Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation
Abstract
:1. Introduction
2. Literature Review
3. Smart AGV Management System (SAMS)
3.1. Physical and Data-Transaction Layers
3.2. Digital Layer
3.3. Shop-Floor Decision-Support
4. Optimisation Approach
4.1. Problem Formulation
- Objective function 1: aims to minimise the total cost associated with the earliness and lateness of the scheduled jobs, and formulated as below.Please note that the authors report based on their project experiences from seat and car manufacturing projects that, overall manufacturing performance, in general, tends to be more affected by the lateness of the jobs. Hence, it is often penalised more than the earliness of the jobs. However, the penalty costs for both earliness and lateness should be configured based on the factory and user requirements.
- Objective function 2: stands for the minimisation of the total energy consumption associated with the AGV loading and cumulative travel distances, and formulated as follows:
- These objectives are subjected to the following constraints:In the above equations, constraint (4) is used to ensure that the precedence relations between stages of a job for every AGVs is not breached. Constraint (5) ensures that multiple jobs cannot be performed by a machine at a time. Constraint (6) is used to fulfil the requirement that a job cannot be performed more than one machine in a stage. Constraint (7) enforces the time difference between start time of machine in the first stage and the release time of the jobs that are assigned to them must be equal or greater zero. Constraint (8) ensures that an AGV cannot perform more than one material transportation task at a time. Constraint (9) states the variables’ binary nature.
4.2. Assumptions
- The parameters of machines, including: set up time and processing time are known and based on continuously updated historical production data;
- The parameters of AGVs, including: energy consumption rate, battery capacity and travelling speed are known and based on continuously updated historical production data;
- The demand information is continuously updated in real time;
- Machine output buffers have a fixed capacity limit;
- The AGV fleet capacity is enough to cover all transportation jobs;
- The AGV will not be called by the machine when the machine output buffer is empty.
4.3. Genetic Algorithm Based Solving Method
4.4. Genetic Algorithm
Algorithm 1. Genetic Algorithm pseudo-code. |
Pseudo-code of the GA
|
4.4.1. Initialising Parameter
4.4.2. Initialising Population
4.4.3. The New Generate Population Generating
- Selection: The stochastic universal selection strategy (see [75]) is used to select parents for producing the next generators. In the stochastic uniform selection, all parents are laid on a line. The algorithm follows the line, and moves to the next point at an equal step size. At each movement, the algorithm chooses the current point as the parent for the next generation. The first step is also a uniform random number, which is smaller than the step size.
- Elitism:All the individuals are sorted based on the fitness values. The first (Equation(10)) best individuals are chosen and passed to the next generation directly. This step guarantees that the best fitness values can survive in the next generation:
- Crossover: Crossover is generated by combining the two parents together. The genes from parents are chosen randomly for crossover, and genes coordinates are the same for both parents, and the crossover children population is specified by the crossover fraction . These rules are applied into both parts of parents. Figure 8 shows an example of crossover strategy.
4.4.4. Evaluation and Iteration
5. Case Study
5.1. Overview of the Experiments
- The shop-floor layout and AGV routing paths were fixed.
- Charging threshold for AGV is set at 20%. If the battery level is lower than 20%, the AGV needs to park at the charging station for re-charge. When AGV battery is fully charged, it will be ready for the new task.
5.2. Results
5.3. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Type | Examples | Strengths | Weaknesses |
---|---|---|---|
Offline | [4,8,20,21,22,23,24,25,26,27,28,29,30,31,44,45,46,47,48,49,50,51,52,53,54,55,56,57,57] | Handles scheduling complexity | Inflexibility |
scheduling | Low CPU overloads | Deterministic behaviours | |
Requires task arrival information | |||
Subjected to a limited execution time | |||
Online | [32,33,34,35,37,38,39,41] | Handles unpredictable workloads | Reduced utilisation of resources |
scheduling | CPU overloads are harder to detect |
Notation | Description |
---|---|
Sets | |
S | Set of stations |
T | Set of production jobs |
Set of AGVs | |
W | Set of workstages |
Number of stations in stage w | |
Indices | |
s | Index of station, |
t | Index of production job, |
n | Index of AGV, |
w | Index of workstage, |
Index of station in stage w, | |
Parameters | |
The weight of no load AGV n | |
The weight of AGV n loaded, when travelling between station i and j for job t | |
Earliness cost penalty coefficient | |
Lateness cost penalty coefficient | |
Processing time of job t allocated to s in stage w | |
Due date of job t | |
Completion date of job t | |
Starting time of job t at station s in stage w | |
Completion time of job t at station s in stage w | |
Distance between station i and j, also, | |
Release time of the job t into the system | |
Decision Variables | |
1 if machine working on job t, else 0 | |
1 if AGV n travels between station i and j for job t, else 0 |
Solutions | Normal Events | Two Machines Breakdown | ||
---|---|---|---|---|
Tardiness | EC | Tardiness | EC | |
Proposed Scheduling | 300.4484 (Earliness) | 701.4404 | 218.6914 (Earliness) | 704.9327 |
FIFO Scheduling | 1575.7169 (Earliness) | 577.4241 | 4657.8487 (Lateness) | 701.1848 |
SPT based on 1st Stage | 1103.9 (Earliness) | 585.7565 | 14,968 (Lateness) | 997.0096 |
SPT based on 2nd Stage | 679.377 (Earliness) | 607.6007 | 13,708 (Lateness) | 923.2472 |
SPT based on 3rd Stage | 1223.6 (Earliness) | 612.8167 | 9681.7 (Lateness) | 833.9795 |
SPT based on 4th Stage | 1179.2 (Earliness) | 613.1612 | 15,750 (Lateness) | 1150.4 |
SPT based on overall Stage | 1710.9 (Earliness) | 576.6980 | 13,956 (Lateness) | 944.900 |
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Yao, F.; Alkan, B.; Ahmad, B.; Harrison, R. Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation. Sensors 2020, 20, 6333. https://doi.org/10.3390/s20216333
Yao F, Alkan B, Ahmad B, Harrison R. Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation. Sensors. 2020; 20(21):6333. https://doi.org/10.3390/s20216333
Chicago/Turabian StyleYao, Fengjia, Bugra Alkan, Bilal Ahmad, and Robert Harrison. 2020. "Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation" Sensors 20, no. 21: 6333. https://doi.org/10.3390/s20216333
APA StyleYao, F., Alkan, B., Ahmad, B., & Harrison, R. (2020). Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation. Sensors, 20(21), 6333. https://doi.org/10.3390/s20216333