Design of predictable production scheduling model using control theoretic approach

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.

This research proposes a two-layered supply chain model consisting of a set of clients and suppliers for dynamic supply chain management considering production and transportation scheduling problems. The model provides a method to optimize both production schedules and transportation schedules simultaneously, in order to estimate suitable prices and delivery times of products. A prototype of simulation system for dynamic supply chain management was developed and some computational experiments were carried out in order to verify the effectiveness of the model. Experimental results show that the prototype system is available to determine suitable production schedules and transportation schedules from the viewpoint of the number of contracted orders and the cost for wasteful shipping.

The annual production scheduling of open pit mines determines an optimal sequence for annually extracting the mineralized material from the ground. The objective of the optimization process is usually to maximize the total Net Present Value (NPV) of the operation. Production scheduling is typically a Mixed Integer Programming (MIP) type problem containing uncertainty in the geologic input data and economic parameters involved. Major uncertainty affecting optimization is uncertainty in the mineralized materials (resource) available in the ground which constitutes an uncertain supply for mine production scheduling. A new optimization model is developed herein based on two-stage Stochastic Integer Programming (SIP) to integrate uncertain supply to optimization; past optimization methods assume certainty in the supply from the mineral resource. As input, the SIP model utilizes a set of multiple, stochastically simulated scenarios of the mineralized materials in the ground. This set of multiple, equally probable scenarios describes the uncertainty in the mineral resource available in the ground, and allows the proposed model to generate a single optimum production schedule. The method is applied for optimizing the annual production scheduling at a gold mine in Australia and benchmarked against a traditional scheduling method using the traditional single “average type” assessment of the mineral resource in the ground. In the case study presented herein, the schedule generated using the proposed SIP model resulted in approximately 10% higher NPV than the schedule derived from the traditional approach.

The production scheduling problem in process industries is a complicated issue of complex multi-objective optimizing that has a significant impact on the production efficiency and economic benefits of enterprises. However, traditional scheduling methods often fail to meet the multi-objective optimization requirements in complex production environments. For improving the production efficiency and economical benefit of process industry enterprises, this study constructs a production scheduling model and solves it using a decomposed multi-objective evolution method. When solving the model using the decomposed multi-objective evolution method, this study also proposes an improvement using self-organizing mapping. The results show that when the optimization objectives are the max completing time and production switches number, the proposed method has a high convergence speed and HV value. The algorithm achieves convergence after 2100 evaluations with an HV value of approximately 0.302. The HypE algorithm achieves convergence after 2400 evaluations with a Hypervolume value of approximately 0.284. The algorithm also exhibits a high level of diversity, with an IGD value of approximately 0.672, which is higher than the other algorithms. The proposed algorithm demonstrates high convergence and diversity when solving the production scheduling model.

A multi-step approach for block-cave production scheduling optimization

Semiconductor production scheduling and industrial economic coordination based on deep learning and heat loss optimization

Some pricing models in production scheduling and planning by Liang Lu thesis 2011 xi, 80 p. : ill. ; 30 cm This thesis consists of three production scheduling and planning problems with pricing issues. The first problem studies the joint…Read more ›

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.

The efficiency and quality of the manufacturing industry are greatly influenced by production scheduling, which makes it a crucial aspect. A well-designed production scheduling scheme can significantly enhance manufacturing efficiency and reduce enterprise costs. This paper presents a tailored optimization model designed to address a more complex production scheduling problem that incorporates parallel machines and preventive maintenance. The proposed solutions aim to achieve a balance between job sequence and machine reliability, considering the minimum maintenance cost rate for determining maintenance cycles of deteriorating machines in real manufacturing scenarios. Furthermore, the objective of minimizing the maximum completion time guides machine assignment and job sequence based on maintenance constraints. The innovation lies in the introduction of a greedy algorithm that utilizes a water injection model to address this NP-hard integrated problem. A pre-distribution model is constructed using the water injection model, and its solution is utilized as input for constructing the production scheduling model, which aids in determining machine assignment and job sequence. This algorithm demonstrates remarkable effectiveness and efficiency, enabling the achievement of an optimal solution. A numerical example is presented to illustrate the computational process, accompanied by an extensive discussion of the results showcasing improved performance. Furthermore, the optimization model developed in this paper can be adapted to tackle the production scheduling problem with modifications tailored for parallel machines.

In view of the lack of consideration of equipment maintenance in the production scheduling of prefabricated components, a production scheduling optimization problem is proposed to meet the needs of prefabricated component production planning and preventive maintenance planning. A joint optimization model of aperiodic preventive maintenance production scheduling is established by considering the production process characteristics of prefabricated components and reliability-centered maintenance. The model is solved with the goal of minimizing the total cost, and finally the optimal production scheduling plan and preventive maintenance plan can be determined. The results of numerical analysis show the feasibility and effectiveness of the optimization model, and it is significantly better than the periodic preventive maintenance production scheduling model.

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

In this paper, integrated preventive maintenance planning and production scheduling for a single machine is formulated as a multi-objective mathematical optimization problem. Despite the inter-dependent relationship between them, production scheduling and preventive maintenance planning decisions are generally analyzed and executed independently in real manufacturing systems. This practice is also found in the majority of the studies found in the relevant literature. The chaotic particle swarm optimization (CPSO) algorithm is proposed to solve the integrated optimization model for production scheduling and preventive maintenance planning. The numerical results on several problem sizes indicate that the methodology's feasibility and efficiency

Manufacturing and production plants operate physical machine that will deteriorate with increased usage and time. Maintenance planning which can keep machines in good operation is thus required for smooth production. However, in previous research, production scheduling and maintenance planning are usually performed individually and not studied as an integrated model. In order to balance the trade-offs between them, this study proposes an integrated scheduling model by incorporating both production scheduling and preventive maintenance planning for a single-machine problem with the objective of minimizing the maximum weighted tardiness. In this model, a variable maintenance time subjected to machine degradation is considered. Finally, a numerical example using this improved production scheduling model is shown. The computational results prove its efficiency.

The reliability of a critical tool like a mould on a machine affects the productivity seriously in many manufacturing firms. In fact, its breakdown frequency is even higher than machines. The decision-making on when mould maintenance should be started become a challenging issue. In the previous study, the mould maintenance plans were integrated with the traditional production schedules in a plastics production system. It was proven that considering machine and mould maintenance in production scheduling could improve the overall reliability and productivity of the production system. However, the previous model assumed that each job contained single operation. It is not workable in other manufacturing systems such as die stamping which may contain multiple operations with multiple moulds in each job. Thus, this study models a new problem for multi-mould production-maintenance scheduling. A genetic algorithm approach is applied to minimise the makespan of all jobs in 10 hypothetical problem sets. A joint scheduling (JS) approach is proposed to decide the start times of maintenance activities during scheduling. The numerical result shows that the JS approach has a good performance in the new problem and it is sensitive to the characteristic of the setup time defined.