Job oriented production scheduling

Aiming at the design and development of integrated production planning and scheduling system(PPSS) of rare metal manufacturing, the goals and main functions of the integrated production planning and scheduling system were analyzed and designed based on the analysis of rare metal manufacturing enterprise’s main characteristics and management status. Through analysis of the present situation and trend of production planning and scheduling, an integrated system architecture was proposed. Key technologies of system integration and system developing were delivered, and some key functions of production planning and scheduling system which include annual plans management, monthly plans management, MRP management, production scheduling management and production Kanban management were developed based on JSP technology and WEB platform. The practical applications in some rare metal manufacturing enterprises were proved that the integrated production planning and scheduling system could enhance the manufacturing management level effectively.

Just-In-Time (JIT) is a popular philosophy in many industrial practices. The concept of JIT in early studies concerned with improving operational efficiency and waste minimization. In recent decades, however, JIT principles have also connected to logistics efficiency particularly for distribution of raw materials and finished goods. In the literature, several attempts have been made to optimize JIT logistics networks. On the one hand, most studies have typically focused on deterministic and small-scale problems which have been solved by exact algorithms. On the other hand, when large-scale problems were considered and usually were solved by metaheuristics algorithms, uncertainty sources and fine-tuning of the metaheuristics parameters were generally ignored. In this paper, we develop a mixed-integer linear optimization model to investigate a large-scale JIT logistics problem with 15 different sizes. To deal with different uncertainty sources, the customers demand and suppliers’ capacity as the two main sources of uncertainty in practice are considered as triangular fuzzy parameters. The proposed model aims to minimize total logistics cost including costs of transportation, inventory holding and backorders. A particle swarm optimization algorithm is applied to solve the problem, and its results are then validated by a harmony search algorithm. Both algorithms parameters are tuned using response surface methodology and Taguchi method. Finally, the conclusion and some directions for future research are proposed.

Production scheduling is a form of decision-making, which plays a crucial role in manufacturing as well as in service industries. Many approaches are used in practice in order to find good and feasible solutions in the scheduling problems. Approaches based on Genetic Algorithms (GAs) are becoming more and more widespread nowadays. This paper is the result of the author's extensive study on the use of advanced technologies in the field of production planning and scheduling and follows his previous research on the use of expert systems in this field (Metaxiotis et al., 2002). The present paper presents famous GAs known in the literature and current applications, analyses the relative benefits and concludes by sharing thoughts and estimations on GAs future prospects in this area.

Digital twin and artificial intelligence (DT-AI) technologies present hitherto unheard-of possibilities for dynamic production scheduling in smart manufacturing. Nevertheless, a careful examination of several studies reveals significant gaps in the current state of the discipline. This paper attempts to review advancements, gaps, and opportunities in the areas of DT-AI-based production scheduling. Articles chosen for this literature analysis were mostly published within the last eight years. Based on the literature, five enabling challenges that are consistently considered in the literature include Dynamic and Unforeseen Disruptions, High System Complexity, Real-Time Data Management, Integration and Interoperability, and Adaptability and Generalizability. This review not only identifies these enabling challenges but also provides tailored outlines of progress and future directions. The findings pave the way for resilient, scalable, and interpretable DT-AI systems for production scheduling that can handle uncertainty and optimize output in real time. DTs and AI can benefit manufacturing with data-driven intelligent planning and decision-making as well as model-based systems engineering principles. This review examines these advancements and trending research directions in production scheduling.

Purpose – In a complex semiconductor manufacturing system (SMS) environment, the implementation of dynamic production scheduling and dispatching strategies is critical for SMS distributed collaborative manufacturing events to make quick and correct decisions. The purpose of this paper is to assist manufacturers in achieving the real time dispatching and obtaining integrated optimization for shop floor production scheduling. Design/methodology/approach – In this paper, an integrated model is designed under assemble to order environment and a framework of a real time dispatching (IRTD) system for production scheduling control is presented accordingly. Both of the scheduling and ordering performances are integrated into the days of inventory based dispatching algorithm, which can deal with the multiple indicators of dynamic scheduling and ordering in this system to generate the “optimal” dispatching policies. Subsequently, the platform of IRTD system is realized with four modules function embedded. Findings – The proposed IRTD system is designed to compare the previous constant work in process method in the experiment, which shows the better performance achievement of the IRTD system for shop floor production dynamic scheduling and order control. The presented framework and algorithm can facilitate real time dispatching information integration to obtain performance metrics in terms of reliability, availability, and maintainability. Research limitations/implications – The presented system can be further developed to generic factory manufacturing with the presented logic and architecture proliferation. Originality/value – The IRTD system can integrate the real time customer demand and work in process information, based on which manufacturers can make correct and timely decisions in solving dispatching strategies and ordering selection within an integrated information system.

The increased availability of data and computer-aided technologies such as MRPI/II, ERP and MES system, allowing producers to be more adaptive to market dynamics and to improve production scheduling. Integration of production scheduling and computer modelling, simulation and visualization systems can be useful in the analysis of production system constraints related to the efficiency of manufacturing systems. A integration methodology based on semi-automatic model generation method for eliminating problems associated with complexity of the model and labour-intensive and time-consuming process of simulation model creation is proposed. Data mapping and data transformation techniques for the proposed method have been applied. This approach has been illustrated through examples of practical implementation of the proposed method using KbRS scheduling system and Enterprise Dynamics simulation system.

