برنامه ریزی تولید و آموزش کارگران در سیستم های تولید پویا

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.

The most important ultimate goal of every activity in every company, including machine industry plants, is generating maximum benefits. All actions taken in a company should be with a step in the way of reaching this goal. The processes of creating production planning are the most complex and the most important elements influencing the financial effect. The goal of production planning is to make planning decisions optimizing the trade-off between economic objectives like cost minimization. To achieve this goal, manufacturing planning systems are becoming more complicated in order to increase both the productivity and the flexibility in satisfying customer demand [1]. In reality, production quantity may not be equal to the demand because it may be satisfied by inventory or there may be backorders. Production quantity should be satisfied based on production planning decisions in order to determine the number and type of machines to be installed in the system. By consideration of machine capacity, the production quantities in each planning period affect the number and types of machines to be installed in manufacturing system. With increasing global competition and shorter product life cycles, there has been a move from planning for static condition (in which system is formed for a single time period with known and constant product mix and demand) to planning for dynamic situation. In dynamic environment a multi-period planning horizon is considered in which in each period has a different product mix and demand requirements. Consequently, the system optimized for a single period may be not optimal and efficient for the next periods. Reconfiguration has two aspects: (1) adding new machines to the system, and (2) removing existing machines from the system. The facts related to workers in developing a production plan may significantly affect the productivity and efficiency of manufacturing. For instance, a study by Park [2] shows how training multi-skill workers can increase production flexibility. It is essential to develop multi-skilled worked who can perform multiple tasks. This, enhances system flexibility, improves worker motivation, and relaxes constraints on workers assignments. Identifying the current levels of skills for each worker can help the decision makers determine the type and duration of training needed for each worker [3]. In this paper, we design a mathematical model for production planning in dynamic environment with an extensive coverage of important manufacturing features considering multi-period production planning, sequence of operations, system reconfiguration, duplicate machines, machine capacity and training of workers. The main constraints are demand satisfaction, machine availability, machine time-capacity, available time of worker and training. The rest of the paper is organized as follows. The literature review related to production planning is presented in Section 2. In Section 3, a mathematical model integrating most of attributes of manufacturing for production planning is formulated and linearization procedure is explained. A clustering method applied for worker training is described in Section 4. We present computational results in Section 5. Finally, conclusions and further research is described in Section 6.

This paper presents a novel integer linear programming model for dynamic manufacturing systems in the presence of worker training and production planning. The objective is to minimize the total costs of machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of workers. This model is capable of determining the system configurations, worker assignment and production plan for each part type at each period over the planning horizon. The performance of the model is illustrated by a small numerical example. The main constraints are demand satisfaction, machine availability, machine time-capacity, available time of worker and training. Sensitive analyses of computational results demonstrate the effects of worker training and reconfiguration in multi-period production planning. Considering these futures in the proposed model can help the efficiency of production planning. The linearized proposed model consists of 2262 variables and 4421 constraints for the example solved in 285 min Obtaining an exact solution for this problem in a reasonable time is computationally intractable. Therefore, it is necessary to develop a heuristic or meta-heuristic approach to solve the proposed model for large-sized problems. This study is still open for incorporating other features in future researches. The advantages of this paper in comparison with the recent studies are as follows: • Integrating most of important principles in dynamic manufacturing systems. • Considering worker assignment and worker training simultaneously. • Clustering of the worker types based on their similarity. The similarity is the common machine types on which corresponding worker types can work. • Training of the worker types in each cluster and each time period. We have shown the role of training in increasing workers flexibility and efficiency of resources leads to more production, decreases the total cost, in all over planning. Some guidelines for future researches can be outlined as follows: • Incorporating machine layout to precisely calculate the material handling cost. • Considering alternate process plans to improve system throughput rate.