Quintiq vandaag en morgen Welke trends tekenen zich af in advanced planning & scheduling? Waar gaat Quintiq heen? Welke nieuwe ontwikkelingen en tools zijn er? Zet u Quintiq in als operationele planningtool, of als tactisch en strategisch planningplatform?.

1. Tactical Engineering Solution
ORDINA ADVANCED PLANNING & SCHEDULING UNIT
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2. Ordina’s Advanced Planning and Scheduling Unit: optimization
experience.
 Optimization within logistics
- Optimization of tactical planning
- Optimization of route schedules
 Optimization within personnel and material
planning
- Minimizing idle time based on forecasted and
actual capacity
 Optimization within the maritime sector
- Optimization of barge planning
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3. Problem Formulation
 In order to provide an optimal service for their customers, 4PL providers
are confronted regularly with a number of fundamental questions:
- Given a typical shipment list or pattern of a customer, what is the most cost-
effective transport plan for that customer within the provider’s network of
carriers and hubs?
- Given an existing customer that is expecting significant changes in his
shipment patterns, is the current transport plan still cost-effective?
If not, how should it be adjusted?
- Given multiple customers, both existing and new, what are the synergies
between their transport plans and can it be beneficial for all of them to jointly
organize their transports?
- Given a number of customers, is the 4PL provider’s network correctly
optimized? I.e. do we have the correct hubs and carriers and do we use
them correctly?
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4. Traditional methodology used throughout the sector
 Often, Excel is used, possibly in combination with off-the-shelf high level
planning applications.
 One of the main problems with high level applications is that they
operate on flow level, simply mapping flows through a network. This
Unrealistic or unfeasible transport plans
regularly leads to non-feasible transport plans.
 One of the main problems with using Excel is that in order to find a
solution within an acceptable time window (typically a few weeks),
several heuristics Slowused. E.g.
are and ponderous analysis process
- Try to make as many full direct transports as possible.
- All shipments that weigh less than X kg are transported via a hub.
 As a consequence, solutions found byoften far from optimal sub-optimal,
Even feasible solutions are providers are often
without knowing how far from an optimal solution their solution actually
lies.
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5. Main goals to achieve for a 4PL engineering tool
 The main goal of a 4PL engineering tool is of course to provide an answer
to the fundamental questions posed earlier in this presentation.
 In finding these answers, the tool should
- Significantly reduce the time spent computing a cost-effective transport plan.
- Provide an unbiased estimate for the effect of volume changes on existing
tactical plans.
- Provide a solution that can be drilled down and evaluated to the level of
individual shipments.
- Allow the tactical engineers to make manual corrections based on their
knowledge and experience.
- Provide a clear estimate of optimality for a given solution.
- Facilitate the evaluation of tactical rules as well as the definition and validation
of business rules
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6. The Tactical Engineering Solution (TES)
Traditional solutions:
A two step optimization solution was developed to TES:
Unrealistic or unfeasible • TES approach gives a
- find a cost-effective transport plan within hours Feasible transport plans
transport plans solution on shipment level.
- provide at the same•time a clearcan manually optimality
Engineers estimate of
overrule at shipment level.
- all the while taking existing business rules into account
 Step 1: flow level optimization.
Traditional solutions: TES:
- optimization on an aggregated level finds solution
using the mathematical technique of
Slow and biased analysis • TES approach Fast and unbiased
MILP within hours.
process • Intuitive interface allows fast
analysis process
 Step 2: shipment level optimization. input and
manipulation of
solution
- refinement of the aggregated solution to shipment level using MILP and
proven milk run heuristics
TES:
Traditional solutions:
• TES approach uses Optimal solution or
Even feasible solutions are
mathematical optimization with indication of minimal level
often far from optimal respect to actual cost functions. of optimality.
• Effect of manual manipulation
on total cost is immediately
visible. 6

7. Performance: computation time
 TES was designed and implemented by Ordina using the Quintiq
planning platform with CPLEX as its underlying optimization engine.
 It has been taken into production running on a server with 16 processor
cores and 48GB of RAM available.
 Performance has been monitored for their actual business cases. To
obtain an optimal solution, calculation time is around 3-4 hours.
 This for formulations with over 4,000,000 variables and 4,000,000
constraints.
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8. Performance: cost optimality compared to manual.
TES best solution:
Customer best 787,884 €
solution: → 6% cheaper!
838,169 €
 Using TES on existing business cases has shown an increase in cost-
effectiveness of up to 17%!
- Existing transport plans that were considered optimal were shown to be
significantly suboptimal.
 Using TES, shortcomings of tactical rules of thumb have been identified.
- E.g. the rule stating all shipments less than 7500 kg should be transported via
hubs turned out to be bad when the distance between factory and hub is
significantly larger than the distance between factory and supplier.
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9. A short TES demo
 Under the motto “put your money where your mouth is” we will illustrate
TES with a little demo.
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10. Questions?
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