Two-machine Robotic Cell Research Articles

A Parallel Algorithm for Scheduling a Two-Machine Robotic Cell in Bicycle Frame Welding Process

Welding frames with differing geometries is one of the most crucial stages in the production of high-end bicycles. This paper proposes a parallel algorithm and a mixed integer linear programming formulation for scheduling a two-machine robotic welding station. The time complexity of the introduced parallel method is O(log2n) on an n3-processor Exclusive Read Exclusive Write Parallel Random-Access Machine (EREW PRAM), where n is the problem size. The algorithm is designed to take advantage of modern graphics cards to significantly accelerate the computations. To present the benefits of the parallelization, the algorithm is compared to the state of art sequential method and a solver-based approach. Experimental results show an impressive speedup for larger problem instances—up to 314 on a single Graphics Processing Unit (GPU), compared to a single-threaded CPU execution of the sequential algorithm.

Two-Machine Robotic Cell Sequencing under Different Uncertainties

Two-Machine Robotic Cell Sequencing under Different Uncertainties

Increasing Throughput for a Class of Two-Machine Robotic Cells Served by a Multifunction Robot

The multifunction robotic cell scheduling problem has been recently studied in the literature. The main assumption in the pertaining literature is that a multifunction robot performs an operation on the part during any loaded move between two adjacent processing stages. For a two-machine cell, these stages are the input hopper, the first machine, the second machine and the output hopper. Consequently, the multifunction robot performs three operations with fixed processing times. In contrast, we assume a class of two-machine cells where none of the processing times of three operations are fixed. However, their summation is fixed and equivalent to the processing time of the unique operation. The processing mode of the unique operation performed by the multifunction robot is “stop resume.” Thus, regardless of the gap interrupts during operations by two machines, the robot continues performing the unique operation of the part when it is reloaded to the robot without any loss of time. The focus lies on $n$ -unit cycles due to their popularity. It is proven one-unit cycles have better performance for the problem under study. The cycle time of one-unit cycles are obtained and optimality conditions are determined for different pickup criteria: free, interval, and no-wait.

A framework for stochastic scheduling of two-machine robotic rework cells with in-process inspection system

This study is focused on the domain of a two-machine robotic cell scheduling problem for three various kinds of pickup scenarios: free, interval, and no-wait pickup scenarios. Particularly, we propose the first analytical method for minimizing the partial cycle time of such a cell with a PC-based automatic inspection system to make the problem more realistic. It is assumed that parts must be inspected in one of the production machines, and this may result in a rework process. The stochastic nature of the rework process prevents us from applying existing deterministic solution methods for the scheduling problem. This study aims to develop a framework for an in-line inspection of identical parts using multiple contact/non-contact sensors. Initially, we convert a multiple-sensor inspection system into a single-sensor inspection system. Then, the expected sequence times of two different cycles are derived based on a geometric distribution, and finally the maximum expected throughput is pursued for each individual case with free pickup scenario. Results are also extended for the interval and no-wait pick up scenarios as two well-solved classes of the scheduling problem. The waiting time of the part at each machine after finishing its operation is bounded within a fixed time interval in cells with interval pickup scenario, whereas the part is processed from the input conveyor to the output conveyor without any interruption on machines in cells with no-wait pickup scenario. We show a simple approach for solving these two scenarios of the problem which are common in practice.

Scheduling of two-machine robotic rework cells: In-process, post-process and in-line inspection scenarios

This study focuses on the domain of a two-machine robotic cell scheduling problem under three inspection scenarios. We propose the first analytical method for minimizing the partial cycle time of cells with in-process and post-process inspection scenarios, and then we convert this cell into a multi-function robotic cell with in-line inspection scenario. For the first scenario, parts are inspected in one of the production machines using multiple sensors, while the inspection process is performed by an independent inspection machine for the second scenario. Alternatively, the inspection can be performed by a multi-function robot for the third scenario. A distinguishing characteristic of this robot is that it can perform inspection of the part in transit. However, the robot cannot complete the part transition and load it on the next destination machine if it identifies a fault in the part. The stochastic nature of the process prevents us from applying existing deterministic solution methods for corresponding scheduling problems. In the first stage, we present a heuristic method that converts a multiple-sensor inspection system into a single-sensor inspection system. The expected cycle times of two different cycles are derived based on a geometric distribution, and then the maximum expected throughput is pursued for in-process and post-process inspection sensors, respectively. In the second stage, we develop the inspection system into an in-line inspection system using a multi-function robot. Finally, we determine if it is technically feasible and profitable to replace the in-process (or post-process) inspection scenario with the in-line inspection scenario.

Bacteria Foraging Optimization Algorithm for Robotic Cell Scheduling Problem

The present study deals with the application of BFO for solving robotic cell problem. One of the major disadvantages of Passino invented BFO is its applicability constrains for solving problems except those contain continuous domain. Thus to make BFO applicable in robotic cell problem, Pairwiseinterchange mutation is chosen for tumbling and swimming operation during chemotaxis.While cyclic-shift neighbourhood mutation is used for random movement duringElimination-Dispersal. The example related to the two-machine robotic cell scheduling problem with sequence-dependent setup times (2RCSDST) was used to demonstrate the performance of the proposed discrete bacteria foraging algorithm. Large part sized problems varying from 200 to 500 part size was considered during this study. The computational results thus obtained were compared with two other existing optimization techniques and found BFO as the better performer to solve such large sized problems.

