We have a huge civic project, includes several sub-projects, which are divided into activities. This project is a project to rehabilitate 550,000 hectares of agricultural land in the provinces of Khuzestan and Ilam. The project has been divided according to the plots of land. The Jehad in Tehran is managing the projects. They need project scheduling as well as resource levelling and "lot-sizing". Levelling and lot sizing are the most important issues in utilizing the limited resources. For determining the scope and the size of those sub-projects as well as their parallel activities, so far, many models have been proposed. However, the models are weak either in higher resource utilization or in solving numerically the problems. In this paper, our effort has been concentrated on developing scheduling, resource levelling, and lot sizing model, based on balancing utilization of resources, so that the real size civil project could be solved within an acceptable duration time. This paper proposes a goal programming linear model for simultaneous project scheduling and resource levelling. This model determines the best schedule of sub-activities (optimal "lot size" of each sub-activity) to reach the minimum amount of diversion of resources consumed from the number of resources available for the entire periods of the planning horizon. In fact, if the best "lot sizes" have been taken, then minimum fluctuation of the active resources is reached. The proposed model has been used to schedule a project with 87 activities. This project has been scheduled and, accordingly, the optimal volume (the "lot size") of sub-activities have been determined for each activity at any period of time. In this way, only 4 resources out of a total of 32 resources are in shortage. In contrast, the scheduling of this project, using the CPM, results in a shortage of 13 resources. Manuscript profile

Predicting unexpected incidents and energy consumption decline is one of the current problems in the industry. The extant study addressed parallel machine scheduling by consideration of failures and energy consumption decline. Moreover, the present paper aimed at minimizing early and late delivery penalties, and enhancing tasks. This research designed a mathematical model for this problem that considered processing times, delivery time, rotation speed and torque, failure time, and machine availability after repair and maintenance. Failure times have been predicated on using machine learning algorithms. The results indicated that the proposed model can be suitably solved for the size of 10 jobs or tasks and five machines. This research addresses the problem in two parts: the first part predicts failures, and the second part includes the sequence of parallel machine scheduling operations. After the previous data were received in the first step, machine failure was predicted by using machine learning algorithms, and a set of rules were obtained to correct the process. The obtained rules were used in the model to improve the machining process. In the second step, scheduling mode was used to determine operations sequence by consideration of these failures and machinery unavailability to achieve the optimal sequence. Moreover, it is supposed to reduce energy consumption and failures. This study used the Light GBM algorithm and achieved 85% precision in failure prediction. The rules obtained from this algorithm contributed to cost reduction. Manuscript profile