In various real-world scenarios, resource allocation plays a crucial role in optimizing efficiency and maximizing productivity. The Resource Assignment Problem (RAP) is a common challenge that involves assigning available resources to specific tasks or jobs in a way that maximizes the overall performance or satisfaction. In this article, we will explore how to formulate and solve the RAP using linear programming techniques and the Gurobi Python API.

Formulating the RAP as a Linear Programming Problem

To begin, we need to import the Gurobi Python library, which provides powerful tools for solving optimization problems. Once imported, we can define the input data for the RAP. In this particular problem, the data consists of two main components: the list of resources and the list of jobs. Additionally, we have matching scores that indicate the compatibility or suitability of each resource for each job.

To represent the resources and jobs, we use lists in Python. For example, let’s assume we have three resources named Carlos, Joe, and Monika, and three jobs: tester, Java developer, and architect. We can initialize these lists as R and J, respectively. To handle the matching scores, we can utilize the multidict function provided by the Gurobi Python API. This function allows us to initialize a dictionary with multiple keys and corresponding values. We define the dictionary keys as combinations of resources and jobs, and the values as the matching scores for each combination.

For instance, if Carlos is assigned as a tester, the matching score would be 53. Similarly, if Joe is assigned as a tester, the matching score would be 80. By using the multidict function, we can efficiently define the indices and keys of the dictionary and associate the matching scores with the respective combinations of resources and jobs.

Defining the RAP Model

To solve the RAP, we need to formulate it as a linear programming problem. We create a model object, denoted as ‘m,’ which encapsulates all the elements of the mathematical optimization model. In this case, we name the model as RAP.

The RAP formulation consists of four main components: data, decision variables, constraints, and the objective function. By defining these components, we construct the model object ‘m’ that encompasses the RAP.

Decision Variables

In linear programming, decision variables represent the unknowns we seek to optimize. To define these variables, we use the ‘addVars’ method provided by the Gurobi Python API. This method operates on the combinations obtained from the multidict function.

The decision variables and matching scores share the same keys for identification. The decision variable name is assigned to ‘x,’ which captures all the decision variables in the linear programming formulation.

Job Constraints

In the RAP, we have constraints related to jobs. To incorporate these constraints into the model, we utilize the ‘addConstrs’ method available in the ‘m’ object. The job constraints are captured in an object called ‘jobs,’ and the constraints are defined using the ‘x.sum’ method in Python.

For each job ‘j’ in the list of jobs ‘J,’ we calculate the summation of all the resources that can be assigned to that job. The ‘x.sum’ method enables us to express the left-hand side of each job constraint accurately. The constraints are defined as equality constraints, indicated by the double equality sign (=), and are set to a value of 1. With this step, we effectively capture all the job constraints within the model.

Resource Constraints

Similar to job constraints, resource constraints also need to be defined. We employ the ‘addConstr’ method within the ‘m’ object to specify these constraints. Once again, we utilize the ‘x.sum’ method to define the summations within each constraint.

For example, for each resource ‘r,’ we define a constraint that encompasses all the jobs to which the resource can be assigned. Since it is possible to not assign all the resources, we express these constraints as less than or equal to (≤) constraints. This allows for the flexibility of not assigning a resource to a particular job. By employing the ‘addConstr’ method, we capture all these resource constraints in the model.

Objective Function

The objective function represents the goal we aim to optimize. In the RAP, our objective is to maximize the total matching score. We use the ‘setObjective’ method provided by the Gurobi Python API to define the equation that represents the objective function.

In this case, we utilize the ‘’ method in Python to calculate the product of each matching score with its associated decision variable. By summing up all these products, we obtain the total matching score. The resulting equation is set as the objective function using the ‘setObjective’ method.

To inform Gurobi that we want to maximize the objective function, we use the ‘GRB.MAXIMIZE’ parameter. Gurobi’s optimization sense is set to maximize the objective function value.

Solving the RAP

Once we have defined the model object ‘m’ with all the necessary components, we are ready to solve the RAP. The Gurobi Python API provides an ‘optimize()’ function, which calls the Gurobi library to solve the defined linear programming problem.

The ‘optimize()’ function leverages the constraints, decision variables, and objective function specified in the model object ‘m’ to find the optimal solution. In this case, Gurobi determines that Carlos should be assigned to the tester job, Joe to the architect job, and Monika to the Java developer job. The resulting total matching score is found to be 193.


In this article, we explored the formulation and solution of the Resource Assignment Problem (RAP) using linear programming techniques and the Gurobi Python API. By defining the input data, decision variables, constraints, and objective function, we constructed a mathematical optimization model to maximize the total matching score. Gurobi’s powerful solver capabilities efficiently solved the RAP and provided an optimal resource assignment solution. By leveraging linear programming and optimization techniques, organizations can effectively allocate resources and improve overall performance and productivity.


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