Comparing an integrated and a standard planning and scheduling model

Author(s):  
W. Roux ◽  
J.-B. Lasserre ◽  
S. Dauzere-Peres
2013 ◽  
Vol 860-863 ◽  
pp. 3094-3099 ◽  
Author(s):  
Bao Lin Zhu ◽  
Shou Feng Ji

Iron and steel production scheduling problems are different from general production scheduling in machine industry. They have to meet special demands of steel production process. The CCR production manner dramatically promotes the revolution in technology and management, especially to planning and scheduling. In this paper, a scheduling model is presented to integrate the three working procedures and the lagrangian relaxation technology is proposed to get the optimal solution of the scheduling model. Finally, numerical examples are given to demonstrate the effectiveness of the integrated model and method.


Author(s):  
Guoxi He ◽  
Yongtu Liang ◽  
Limin Fang ◽  
Qi Zheng ◽  
Liying Sun

The disconnect between the optimization systems of upstream production and downstream demand poses a legitimate problem for China’s refined oil industry in terms of overproduction waste. Established methods only partially model the refinery system and are unable to integrate detailed production plans or meet market demands. Therefore, the research on production scheduling optimization combined with the demand of downstream pipeline network has very real applications that not only reduce the consumption of human/material resources, but also increase economic efficiency. This paper aims to optimize the production scheduling of refined oil transportation based on the demand of downstream product pipelines by analyzing the relationships between crude oil supply, refinery facility capacities and refinery tanks storage. The new model will minimize the refined production surplus therefore minimizing refinery costs and wastage. This is done by implementing models custom designed to optimize the three subsystems of the overall process: oil product blending scheduling optimization, producing and processing equipment scheduling optimization, and mixed crude oil scheduling optimization. We first analyzed the relationship between all the production units from the crude oil to the distributional destinations of oil products. A mathematical model of the refinery production scheduling was then built with minimum total surplus inventory as the objective function. We assumed a known downstream demand and used a step by step model to optimize oil stocks. The oil blending plan, production scheduling, amount of crude oil, and refined oil mixing ratios were all derived from the model using three methods: a nonlinear method called Particle Swarm Optimization (PSO), the simplex method and the enumeration method. The evidence laid out in this paper verifies our models functionality and suggests that systems can be significantly optimized by using these methods which can provide solutions for industries with similar challenges. Optimization of the refinery’s overall production process is achieved by implementing models for each of the three distinguished subsystems: oil blending model, plant scheduling model, and the mixed crude oil refining model. The demand dictates the final production quantities. From those figures we are able to place constraining limits on the input crude oil. The refined oil production scheme is continuously enhanced by determining the amount of constituent feed on the production equipment according to the results of previous production cycle. After optimization, the minimum surplus inventory of the five oil components approach their lower limits that were calculated using our models. We compare the literature on scheduling optimization challenges both in China and abroad while providing a detailed discussion of the present situation of Chinese refineries. The interrelationships of production processes on each other are revealed by analyzing the system and breaking it down to three fundamental parts. Basing the final production predictions on the downstream demand, we are able to achieve a minimum refinery surplus inventory by utilizing a comprehensive refinery scheduling model composed of three sub-models.


2017 ◽  
Vol 17 (1) ◽  
pp. 41-44
Author(s):  
J. Duda ◽  
A. Stawowy

Abstract A novel approach for treating the uncertainty about the real levels of finished products during production planning and scheduling process is presented in the paper. Interval arithmetic is used to describe uncertainty concerning the production that was planned to cover potential defective products, but meets customer’s quality requirement and can be delivered as fully valuable products. Interval lot sizing and scheduling model to solve this problem is proposed, then a dedicated version of genetic algorithm that is able to deal with interval arithmetic is used to solve the test problems taken from a real-world example described in the literature. The achieved results are compared with a standard approach in which no uncertainty about real production of valuable castings is considered. It has been shown that interval arithmetic can be a valuable method for modeling uncertainty, and proposed approach can provide more accurate information to the planners allowing them to take more tailored decisions.


2000 ◽  
Vol 13 (4) ◽  
pp. 191-200 ◽  
Author(s):  
Samson Tam ◽  
W.B. Lee ◽  
Walter W.C. Chung ◽  
Henry C.W. Lau

1973 ◽  
Vol 21 (3) ◽  
pp. 693-711 ◽  
Author(s):  
William J. Abernathy ◽  
Nicholas Baloff ◽  
John C. Hershey ◽  
Sten Wandel

Author(s):  
Tiago Alves ◽  
António R. Andrade

Abstract This paper presents a mathematical programming model that optimizes the daily schedule of maintenance technicians in a railway depot. The aim of the model is the minimization of the associated labor costs, while assigning the different technicians and skills required for each maintenance task. A case study of a Portuguese train operating company is explored, including many technical constraints imposed by the company. A mixed-integer linear programming model is formulated and applied to the case study, while observing the rolling stock schedule and the maintenance tactical plan. The optimized solution shows that the maintenance team could be shortened, as some workers are not necessary to carry out all maintenance actions, suggesting the need for more flexible maintenance crew scheduling and associated labor conditions. The present model is integrated within a tactical maintenance planning model, which finds a feasible annual maintenance plan for the entire fleet, and an operational maintenance scheduling model, which assigns train units to service tasks and schedules the maintenance tasks within the rolling stock. Together, the three models provide a decision framework that can support maintenance planning and scheduling decisions. Finally, the present maintenance crew scheduling model adds a key aspect to the literature: the skills of maintenance technicians.


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