Simulation-Based Optimization of a Transport Robot via Super-Efficiency DEAGP Approach

Introduction

Transport has become a crucial part of the entire industrial operational schedules. In modern manufacturing, the final goal is to decrease cycle time and produce more complex products. The role of robots in industry is to upgrade the processes, thereby acting as a material handling device in robotic manufacturing cells would improve the transportation task. Computer numerical control (CNC) machines with at least a robot constitute a robotic manufacturing cell wherein the robot is responsible for picking-up the products, loading & unloading the machines, and also material displacement operations within the cell. Applying the robots facilitate the process and promote the productivity of system as well. Many researchers have focused their attention on sequencing of machine feedings and robot moves in robotic cells to promote total system productivity. In most of their studies machine breakdowns and transportation times have been relaxed on scheduling and sequencing optimization process, though this may lead to change the solutions. A survey of recent developments in robotic manufacturing systems between 2005 and September 2021 was appeared by Vaisi (2022), and part sequencing as one of the optimization problems in robotic cells was also reviewed.

In the part sequencing problem determining the part input sequence into the robotic cell is the main issue. This problem was recently studied among others, by (Abdulkader et al., 2013; Gultekin et al., 2018; Majumder et al., 2019; Vaisi et al., 2018; 2020; Zahrouni & Kamoun, 2012; Zahrouni & Kamoun, 2021; Zhao & Guo, 2018). There are few studies on the sequencing of parts in robotic manufacturing cells reported in the literature. In some of the new studies the problem was formulated based on the traveling salesman problem (TSP), the rest was solved by heuristics or some special algorithms. The reader is referred to (Abdulkader et al., 2013; Lei et al., 2014) for studying the relevant papers. In some other studies simulation based optimization is applied such as (Vaisi et al., 2018, 2020, 2021). While Data envelopment analysis (DEA) method was proposed by Vaisi et al. (2018) to find the best sequences in two-machine robotic cells, robust sequencing in three-machine robotic cells was modeled and response surface methodology concerned as the solution method in other cited simulation based optimization researches. The review of the related literature reveals that scenario design and scenario evaluation by applying data envelopment analysis-goal programming (DEAGP) and super efficiency approach in robotic cells have not been examined before. Therefore, to bridge this gap, scenario concept is being developed. Some of the novelties of the current research are: (a) considering scenario concept for the order of arrival of parts in the cell where one of robot move cycles in 2-machine robotic manufacturing cell is measured; (b) the simulated scenarios are run; (c) a comparison is done between DEA and DEAGP methods to trace the optimal cyclic sequences for the parts; and (d) using Andersen and Petersen’s super-efficiency approach based on data envelopment analysis-goal programming (DEAGP-AP) the best cyclic sequence is selected.

The contribution of this research is to present a new approach based on DEAGP-AP in order to optimize the cycle time, total production cost, and output rate simultaneously. Concretely, DEAGP-AP is proposed as a solution approach to determine the efficient sequence of parts entrance to the 2-machine robotic cell by the help of simulation based optimization.

The chapter is presented as follows. Section 1 introduces the problem and discusses the preliminaries. A summary of the methods used to obtain sequencing pattern and the associated properties are provided in Sections 2 and 3. In Section 4 designated scenarios are given and through numerical examples discussion based on optimal part input sequences under different methods are revealed. Conclusions are made in Section 5.