Shop Production Scheduling Research Articles

THE SELECTION OF PROCESS SEQUENCE FOR MULTI MACHINE ARRANGED IN SERIES IN THE JOB SHOP INDUSTRY

PT. KOP operates within the oil and gas drilling sector, utilizing job shop production scheduling. The company produces 4 primary product types, employing 2 identical machines operated in parallel, as is common in the job shop industry. However, production planning faces challenges, notably delivery delays stemming from inefficient scheduling, indicated by high work-in-process inventory and machine tooling availability issues. This research proposes a series-based machine scheduling method, evaluating dispatching methods such as SPT (Shortest Processing Time), LPT (Longest Processing Time), and the Johnson algorithm. Results demonstrate that the Johnson method outperforms SPT and LPT, minimizing delay times to 1 hour in the first week, 16 hours in the second, 48 hours in the third, and 86 hours in the fourth. By arranging machines in series, tooling requirements are reduced by 50%. Specifically, series machines only require 4 tooling per product, while parallel machines require 8.

Digital twin rehabilitation system based on self-balancing lower limb exoskeleton.

The digital twin concept is the virtual model based on entity design measures, which is used in many enterprises' virtual workshop design models for workshop production scheduling and optimization. However, in the field of medical rehabilitation, the integration of digital twin technology started late compared to traditional industrial manufacturing. Many current digital models are not well suited for information interaction between patients and devices. In order to address the lack of interaction between patients and devices in the field of medical rehabilitation, this paper proposes an automatic gait data control system (AGDCS) for fully actuated lower limb exoskeleton digital twinning. This system improves the integration of digital twinning system with the medical rehabilitation field and analyzes the patient's gait data through simulation experiments. The digital twin system was designed in several steps. Firstly, the upper computer function module was designed and developed according to the rehabilitation treatment needs. After that, the combination of exoskeleton robot and software was carried out, and finally the real rehabilitation treatment environment of patients was simulated through experiments. The proposed system was very reliable in the experimental tests of the host computer and exoskeleton robot. In the upper computer test, the patient specific gait can be generated, and the motion of the exoskeleton robot can be observed in real-time. During the walking test of the exoskeleton robot, the exoskeleton robot completed the specified gait. The result verified the superiority and effectiveness of the digital twin system AGDCS in the field of rehabilitation. The digital twin system proposed in this paper improves the interaction between self-balancing exoskeleton robot and patients, and improves the autonomy and safety of patients in rehabilitation treatment.

Intelligent scheduling of double-deck traversable cranes based on deep reinforcement learning

ABSTRACT Cranes are used extensively in manufacturing workshops to move jobs, but their high complexity and dynamics lead to difficult workshop production scheduling. To address this issue, this article proposes a deep reinforcement learning-based method combined with discrete event simulation to minimize the makespan of the double-deck traversable crane flexible job-shop scheduling problem (DTCFJSP). Specifically, the problem is first formulated as a finite Markov decision process by introducing state representation, an action space and a reward function. Then, a new double-deep Q-learning network is incorporated to create a selection strategy for optimal actions in different states. The results of experiments conducted in this study show that the average efficiency of the double-deck traversable crane is approximately 12% higher than that of regular cranes, and the application of deep reinforcement learning in crane scheduling is feasible and effective.

Research on Assembly Scheduling of Complex Electromechanical Products Based on Improved Particle Swarm Optimization

The assembly shop production scheduling of complex electromechanical products is a typical discrete variable NP-hard problem. In order to solve the assembly job scheduling problem of a large transformer assembly shop, an improved particle swarm algorithm is used to solve the optimal assembly task scheduling result. In order to minimize the maximum completion time, the traditional particle swarm optimization (PSO) algorithm was improved according to the characteristics of large transformer assembly process. First, the two-stage coding method of process and assembly team is adopted. Then, in order to improve the quality of the initial solution, the initialization method that minimizes the completion time is selected, and the adaptive inertia weight optimization speed update formula is adopted. Finally, the simulation study is carried out with relevant example data. Compared with the traditional PSO, the maximum completion time and convergence speed are significantly improved, which verifies the effectiveness of the improved PSO, and obtains the production scheduling Gantt chart of the workshop.

