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Lot-sizing and scheduling with energy constraints and costs Journée P2LS "Lot-sizing dans l'industrie" LPI6 Paris 20 Juin 2014 Grigori German, Claude Lepape, Chloé Desdouits

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Dealing with energy constraints and costs Scheduling versus lot-sizing A case study of manufacturing scheduling with energy costs Lot-sizing perspectives Agenda

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Energy constraints and costs

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Introduction Test Data Day Night Time Cost

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Determine whether it is worth considering energy costs in the planning and scheduling of a given factory or workshop Determine what kinds of tradeoffs are worth considering between energy and: Intermediate or final product inventory Work shift organization Other production costs Tardiness risks … Determine what kinds of models and techniques can be used to answer the questions above Process simulation Scheduling with energy costs Scheduling with energy (power) constraints, i.e., do not exceed a given power limit Lot-sizing … Determine how generic can such models and techniques be? Objectives

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Production planning and scheduling taking into account given energy tariffs Reducing energy-intensive production during high-cost days and hours Can mean different things: producing less, producing less energy-intensive products, avoiding energy-intensive steps, during the high-cost days and hours Often impacting indirect CO2 emissions Selecting or negotiating a better contract based on the energy-aware planning and scheduling capability In particular concerning power subscription levels and penalties Identifying demand-response opportunities Maintaining a higher stock level to be able to reduce power consumption under rather short notice When demand-response is “likely” Several questions

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www.arrowhead.eu An example: the Sarel plant

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www.arrowhead.eu Measuring chain Energy sensor Self powered Wireless communication Non intrusive installation Accumulator Provide the Energy value Collector transmitter Send historical data periodically to the time series repository 8

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www.arrowhead.eu Simulation Optimization Based on a commercial production flow simulator (Rockwell Arena ) Optimization Input Day Night Time Cost Constraints Objectives

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Scheduling versus lot sizing

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What are the time scales? Duration for the execution of a recipe or of its critical activities Versus the frequency of tariff changes What is the relationship between the critical resources time-wise and the critical resources energy-wise? Do I have batch sizing flexibility and can it impact energy consumption? Ovens, etc. Energy-consuming setups / cleaning steps Scheduling versus lot-sizing: differentiating questions Time Cost Time Cost M1M2 M1M2

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To exploit batch sizing flexibility As an abstraction of the scheduling problem Less variables Easiest constraints … As a tool to decompose the scheduling problem Depending on the plant, coupled lot-sizing and scheduling can be the best solution Three motivations for lot-sizing

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A case study of manufacturing scheduling with energy costs

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Overview of the scheduling problem

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Adding the energy dimension act 3 time Res r cap r stet calendar capacity cost interval capacity calendar interval cmax cmin act 1 act 2

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Optimization Input Day Night Time Cost Constraints Objectives

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Simple, classical formulation Branching strategy: Earliest Due Date No simple formulation for computing the energy cost Time-based formulation Perspective: global constraint Generates a good first solution Method 1: Constraint Programming

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Overlap Variables Method 2: MIP How to express the energy cost? Taille du bucket act dépasse à gauche Durée de act act dépasse à droite act et le bucket sont disjoints

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Constraints Method 2: MIP How to express the energy cost?

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Other constraints and variables Disjunctive constraints: Applegate and Cook (1991) formulation Relaxed MIP Too many variables and constraints (e.g., 700k+ variables and 1.2M+ constraints with 200 activities and a 400 days horizon) Energy binary variables continuous in [0,1] Stills leads CPLEX towards a good solution Perspectives Explore different strategies (e.g., branch on all the variables before the energy variables) Other formulations with precomputed intervals Method 2: MIP

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Algorithm Perspectives Adapted time windows size Sliding time windows Intensification Method 3: Hybrid local search Constraint Programming S Local search While there is still time Find a time window F Set all the variables outside F Keep the best between S and S’ Optimize F with MIP S’ S

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Adapted benchmark instances from the literature CP, MIP & LS versus best known results CPMIPLS All instances (38) = Best known results 253026 Relative deviation 20%47%7% NCGS (20 instances) = Best known results 141814 Relative deviation 30%0%11% NCOS (18 instances) = Best known results 1112 Relative deviation 8%99%3% CPMIPLS All instances (38) = Best known results 253026 Relative deviation 20%4%7% NCGS (20 instances) = Best known results 141814 Relative deviation 30%0%11% NCOS (18 instances) = Best known results 1112 Relative deviation 8% 3% Comparison of the 3 methods without the energy

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MIP versus LS Local search with and without energy And with energy ? MIP All instances (34) ≤ Local search 9 Relative deviation 14% NCGS (18 instances) ≤ Local search 3 Relative deviation 0% NCOS (16 instances) ≤ Local search 6 Relative deviation 31% ObjectivesSavings All instances (29) Tardiness0% Energy-0,95% Total cost-0,12%

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Application to the SAREL use case Multi-objectives: Pareto-optimal schedules Piecewise linear energy costs Scheduling perspectives

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Lot-sizing perspectives

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Makes sense only if recipe or critical activity execution duration is smaller than tariff intervals duration Recipe-based model Quantity of recipe r executed in period p for each period p and recipe r Linked to the energy consumption in period p and hence to the energy cost (with a linear or piecewise linear relation between consumption and cost – could be subtle in some cases, e.g., if several resources in parallel consume and there is a penalty for exceeding a given amount of power …) Linked to quantity of materials produced (or consumed) in period p Linked to customer demands in different ways: either (i) no tardiness authorized with the risk that there is no solution, or (ii) delivering the demand when ready, either early or late, or (iii) delivering either just in time or late … As a result linked to an evaluation of storage and tardiness costs Activity-based model For relevant activities of given batches, deciding in which period they execute Variation (relaxation) of the model used in our scheduling study With subtleties to look at when there are multiple energy-relevant activities or if the energy-relevant activity is not the bottleneck time-wise … Models with lot-sizing periods corresponding to tariff intervals (buckets)

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Assuming recipe or critical activity execution duration is smaller than the lot-sizing period An open question is how to approximate the energy cost An optimistic viewpoint assumes that inside each period we will be able to exploit intervals with the lowest tariffs, up to some given maximal power Can we use historical data to better evaluate an expected cost? Shall we do this through some smart coupling of lot-sizing and scheduling? Models with lot-sizing periods exceeding or not consistent with tariff intervals Energy Cost 1 week period Max power = 10kW 88 hours at 0.05€/kWh 80 hours at 0.10€/kWh (0, 0) (880, 44) (1680, 124) Energy Cost 1 week period Max power = 10kW 88 hours at 0.05€/kWh 80 hours at 0.10€/kWh (0, 0) (880, 44) (1680, 124)

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Energy cost reduction is a growing concern But usually one among multiple optimization criteria Multiple technical approaches and models can be considered Lot-sizing is one of them Depending on time scales, relationships between the critical resources time-wise and the critical resources energy-wise, and on batch sizing flexibility Sometimes (often) to be coupled with detailed scheduling A very open topic at this point Conclusion

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Thank you for your attention!

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