Wenny H. M. Raaymakers

Technische Universiteit Eindhoven, Eindhoven, North Brabant, Netherlands

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Publications (9)12.84 Total impact

  • W.H.M Raaymakers, A.J.M.M Weijters
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    ABSTRACT: Batch processing is becoming more important in the process industries, because of the increasing product variety and the decreasing demand volumes for individual products. In batch process industries it is difficult to estimate the completion time, or makespan, of a set of jobs, because jobs interact at the shop floor. We assume a situation with hierarchical production control consisting of a planning level and a scheduling level. In this paper we focus on the planning level. We use two different techniques for estimating the makespan of job sets in batch process industries. The first technique estimates the makespan of a job set by developing regression models, the second technique by training neural networks. Both techniques use aggregate information. By using aggregate information the presented techniques are less time consuming in assessing the makespan of a job set compared with methods based on detailed information.Tests on newly generated job sets showed that both techniques are robust for changes in the number of jobs, the average processing time, a more unbalanced workload and for different resource configurations. Finally, the estimation quality of the neural network models appears significantly better than the quality of regression models.
    European Journal of Operational Research 02/2003; · 2.04 Impact Factor
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    W. H. M. Raaymakers, A. J. M. M. Weijters
    European Journal of Operational Research 02/2003; 145(1):14-30. · 2.04 Impact Factor
  • Wenny H.M. Raaymakers, J. Will M. Bertrand, Jan C. Fransoo
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    ABSTRACT: To properly conduct aggregate control functions such as order acceptance and capacity loading, good estimates of the available production capacity need to be on hand. Capacity structures in batch process industries are generally so complex that it is not straightforward to estimate the capacity of a production department. In this paper, we assess the quality of estimation models that are based on regression. The paper builds on earlier results, which have demonstrated that a limited number of factors can explain a large share of the variance in makespan estimation based on regression models.
    International Journal of Production Economics 02/2001; 70(2):145-161. · 2.08 Impact Factor
  • Jan C. Fransoo, Wenny H. M. Raaymakers
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    ABSTRACT: In the batch chemical industry, making aggregate estimations of available capacity is a difficult task, due to the many interactions between jobs at the shop floor. In this Chapter, we present an approach for aggregate capacity modelling that is based on linear regression. In the regression model, we include resource structure characteristics of the jobs to be completed. We show for a large variety of settings that this approach is superior to a workload approach, and further argue that this type of aggregate modelling may also very well address the aggregate modelling of a human scheduler's scheduling performance.
    01/2001;
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    W. H. M. Raaymakers, A. J. M. M. Weijters
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    ABSTRACT: Batch processing becomes more important in the process industries, because of the increasing product variety and the decreasing demand volumes for individual products. In batch process industries it is difficult to predict the completion time, or makespan, of a set of jobs, because jobs interact at the shop floor. In this paper, we compare two different methods for predicting the makespan of job sets in batch process industries. The first method predicts the makespan of a job set by developing regression models, the second method by training neural networks. Both methods use aggregate job-set information. 1. Introduction Batch processes are frequently found in food, specialty chemicals and pharmaceutical industries, where production volumes of individual products do not justify continuous production and dedicated production lines. Nowadays, batch process industries become more important because of the increasing product variety and decreasing demand volumes for individual prod...
    10/2000;
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    Wenny H.M. Raaymakers, J. Will M. Bertrand, Jan C. Fransoo
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    ABSTRACT: Aggregate models of detailed scheduling problems are needed to support aggregate decision making such as customer order acceptance. In this paper, we explore the performance of various aggregate models in a decentralized control setting in batch chemical manufacturing (no-wait job shops). Using simulation experiments based on data extracted from an industry application, we conclude that a linear regression based model outperforms a workload based model with regard to capacity utilization and the need for replanning at the decentralized level, specifically in situations with increased capacity utilization and/or a high variety in the job mix.
    IIE Transactions 09/2000; 32(10):989-998. · 1.29 Impact Factor
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    Wenny H. M. Raaymakers, J.Will M. Bertrand, Jan C. Fransoo
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    ABSTRACT: We investigate the performance of workload rules used to support customer order acceptance decisions in the hierarchical production control structure of a batch chemical plant. Customer order acceptance decisions need to be made at a point in time when no detailed information is available about the actual shop floor status during execution of the order. These decisions need therefore be based on aggregate models of the shop floor, which predict the feasibility of completing the customer order in time. In practice, workload rules are commonly used to estimate the availability of sufficient capacity to complete a set of orders in a given planning period. Actual observations in a batch chemical manufacturing plant show that the set of orders accepted needs to be reconsidered later, because the schedule turns out to be infeasible. Analysis of the planning processes used at the plant shows that workload rules can yields reliable results, however at the expense of a rather low capacity utilization. In practice this is often unacceptable. Since, solving a detailed scheduling problem is not feasible at this stage, this creates a dilemma that only can be solved if we can find more detailed aggregate models than workload rules can provide.
    Journal of Intelligent Manufacturing 03/2000; 11(2):217-228. · 1.28 Impact Factor
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    Wenny H.M Raaymakers, Jan C Fransoo
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    ABSTRACT: We study multipurpose batch process industries with no-wait restrictions, overlapping processing steps, and parallel resources. To achieve high utilization and reliable lead times, the master planner needs to be able to accurately and quickly estimate the makespan of a job set. Because constructing a schedule is time consuming, and production plans may change frequently, estimates must be based on aggregate characteristics of the job set. To estimate the makespan of a complex set of jobs, we introduce the concept of job interaction. Using statistical analysis, we show that a limited number of characteristics of the job set and the available resources can explain most of the variability in the job interaction.
    International Journal of Production Economics 02/2000; · 2.08 Impact Factor
  • W.H.M. Raaymakers, J.A. Hoogeveen
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    ABSTRACT: Scheduling problems in multipurpose batch process industries are very hard to solve because of the job shop like processing structure in combination with rigid technical constraints, such as no-wait restrictions. This paper shows that scheduling problems in this type of industry may be characterized as multiprocessor no-wait job shop problems with overlapping operations. A simulated annealing algorithm is proposed that obtains near-optimal solutions with respect to makespan. This paper shows that the no-wait restrictions require several adaptations of the neighborhood structure used by simulated annealing. The performance of the algorithm is evaluated by scheduling industrial instances from a multipurpose batch plant in the pharmaceutical industry. Our results indicate that simulated annealing consistently gives better results for a number of realistic instances than simple heuristics within acceptable computation time.
    European Journal of Operational Research 02/2000; · 2.04 Impact Factor