One of the major factors of competitive advantage in service and manufacturing systems is establishing a proper maintenance system .The productivity of the production line and equipment efficiency are among the significant points in most manufacturing companies which are directly related to their production style. Achieving the highest possible productivity improves the operations and functions of the line and finally increases the production volume. Various variables are effective on the productivity and efficiency of the line, among which, the preventive maintenances is one of the most important factors for cost reduction, equipment defect reduction, quality increase and efficiency increase. Multi-criteria decision-making methods are able to include and concern various variables related to competitive factors of a system. The purpose of this study is applying simulations to evaluate different scheduling techniques for preventive maintenances and combining the simulations as a decision-making tool, with the decision-making process. In this article three different preventive maintenance techniques have been implemented and simulated by using Arena-based simulation with a numerical example and finally, their results are compared with each other through TOPSIS and AHP, and the most efficient preventive maintenance technique is determined.

Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem’s criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.

Production planning is one of the most important functions in the process of production and operation management. The production planning should be made under uncertainty and considering these uncertainties will result in more realistic production planning model. Simulation optimisation methods have been proved to be useful for analysis of different system configurations and/or alternative operating procedures for complex logistic or manufacturing systems under uncertainty. The present article suggested a multi-period multi-product production planning model in a stochastic situation. Due to the probability of three problem parameters, including demand rate, process time and also setup time of every product in each period, the simulation optimisation technique was employed. In fact, through simulation, the system response rate (the mathematical expectation of objective) for various numbers of production rates in different periods was determined, and consequently, we approached the optimal response by simulated annealing algorithm. Finally, a numeral exam was presented and solved through the suggested approach.