On the integration of due date setting and order release control

Thürer, Matthias; Land, Martin; Stevenson, Mark; Fredendall, Lawrence D.

doi:10.1080/09537287.2017.1302102

Cited by 12 publications

(8 citation statements)

References 42 publications

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“…As in previous simulation studies on Workload Control (e.g. Land and Gaalman 1998;Fredendall et al 2010;Thürer et al 2012Thürer et al , 2017Fernandes et al 2017), it is assumed that all materials are available, and all necessary information regarding shop floor routings, processing times, etc. is known.…”

Section: Workload Control Order Release and Time Limit Policiesmentioning

confidence: 99%

Balancing earliness and tardiness within workload control order release: an assessment by simulation

2022

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Most Workload Control literature assumes that delivery performance is determined by tardiness related performance measures only. While this may be true for companies that directly deliver to end-customers, for make-to-stock companies or firms that are part of supply chains, producing early often means large inventories in the finished goods warehouse or penalties incurred by companies downstream in the supply chain. Some earlier Workload Control studies used a so-called time limit, which constrains the set of jobs that can be considered for order release, to reduce earliness. However, recent literature largely abandoned the time limit since it negatively impacts tardiness performance. This study revisits the time limit, assessing the use of different adaptive policies that restrict its use to periods of either low or high load. By using a simulation model of a pure job shop, the study shows that an adaptive policy allows to balance the contradictory objectives of delaying the release of orders to reduce earliness and to release orders early to respond to periods of high load as quick as possible. Meanwhile, only using a time limit in periods of high load was found to be the best policy.

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Section: Workload Control Order Release and Time Limit Policiesmentioning

confidence: 99%

Balancing earliness and tardiness within workload control order release: an assessment by simulation

2022

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“…They concluded that the system's performance is improved for mean tardiness and SDL. Thürer et al 18) integrated due date setting rules and ORR considering SIDST using simulation. They considered two ORR policies, i.e., periodic and LUMSCOR with PRT sequencing rule.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Effect of Setup Times and Shop Utilization Levels on Performance of ORR Policies in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Times: A Simulation Approach

Rani¹,

Jain²,

Angra³

2022

Evergreen

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Order review and release (ORR) controls making jobs from planning stage to execution stage. This article assesses the effect of setup times and shop utilization levels on ORR policies performance in stochastic dynamic job shop (SDJS) considering sequence-dependent setup time (SDST). The system performance indicators such as mean throughput time (MTT), mean lead time (MLT), number of tardy jobs (NOTJ), and total setups (TS) are used to assess system's performance. For conducting experiments, the simulation model is created using Promodel ® simulation software. Results indicate that for a given ORR policy, at a given setup time and shop utilization level, as workload trigger level, increases MTT increases. In contrast, the other performance measures such as MLT, NOTJ, and TS decrease. Further, for a given ORR policy, as shop utilization decreases, MTT, MLT, and NOTJ decrease at all workload trigger levels for all setup time levels. In contrast, TS increase at all workload trigger levels for all setup time levels. Further, as setup time increases for a given ORR policy, MTT and MLT increase at all workload trigger levels for all shop utilization levels. In contrast, TS and NOTJ decrease.

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“…One of the early implementations of WL was Bertrand's workload concept presented in Land and Gaalman (1996). It has received much attention in the literature, with researchers testing different methodologies to account for job workload (Oosterman et al 2000), investigating the integration of due-date information (Thürer et al 2017), and trying to integrate O/C (Thürer et al 2016a, b). In all cases, the most commonly implemented version is as follows:…”

Section: Wlworker+mentioning

confidence: 99%

Labor flexibility integration in workload control in Industry 4.0 era

Costa

Staudacher

2021

Oper Manag Res

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The paradigm shift toward Industry 4.0 is facilitating human capability, and at the center of the research are the workers—Operator 4.0—and their knowledge. For example, new advances in augmented reality and human–machine interfaces have facilitated the transfer of knowledge, creating an increasing need for labor flexibility. Such flexibility represents a managerial tool for achieving volume and mix flexibility and a strategic means of facing the uncertainty of markets and growing global competition. To cope with these phenomena, which are even more challenging in high-variety, low-volume contexts, production planning and control help companies set reliable due dates and shorten lead times. However, integrating labor flexibility into the most consolidated production planning and control mechanism for a high-variety, low-volume context—workload control—has been quite overlooked, even though the benefits have been largely demonstrated. This paper presents a mathematical model of workload control that integrates labor flexibility into the order review and release phase and simulates the impact on performance. The main results show that worker transfers occur when they are most needed and are minimized compared to when labor flexibility is at a lower level of control—shop-floor level—thus reducing lead time.

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On the integration of due date setting and order release control

Cited by 12 publications

References 42 publications

Balancing earliness and tardiness within workload control order release: an assessment by simulation

Balancing earliness and tardiness within workload control order release: an assessment by simulation

Assessing the Effect of Setup Times and Shop Utilization Levels on Performance of ORR Policies in a Stochastic Dynamic Job Shop with Sequence-Dependent Setup Times: A Simulation Approach

Labor flexibility integration in workload control in Industry 4.0 era

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