Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks
<p>Classification of supply chain mapping hierarchy concerning planning levels and focus.</p> "> Figure 2
<p>Co-occurrence network of keywords in buffer allocation and queueing networks for manufacturing systems.</p> "> Figure 3
<p>GEM steps for a tandem network (adapted from Kerbache and Smith [<a href="#B73-applsci-13-09525" class="html-bibr">73</a>]).</p> "> Figure 4
<p>Steps of optimisation approach used in Anylogic software.</p> "> Figure 5
<p>Process flowchart for the inter-facility material transfer operations.</p> "> Figure 6
<p>Layout plan of SM’s raw material storages and billet plant.</p> "> Figure 7
<p>DES model for SM inter-facility material transfer operations.</p> "> Figure 8
<p>Process flowchart of outbound logistics at SM.</p> "> Figure 9
<p>DES model for outbound logistics process.</p> "> Figure A1
<p>Optimisation run window—scenario 2.</p> "> Figure A2
<p>Optimisation run window—scenario 3.</p> "> Figure A3
<p>Optimisation run window—scenario 4.</p> "> Figure A4
<p>Optimisation run window—scenario 5.</p> ">
Abstract
:1. Introduction
2. Literature Review
2.1. Process Maps and Applications
2.2. Order Releasing and Scheduling Using Heuristic Based on Drum Buffer (DRB) Rope Technique
2.3. Multi-Level Rolling Horizon Planning and Scheduling Using DBR Approach Considering Material Constraints
2.4. Buffer Allocation Problems
Uniform and Non-Uniform Buffers
2.5. Solving Finite Queueing Networks
Blocking Phenomenon
2.6. Scholarly Contributions and Future Extensions of the Study
3. Methodology
3.1. Modelling the Material Transfer Processes Using a Finite Queueing Network
3.2. Generalized Expansion Method (GEM)
Notations and Definitions
3.3. Formulation of Optimisation Problem
4. Case Study—Numerical Experiments
4.1. Inter-Facility Material Transfer Process
4.1.1. Development of the DES Model for Inter-Facility Material Transfer Operations
4.1.2. Optimal Buffer Allocation
4.1.3. Utilisation Rates Comparison
- Subdividing specific processes into two sub-processes,
- merging idle stations to consolidate workloads, or
- employing additional servers to alleviate the burden on busy servers.
4.1.4. Sensitivity Analysis
4.1.5. Comparison of Analytical and Simulation Methodologies
4.2. Out-Bound Logistics Process
4.2.1. DES Model for SM’s Outbound Logistics Operation
4.2.2. Optimal Buffer Allocation for Outbound Logistics Process
4.2.3. Cycle Time Comparison with Analytical and Simulation Method
4.3. Managerial Insights
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Node # | Process | Aver. Service Time (min) | Node # | Process | Aver. Service Time (min) |
---|---|---|---|---|---|
1 | Gate operation | 1.5 | 7 | Weigh bridge 2 | 1.5 |
2 | Weigh bridge1 | 1.5 | 8 | Unloading—A | 2.5 |
3 | Loading—A | 6 | 9 | Unloading—B | 2 |
4 | Loading—B | 4 | 10 | Unloading—C | 3.5 |
5 | Loading—C | 7 | 11 | Return trip | 6 |
6 | Quality check | 2 |
Raw Material (Product Class) | Full Truck Load (Tons) |
---|---|
A | 7 |
B | 3 |
C | 9 |
Scenario # | Number of Trucks | Total Number of Trucks | ||
---|---|---|---|---|
A | B | C | ||
Scenario 1 | 3 | 3 | 3 | 9 |
Scenario 2 | 4 | 4 | 4 | 12 |
Scenario 3 | 6 | 6 | 6 | 18 |
