Abstract This research aims to incorporate human factors engineering into the scheduling theory in order to exploit optimized human performance. Staff scheduling issue in which part-time employees have variable performance was studied in this paper. Objective function of the mathematical model is to minimize staffing costs and provided model tries to determine best shifts duration and employees assignments. The unique characteristic and novelty of this study is consideration of ergonomic aspect (fatigue rate of employees) in staff scheduling problem. We used genetic algorithm to solve model. In order to examine effectiveness and efficiency of the model, a set of problems were solved. Also efficiency of GA algorithm results were compared against LINGO results. Comparison of results demonstrated that GA algorithm has good ability to find satisfying solution in reasonable computational running time. This study showed that we can model human fatigue in employee scheduling and planning and consider flexible working shifts to decrease labor cost and increase production efficiency. In order to study policies of decreasing labor fatigue and increasing his/her working capacity, applying provided model were suggested for comparing cost of policies (such as education, job rotation, automation and …) against economic benefits of them in scheduling and choosing the best.

Abstract External factors such as sanctions and the lack of cooperation from foreign suppliers, and internal factors such as lack of proper management of cash flow or inappropriate policy board, can lead to a lack of inventory. Inventory shortage is an important factor in creating the crisis in the manufacturing processes. This article seeks to provide a system for inventory control and production. The proposed model for the production and inventory control in uncertainty modeling and it is assumed that the customer demand as well as follow a normal distribution. In this study, a mathematical model presented integer, nonlinear and is NP-hard. To solve this complex model of genetic algorithm is used. The results of the numerical model shows that a point mutation operators intersection and a good ability in the search space is justified and unjustified away from space. The convergence of the algorithm results also show that the algorithm can be used to find the optimum solution at the appropriate time.

Abstract The goal of this research is providing optimization approach for bi-objective scheduling work shifts and job rotation problem in order to exploit efficient performance of employees. In this article scheduling work shifts and job rotation problems had been modeled in one mathematical model with two objective functions: minimizing labor cost and maximizing number of job rotation. Also human factors (fatigue, learning and forgetting) that have effect on workers performance were modeled. Presented model was mixed integer and nonlinear and genetic algorithm and ε-constraint technique have been used to solve it and gain Pareto sets. To illustrate efficiency of provided algorithm, its performance has been compared to results of LINGO. The results indicated that performance of genetic algorithm is better than that of LINGO in terms of computational time and objective value. To relate relationship between objectives, set of problems have been solved. Obtained Pareto indicated that there is a conflict between objectives. Hence with considering human factors that have effect on workforce’s performance it is needed to plan work shifts and job rotation simultaneously. Results indicated that the proposed optimization approach is capable to provide suitable alternatives while managers try to consider decreasing cost, increasing jobs variety and multi-skilled training.