Order acceptance under uncertainty in batch process industries

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On the selectivity of order acceptance procedures in batch process industries

Job and resource structures in batch process industries are generally very complex, which renders the assessment of what workload can be completed during a specific period very difficult. Order acceptance procedures have a considerable impact on the mix of jobs that need to be scheduled, by refusing specific jobs from the total demand. In this paper, we investigate whether jobs with specific characteristics are systematically rejected by an aggregate acceptance procedure and a detailed acceptance procedure. We find out that, while both procedures are selective in the kind of jobs they accept when job mix variety is high, the detailed acceptance procedure underestimates the consequences on the total makespan of significantly changing the job mix

Bootstrapping to solve the limited data problem in production control : an application in batch process industries

Batch process industries are characterized by complex precedence relationships among operations, which makes the estimation of an acceptable workload very difficult. Previous research indicated that a regression-based model that uses aggregate job set characteristics may be used to support order acceptance decisions. Applications of such models in real life assume that suffcient historical data on job sets and the corresponding makespans are available. In practice, however, historical data may be very limited and may not be suffcient to produce accurate regression estimates. This paper shows that such a lack of data significantly impacts the performance of regression-based order acceptance procedures. To resolve this problem, we devised a method that uses the bootstrap principle. A simulation study shows that performance improvements are obtained when using the parameters estimated from the bootstrapped data set, demonstrating that this bootstrapping procedure can indeed solve the limited data problem in production control

On the selectivity of order acceptance procedures in batch process industries

Job and resource structures in batch process industries are generally very complex, which renders the assessment of what workload can be completed during a specific period very difficult. Order acceptance procedures have a considerable impact on the mix of jobs that need to be scheduled, by refusing specific jobs from the total demand. In this paper, we investigate whether jobs with specific characteristics are systematically rejected by an aggregate acceptance procedure and a detailed acceptance procedure. We find out that, while both procedures are selective in the kind of jobs they accept when job mix variety is high, the detailed acceptance procedure underestimates the consequences on the total makespan of significantly changing the job mix

A hybrid policy for order acceptance in batch process industries

Abstract Customer order acceptance is an important process in make-to-order industries. Acceptance policies should operate such that a pre-specified delivery reliability is achieved, while maximizing resource utilization. By selecting orders with specific characteristics that maximize resource utilization, an important and often unforeseen effect occurs: the mix of orders changes such that the expected delivery reliability is no longer met. This paper investigates the selectivity of an aggregate and a detailed acceptance procedure, for batch process industries featuring complex job and resource structures. We found that the detailed policy maximizes resource utilization but underestimates the consequences on the realized makespan of significantly changing the job mix. The aggregate policy, while being selective, performs much better with respect to the delivery reliability, but achieves a lower capacity utilization. We propose a third procedure, the hybrid policy, which combines the strengths of both the detailed and aggregate acceptance procedures. Simulation experiments show that the hybrid policy successfully controls the delivery reliability, without loosing much of the beneficial effect of the selectivity on utilization

Bootstrapping to solve the limited data problem in production control: an application in batch process industries

Batch process industries are characterized by complex precedence relationships among operations, which makes the estimation of an acceptable workload very difficult. Previous research indicated that a regression-based model that uses aggregate job set characteristics may be used to support order acceptance decisions. Applications of such models in real-life assume that sufficient historical data on job sets and the corresponding makespans are available. In practice, however, historical data maybe very limited and may not be sufficient to produce accurate regression estimates. This paper shows that such a lack of data significantly impacts the performance of regression-based order acceptance procedures. To resolve this problem, we devised a method that uses the bootstrap principle. A simulation study shows that performance improvements are obtained when using the parameters estimated from the bootstrapped data set, demonstrating that this bootstrapping procedure can indeed solve the limited data problem in production control

Bootstrapping to solve the limited data problem in production control: an application in batch process industries

Batch process industries are characterized by complex precedence relationships among operations, which makes the estimation of an acceptable workload very difficult. Previous research indicated that a regression-based model that uses aggregate job set characteristics may be used to support order acceptance decisions. Applications of such models in real-life assume that sufficient historical data on job sets and the corresponding makespans are available. In practice, however, historical data maybe very limited and may not be sufficient to produce accurate regression estimates. This paper shows that such a lack of data significantly impacts the performance of regression-based order acceptance procedures. To resolve this problem, we devised a method that uses the bootstrap principle. A simulation study shows that performance improvements are obtained when using the parameters estimated from the bootstrapped data set, demonstrating that this bootstrapping procedure can indeed solve the limited data problem in production control

Makespan estimation and order acceptance in batch process industries when processing times are uncertain

Batch process industries are characterized by complex precedence relationships between operations, which renders the estimation of an acceptable workload very difficult. A detailed schedule based model can be used for this purpose, but for large problems this may require a prohibitive large amount of computation time. We propose a regression based model to estimate the makespan of a set of jobs. We extend earlier work based on deterministic processing times by considering Erlang-distributed processing times in our model. This regression-based model is used to support customer order acceptance. Three order acceptance policies are compared by means of simulation experiments: a scheduling policy, a workload policy and a regression policy. The results indicate that the performance of the regression policy can compete with the performance of the scheduling policy in situations with high variety in the job mix and high uncertainty in the processing times