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Articles

Order release planning with predictive lead times: a machine learning approach

ORCID Icon, ORCID Icon &
Pages 3285-3303 | Received 11 Mar 2019, Accepted 15 Oct 2020, Published online: 26 Dec 2020

Figures & data

Figure 1. Production System of the Simulation Model with routing, processing time distributions and demand interarrival time distributions.

Figure 1. Production System of the Simulation Model with routing, processing time distributions and demand interarrival time distributions.

Figure 2. Setting adaptive lead times.

Figure 2. Setting adaptive lead times.

Figure 3. Safety lead time under the assumption of normally distributed deviations of the flow time forecasts.

Figure 3. Safety lead time under the assumption of normally distributed deviations of the flow time forecasts.

Figure 4. The artificial neural network architecture.

Figure 4. The artificial neural network architecture.

Table 1. Result of feature selection for bottleneck products (top) and non-bottleneck products (bottom), where features with 5%-significance are printed in bold.

Figure 5. Flow time forecast accuracy of all products for all scenarios.

Figure 5. Flow time forecast accuracy of all products for all scenarios.

Table 2. Results for a bottleneck utilisation of 70%.

Table 3. Results for a bottleneck utilisation of 80%.

Figure 6. Job Shop Simulation Model with routing, processing time distributions and demand interarrival time distributions.

Figure 6. Job Shop Simulation Model with routing, processing time distributions and demand interarrival time distributions.

Table 4. Results for job shop production environment.

Table 5. Results for 80-Exp-Exp with (top) and without safety lead times (bottom).

Table 6. Sensitivity analysis for modified cost ratios of FGIC and BOC.

Table 7. Results for 80-Exp-Exp with limited release quantity.

Table A1. Results for 80-Exp-Exp with (top) and without safety lead times (bottom).

Table A2. Sensitivity analysis for modified cost ratios of FGIC and BOC.

Table A3. Results for 80-Exp-Exp with limited release quantity.