Design of Pipeless Chemical Batch Plants with Queueing Networks

Yoo, Dong Joon; Lee, In‐Beum; Jung, Jae Hak

doi:10.1021/ie049514n

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…1. This layout is based on the cyclic operations as presented in literature [25] and [26]. In this system, multiple robots are in operation, i.e., MHRs, which transport materials of products to process stations, and MPRs, which have mobility and varied equipment to conduct chemical processing tasks, such as coupling, feed, blending, separation, discharge, and cleaning operations at process stations.…”

Section: Robotic Batch Manufacturing Systemmentioning

confidence: 99%

Multirobot Coordination for Flexible Batch Manufacturing Systems Experiencing Bottlenecks

Hoshino

Seki

Naka

et al. 2010

IEEE Trans. Automat. Sci. Eng.

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In this paper, we focus on a batch manufacturing system with multiple industrial robots. In this system, material-handling robots (MHRs) and material-processing robots (MPRs) are operating. Since various operations are conducted at certain places, one of them might include a localized bottleneck. A localized bottleneck is a constraint that dominates the maximum amount of production in a system, that is, the productivity. In addition, the bottleneck induces congestion; as a result, the productivity declines. For these issues, there are two primary challenges: bottleneck prevention and restraint. To solve them, suitable operational techniques with respect to the MHRs and MPRs are needed in order for them to operate appropriately while relating to each other. In this paper, a constructive approach toward multi-robot coordination problems is taken. In this approach, we propose applicable operational techniques for the robots. Through simulation experiments, we examine the effectiveness of the proposed techniques and their combinations and, finally, show an integrated operational technique. Each of the operational techniques solves a localized bottleneck and the congestion, and the integrated technique successfully improves the productivity and results in the most efficient system.Note to Practitioners-The primary motivation for this study was the pursuit of answers to the problems associated with production operations for a pipeless batch plant system in chemical process industries. These solutions will also apply to other manufacturing systems for high-mix low-volume production with a number of machines or robots. In many previous studies, scientists addressed the optimization problem of production scheduling; in this study, however, we focus on a simulation study and aim to develop an applicable integration technology for the coordination of operating robots in the interest of the automation of a highly flexible production activity. In practical batch manufacturing systems, as an entire operational strategy, it is necessary to equalize the flow of products, i.e., bottlenecks, throughout the system. Furthermore, once a localized bottleneck appears, its harmful effect on the system has to be restrained. Therefore, we suggest efficient operational techniques to tackle these challenging issues. Through various simulation experiments, we show that some good combinatorial techniques enable to improve productivity. The results of this paper are feasible and sufficient for flexible batch manufacturing systems, but the application is limited to a system in which the safety of production operations and processes is guaranteed. In future research, we will address design problems regarding safety and reliability in a plant system.

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Section: Robotic Batch Manufacturing Systemmentioning

confidence: 99%

Multirobot Coordination for Flexible Batch Manufacturing Systems Experiencing Bottlenecks

Hoshino

Seki

Naka

et al. 2010

IEEE Trans. Automat. Sci. Eng.

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“…Realff (1998a, 1998b) combine MILP with discrete event simulation to model the processing durations. Panek et al (2005) include scheduling heuristics and Yoo et al (2005) extend their MINLP by a queuing approach. Huang and Chung (2005) consider constraint satisfaction techniques to find an optimal schedule and deal with vessels that possess their own moving device.…”

Section: Routing Of Agvsmentioning

confidence: 99%

Hybrid evolutionary optimization of the operation of pipeless plants

Piana

Engell

2009

J Heuristics

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Pipeless plants are a new production concept in chemical engineering in which automated guided vehicles (AGVs) transport the substances in mobile vessels between processing stations. In the operation of such plants, decisions have to be made on the scheduling of the production, the assignment of the equipment and the routing of the AGVs that carry the vessels. The large number of interacting degrees of freedom prohibit the use of exact mathematical algorithms to compute optimal schedules. This paper describes the combination of an evolutionary scheduling algorithm with a simulation based schedule builder. The algorithm is tested on a real-life example and on a benchmark problem from the literature and yields considerably shorter makespans than a heuristic solution.

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“…Huang and Chung considered the simultaneous solution of scheduling and routing using constraint-based methodology suitable for different layouts. Patsiatzis et al and Yoo et al incorporated different layout and design aspects into the scheduling problem. Zeballos and Mendez proposed a constraint-programming-based efficient search method for solving the simultaneous scheduling and routing problem.…”

Section: Introductionmentioning

confidence: 99%

Generalization of Scheduling Models for Batch Plants and Pipeless Plants

Shaik

Mathur

2019

Ind. Eng. Chem. Res.

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Unlike in batch operations where significant progress has been made, there is no adequate research work done on optimal operations of pipeless plants. Pipeless plants were initially explored as an alternative production setup with potential applications in production of paints, adhesives, lubricants, food, pharmaceuticals, and fine chemicals. The concept of pipeless plants requires a renewed momentum to explore if they could become competitive. Unlike in the literature, where researchers have presented different aspects of scheduling models for pipeless plants through different models, in this work the focus is to develop a unified/generalized approach whereby the advancements in the batch scheduling literature can be readily extended to pipeless plants. We explore how batch scheduling models can be extended and generalized for the optimal operation of pipeless plants with fixed design, layout, and routing aspects. The important modifications and additions made to the batch scheduling models for handling scheduling of pipeless plants include: (i) modifications in state−task−network representation to bring in vessel, product, and equipment identities, leading to a "generalization" of models for batch and pipeless plants; (ii) treatment of moveable vessels as states, and not as separate resources, which are consumed only by the first unit and subsequently released only by the last unit in the production sequence for each product; and (iii) elimination of material balances and batch size aspects to simplify the models and reduce the problem size. Emphasis has been laid on development of a systematic procedure for effective STN representation for pipeless plants leading to unification of scheduling models for batch plants and pipeless plants. Two scheduling models are presented with varying problem size and complexity to explore the best suitable model for the optimal operation of pipeless plants. The proposed models have been validated on two benchmark problems, drawn from the literature, involving multiproduct and multipurpose plant features.

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Design of Pipeless Chemical Batch Plants with Queueing Networks

Cited by 5 publications

References 26 publications

Multirobot Coordination for Flexible Batch Manufacturing Systems Experiencing Bottlenecks

Multirobot Coordination for Flexible Batch Manufacturing Systems Experiencing Bottlenecks

Hybrid evolutionary optimization of the operation of pipeless plants

Generalization of Scheduling Models for Batch Plants and Pipeless Plants

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