Scenario 4 | 4 | 5 | 6 | 15 |
Scenario 5 | 6 | 4 | 5 | 15 |
Scenario # | Optimal Buffer Allocation | Throughput (Tons of Materials Transferred) | Total # of Buffers Used |
---|---|---|---|
Scenario 1 | 4,1,4,3,1,3,2,2,1,1 | 3101 | 22 |
Scenario 2 | 1,1,3,2,2,4,3,2,2,1 | 3656 | 21 |
Scenario 3 | 1,3,2,4,1,1,2,7,2,1 | 3677 | 24 |
Scenario 4 | 1,1,1,1,1,10,1,1,1,1 | 3628 | 18 |
Scenario 5 | 2,1,6,2,1,5,1,1,3,3 | 3794 | 25 |
Scenario # | Scenario 1 | Scenario 5 | ||||
---|---|---|---|---|---|---|
Node # | GEM Method | Simulation Model | Diff. | GEM Method | Simulation Model | Diff. |
Node1 | 63.30% | 66% | −2.70% | 75.9% | 79% | −3.1% |
Node2 | 63.30% | 66% | −2.70% | 77.8% | 78% | −0.2% |
Node3 | 68.90% | 66% | 2.90% | 72.9% | 78% | −5.1% |
Node4 | 69.80% | 73% | −3.20% | 92.4% | 99% | −6.6% |
Node5 | 54.10% | 55% | −0.90% | 54.1% | 58% | −3.9% |
Node6 | 85.80% | 82% | 3.80% | 87.5% | 98% | −10.5% |
Node7 | 87.80% | 84% | 3.80% | 87.1% | 99% | −11.9% |
Node8 | 28.70% | 32% | −3.30% | 37.5% | 44% | −6.5% |
Node9 | 35.80% | 30% | 5.80% | 25.0% | 30% | −5.0% |
Node10 | 38.10% | 43% | −4.90% | 50.0% | 51% | −1.0% |
Scenario # | Node Utilisation Rate | Throughput | Total # of Buffers | |||
---|---|---|---|---|---|---|
1 Server | 2 Servers | 1 Server | 2 Servers | 1 Server | 2 Servers | |
Scenario 1 | 84% | 42% | 3101 | 3126 | 22 | 10 |
Scenario 3 | 99% | 67% | 3677 | 4199 | 24 | 18 |
Process | Aver. Service Time (min) | Process | Aver. Service Time (min) |
---|---|---|---|
Gate operation | 2 | Documentation and compliance | 2 |
Creditworthiness evaluation | 3 | Loading and staging | 12 |
Order processing—standard | 7 | Truck inspection | 2 |
Order processing—spot | 10 | Truck dispatch and delivery confirmation | 2 |
Scenario # | Arrival Rate (per Hour) | Stand.: Spot |
---|---|---|
Scenario 1 | 4 | 80 to 20 |
Scenario 2 | 4 | 90 to 10 |
Scenario 3 | 5 | 90 to 10 |
Scenario 4 | 8 | 90 to 10 |
Scenario # | Optimal Buffer Allocation | Throughput (# of Orders Processed) | Aver. Order Processing Time (min) | Total Number of Buffer Spaces Used |
---|---|---|---|---|
Scenario 1 | 1,1,5,1,7,1,4,3 | 49 | 14.69 | 23 |
Scenario 2 | 2,2,2,3,3,1,1,2 | 49 | 14.69 | 16 |
Scenario 3 | 1,1,1,1,1,1,1,1 | 51 | 14.12 | 8 |
Scenario 4 | 5,12,3,3,20,7,2,16 | 57 | 12.63 | 68 |
Scenario # | Cycle Time (min) | ||
---|---|---|---|
GEM Method | Simulation Model | Diff. | |
Scenario 1 | 13.471 | 14.69 | −8.30% |
Scenario 2 | 13.851 | 14.69 | −5.71% |
Scenario 3 | 12.981 | 14.12 | −8.07% |
Scenario 4 | 12.135 | 12.63 | −3.92% |
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Amjath, M.; Kerbache, L.; Smith, J.M.; Elomri, A. Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks. Appl. Sci. 2023, 13, 9525. https://doi.org/10.3390/app13179525
Amjath M, Kerbache L, Smith JM, Elomri A. Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks. Applied Sciences. 2023; 13(17):9525. https://doi.org/10.3390/app13179525
Chicago/Turabian StyleAmjath, Mohamed, Laoucine Kerbache, James MacGregor Smith, and Adel Elomri. 2023. "Optimisation of Buffer Allocations in Manufacturing Systems: A Study on Intra and Outbound Logistics Systems Using Finite Queueing Networks" Applied Sciences 13, no. 17: 9525. https://doi.org/10.3390/app13179525