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Intelligent Scheduling Based on Reinforcement Learning Approaches: Applying Advanced Q-Learning and State–Action–Reward–State–Action Reinforcement Learning Models for the Optimisation of Job Shop Scheduling Problems
Flexible job shop scheduling problems (FJSPs) have attracted significant research interest because they can considerably increase production efficiency in terms of energy, cost and time; they are considered the main part of the manufacturing systems which frequently need to be resolved to manage the variations in production requirements. In this study, novel reinforcement learning (RL) models, including advanced Q-learning (QRL) and RL-based state–action–reward–state–action (SARSA) models, are proposed to enhance the scheduling performance of FJSPs, in order to reduce the total makespan. To more accurately depict the problem realities, two categories of simulated single-machine job shops and multi-machine job shops, as well as the scheduling of a furnace model, are used to compare the learning impact and performance of the novel RL models to other algorithms. FJSPs are challenging to resolve and are considered non-deterministic polynomial-time hardness (NP-hard) problems. Numerous algorithms have been used previously to solve FJSPs. However, because their key parameters cannot be effectively changed dynamically throughout the computation process, the effectiveness and quality of the solutions fail to meet production standards. Consequently, in this research, developed RL models are presented. The efficacy and benefits of the suggested SARSA method for solving FJSPs are shown by extensive computer testing and comparisons. As a result, this can be a competitive algorithm for FJSPs.
Journal Article
Stick with me!
When Rox and Blair get stuck together from some very sticky hairspray, Sunny must save them before their special night at the ballet.
Book
Preface
The 6th European Advanced Accelerator Concepts Workshop (EAAC) took place from September 17 to 23, 2023, at La Biodola Bay on Elba Island. With 204 participants gathered in person, it was a pleasure to once again experience the energy, creativity, and collaborative spirit that make EAAC such a special event.The mission of the workshop remains both ambitious and vital: to explore new methods of beam acceleration that can achieve gradients beyond those available in today’s facilities. This year’s program reflected the breadth and depth of the field, ranging from laser- and beam-driven plasma wakefield accelerators to high-gradient vacuum structures, advances in beam instrumentation, beam dynamics and simulations, and applications of novel accelerator concepts.A defining feature of EAAC is its format. While presentations are central for reviewing the impressive progress of recent years, the program is intentionally designed with generous space for open discussions. These sessions—often as lively and productive as the talks themselves-allow participants to look ahead, challenge ideas, and spark collaborations that may shape the field in years to come. This balance between formal presentations and dedicated discussion time has proven to be one of the workshop’s greatest strengths.List of Workshop Organizing Committee, Programme, International Advisory Committee and Local Organizing Committee are available in this PDF.
Journal Article
Glam opening!
When their mothers join forces and open a new salon together, Aly and her nemesis Suzy are forced to work side by side in a new Sparkle Spa.
Book
An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop
Distributed welding flow shop scheduling problem is an extension of distributed permutation flow shop scheduling problem, which possesses a set of identical factories of welding flow shop. On account of several machines can process one job simultaneously in welding shop, increasing the amount of machines can short the processing time of operation while waste more energy consumption at the same time. Thus, energy-efficient is of great significance to take total energy consumption into account in scheduling. A multi-objective mixed integer programming model for energy-efficient scheduling of distributed welding flow shop is presented based on three sub-problems with allocating jobs among factories, scheduling the jobs in each factory and determining the amount of machines upon each job. A multi-objective whale swarm algorithm is proposed to optimize the total energy consumption and makespan simultaneously. In the proposed algorithm, a new initialization method is designed to improve the quality of the initial solution. And various update operators, as well as local search, are designed according to the feature of the problem. To conduct the experiment, diversified indicators are applied to evaluate the proposed algorithm and other MOEAs performance. And the experiment results demonstrate the effectiveness of the proposed method. The proposed algorithm is applied in the real-life case with great performance compared with other MOEAs.
Journal Article
Legends & lattes : a novel of high fantasy and low stakes
\"The much-beloved BookTok sensation from Travis Baldree, Legends & Lattes is a novel of high fantasy and low stakes. *The new paperback edition will include a very special, never-before-seen bonus story, 'Pages to Fill.'* Come take a load off at Viv's cafe, the first & only coffee shop in Thune. Grand opening! Worn out after decades of packing steel and raising hell, Viv, the orc barbarian, cashes out of the warrior's life with one final score. A forgotten legend, a fabled artifact, and an unreasonable amount of hope lead her to the streets of Thune, where she plans to open the first coffee shop the city has ever seen. However, her dreams of a fresh start filling mugs instead of swinging swords are hardly a sure bet. Old frenemies and Thune's shady underbelly may just upset her plans. To finally build something that will last, Viv will need some new partners, and a different kind of resolve. \"Take a break from epic battles and saving the world. Legends & Lattes is a wholesome, cozy novel that feels like a warm hug. This is my new comfort read.\"-Genevieve Gornichec, author of The Witch's Heart\"-- Provided by publisher.
Book
Adaptive job shop scheduling strategy based on weighted Q-learning algorithm
Given the dynamic and uncertain production environment of job shops, a scheduling strategy with adaptive features must be developed to fit variational production factors. Therefore, a dynamic scheduling system model based on multi-agent technology, including machine, buffer, state, and job agents, was built. A weighted Q-learning algorithm based on clustering and dynamic search was used to determine the most suitable operation and to optimize production. To address the large state space problem caused by changes in the system state, four state features were extracted. The dimension of the system state was decreased through the clustering method. To reduce the error between the actual system states and clustering ones, the state difference degree was defined and integrated with the iteration formula of the Q function. To select the optimal state-action pair, improved search and iteration update strategies were proposed. Convergence analysis of the proposed algorithm and simulation experiments indicated that the proposed adaptive strategy is well adaptable and effective in different scheduling environments, and shows better performance in complex environments. The two contributions of this research are as follows: (1) a dynamic greedy search strategy was developed to avoid blind searching in traditional strategy. (2) Weighted iteration update of the Q function, including the weighted mean of the maximum fuzzy earning, was designed to improve the speed and accuracy of the improved learning algorithm.
Journal Article
Biscuit feeds the pets
Biscuit the puppy finds his own way to help feed the pets at Mr. Gray's Pet Shop.
Book
Scheduling a dual-resource flexible job shop with makespan and due date-related criteria
Current struggles for customer satisfaction in make-to-order companies focus on product customization and on-time delivery. For better management of demand-mix variability, production activities are typically configured as flexible job shops. The advent of information technology and process automatization has given rise to very specific training requirement for workers, which indeed turns production scheduling into a dual-resource constrained problem. This paper states a novel dual-resource constrained flexible job-shop problem (DRCFJSP) whose performance considers simultaneously makespan and due date-oriented criteria, where eligibility and processing time are both dependent on worker expertise. Our research comes from an automobile collision repair shop with re-scheduling needs to react to real-time events like due date changes, delay in arrival, changes in job processing time and rush jobs. We have developed constructive iterated greedy procedures that performs efficiently on the large-scale bi-objective DRCFJSP arisen (good schedules in < 5 s), hence providing planners with the required responsiveness in their scheduling of repairing orders and allocation of workers at the different work centres. In addition, computational experiments were conducted on a test bed of smaller DRCFJSP instances generated for benchmarking purposes. Off-the-shelf resolution for an 80% of the medium-sized instances is not fruitful after 9000 s.
Journal Article