Scheduling in two-machine robotic cells with a self-buffered robot

ABSTRACTThis study considers a production cell consisting of two machines and a material handling robot. The robot has a buffer space that moves with it. Identical parts are to be produced repetitively in this flowshop environment. The problem is to determine the cyclic schedule of the robot moves that maximizes the throughput rate. After developing the necessary framework to analyze such cells, we separately consider the single-, double-, and infinite-capacity buffer cases. For single- and double-capacity cases, consistent with the literature, we consider one-unit cycles that produce a single part in one repetition. We compare these cycles with each other and determine the set of undominated cycles. For the single-capacity case, we determine the parameter regions where each cycle is optimal, whereas for the double-capacity case, we determine efficient cycles and their worst-case performance bounds. For the infinite-capacity buffer case, we define a new class of cycles that better utilizes the benefits of the buffer space. We derive all such cycles and determine the set of undominated ones.We perform a computational study where we investigate the benefits of robots with a buffer space and the effects of the size of the buffer space on the performance. We compare the performances of self-buffered robots, dual-gripper robots, and robots with swap ability.

Applying Metaheuristic Algorithms for Output Rate Analysis in Two-Machine Robotic Manufacturing Cells

This article analyses the output rate in two-machine flexible robotic manufacturing cells. The flexible CNC machines in this manufacturing cell can process different operations. The manufactured parts in the cell are identical and it is assumed that different operations are required to manufacture each part. Moreover, loading/unloading time of a part by the robot (ε), robot movement time between the machines and input and output areas (σ), and processing time of the jth part on the machines (tj) are considered to be fixed. The main objective of this article is to minimize cycle time in order to increase the output rate of the manufacturing cell. To achieve this goal, it is important to optimally assign operations required for manufacturing a part to each machine and to determine the optimal robot moves sequence. Accordingly, existing feasible movement policies in the cell and their cycle times have been reviewed, and then these policies have been considered in a new machine layout and their cycle times have been calculated based on the new robot moves sequence. Afterwards, a mathematical model has been presented to select optimal cycle time in the manufacturing cell and this model has been solved by a branch and bound exact algorithm; since the mathematical model is non-linear and the optimal solution cannot be obtained, two metaheuristic algorithms—genetic and simulated annealing algorithms—have also been proposed to solve the model and their results have been compared.

Two-machine robotic cell scheduling problem with sequence-dependent setup times

In this paper, we introduce a new and practical two-machine robotic cell scheduling problem with sequence-dependent setup times (2RCSDST) along with different loading/unloading times for each part. Our objective is to simultaneously determine the sequence of robot moves and the sequence of parts that minimize the total cycle time. The proposed problem is proven to be strongly NP-hard. Using the Gilmore and Gomory (GnG) algorithm, a polynomial-time computable lower bound is provided.Based on the input parameters, a dominance condition is developed to determine the optimal sequence of robot moves for a given sequence of parts. A mixed-integer linear programming (MILP) model is provided and enhanced using a valid inequality based on the given dominance condition. In addition, a branch and bound (BnB) algorithm is exploited to solve the problem, and due to the NP-hardness, an improved simulated annealing (SA) algorithm is proposed to address large-sized test problems.All the solution methods are evaluated using small-, medium- and large-sized test problems. The numerical results indicate that the optimal solution of the MILP model is attained for the medium- and some large-sized test problems, and the proposed SA, tuned using the Taguchi technique, provides an acceptable, near-optimal solution with markedly reduced CPU time. Moreover, the lower bound is observed to be significantly near the optimal solution. Thus, this lower bound is exploited to validate the results of the SA algorithm for large-sized test problems.

Optimal Scheduling for a Two-Machine Robotic Cell of Jobshop Type

In this paper we consider a scheduling problem for a two-machine robotic cell of jobshop type. The problem is an extension of the classical two-machine jobshop scheduling addressed by Jackson 6), but is NP-hard. We propose a greedy heuristic algorithm and test for its performance with numerical experiments.

Optimal Scheduling for a Two-Machine Robotic Cell with Finite Buffer

Optimal Scheduling for a Two-Machine Robotic Cell with Finite Buffer

Sequencing of robot activities and parts in two-machine robotic cells

This paper deals with the development of analytical methods for determining an optimal sequence of parts and robot activities to minimize cycle time in robotic cells with two machines. A robot is used to feed parts to the two machines or in the absence of machines two robots perform processing operations on parts. Three different types of robotic cellular layouts, namely the robot-centered cell, the mobile robot cell, and the in-line robot cell have been considered in an automated flow line manufacturing system with no storage buffers. Optimal sequences have been established for both single and multiple part types when part production is performed in a repetitive fashion. In order to keep abreast with the current trend toward just-in-time manufacturing, production of a quantity known as the minimum part set (MPS) is considered for multiple part types. The analysis for a single MPS has further been extended to include the cyclic production of multiple MPSs. The problem of determining the optimal sequence of multiple part types has been shown equivalent to a two machines no-wait flow shop problem, and has been solved by Gilmore and Gomory's (1964) algorithm.

Scheduling a two-machine robotic cell: A solvable case

The paper deals with the scheduling of a robotic cell in which jobs are processed on two tandem machines. The job transportation between the machines is done by a transportation robot. The robotic cell has limitations on the intermediate space between the machines for storing the work-in-process. What complicates the scheduling problem is that the loading/unloading operation times are non-negligible. Given the total number of operationsn, an optimalO(n logn)-time algorithm is proposed together with the proof of optimality.