Jacob Rubinovitz

A weighted approach for assembly line design with station paralleling and equipment selection

This paper studies the problem of assembly line design, focusing on station paralleling and equipment selection. Two problem formulations, minimizing the number of stations, and minimizing the total cost, are discussed. The latter formulation is demonstrated by several examples, for different assembly system conditions: labor intensive or equipment intensive, and with task times that may exceed the required cycle time. It is shown that the problem of assembly system design with parallel stations can be treated as a special case of the problem of equipment selection for an assembly line. A branch and bound optimal algorithm developed for the equipment selection problem is adapted to solve the parallel station problem. Experiments are designed to investigate and demonstrate the influence of system parameters, such as assembly sequence flexibility and cycle time, on the balancing improvement due to station paralleling. An ILP formulation is developed for the combined problem of station paralleling with equipment selection, and an optimal solution of an example problem is presented.

RALB: a heuristic algorithm for design and balancing of robotic assembly lines

Summary This paper describes an heuristic approach for design and balancing of a robotic assembly line. The objective of RALB (Robotic Assembly Line Balancing) algorithm is to balance the assembly line, by allocating equal amount of work to the stations on the line, while assigning the most efficient robot type, out of several different types of robots available for the assembly task, to each workstation, and minimizing the number of workstations and robots used. RALB uses heuristics to limit and guide a Branch and Bound frontier starch, thus leading to solution of very large or difficult problems. A recommendation of the optimal set of heuristic rules is made based on results of extensive testing of RALB with a variety of assembly problems.

Multicriteria dynamic scheduling methodology for controlling a flexible manufacturing system

A methodology is presented for the dynamic scheduling of flexible manufacturing systems (FMSs). A two-level control hierarchy is suggested. The higher level is used for determining a dominant decision criterion and relevant scheduling rules, based on an analysis of the actual shop status. The lower level uses simulation for determining the best scheduling policy to be selected. Simulation is used to evaluate different control options, and once a control decision is made, it is operated in real time to serve as the FMS controller. The suggested scheduling and control scheme is being developed, implemented and tested in a physical computer integrated manufacturing (CIM)/FMS environment at the CIM and Robotics Lab of the Faculty of Industrial Engineering and Management, Technion. This will serve as a test-bed to study the performance of the FMS under different scheduling rules and control options, and to recommend the best combination of control policies and parameters for specific system conditions and global production objectives.

Team-oriented assembly system design: A new approach

Abstract A new design methodology for team-based assembly systems is presented in this paper. Team-oriented assembly (TOA) systems support the objectives of modern assembly systems, while creating a more satisfactory working environment. Each team is defined as semi-autonomous with well-defined responsibilities. Design parameters are: number of teams, precedence relationship between them, team size, and work content of each team. The problem-solving approach is product oriented and based on assigning Bill of Material (BOM) elements to teams. Performance measures are introduced, and a hierarchical design approach which consists of two stages is presented.

Using learning theory in assembly lines for new products

Abstract This paper presents a planning model for the assembly of new products, generally required in small quantities, and whose basic task times are relatively long. As a consequence, some consideration of Industrial Learning is incorporated into the planning model. An algorithm based on this model was successfully implemented for on-line planning and scheduling of a new production line for the assembly of aircraft engine housings in which the plant had limited experience. The jigs used for production are very expensive and would not be duplicated. Also, the arrival time of parts needed for the assembly could not be guaranteed, and updating of due dates needs to be considered by the planning model. Initial estimates of the learning parameters are updated continuously by the model, and as a result, line rebalancing is undertaken to account for task times reduction and modification due to both learning and correction of initial estimates. The powerful MUST algorithm was selected to generate the station balances since their requirement is for frequent rebalancing of the production line due to changes in task-times. Trial runs yield good solutions for rebalancing the line, while satisfying “zoning” constraints, and keeping the maximum number of activities in their original stations, so as to minimize potential learning losses.

Task level off-line programming system for robotic arc welding — an overview

Abstract A structure for a CAD based, task level off-line programming system for robotic arc welding is presented. The welding task is specified using a solid modeling CAD system. A model of the robot motion capabilitie are used to plan the path of motion between weld seams for the robotic welding process. The output of the system, in the form of a robot level program, is transferred to a specific robot controller using an interface module, which was also created for this research. An example welded part is used to show that the system can enhance the productivity and efficiency of robotic arc welding, for both low batch and high batch production.

Sequencing, Scheduling and Tooling Single-Stage Multifunctional Machines in a Small Batch Environment

Single-Stage multifunctional Machining Systems (SSMSs) are an important integral part of automated, flexible manufacturing systems. However, the SSMS frequently becomes the bottleneck of the entire manufacturing system by virtue of protracted internal set-up requirements. A key aspect of achieving full SSMS utilization is the ability to reduce as much as possible these demands at the expense of increasing external set-up. As of today, fixturing devices have not received much attention in the modeling literature. Nevertheless, fixturing devices are in some cases a limited resource which, may affect the shop scheduling decisions and the shop performance and as such can not be omitted. This study presents a {0–1} programming model which takes the fixturing devices limitation in to consideration when determining the weekly sequence of jobs to be produced. The model tries to minimize the nonproductive machine time. Based on this model, a heuristic procedure is developed and tested using data of four production weeks obtained from a hi-tech company that produces medical imaging systems.

Computer-aided-design-based interactive off-line programming of spray-glazing robots

Abstract Off-line programming of robots is an important step in the effective integration of computer-aided design (CAD) and computer-aided manufacturing. This paper identifies the steps involved in the development of such an off-line programming system for robotic spray glazing. Various robot path specification methods and path-planning strategies are described and evaluated The basic methodology is to use the CAD representation of the component to determine a robot path that ensures good glaze coverage. A CAD model of a sample robot work cell is created to simulate the spray-glazing operation, and to test the performance of the path specification methods. Two path planning strategies are evaluated for this work cell, for two sample surfaces.

Genetic algorithm for linear and cyclic assignment problem

Abstract A hybrid algorithm based on a genetic approach and a pairwise exchange procedure is developed for solving the linear and cyclic assignment problems. A technique which minimizes the amount of calculations during the exchange procedure is used. A comparison is made between the quality of solutions obtained by the genetic algorithm, and solutions achieved by another efficient algorithm based on a constructive procedure. Evaluation of modifications to the basic genetic procedure assists in recommending an improved algorithm. These modifications include various reproduction and selection rules and changes to initial population size.

Expert system approaches to the selection of materials handling and transfer equipment

Materials handling and transfer (MHT) is defined as the movement of physical objects -raw materials, components parts subassemblies, assemblies and finished goods -along the factory floor from receiving through shipping (Eastman, 1987, p. 1). The purpose of moving the material should be to increase its value. However, ‘the handling, transporting, housing and controlling of materials and goods adds nothing but cost to the system’ (Sims, 1991, p. xiii). Thus MHT is usually regarded as a burden; and MHT design is, therefore, often carried out as a final step after product, process and layout design has been completed, and in isolation from the overall design process.