A toolbox of agents for scheduling the paint shop in bicycle industry

Siatras, Vasilis; Nikos, Nikolakis; Alexopoulos, Kosmas; Mourtzis, Dimitris

doi:10.1016/j.procir.2022.05.124

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…For flowshop scheduling problems, there are various variants, considering different constraints, scheduling scenarios, and optimization objectives [3]. The main constraints include setup time [4], limited buffers [5], uncertain processing time [6], due dates [7], etc. Many researchers attempt to solve such scheduling problems by using mathematical models [4,8], meta-heuristics algorithms [5,9], artificial intelligence [4], and methods based on scheduling policy [10,11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The main constraints include setup time [4], limited buffers [5], uncertain processing time [6], due dates [7], etc. Many researchers attempt to solve such scheduling problems by using mathematical models [4,8], meta-heuristics algorithms [5,9], artificial intelligence [4], and methods based on scheduling policy [10,11]. Interested readers can refer to the surveys in [3,12,13] for more details about the scheduling analysis of flow-shop systems.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Two-Step Approach to Scheduling a Class of Two-Stage Flow Shops in Automotive Glass Manufacturing

Qiao

et al. 2023

Machines

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Driven from real-life applications, this work aims to cope with the scheduling problem of automotive glass manufacturing systems, that is characterized as a two-stage flow-shop with small batches, inevitable setup time for different product changeover at the first stage, and un-interruption requirement at the second stage. To the best knowledge of the authors, there is no report on this topic from other research groups. Our previous study presents a method to assign all batches to each machine at the first stage only without sequencing the assigned batches, resulting in an incomplete schedule. To cope with this problem, if a mathematical programming method is directly applied to minimize the makespan of the production process, binary variables should be introduced to describe the processing sequence of all the products, not only the batches, resulting in huge number of binary variables for the model. Thus, it is necessary and challenging to search for a method to solve the problem efficiently. Due to the mandatory requirement that the second stage should keep working continuously without interruption, solution feasibility is essential. Therefore, the key to solve the addressed problem is how to guarantee the solution feasibility. To do so, we present a method to determine the minimal size of each batch such that the second stage can continuously work without interruption if the sizes of all batches are same. Then, the conditions under which a feasible schedule exists are derived. Based on the conditions, we are able to develop a two-step solution method. At the first step, an integer linear program (ILP) is formulated for handling the batch allocation problem at the first stage. By the ILP, we need then to distinguish the batches only, greatly reducing the number of variables and constraints. Then, the batches assigned to each machine at the first stage are optimally sequenced at the second step by an algorithm with polynomial complexity. In this way, by the proposed method, the computational complexity is greatly reduced in comparison with the problem formulation without the established feasibility conditions. To validate the proposed approach, we carry out extensive experiments on a real case from an automotive glass manufacturer. We run ILP on CPLEX for testing. For large-size problems, we set 3600 s as the longest time for getting a solution and a gap of 1% for the lower bound of solutions. The results show that CPLEX can solve 96.83% cases. Moreover, we can obtain good solutions with the maximum gap of 4.9416% for the unsolved cases.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Two-Step Approach to Scheduling a Class of Two-Stage Flow Shops in Automotive Glass Manufacturing

Qiao

et al. 2023

Machines

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“…The heuristic algorithm was used both for the painting and the wheel assembly lines' allocation of orders, while the MIP and DRL were used individually for the painting and The industrial case came from a bicycle Original Equipment Manufacturing (OEM) industry specifically for the departments of painting and wheel assembly. Three different schedulers were deployed: (1) a heuristic multi-objective scheduling framework, (2) a Mixed Integer Programming [68] (MIP) model optimizer for production scheduling, and (3) a deep reinforcement learning (DRL) scheduler for dynamic production scheduling. For the latter two (2, 3), two Flask applications were deployed to integrate the algorithms as REST services.…”

mentioning

confidence: 99%

On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

Siatras,

Bakopoulos,

Mavrothalassitis

et al. 2023

Applied Sciences

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Industry 4.0 (I4.0) aims at achieving the interconnectivity of multiple industrial assets from different hierarchical layers within a manufacturing environment. The Asset Administration Shell (AAS) is a pilar component of I4.0 for the digital representation of assets and can be applied in both physical and digital assets, such as enterprise software, artificial intelligence (AI) agents, and databases. Multi-agent systems (MASs), in particular, are useful in the decentralized optimization of complex problems and applicable in various planning or scheduling scenarios that require the system’s ability to adapt to any given problem by using different optimization methods. In order to achieve this, a universal model for the agent’s information, communication, and behaviors should be provided in a way that is interoperable with the rest of the I4.0 assets and agents. To address these challenges, this work proposes an AAS-based information model for the description of scheduling agents. It allows multiple AI methods for scheduling, such as heuristics, mathematical programming, and deep reinforcement learning, to be encapsulated within a single agent, making it adjustable to different production scenarios. The software implementation of the proposed architecture aims to provide granularity in the deployment of scheduling agents which utilize the underlying AAS metamodel. The agent was implemented using the SARL agent-oriented programming (AOP) language and deployed in an open-source MAS platform. The system evaluation in a real-life bicycle production scenario indicated the agent’s ability to adapt and provide fast and accurate scheduling results.

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