Job Scheduling Algorithm for a Hybrid MTO-MTS Production Process

Danilczuk, Wojciech; Gola, Arkadiusz; Grznár, Patrik

doi:10.1016/j.ifacol.2022.04.235

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Peeters and van Ooijen (2020) reviewed and classified the hybrid make-to-stock/make-to-order production control systems, introducing a taxonomy of different types of such systems. Danilczuk et al (2022) presented the premises of a procedure and a main algorithm for manufacturing job scheduling in hybrid make-to-order/make-tostock production systems. They developed a shop job scheduling approach that enabled both to select jobs to be produced with the help of a make-to-stock strategy.…”

Section: Related Literaturementioning

confidence: 99%

Matheuristics for the Order-picking Problem with Sequence-dependant Constraints in a Logistic Center with a One-directional Conveyor Between Buffers

Czerniachowska,

Wichniarek,

Żywicki

2024

Management and Production Engineering Review

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In the logistics center (warehouse or distribution center), customer orders need to be picked up by the pickers. In this research, we examine the order-picking problem with sequencedependent constraints with two decision variables (container start time and product quantity) in a distribution center with a one-directional conveyor. The decision-making is based on the developed two variations of two-step matheuristics. At first, the main order-picking problem is divided into two subproblems. Next, each step of each variant of the subproblem is solved using a mathematical programming-based technique. Both matheuristics were better in 85 of 120 test instances compared to the initial model solved by mathematical programming. Pickers matheuristics were better on average at 46.56%, while Buffers matheuristics were better on average at 46.87%. The proposed matheuristics approach allows distributors to schedule orders in the logistics center fast enough and with fewer resources.

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Section: Related Literaturementioning

confidence: 99%

Matheuristics for the Order-picking Problem with Sequence-dependant Constraints in a Logistic Center with a One-directional Conveyor Between Buffers

Czerniachowska,

Wichniarek,

Żywicki

2024

Management and Production Engineering Review

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“…The problem of production and production process scheduling has been investigated in numerous studies. In these studies, scheduling processes are analysed by classifying scheduling problems depending on: production system type [17], randomness [18], process dynamics and change over time [19], practice-related aspects [20]. The literature review shows that there also exist studies that focus on industrial robot scheduling.…”

Section: Robotic Task Schedulingmentioning

confidence: 99%

A Lean Robotics Approach to the Scheduling of Robotic Adhesive Dispensing Process

Sobaszek¹

2022

Adv. Sci. Technol. Res. J.

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Modern implementations of industrial robots require the use of extensive knowledge and novel concepts. To bring real benefits, robotized processes must be analysed in detail. One can now observe an increasing use of lean robotics, a concept that is primarily intended to simplify processes and eliminate inefficient activities. This paper deals with industrial robot task scheduling in the adhesive dispensing process. The first part of the paper presents the modern concept of production process robotisation and reviews the literature on industrial robot task scheduling. After that, the problem of robotic adhesive dispensing on the electronic components of a printed circuit board (PCB) is presented. Another section of the paper describes the scheduling of effective and supporting tasks of the robot in the analysed process with the use of alternative dispatching rules. The determination of a schedule that is optimal in terms of the defined objective made it possible to discuss the results and reach valid conclusions. The study has confirmed that modern concepts are useful for simplifying robotic production processes.

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“…Some attempt to estimate assembly time from data of a complex product was presented by Eigner, Roubanov and Sindermann [8]. An interesting proposal to support assembly time prediction using Markov model and hybrid developments was proposed by Gellert et al [9]. More recently, an attempt to use artificial neural networks for assembly operation time prediction was presented by Rueckert, Birgy and Tracht [10].…”

Section: Introductionmentioning

confidence: 99%

Problems of forecasting the length of the assembly cycle of complex products realized in the MTO (make-to-order) model

Brzozowska,

Gola,

Kulisz

2023

TiAM

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This article presents the problem of forecasting the length of machine assembly cycles in make-to-order production (Make-to-Order). The model of Make-to-Order production and the technological process of manufacturing the finished product are presented. The possibility of developing a novel method, using artificial intelligence solutions, to estimate machine assembly times based on historical company data on manufacturing times for structurally similar components, is described. It is assumed that the result of the developed method will be an intelligent system supporting efficient and accurate estimation of machine assembly time, ready for implementation in production conditions. Such data as part availability, human resource availability and novelty factor will be used as input data for learning the neural network, while the output variable during learning the neural network will be the actual machine assembly time.

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Job Scheduling Algorithm for a Hybrid MTO-MTS Production Process

Cited by 4 publications

References 31 publications

Matheuristics for the Order-picking Problem with Sequence-dependant Constraints in a Logistic Center with a One-directional Conveyor Between Buffers

Matheuristics for the Order-picking Problem with Sequence-dependant Constraints in a Logistic Center with a One-directional Conveyor Between Buffers

A Lean Robotics Approach to the Scheduling of Robotic Adhesive Dispensing Process

Problems of forecasting the length of the assembly cycle of complex products realized in the MTO (make-to-order) model

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