What is optimization modeling?

Optimization modeling is a powerful tool used in various fields, including operations research, engineering, economics, finance, logistics and more. By optimizing resource allocation, production processes or logistics, mathematical optimization modeling can reduce costs and improve operational efficiency across workflows.

Additionally, optimization modeling enhances strategic planning and long-term decision-making. It enables organizations to evaluate various scenarios and alternatives, helping them understand the potential consequences of different choices before implementing them. This can be particularly valuable in industries like finance, for example, where portfolio optimization can lead to better investment strategies.

Optimization models are designed to help organizations and individuals make informed decisions by maximizing or minimizing an objective function while adhering to specific constraints.

Objective functions are the mathematical expressions that define what you want to maximize (e.g., profit, revenue, efficiency) or minimize (e.g., cost, waste, time). The objective function is the core of the optimization problem.

Decision variables are the variables that you can control or adjust to influence the outcome, typically represented by symbols and subject to certain constraints. These constraints are mathematical expressions that limit the values or relationships between the decision variables. Constraints represent real-world limitations, such as resource availability, capacity limits or regulatory requirements.

There are different kinds of optimization modeling that serve different purposes. Stochastic optimization is a branch of mathematical optimization that deals with optimization problems involving uncertainty or randomness. In stochastic optimization, the objective function and/or constraints are influenced by probabilistic or random variables, making the optimization process more complex than traditional deterministic optimization.

Nonlinear optimization modeling deals with mathematical optimization problems where either the objective function, constraints, or both contain nonlinear functions of the decision variables.

Unconstrained optimization modeling is a type of mathematical optimization where the goal is to find the maximum or minimum of an objective function without any constraints on the decision variables.

In optimization modeling, a heuristic is a problem-solving approach or technique that aims to find approximate solutions to complex optimization problems, especially when finding an exact optimal solution is computationally infeasible within a reasonable time frame. Heuristics often involve trade-offs between solution quality and computation time.

Let's consider a hypothetical scenario where a delivery company, "RapidLogistics," wants to optimize its delivery routes for a fleet of vehicles to minimize fuel costs while ensuring timely deliveries. Here's how optimization modeling can be applied to this scenario, step by step:

Begin by understanding the problem you want to solve and clearly articulate its objectives. Determine the variables that you can control or adjust to achieve your objectives. Create a mathematical expression that represents what you want to maximize (e.g., profit, efficiency) or minimize (e.g., cost, waste) in terms of the decision variables.

RapidLogistics wants to minimize fuel costs while delivering packages to various customers within a city. The decision variables are the routes each vehicle takes, and the objective is to minimize fuel consumption.

List all the constraints that restrict the values or relationships of the decision variables. These could be resource constraints, capacity limits, or regulatory requirements. Express each constraint as a mathematical equation or inequality involving the decision variables.

For RapidLogistics, constraints include:

• Time windows: Each customer has a specified time window during which deliveries can be made.

• Vehicle capacity: Each vehicle has a maximum weight and volume capacity for packages.

• Must visit all customers: Every customer must be visited exactly once.

• Fuel limit: The total fuel capacity of all vehicles combined is limited.

Decide whether your problem can be represented as linear programming (or linear optimization), nonlinear programming, integer programming, quadratic programming, or some other type of mathematical programming. This choice depends on the nature of your objective function and constraints. For example, linear constraints are a fundamental component of linear optimization modeling.

Our type of problem can be represented as a mixed-integer linear programming (MILP) problem. The objective function is to minimize the total fuel consumption, which is a linear function of the decision variables. The constraints involving time windows and vehicle capacity can be linearized.

Collect all necessary data, including parameter values for the objective function and constraints, such as costs, coefficients and bounds on decision variables.

RapidLogistics must gather data on customer locations, their time windows, package sizes, vehicle fuel consumption rates, vehicle capacities and the fuel limit for all vehicles.

Combine the objective function and constraints into a complete mathematical model that represents your optimization problem.

In RapidLogistics’ case, the objective function might be to minimize the sum of fuel consumption across all vehicles, and the decision variables are binary variables indicating whether a vehicle visits a customer or not.

Choose the appropriate optimization software (sometimes called a “solver”) or programming language that supports the type of model you are using. Enter the mathematical model and data into the selected optimization software or tool, and use it to find the optimum solution. Modern software will typically employ various machine learning techniques and optimization algorithms to find the best solution within the feasible region.

RapidLogistics might want to select an optimization tool or software that supports mixed-integer linear programming, such as Gurobi, CPLEX or open-source libraries like PuLP in Python.

Examine the values of the decision variables to understand the recommended course of action. Determine the value of the objective function at the optimal solution, which represents the best possible outcome.

This process helps RapidLogistics discover certain routes that are more efficient than others, leading to cost savings. It can now implement optimized delivery routes and update vehicle assignments and schedules accordingly. The company can regularly monitor the performance of the optimized routes and make adjustments as needed to adapt to changing conditions, such as new customers or updated fuel costs.

We’ve already reviewed a use case for logistics. Here are some other common areas where optimization modeling can aid decision makers: