Scope for industrial applications of production scheduling models and solution methods. Highlights. Above all, the aim of the paper is to focus on the industrial aspects of scheduling and discuss the main characteristics, including strengths and weaknesses of the presented approaches. It is claimed that optimization tools of today can effectively support the plant level production.
However there is still clear potential for improvements, especially in transferring academic results into industry. For instance, usability, interfacing and integration are some aspects discussed in the paper. After the introduction and problem classification, the paper discusses some lessons learned from industry, provides an overview of models and methods and concludes with general guidelines and examples on the modeling and solution of industrial problems. Keywords. Scheduling; Industrial applications; Best practices; Integration; Challenges.
Introduction and motivation. Scheduling is a decision- making process that plays an important role in most manufacturing and service industries (Pinedo & Chao, 1. Scheduling problems arise in almost any type of industrial production facilities (Pulp and Paper, Metals, Oil and Gas, Chemicals, Food and Beverages, Pharmaceuticals, Transportation, Service, Military, etc.) where given tasks need to be processed on specified resources. For instance, in a chemical process, the production must be appropriately planned to ensure that the equipment, material, utilities, personnel and other resources are available at the plant when they are needed to realize the production tasks.
Production scheduling comprises the activity of planning the production of e. In a nutshell, it commonly boils down to the following main decisions. In some cases, only parts of these decisions need to be made, however often they are all relevant to the production. While they are typically also strongly coupled through the synchronization of the resource utilization, ideally the decisions should be taken simultaneously. Traditionally, production scheduling or short- term planning has been done manually by trained individuals using pen and paper, planning cards, e.
Kanban or spreadsheets. In the course of time, many companies have identified and documented best operating practices for meeting their specific production targets. Along increasing production volumes, larger product portfolio, alternative production recipes and – especially more recently – volatile customer orders and high pressure to save on production and energy costs, manual scheduling has become extremely challenging. Due to this complexity and required flexibility it is very difficult to ensure a profitable production without any optimization support.
A good optimization solution can result in significant savings through better capacity utilization. Apart from economic benefits, good schedules can also contribute to reducing the environmental load, energy demand, violations of various regulations and help coping more efficiently with uncertainties, both in production as well as in customer order levels. A number of review papers on scheduling have been written across different scientific communities, e. Floudas and Lin (2.
M. Due to the wide range of scheduling problems, a number of approaches have been developed to make production scheduling easier and yield better solutions. Some of the approaches comprise computer- supported manual scheduling (e. Recently, also approaches trying to tackle uncertainty have been introduced (stochastic optimization). Most of the above methods are often presented in the literature from a purely modeling point of view and they are mainly tested only on small- scale examples. One of the main targets of this paper is to focus on the industrial applicability of existing scheduling solutions and provide some ideas or guidelines on how the gap toward industrial applicability could be reduced or even closed. As the topic itself is much too broad to be completely covered in one paper, we mainly focus on the activities done within the process systems engineering (PSE) community.
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In an industrial environment it is crucial to be able to connect any planning solution into the existing information systems. Often there are close interdependencies to other planning systems and control decisions and thus it is also relevant how to integrate all these together into a functional system (see Fig.
A central processing unit (CPU) is the electronic circuitry within a computer that carries out the instructions of a computer program by performing the basic arithmetic, logical, control and input/output (I/O) operations. We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers are. Computer Science quizzes for geeks. GATE Computer science previous year solved papers, Quizzes on GATE CS, Data Structures, Algorithms, DBMS, OS, Theory of Computation, Computer Architecture, Compilers, C, C++, Java, Python. 16 Chapter 6 CPU Scheduling that two shorter processes would arrive soon. Compute what the average turnaround time will be if the CPU is left idle for the . Remember that processes.