Solving machine loading problem of flexible manufacturing systems using a modified discrete firefly algorithm

Bottani, Eleonora; Centobelli, Piera; Cerchione, Roberto; Gaudio, Luciano; Murino, Teresa

doi:10.5267/j.ijiec.2016.12.002

Cited by 19 publications

(12 citation statements)

References 26 publications

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“…It can be extended as a multi-objective, such as minimizing the makespan and tardiness as they are conflicted. As another future research, the performance of the proposed algorithm can be compared with other heuristic algorithms that were presented in other studies [33].…”

Section: Discussionmentioning

confidence: 99%

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

2020

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In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q-network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

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Section: Discussionmentioning

confidence: 99%

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

2020

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“…The research result showed that significantly savings were realized as compared to a production plan without considering energy consumption. Additional works (Khouja & Mehrez, 1994;Giri & Dohi, 2005b;Sana, 2010;Liu et al, 2017b;Bottani, et al, 2017;Chiu et al, 2018c,d;Ameen et al, 2018) were also conducted to address various issues and influences of variable fabrication rates on manufacturing systems. As little attention has been paid to study the joint influences of stochastic failures, random scrap, and expedited fabrication rate on the manufacturing runtime decision, this work aims to link the gap.…”

Section: Introductionmentioning

confidence: 99%

Manufacturing runtime problem with an expedited fabrication rate, random failures, and scrap

Chiu¹,

Huang²,

Chou³

et al. 2020

10.5267/j.ijiec

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When operating in highly competitive business environments, contemporary manufacturing firms must persistently find ways to fulfill timely orders with quality ensured merchandise, manage the unanticipated fabrication disruptions, and minimize total operating expenses. To address the aforementioned concerns, this study explores the optimal runtime decision for a manufacturing system featuring an expedited fabrication rate, random equipment failures, and scrap. Specifically, the proposed study considers an expedited rate that is linked to higher setup and unit costs. The fabrication process is subject to random failure and scrap rates. The failure instance follows a Poisson distribution, is repaired right away, and the fabrication of interrupted batch resumes when the equipment is restored. The defective goods are identified and scrapped. Mathematical modeling and optimization method are used to find the total system cost and the optimal runtime of the problem. The applicability and sensitivity analyses of research outcome are illustrated through a numerical example. Diverse critical information regarding the individual/joint impacts of variations in stochastic time-to-failure, expedited rate, and random scrap on the optimal runtime decision, total system expenses, different cost components, and machine utilization, can now be revealed to assist in in-depth problem analyses and decision makings.

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“…Swarm-based algorithms are popularly used algorithms for solving scheduling problems. Bottani et al [24] developed an efficient heuristic based on firefly algorithm, defined two vectors for each firefly to represent a candidate solution and optimized by the objective function, the aim to formulate an objective function which takes into account both the throughput and the system unbalance, and the results show that the algorithm is feasible. Zhou et al [25] presented a multi-population discrete firefly algorithm and combined with the k-opt algorithm to solve the traveling salesman problem.…”

Section: Introductionmentioning

confidence: 99%

Mixed Model Assembly Line Scheduling Approach to Order Picking Problem in Online Supermarkets

et al. 2018

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Online retail orders, especially online supermarket orders, have been highlighted to have several distinguished features from traditional online retailers. These include a huge amount of daily orders and orders containing multiple items. Tens of thousands of Stock Keeping Units (SKUs) sold by online retailers have to be stored at multiple storage zones due to the limit capacity of one area, and ordered items should to be picked with a parallel picking strategy. What is the most efficient and accurate method of picking, sorting and packaging the ordered items from SKUs for online orders? This paper focuses on scheduling the three processes of order picking problems in a warehouse for an online supermarket. Referring to the principle of the mixed-model assembly line, it presents a new optimization method of group order picking. With an objective of minimizing the picking and packaging time, this paper studies order batching and order sequencing. In order batching, considering the workload balance, it builds a mathematical optimization model and applies a bi-objective genetic algorithm to solve it. Then an order batching sequencing model is built, and a solving algorithm based on Pseudo-Boolean Optimization is developed. Case study and sensitivity analyses are conducted to verify the effectiveness of the method.

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Solving machine loading problem of flexible manufacturing systems using a modified discrete firefly algorithm

Cited by 19 publications

References 26 publications

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

Manufacturing runtime problem with an expedited fabrication rate, random failures, and scrap

Mixed Model Assembly Line Scheduling Approach to Order Picking Problem in Online Supermarkets

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