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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.

Nowadays, establishing a sustainable management plan will help organizations use resources effectively and efficiently and help the business to have a competitive advantage in the long term. In a machine shop where the operation cost is high and resources are not effectively used, a worker-machine relationship-based strategy has been introduced. Many studies explored the use of different problem solving tools in different fields, however there is a lack of studies in the application of gang process chart, random and synchronous servicing especially in machine shop. Thus, the purpose of this study was to evaluate the job distribution during service operation, to determine which worker and machine will require reassignment to operate effectively, and lastly propose a sustainable management strategy to optimize the operation and reduce the operation cost. This study used a descriptive research design with an overt observational approach in a well-established machine shop in Oriental Mindoro, Philippines. A total of fifteen operators who were directly involved with the machines have been observed. The study discovered that Operators 1,2,3.4 and 5 are already operating on the ideal number of machines, Operator 6 will require one more additional machine, Operator 7,8 9 and 10 will require one less machine to operate on an ideal number and save operation cost.

The company says that the product's features include: [black square] The ability to create reports using an intuitive drag-and-drop interface. [black square] The ability to feed live data into custom-built dashboards where results are displayed in tables, charts, indicators, gauges, etc. [black square] Key real-time information. [black square] Rotating tabs that help organise dashboards into categories (eg, statistics for branch surgeries or different reporting types). [black square] Creation of dashboards for use by different members of staff or to suit individual practice requirements.

Becky Richardson, Young Vet Network representative on BVA Council, jointly hosted a meeting attended by BVA President Gudrun Ravetz.Becky Richardson, Young Vet Network representative on BVA Council, jointly hosted a meeting attended by BVA President Gudrun Ravetz.

Generally, ideal manufacturing system environments are assumed before determining effective scheduling. However, the original schedule is no longer optimal or even to be infeasible due to many uncertain events. This paper investigates a multi-objective inverse scheduling problem in single-machine shop system with due-dates and uncertain processing parameters. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing parameters are considered to be uncertain stochastic parameters. First, a comprehensive mathematical model for multi-objective single-machine inverse scheduling problem (MSMISP) is addressed. Second, an effective hybrid multi-objective evolutionary algorithm (HMNL) is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In HMNL, using an effective decimal system encoding scheme and genetic operators, the non-dominated sorting based on NSGA-II is adapted for the MSMISP. In addition, hybrid HMNL are proposed by incorporating an adaptive local search scheme into the well-known NSGA-II, where applies a separate local search process, total six strategies, to improve quality of solutions. Furthermore, an on-demand layered strategy is embedded into the elitism strategy to keep the population diversity. Afterwards, an external archive set is dynamically updated, where a non-dominated solution is selected to participate in the creation of the new population. Finally, 36 public problem instances with different scales and statistical performance comparisons are provided for the HMNL algorithm. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve MSMISP in inverse scheduling domain.

Rotary screw machine (or a machine on the screw) has been widely used in Russia in 1960-1970. In contrast to vehicles equipped with conventional types of propulsion, the dynamics of screw machines is poor. The uniqueness of calculation of screw machines in the geometric linear movement of the screw is considered.

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.

Officers of the BVA President: Mr S. Wensley Senior Vice-President: Mr J. Blackwell Junior Vice-President: Mrs G. Ravetz Address 7 Mansfield Street, London W1G 9NQ Telephone 020 7636 6541 Fax 020 7908 6349 e-mail: bvahq@bva.co.uk www.bva.co.uk HEADQUARTERS ACTIVITIES Officers' diary The BVA officer team's activities in the week ending October 23 included the following: [black square] The President attended a Canine Health Schemes management meeting. [black square] All the officers attended BVA's annual House of Commons reception, to brief parliamentarians on key topics for the veterinary profession. [black square] All the officers participated in a Vet Futures project board meeting. [black square] The senior vice-president and junior vice-president participated in BVA Member Services Group meeting. [black square] The President attended a meeting of the Kennel Club Dog Health Group. [black square] The senior vice-president gave a talk on large animals in emergencies to Bristol vet school's clinical club. [black square] The senior vice-president attended a Veterinary Defence Society graduate reunion in Liverpool.

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.