Family-based dispatching with parallel machines

Zee, Durk-Jouke van der

doi:10.1080/00207543.2015.1011289

Cited by 5 publications

(2 citation statements)

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“…Whereas exhaustive heuristics only allow decision-making at the moment the queue of jobs for the current family is exhausted, non-exhaustive heuristics specify a truncation-mechanism for deciding on the switching of families. Various truncation mechanisms have been studied that link a decision moment to restrictions on batch size (Mosier, Elvers, and Kelly 1984;Ruben, Mosier, and Mahmoodi 1993;van der Zee 2010van der Zee , 2015 or a specified time lapse (Russell and Philipoom 1991). Despite a long-lasting debate in literature on the benefits of either subclass -exhaustive or non-exhaustive -no single heuristic has been proposed indicating overall best performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Coordinating batching decisions in manufacturing networks

Zee

2017

International Journal of Production Research

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Family-based dispatching heuristics aim for improving job flow times by reducing time spent on set-ups. They realise set-up efficiencies by batching similar types of jobs. By their intuitiveness and the simplicity of their decision logic, they may contribute to an easy to implement and viable strategy in many practical settings. Similar to common dispatching rules most existing family-based dispatching heuristics are myopic, i.e. their decision scope is restricted to a single manufacturing stage. Hence, they neglect opportunities for improving shop performance by coordinating batching decisions with other manufacturing stages. Case examples from industry underpin the need for exploring these opportunities. We do so by studying a simple two-stage flow shop, entailing a serial and a batch stage. To facilitate shop coordination we propose extensions to existing family-based dispatching heuristics. Extended heuristics seek to further increase set-up efficiencies by allowing for upstream job re-sequencing, and pro-active set-ups, i.e. set-ups that may be initiated prior to the arrival of a job. Outcomes of an extensive simulation study indicate significant performance gains for extended heuristics vs. existing heuristics. Performance gains are largest for moderate and high set-up to run-time ratios.

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Section: Literature Reviewmentioning

confidence: 99%

Coordinating batching decisions in manufacturing networks

Zee

2017

International Journal of Production Research

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“…Eom et al (2002) developed an efficient heuristic to minimize the total weighted tardiness, based on the Apparent Tardiness Cost with Setups (ATCS) composite rule (Lee and Pinedo 1997), with sequence-dependent family setup times. Van Der Zee (2015) developed a survey on dispatching methods to address parallel machines scheduling problems with family setup times.…”

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confidence: 99%

Solving the Deterministic and Stochastic Pipe-Laying Support Vessel Scheduling Problem

CUNHA¹

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A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups

Lang

Hong

et al. 2023

J Intell Manuf

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As an essential scheduling problem with several practical applications, the parallel machine scheduling problem (PMSP) with family setups constraints is difficult to solve and proven to be NP-hard. To this end, we present a deep reinforcement learning (DRL) approach to solve a PMSP considering family setups, aiming at minimizing the total tardiness. The PMSP is first modeled as a Markov decision process, where we design a novel variable-length representation of states and actions, so that the DRL agent can calculate a comprehensive priority for each job at each decision time point and then select the next job directly according to these priorities. Meanwhile, the variable-length state matrix and action vector enable the trained agent to solve instances of any scales. To handle the variable-length sequence and simultaneously ensure the calculated priority is a global priority among all jobs, we employ a recurrent neural network, particular gated recurrent unit, to approximate the policy of the agent. The agent is trained based on Proximal Policy Optimization algorithm. Moreover, we develop a two-stage training strategy to enhance the training efficiency. In the numerical experiments, we first train the agent on a given instance and then employ it to solve instances with much larger scales. The experimental results demonstrate the strong generalization capability of the trained agent and the comparison with three dispatching rules and two metaheuristics further validates the superiority of this agent.

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Family-based dispatching with parallel machines

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Cited by 5 publications

References 40 publications

Coordinating batching decisions in manufacturing networks

Coordinating batching decisions in manufacturing networks

Solving the Deterministic and Stochastic Pipe-Laying Support Vessel Scheduling Problem

A two-stage RNN-based deep reinforcement learning approach for solving the parallel machine scheduling problem with due dates and family setups

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