Local Search and Tabu Search Algorithms for Machine Scheduling of a Hybrid Flow Shop Under Uncertainty

Schumacher, Christin; Buchholz, Peter; Fiedler, Kevin; Gorecki, Nico

doi:10.1109/wsc48552.2020.9383964

Cited by 4 publications

(4 citation statements)

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“…On the other hand, we test different values for the quantile to reduce and evaluate the risk of producing less demands than the customer order (Sections 5 and 6) and simulate the calculated schedules with our simulation model, described in Section 7. For the evaluation of different security factor levels, e.g., for SF ij , we refer to Schumacher et al [30].…”

Section: Resultsmentioning

confidence: 99%

“…In Schumacher et al [30], we also analyzed the variation in objective function values caused by stochastic components in the metaheuristics for an exemplary week. Steepest Descent and tabu search algorithms provide the same objective value without variation over different runs.…”

Section: Resultsmentioning

confidence: 99%

“…At first, the first stage is scheduled with one of the algorithms, which then is combined with ECT in each case to schedule the second stage. Thus, we obtain Algorithms 1-4, which were already presented in our conference paper Schumacher et al [30].…”

Section: Computation Of Schedulesmentioning

confidence: 89%

“…The clustering procedure was first mentioned in Schumacher et al [30]. For clustering demand data and identifying high-risk jobs (see Figure 4), k-means++ is applied with three parameters and the result is shown in Figure 5.…”

Section: Clustering and Forecasting In The Application Casementioning

confidence: 99%

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Scheduling Algorithms for a Hybrid Flow Shop under Uncertainty

Schumacher

Buchholz

2020

Algorithms

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In modern production systems, scheduling problems have to be solved in consideration of frequently changing demands and varying production parameters. This paper presents a approach combining forecasting and classification techniques to predict uncertainty from demands, and production data with heuristics, metaheuristics, and discrete event simulation for obtaining machine schedules. The problem is a hybrid flow shop with two stages, machine qualifications, skipping stages, and uncertainty in demands. The objective is to minimize the makespan. First, based on the available data of past orders, jobs that are prone to fluctuations just before or during the production phase are identified by clustering algorithms, and production volumes are adjusted accordingly. Furthermore, the distribution of scrap rates is estimated, and the quantiles of the resulting distribution are used to increase corresponding production volumes to prevent costly rescheduling resulting from unfulfilled demands. Second, Shortest Processing Time (SPT), tabu search, and local search algorithms are developed and applied. Third, the best performing schedules are evaluated and selected using a detailed simulation model. The proposed approach is validated on a real-world production case. The results show that the price for a very robust schedule that avoids underproduction with a high probability can significantly increase the makespan.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Computation Of Schedulesmentioning

confidence: 89%

Section: Clustering and Forecasting In The Application Casementioning

confidence: 99%

See 2 more Smart Citations