Adaptive Model Building Framework for Production Planning in the Primary Wood Industry
Abstract
:1. Introduction
2. Literature Review
2.1. Linear or Mixed-Integer Programming
2.2. Heuristic Approaches
2.3. Simulation
2.4. Uncertainties
2.5. Extended Models
3. Material and Methods
3.1. Application Field
3.1.1. Case 1: Procurement and Sales Support
3.1.2. Case 2: Production Preview
3.1.3. Case 3: Operations Requirements
3.1.4. Case 4: Order Release, Loading and Scheduling
3.2. Planning Process
3.3. Modelling Approach
Set of products | |
Set of log types | |
Set of periods | |
Set of locations | |
Set of production recipes | |
Set of cutting recipes | |
Set of drying recipes | |
Set of planing recipes | |
Set of sawing lines | |
Set of kilns | |
Set of planing lines | |
Set of customers | |
Raw material types (logs, green timber, dried timber, planed timber) | |
Set of green products | |
Set of dried products | |
Set of planed products |
Y | Yield of production with recipe in percentage |
B | Assignment of raw material to recipe |
N | Demand of product |
R | Revenue of product |
A | Available raw material |
C | Costs |
G | Duration of recipe per unit |
M | Capacity for recipes |
U | (Total) storage capacity |
z | Volume of raw material processed with recipe |
i | Stored volume |
x | Sold volume |
w | Backorder volume of product |
f | Bought volume of product |
4. Industrial Application
5. Conclusions and Discussion
6. Outlook
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Paper | Objective | Method | Application | Planning Horizon |
---|---|---|---|---|
Alayet et al. [6] | Minimise costs | Mixed-integer programming model with rolling planning horizon | Forest products supply chain, different inventory management policies | month [week] for a year |
Alvarez and Vera [25] | Maximise economic benefit | Robust optimisation | Production planning sawmill (sawing, drying, outsourcing and reprocessing) | year [month] |
Bajgiran et al. [12] | Maximise global net profit | Mixed-integer program, heuristic | Plan lumber supply chain (harvesting, procurement, production, distribution and sales activities) | year [month] |
Ben Ali et al. [18] | Maximise net profit | Order promising model based on nested booking limits, simulation | Sales and operations planning, customer service level | 8 weeks [week] for a year |
Broz et al. [7] | Weighted objective function of maximising profit, production, minimising raw material loss, inventory, unsatisfied demand | Mixed-integer linear program | Determine daily production plan satisfying several objectives | day [day] |
Gaudreault et al. [11] | Minimise backorder | Mixed-integer program, constraint program, search procedure | Planning and scheduling drying and finishing | 60 days [day] |
Greigeritsch [16] | Maximise contribution margin | Linear model, simulation | Sequence planning for sawmill production, material flow simulation, dry kiln occupancy | 14 weeks [1–2 weeks] |
Huka and Gronalt [1] | Maximise contribution margin | Mixed-integer program, heuristics | Plan sawing, investigate loss of not following optimal plan, analyse inventory effects, different planning frequencies | 12 weeks [2 weeks] |
Jerbi et al. [17] | Maximise profit | Aggregated optimisation, discrete-event simulation | Selection of supply chain management policies for lumber supply chain network, tactical and operational planning | year [week], week [day] |
Kühle et al. [8] | Minimise costs | Linear programming model | Develop future production concept using form postponement | year [month] |
Lobos and Vera [27] | Minimise costs | Two-stage stochastic programming | Determine log purchase and labour hiring under uncertainties in log supply | 50 months [month], 1 month [week] |
Maness and Norton [2] | Maximise profit | Linear program | Production and sales planning for sawmills | quarter [month] |
Marier et al. [4] | Maximise gross margin | Linear program | Production and sales planning for sawmills | year [week] |
Maturana et al. [10] | Minimise costs | Mixed-integer program, heuristic | Production planning at sawmill with increasing uncertainty (rolling planning horizon, sub-periods in first period) | 6 weeks [week] |
Mendoza et al. [15] | Maximise economic return | Combined linear inventory and simulation model | Determine optimal lumber production schedule | 10 h [hour] |
Ouhimmou et al. [9] | Minimise costs | Mixed-integer program, heuristic | Furniture supply chain | year [week] |
