Formulate scheduling and allocation problems as objective functions, decision variables and constraints suitable for a solver.
Digital Transformation & Artificial Intelligence
Prescriptive Analytics with Optimisation Solvers for Scheduling and Allocation
Build prescriptive analytics models that use optimisation solvers to generate feasible schedules and resource allocations under real operating constraints.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Most organisations that schedule people, vehicles or capacity still rely on spreadsheets, rules of thumb or descriptive dashboards that report what happened without recommending what to do next. Prescriptive analytics closes that gap by turning scheduling and allocation problems into mathematical models that an optimisation solver can search for the best feasible answer, given the actual constraints of shift patterns, contract hours, vehicle capacity, budgets and service-level rules. This course teaches participants to formulate real scheduling and allocation problems as decision variables, objective functions and constraints, choose between linear programming, mixed-integer programming and constraint programming, and build working models in a modelling language connected to a solver. Sessions work through live formulation exercises, solver runs on realistic datasets, and the diagnostic steps needed when a model returns no feasible solution or takes too long to solve. Participants leave with a working optimisation model for a scheduling or allocation problem from their own organisation, the skills to defend its assumptions to operational stakeholders, and a plan for embedding solver output into the planning tools that schedulers already use.
Expected Learning Outcomes
Select between linear programming, mixed-integer programming and constraint programming based on problem structure.
Build and run optimisation models using a modelling language and a commercial or open-source solver.
Diagnose infeasible or unbounded models and apply relaxation, bound-tightening and reformulation techniques.
Design workforce, vehicle routing or capacity allocation models that reflect real shift patterns and service-level rules.
Integrate solver output into planning dashboards so schedulers can review, override and approve recommendations.
Establish a review cadence that keeps optimisation models aligned with changing costs, capacities and policies.
Who Should Attend
Operations research analysts moving from descriptive reporting into prescriptive modelling work.
Workforce planners responsible for shift rosters, staffing levels and service-level commitments.
Logistics and supply chain planners handling vehicle routing, fleet capacity and warehouse allocation.
Business analysts tasked with automating manual scheduling spreadsheets.
IT and data teams selecting or implementing an optimisation solver for a planning system.
Operations managers who approve schedules and need to interpret solver recommendations.
Course Modules
Select any module to see its sessions and points.
01Formulating Scheduling and Allocation Problems as Optimisation Models
2 sessions · 8 points
Session 1Translating Operational Constraints into Mathematical Formulations
- Define decision variables that represent shift assignments, vehicle routes or resource allocations precisely enough for a solver to evaluate.
- Express operational rules such as minimum rest periods, contract hours and capacity limits as linear or logical constraints.
- Build an objective function that balances cost, service level and fairness rather than optimising a single metric in isolation.
- Distinguish hard constraints that a schedule must satisfy from soft constraints that can be traded off through penalty terms.
Session 2Choosing Between Linear, Integer and Constraint Programming
- Compare linear programming, mixed-integer programming and constraint programming against the structure of a given scheduling problem.
- Recognise when binary or integer variables are required to represent shift assignments, vehicle selection or on/off decisions.
- Assess when constraint programming's logical expressiveness suits complex rostering rules better than arithmetic formulations.
- Estimate model size and solve-time risk before committing a formulation to a production planning process.
02Working with Optimisation Solvers and Modelling Languages
2 sessions · 8 points
Session 1Building Models in Solver Environments
- Translate a mathematical formulation into a modelling language such as a solver-specific API or a Python-based modelling library.
- Load real scheduling data, including demand forecasts, staff availability and vehicle capacity, into a solver-ready dataset.
- Configure solver parameters such as time limits, optimality gaps and warm starts to balance solution quality against run time.
- Interpret solver logs to distinguish an optimal solution from a good-enough solution stopped early by a time or gap limit.
Session 2Diagnosing Infeasibility and Improving Solve Performance
- Apply infeasibility diagnosis techniques, including conflict sets and constraint relaxation, to find which rules make a model unsolvable.
- Tighten variable bounds and reformulate constraints to reduce solver search space and cut solve time.
- Use decomposition approaches to break a large scheduling problem into solvable sub-problems solved in sequence or in parallel.
- Validate a solver's proposed schedule against operational reality before it is released to staff or drivers.
03Applying Prescriptive Analytics to Scheduling and Allocation Scenarios
2 sessions · 8 points
Session 1Workforce and Shift Scheduling Under Demand Variability
- Build a shift-scheduling model that matches staffing levels to forecast demand while respecting contract and labour-law constraints.
- Model skill-based assignment so that staff are only scheduled to tasks they are qualified and available to perform.
- Incorporate fairness constraints that distribute unpopular shifts and overtime evenly across a workforce over a rolling period.
- Run scenario comparisons that show the cost and service-level impact of adding, removing or reshaping shift patterns.
Session 2Vehicle Routing, Capacity Allocation and Network Flow Problems
- Formulate a vehicle routing problem with time windows, capacity limits and multiple depots as a mixed-integer model.
- Apply network flow formulations to allocate shared capacity, such as warehouse space or production lines, across competing demands.
- Use heuristics and metaheuristics as a practical alternative when an exact solver cannot reach a solution within an operational time limit.
- Compare exact and heuristic solutions on cost, service level and computation time to choose the right approach for each use case.
04Embedding Optimisation into Operational Decision-Making
2 sessions · 8 points
Session 1Integrating Solvers into Planning Systems and Dashboards
- Design an interface that lets planners review, adjust and approve solver recommendations rather than accepting them automatically.
- Automate the data pipeline that feeds demand forecasts, availability and cost data into the optimisation model on a set schedule.
- Present trade-offs between cost, service level and fairness in a dashboard that non-technical schedulers can interpret quickly.
- Build override and audit features so manual changes to a solver's schedule are recorded and explained.
Session 2Governance, Trust and Continuous Improvement of Optimisation Models
- Establish a review cadence that re-validates model assumptions against changing costs, capacities and regulations.
- Track the gap between solver-recommended schedules and what was actually implemented to identify where trust is lacking.
- Assign ownership for maintaining constraints, objective weights and data feeds as operating conditions change.
- Build a business case that quantifies the cost and service-level improvement a prescriptive model delivers over the prior manual process.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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