ANALISA PERBANDINGAN PENJADWALAN PRODUKSI DENGAN METODE CDS (CAMPBELL DUDEK SMITH), EDD (EARLIEST DUE DATE) DAN FCFS (FIRST COME FIRST SERVE) PADA PT. SARI WARNA ASLI UNIT V

Scheduling is defined as the process of sequencing the manufacture of a product as a whole on several machines. All industries need proper scheduling to manage the allocation of resources so that the production system can run quickly and precisely as of it can produce optimal product. PT. Sari Warna Asli Unit V is one of the companies that implements a make to order production system with the FCFS system. Thus, scheduling the production process at this company is also known as job shop production scheduling.
 The methods used in this research are the CDS method, the EDD method and the FCFS method. The purpose of this research is to minimize the production time and determine the best method that can be applied to the company.
 The results of this research showed that the makespan obtained in the company's scheduling system with FCFS rules was 458 minutes, and the results of scheduling using the CDS method obtained a makespan value of 329 minutes, then the best production scheduling method that had the smallest makespan value was the CDS method.

Real-time workshop digital twin scheduling platform for discrete manufacturing

Compared with other production methods, multi-variety, small-batch production driven by order become a lot more sensitive to the disturbances of workshop environment, orders, and processing equipment. This makes a great impact on workshop scheduling. Aiming at this problem, this paper designs a real-time workshop digital twin scheduling platform for discrete manufacturing. It improves the flexibility of the intelligent workshop and the response processing speed after dynamic disturbance. The platform can real-time monitor of the physical workshop and track orders, products, equipment and other information. Further, the platform and visualization technology were explored to work together. Then, the true display of changes after production line decision was displayed. It enables managers to carry out production scheduling verification in the virtual workshop, which reduces the repeated changes of physical workshop production scheduling. Finally, the platform was verified in a discrete manufacturing workshop. The scheduling platform based on digital twins helps manager to immersivity develop factory production strategies. The production efficiency was then enhanced. The platform can ensure the effective operation of the manufacturing system.

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

With the rapid economic development, manufacturing enterprises are increasingly using an efficient workshop production scheduling system in an attempt to enhance their competitive position. The classical workshop production scheduling problem is far from the actual production situation, so it is difficult to apply it to production practice. In recent years, the research on machine scheduling has become a hot topic in the fields of manufacturing systems. This paper considers the batch processing machine (BPM) scheduling problem for scheduling independent jobs with arbitrary sizes. A novel fast parallel batch scheduling algorithm is put forward to minimize the makespan in this paper. Each of the machines with different capacities can only handle jobs with sizes less than the capacity of the machine. Multiple jobs can be processed as a batch simultaneously on one machine only if their total size does not exceed the machine capacity. The processing time of a batch is determined by the longest of all the jobs processed in the batch. A novel and fast 4.5-approximation algorithm is developed for the above scheduling problem. For the special case of all the jobs having the same processing times, a simple and fast 2-approximation algorithm is achieved. The experimental results show that fast algorithms further improve the competitive ratio. Compared to the optimal solutions generated by CPLEX, fast algorithms are capable of generating a feasible solution within a very short time. Fast algorithms have less computational costs.

Comparison of job shop production scheduling by using the non-delay method and the Heijunka method at PT XYZ

The problem that often occurs in this company is the accumulation of orders at one time with a large quantity of products and human resources that are still relatively low can be one of the causes of delays in completing orders for customers. We using Non Delay Method and the Heijunka Method. Where, the Non Delay method is a method that does not let the machine idle if an operation can be started. While the Heijunka method is more concerned with equity in its production. In this study, scheduling using the Non Delay Method is better than using the Heijunka Method or using the existing scheduling method used by the company, because it can produce a faster makespan value. With this Non Delay method, to produce 3 Job 5 machines produce makespan of 3772.01 minutes smaller than using the Heijunka Method which results in makespan 3820 minutes. While the results of existing scheduling makespan only produce 3880.35 minutes.