Pradenas et al. [3] | Maximise revenue | Linear model, simplex primal and dual, barrier method | Aggregated production planning for multiple sawmills and periods, determine levels of production, subcontracting and inventory | 2–8 periods |
Vanzetti et al. [13] | Maximise net benefit | Mixed-integer linear program, heuristic to determine cutting patterns | Multi-period production planning with integrated cutting pattern generation | 5 days [day] |
Vanzetti et al. [14] | Maximise profit | Mixed-integer linear program, heuristic to determine cutting patterns | Daily production planning with integrated primary cutting pattern generation | day [day] |
Varas et al. [26] | Minimise cost (inventory, backorder) | Robust optimisation | Scheduling production for a sawmill with uncertainties in product demand and availability of raw materials | 6 weeks [week] |
Vergara et al. [5] | Minimise cost, waste, number of logs, production time, maximise profit | Linear model | Production planning sawmill, compare objective functions | 3 days [hour] |
Vila et al. [19] | Maximise expected corporate profits | Two-stage stochastic programming, sampling average approximation method | Design of forest industry supply chains, deploying production-distribution network | years [season] |
Zanjani et al. [20] | Minimise costs (inventory, backorder, raw material) | Robust optimisation, stochastic programming | Sawmill production planning with random yields for robust customer service levels | month [day] |
Zanjani et al. [21] | Minimise costs (inventory, backorder, raw material) | Multi-stage stochastic program, scenario tree | Production planning with uncertainty in the quality of raw materials and demand for a sawmill | month [day] |
Zanjani et al. [22] | Minimise costs (inventory, backorder, raw material) | Stochastic mixed-integer model, accelerated scenario updating heuristic | Scheduling production for sawmills with randomness in yield and demand, | month [day] |
Zanjani et al. [23] | Minimise costs (inventory and backorder) | Two-stage stochastic linear program | Randomness in yields modelled as scenarios with probability distributions | month [day] |
Zanjani et al. [24] | Minimise costs (inventory, backorder, raw material) | Stochastic mixed-integer model, decomposition and cutting plane algorithms | Stochastic production planning in sawmills with randomness in yield and demand and set-up constraints | month [day] |
Case | Insights | Adaptable Constraints | Actions/Decisions |
---|---|---|---|
1 | Best supply | Min/Max quantity per log type | Purchase guidelines |
Sales quantities | Min/Max quantity per product | Sales guidelines | |
Unused logs | Capacity | Capacity planning | |
By-products | |||
Used capacity | |||
2 | Approximate production plan | Min/Max quantity per product | Sales guidelines |
Aggregated dry kiln scheduling | New cutting patterns | Delivery promises | |
Production quantities | |||
Unused logs | |||
Unsold by-products | |||
Used capacity | |||
3 | Production plan | Min/Max quantity per product | Dry kiln scheduling |
Used capacity | Stock quantities | External logistics | |
Minimal production quantities | |||
4 | Production sequence scheduling | Capacities | Bottleneck analyses |
Detailed dry kiln scheduling | Staff allocation |
Year | Quarter [Month] | Week [Shift/Day] | Day [Hour] | |
---|---|---|---|---|
Entire production | Vanzetti et al. [13] | Mendoza et al. [15] | ||
Production steps | Alvarez and Vera [25] | Alayet et al. [6] | Maturana et al. [10] | Broz et al. [7] |
Vila et al. [19] | Greigeritsch [16] | Varas et al. [26] | Vanzetti et al. [14] | |
Huka and Gronalt [1] | Zanjani et al. [20,21,22,23,24] | Vergara et al. [5] | ||
Maness and Norton [2] | ||||
Pradenas et al. [3] | ||||
Machines | Bajgiran et al. [12] | Ben Ali et al. [18] | Gaudreault et al. [11] | |
Jerbi et al. [17] | Lobos and Vera [27] | Jerbi et al. [17] | ||
Marier et al. [4] | ||||
Ouhimmou et al. [9] |
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Kaltenbrunner, M.; Huka, M.A.; Gronalt, M. Adaptive Model Building Framework for Production Planning in the Primary Wood Industry. Forests 2020, 11, 1256. https://doi.org/10.3390/f11121256
Kaltenbrunner M, Huka MA, Gronalt M. Adaptive Model Building Framework for Production Planning in the Primary Wood Industry. Forests. 2020; 11(12):1256. https://doi.org/10.3390/f11121256
Chicago/Turabian StyleKaltenbrunner, Matthias, Maria Anna Huka, and Manfred Gronalt. 2020. "Adaptive Model Building Framework for Production Planning in the Primary Wood Industry" Forests 11, no. 12: 1256. https://doi.org/10.3390/f11121256