the physical layout of a facility.
Such restrictions can make the problem more difficult to solve as they can limit the number of possible solutions or make it difficult to find an optimal Diaxle. The complexity of the problem also depends on the number of work orders, the number of resources available and the need to satisfy the integrity limit for binary decision variables. It is important to consider these limitations both while formulating the problem as a Minlp problem and while designing the optimization algorithm to solve the problem.
Apt
In the case of APT, the algorithm is implemented as a wood search where the rodnod represents the original problem and the children for each node under problems generated by branching. The search continues until all nodes have been examined or a feasible solution has been found.
We have used Xpress solver to solve this problem with multiple restrictions and a mixture of integer/continuous variable decision. Our studies show that APT is able to find optimal solutions for work order allocation problems with non-linear objective functions in a relatively short love of time.
AWS-driven implementation
APT is written using Python and uses Amazon Web Services (AWS) infrastructure for data storage and calculation. AWS’s ECS FARGATE enabled us to scale apt in an affordable way without any upper cap on the number of users. ECS FARGATE is a technology provided by AWS to run Docker containers on AWS Elastic Container Service (ECS) without the need to control the underlying infrastructure. Fargate eliminates the need for users to deliver and manage the EC2 deposits on which their containers are running, allowing them to focus on developing and implementing their applications. It also enables automatic scaling of containerized applications and integrates with other AWS services, such as load balance and security groups.
The automated planning tool (APT) is a powerful and effective method for solving work orglocation problems. Its ability to handle non-linear goals and its use of a branch-and-bound makes it a valuable tool for planning and resource allowance.