Scheduling Optimization Specialist
">
At the forefront of scheduling optimization lies a realm where data-driven professionals converge to tackle complex challenges.
With unwavering passion, you strive to apply advanced optimization techniques to real-world problems, striking a delicate balance between mathematical rigor and practical feasibility.
As a skilled analytical thinker with strong modeling abilities, you craft solutions that boost efficiency, minimize costs, and ensure seamless compliance with operational and regulatory requirements.
You bring to this role:
* Expertise in optimization modeling, encompassing integer programming, constraint programming, and heuristics/metaheuristics.
* Proven experience in developing and maintaining large-scale scheduling models within applied environments.
* Ability to seamlessly integrate operational rules, contractual agreements, regulatory requirements, and business constraints into model formulations.
* Familiarity with optimization solvers such as Gurobi, CPLEX, and OR-Tools.
* Proficiency in programming languages like Python, Julia, or C++.
* A deep understanding of data analysis for planning, focusing on event calendars, service times, passenger volumes, and destination planning.
* An analytical mindset coupled with strong problem-solving skills and attention to detail.
* Excellent communication skills to effectively collaborate with technical and non-technical stakeholders.
Develop and maintain sophisticated optimization models to solve intricate scheduling problems.
Construct models that consider event calendars, service times, passenger volumes, and destinations using available data.
Integrate operational rules, contractual obligations, regulatory requirements, and business constraints into model formulations.
Apply optimization solvers to generate feasible, efficient schedules.
Collaborate with operations, planning, and technology teams to ensure outputs align with real-world needs.
Analyze results to identify cost savings, efficiency improvements, and compliance risks.
Continuously refine models to adapt to changes in resource availability, demand patterns, and operational constraints.