A batch scheduling problem arising in the chemical industry. C. Gicquel, L. Hege, M. Minoux Centrale Recherche S.A.

Size: px

Start display at page:

Download "A batch scheduling problem arising in the chemical industry. C. Gicquel, L. Hege, M. Minoux Centrale Recherche S.A."

Piers Gilmore
8 years ago
Views:

1 A batch scheduling problem arising in the chemical industry C. Gicuel, L. Hege, M. Minoux Centrale Recherche S.A. 1

2 Context 2

3 CAP-SCHED european project Partners: Princeps (F), TTS (I), ManOpt (Ir), Genencor (B) Ghent University (B), Telecom Bretagne (F), CRSA (F) Purpose: Develop softwares based on optimisation and simulation for production planning and scheduling in chemical industries Budget: 775k (among which around 350k for CRSA) 3

Develop softwares based on optimisation and simulation for production planning

4 Enzyme production process 4

5 Enzyme production process Fermentation Recovery Production of the enzyme by living microorganisms Purification of the enzyme by fitration Bottleneck stages Formulation Stabilization/standardization of the enzyme (liuid or granules) Packaging Packaging in drums, barrels, cubitainers 5

fitration Bottleneck stages Formulation Stabilization/standardization

6 Enzyme production process SF MF BT R Seed fermentation Main fermentation Broth preparation in buffer tanks Recovery 6

7 Enzyme production process Filtration Top of a main fermentor 7

8 Enzyme production process Main features For a given type of enzyme: Fixed batch size Fixedprocessingtime in seedand main fermentation Setup/Production/Cleaning Fixed processing time for the broth preparation Maximum duration for the storage Fixedprocessingtime in recovery Seed/main fermenters Buffer tanks Recovery line 8

Fixed processing time for the broth preparation Maximum duration for the storage

9 Enzyme production process Main features Precedence constraints: SF MF BT R Zero wait betwen SF and MF Zero wait between MF and BT A batch stays in the buffer tank as long as its filtration is not finished. A buffer tank contains a single batch at a time. Restrictions on main fermentation setup due to manpower limitation Batch scheduling problem for a seuential process with shared storage tanks Flowshop scheduling problem with parallel resources at each stage 9

A buffer tank contains a single batch at a time.

10 Discrete time MILP model 10

11 Proposed MIP model Parameters Discretetime horizon: t=1..t 1 period = 8 hours / 12 hours / 24 hours Events can occur only at the beginning of a time period End products (enzymes): Number of batches to be processed For each batch, due date Data obtained from the planned orders of SAP APO 11

beginning of a time period End products (enzymes): Number of batches to

12 Proposed MIP model Parameters Simplified recipe for each product p SF: setup/production/cleaning time MF: setup/production/cleaning time BP: processing time Storage before recovery: maximum duration R: processing time Processing times = integer number of planning periods 12

time BP: processing time Storage before recovery: maximum duration

13 Proposed MIP model Parameters Proposal for product FAA Processing times Seed fermentation Main Fermentation Broth preparation Maximum storage duration Recovery Recipe in SAP\R3 (hours) 12/34/4 19/191/ Model (hours) 16/40/8 24/192/ Model (number of 8h-periods) 2/5/1 3/24/ SF/MF time = setup / fermentation / cleaning 13

$Recipe in SAP\R3 (hours) 12/34/4 19/191/15 22 72 26 Model (hours) 16/40/8 24/192/16 24$

14 Proposed MIP model Parameters Available ressources S=2 identicalseedfermenters M=5 identicalmain fermenters B=4 buffer tanks (3 small tanks, 1 large tank) R=1 recovery line Storage in the buffer tanks For small-size batches: 1 single buffer tank (either small or large) For large-size batches: 1 large buffer tank or 2 small buffer tanks 14

line Storage in the buffer tanks For small-size batches: 1 single buffer tank

15 Proposed MIP model Optimization problem Binary decision variable : starting date in MF starting date in R For each batch to be processed Constraints Aggregate production capacity in SF, MF, R Aggregatestoragecapacityin BT Fixed processing times for SF, MF, BP, R Maximum storage time in the buffer tanks Precedence constraints between the operations of a given batch Objective: minimize total tardiness 15

Aggregatestoragecapacityin BT Fixed processing times for SF, MF, BP, R Maximum storage time in the

16 Continuous time MIP model 16

17 Proposed MIP model Parameters Continuous time horizon Events can occur at any moment within the scheduling horizon List of batches to be processed, with due dates Simplified recipe with real processing times Available resources 17

horizon List of batches to be processed, with due dates

18 Proposed MIP model Optimization problem Continuous decision variables starting date in MF starting date in R For each batch to be processed Binary decision variables allocation of a SF/MF for each batch allocation of one or two BT for each batch production seuence on each SF/MF/BT 18

Binary decision variables allocation of a SF/MF for each batch

19 Proposed MIP model Optimization problem Constraints Allocation of the SF/MF/BT used for each batch Seuencing constraints for each pair of batches allocated to the same resource (for each SF, MF, BT and R) A single batch processed at a time Fixed setup/production/cleaning time Precedence constraints between the operations of a given batch Maximum storage time in the buffer tank Objective: minimize total tardiness 19

R) A single batch processed at a time Fixed setup/production/cleaning time Precedence constraints

20 Small example 20

21 Small example Processing times (hours) Product ID SF MF BT F Type of batch (small/big) Number of batches to be scheduled FAA big 1 XEL big 2 NPR small 2 GCF small 2 SAL small 1 TAL small 2 10 batches to be scheduled Planning horizon: 21 days 63 periods of 8h 21

22 Small example Gantt diagram: minimize total tardiness Total tardiness : 10 Continuous relaxation : 1.57 CPU (s) :

23 Small example Gantt diagram: use previous seuence to solve continuous model Total tardiness : 3 CPU (s) :

24 Investigated solution approaches 24

25 Possible solution approaches Exact solution approach Discrete time MILP formulation Cut generation based on two families of valid ineualities Heuristic solution approach Continuous time MIP formulation Production seuence of the single recovery line = starting point to find the production seuences on the MF/BT Once the allocation and seuencing decisions are made, the starting dates can be found solving a simple linear program. 25

26 Conclusion 26

27 Conclusion and perspectives Batch scheduling problem Models Seuential process Shared storage tanks Investigated solution approaches Discrete time MILP formulation Continuous time MIP formulation Directions for future research : Exact/heuristic solution approaches Cooperation with Ghent University and Telecom Bretagne 27

28 Thank you for your attention! 28

How To Plan Production

How To Plan Production Journal of Engineering, Project, and Production Management 2014, 4(2), 114-123 Finite Capacity Scheduling of Make-Pack Production: Case Study of Adhesive Factory Theppakarn Chotpradit 1 and Pisal Yenradee