Workspace Allocation Optimization for the Renault Group

renault-workspace-allocation-optimization

Renault Group is a French multinational automobile manufacturer, and one of the largest automakers in the world.¬†They have been using LocalSolver since 2016 to solve various optimization problems they face. In this case, Renault’s Operations Research department sought to optimize the workspace allocation with LocalSolver.

Renault’s workspace allocation problem

To optimize the organization of its staff in buildings, the Renault team goes through two stages. The first step is macro zoning. It consists of allocating workspaces to Renault’s various departments. The next step is to assign the desks to people; it’s called micro zoning.

Here is an overview of the optimization model to be solved by Renault Group:

Input data

  • 5 departments
  • 27 workspaces
  • 1109 people

Decisions

  • Allocate workspaces to departments
  • Assign the desks to people

Constraints

  • Respect the capacity of the modules
  • Assign all staff members
  • Reserve some modules for departments (modules with specific equipment)
  • Departments requiring a high degree of confidentiality do not have to share their modules with other departments

Objectives

  • Smoothing the filling rate of the different modules
  • Group members of the same department in the same module or neighboring modules
  • Grouping in the same module (or neighboring modules) services from 2 departments, which collaborate closely
  • Assign as much as possible a single department to a module
renault-workspace-allocation-solution
Solution for 5 directions in 27 modules (1 department = 1 color)

Why LocalSolver?

The workspace allocation problem is hard to solve because of the coupling of matching and packing constraints, as well as the multiple nonlinear objectives to optimize. For example, smoothing the filling rate of the different modules induces quadratic expressions. Despite its hardness, LocalSolver provides near-optimal solutions in less than 1 minute of runtime. In addition, the intuitive and powerful modeling language offered by LocalSolver, namely LSP, allowed the Renault OR team to develop the mathematical model in just a few hours.

We use LocalSolver for several optimization problems at Renault Group. What we appreciate most is the incomparable ease of modeling provided by LocalSolver mathematical formalism. Besides, exchanging with the LocalSolver team is always extremely fruitful and highly pleasant.

Alain Nguyen Head of Operations Research, Renault
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