A method for evaluating management capability of workshop production scheduling

The management capability of workshop production scheduling has been recognized as one of the important factors for the resource efficiency, delivery time and customer satisfaction of manufacturing enterprises. Therefore, a method for evaluating the management capability of production scheduling is proposed to optimize the production management. The accuracy, timeliness and effectiveness of production scheduling are measured by the formulation, execution, change and completion of the management process of production. The index model for management capability of production scheduling is established, which was driven by the data of actual production. Then, (1) scheduling generation time index, (2) scheduling execution deviation index, (3) scheduling change index and (4) scheduling completion index are built to quantitatively evaluate the capacity of production scheduling management. Finally, the usefulness and feasibility have been proved in an automobile parts–manufacturing enterprise. This article quantitatively evaluates the management capability of production scheduling. Both theoretical and results are demonstrated that the method is highly effective. The evaluation can help decision makers to better organize the production scheduling.

Proposed algorithm to improve job shop production scheduling using ant colony optimization method

This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

Study of Production Scheduling of the Digital Workshop on Limited Cache Area

In view of the limitations of EPR infinite capacity theory, and based on that the infinite capacity of production scheduling methods already can not adapt to modern enterprise management requirements, this paper regards the digital unmanned workshop as the research object, and build a kind of digital workshop production scheduling model based on limited buffer zone, and present the solving method. The model can improve the utilization rate of machine tools under the circumstances of limited buffer area. At the same time it provides a practical and effective solution to solve the emergency single plug.

A heuristic for adaptive production scheduling and control in flow shop production

Although research in flow shop production scheduling has been carried out for many decades, there is still a gap between theoretical research and industrial applications – especially in some manufacturing paradigms in which adaptive production control is necessary, such as in one-of-a-kind production (OKP). This paper presents an average processing time and lever (APT-LVR) heuristic, integrated with a closed-loop feedback control scheme, to achieve adaptive production scheduling and control in flow shop production. Having been implemented in a local OKP company, our APT-LVR based production scheduling and control system demonstrates a significant potential to improve the company's production efficiency and competitiveness.

Flow Shop Production Scheduling under Uncertainty within Infinite Intermediate Storage

As to current situation of research on production scheduling under uncertainties in enterprises, Flow Shop production scheduling model is established based on the theory of fuzzy programming, in which fuzzy processing time is considered and the duration time of intermediate is unlimited. The maximum membership function of mean value has been applied to solve the non-linear fuzzy scheduling model in order to convert the fuzzy optimization problem to the general optimization problem. Finally, a cooperative co-evolutionary particle swarm optimization algorithm based on catastrophe added to improve the diversity of the swarm (CCPSO) is adopted to solve Flow Shop Production Scheduling Under Uncertainty within Infinite Intermediate Storage and results are obtained effectively.

A Cooperative Co-evolution PSO for Flow Shop Scheduling Problem with Uncertainty

Considering current situation of production scheduling with uncertainties in modern manufacturing enviroments, flow shop production scheduling model is established based on the theory of fuzzy programming, in which fuzzy processing time is considered and the duration time of intermediate is unlimited. The maximum membership function of mean value has been applied to solve the non-linear fuzzy scheduling model in order to convert the fuzzy optimization problem to the general optimization problem. Finally, a cooperative co-evolutionary particle swarm optimization algorithm based on catastrophe added to improve the diversity of the swarm (CCPSO) is adopted to solve flow shop production scheduling with uncertainty within infinite intermediate storage and the simulation results obtained are effective and satisfactory.

An efficient heuristic for adaptive production scheduling and control in one-of-a-kind production

Even though research in flow shop production scheduling has been carried out for many decades, there is still a gap between research and application—especially in manufacturing paradigms such as one-of-a-kind production (OKP) that intensely challenges real time adaptive production scheduling and control. Indeed, many of the most popular heuristics continue to use Johnson's algorithm (1954) as their core. This paper presents a state space (SS) heuristic, integrated with a closed-loop feedback control structure, to achieve adaptive production scheduling and control in OKP. Our SS heuristic, because of its simplicity and computational efficiency, has the potential to become a core heuristic. Through a series of case studies, including an industrial implementation in OKP, our SS-based production scheduling and control system demonstrates significant potential to improve production efficiency.

A Simulation-Based Method for Capacity Adjustments in Job Shop Production Scheduling

A Simulation-Based Method for Capacity Adjustments in Job Shop Production Scheduling