Abstract
This study discusses the model development of three-stage flow-shop scheduling involving Batch Processing Machine (BPM) and Discrete Processing Machine (DPM). Each job passes several stages of the production process from the first stage in DPM-a, the second stage in BPM, to the third stage in DPM-b. Each job is sequentially processed in DPM-a and DPM-b. in BPM; every job is handled simultaneously at the same time. The maximum weight determines the capacity of BPM. This study uses mathematic modeling approach. The result model produced in this study is Mixed Integer Linear Programming (MILP) Model. Purpose function model is minimizing total completion time. Model testing is done by using numerical examples with several data scenarios. The results showed that the model produced was the optimum model and provided a unique schedule pattern. In the future research can be formulated the heuristic model.
Highlights
Flow-shop scheduling involving Batch Processing Machine (BPM) and Discrete Processing Machine (DPM) has been started since decades ago
The model in this study aims to minimize total completion time in DPM-b
The following numerical example is used to see if the resulting model can provide the optimum solution: Six jobs are about to be scheduled in the process of DPM-a BPM DPM-b routing
Summary
Flow-shop scheduling involving Batch Processing Machine (BPM) and Discrete Processing Machine (DPM) has been started since decades ago. The use of BPM in the process of flow-shop production does involve job scheduling and determine the batch of every job. There are two schools of thought in understanding BPM [1] It is understood as a machine processing a batch consisting of a number of jobs by sharing the setup process [2], [3], [4], [5], [6], [7], [8], [9], [10], [11],[12]. BPM is understood as a machine processing a number of jobs in a batch at the same time whose time processing is not changing and influenced by the number of jobs in the batch [13], [14], [15], [16], [17], [18], [19], [20], [21], [22]
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