Process planning output can be post-processed into criteria for job scheduling decisions in printed circuit board assembly using surface-mount technology (SMT). Artificial intelligence-based techniques used in computerized planning and scheduling systems in other industries can be extended to printed circuit board operations. These techniques include blackboard architectures, object-oriented programming systems, and nonmonotonic reasoning systems. These techniques were used to demonstrate a unique architecture of blackboard systems that communicate via object-oriented messages to arrive at a shop-floor process plan and production schedule. The methodology was specialized to the assembly of printed circuit boards using surface-mounted components in a high-variety/low-volume product mix. The assembly facility was represented as a hierarchical object of product, process, and organizational knowledge. The system of working heuristics was integrated within a prototype environment with the practitioners that assisted in its development. The end result is a good working methodology for system development, implementation, and maintenance by knowledge worker involvement.

Evaluation and comparison of production schedules

This research was driven by the need for a more efficient production scheduling system in a consumable liquid product division of a large consumer products company. The manufacturing process under inspection consists of continuous and discrete elements, on both production and packaging lines. The production lines are split into continuous production lines and batch production lines which produce the product in fixed batch amounts. Then there are several bottling lines, some of which package a particular bottle size and others that can package multiple bottle sizes. The main objective of this research was to reduce the amount of time it takes for the client to create production and bottling schedules. An optimization model was developed to automate this process and provide the client with the best possible schedule. The objective of the model is to minimize cost by minimizing the number of switches across the production and bottling lines, as well as minimizing the amount of overproduction. Inputs into the model include model parameters, like the number of shifts to schedule, and monthly demand numbers for each stock keeping unit (SKU). The variables being solved for are the amount of each flavor to be produced across the production lines during each shift, and the number of bottles of each SKU to be bottled across the bottling lines during each shift. Due to the unique constraints and resources of the client, a custom formulation using mixed integer programming was necessary to achieve these objectives. Overall, our model fell short in some areas but succeeded in others. Our analysis showed that the model had a 13% average decrease in production switches but an 87% average increase in bottling switches compared to the current manual scheduling system. However, the ability of our system to create a good enough initial schedule reduces the time it takes expert human schedulers to develop a final schedule by up to 85%. Runtime and computational constraints barred us from creating an optimal, cost-minimized solution for our client, and future work can be directed toward solving these issues.

This paper describes an expert system for paper production scheduling which has been in operation since January 1989 at Tomakomai Mill of Oji Paper Co., Ltd. The mill has ten papermaking machines and produces 200 different types of paper products each month.The problems associated with the production process are complex and large in scale, because of cross-machinery constraints such as limits on the supply of energy and pulp, and the combination of different products during the same production period. Production scheduling is implemented in three steps : group product allocation, individual allocation, and pulp and energy balance adjustment.The system provides a knowledge maintenance tool by employing several CRT screens, and the expert shell used is Toshiba's “ASIREX” which runs on the AS 3000 series engineering workstations.

Smart factories and intelligent manufacturing systems are the key drivers of knowledge economy in the era of Industry 4.0. One of the critical aspects is a flexible and optimised production planning and scheduling system. An efficient scheduling system can minimise the production cost and maximise machine utilization, leading to improvement in the production rate. In this study, we design an ensemble-based Harris' Hawk optimisers (EN-HHO) to address the multi-objective flexible job shop problem (MOFJSP), which is an NP-hard and complex combinatorial task. The developed ensemble model is evaluated using well-known benchmark problems, and the results compare favourably with those from similar methods in the literature.

The following investigation shows the problems in “Industria Granero del Centro EIRL”, it is a company that is dedicated to the production of grains such as split bean, split wheat, american morón and corn flour. The goal is to improve the production planning and scheduling system, and eliminate overproduction and economic losses. VSM (Value Stream Mapping) was used to identify the improvement in the production process. The root causes of the problem were then determined using the Ishikawa diagram. The activities to be carried out are: identification of times of each process of the evaluated product, carry out the implementation of the master production plan, application of Kanban and application of Heijunka for load balancing [1]. The results of this implementation will be visualized through a simulation making use of efficiency, effectiveness and productivity indicators to meet the objective.

Thin‐film transistor liquid‐crystal displays (TFT‐LCDs) have gradually replaced cathode‐ray tubes because of their compact size and efficient use of power. Since product lifecycles are shortening, consumers require instant order delivery, which affects fluctuations in supply and demand. As various product types in the LCD industry exist, manufacturing time has to be reduced to satisfy consumer demand. This study investigates the dispatching of parallel machines for TFT‐LCD manufacturing. Since customers’ orders and suppliers’ deliveries vary significantly and the manual adjustment of production planning is time consuming, an appropriate production planning and scheduling system is necessary. Consequently, this study develops a genetic algorithm to solve the scheduling problem of unrelated parallel machines, taking account of multiple performance measurement indicators. We also adopt LINGO to solve the problem and compare the obtained solution quality and computation time of the two approaches to justify the effectiveness of the proposed genetic algorithm.

A production scheduling and control system for CAR T cell manufacturing

The manufacturing systems’ success directly relates to their accurate, reliable and flexible schedules, including how production is planned and scheduled and which constraints are considered in generating the schedules. The study's objective arises from the need to generate an optimal production scheduling system in a connecting plates manufacturing company that works on a Make-To-Stock basis. This research investigates the impact of demand and operational constraints on production schedules, including the facility capacity, operators and machines availability, raw materials availability, inventory level and warehouse capacity. A multi-agent-based optimisation model is developed to face the complexity of considering demand and operational constraints and reflects their impact on generating a reliable production schedule. This model involves a proposed heuristic algorithm that considers demand and operations constraints in such a manufacturing environment and optimises the production schedule based on these restrictions/requirements. A real-life case study based on a connecting plates manufacturer company is used as a test bench of the proposed agent-based heuristic optimisation model. The proposed algorithm is compared with other related approaches to check its superiority based on key criteria, including inventory levels, missed/unsatisfied orders and total production time. Results show that the proposed heuristics algorithm reduced the number of missed orders by 34% compared with similar approaches.