Design of an automated guided vehicle-based material handling system for a flexible manufacturing system

Automated guided vehicle (AGV)-based material handling systems (MHSs), which are widely used in several flexible manufacturing system (FMS) installations, require a number of decisions to be made. These include the number of vehicles required, the track layout, traffic pattern along the AGV tracks, and solving traffic control problems. This paper addresses the key issues involved in the design and operation of AGV-based material handling systems for an FMS. The problems arising from multi-vehicle systems are analysed, and strategies for resolving them are examined using analytical and simulation models.     

Guide path design and location of load pick-up/drop-off points for an automated guided vehicle system

The guide path layout for an automated guided vehicle system (AGVS) is a critical component in the overall design of a flexible manufacturing system (FMS) that utilizes AGVs for materials handling. Not only does it affect the total distance travelled by the vehicles but it also affects vehicle requirements and space utilization. In this study, the problem of selecting the guide path as well as the location of pick-up and drop-off points for outward and inward bound parts to departments is addressed. The problem is modelled and solved as a linear integer program with the objective of minimizing the total distance travelled.     

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS²O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.     

Design of an FMS decision support system

Flexible manufacturing system (FMS) is an integrated system consisting of computer numerical control machine tools and automated material handling system controlled by an overall control computer system. The FMS design is broken down into strategic, tactical and operational level designs. This research is concerned with the development of a decision support system for the design of FMS (FMSDDS. Simulation is used as a primary analysis tool, making the DSS capable of solving problems at all three levels. The decision making ability is provided by a multi-attribute utility model which accounts for both quantitative and qualitative factors that affect a decision. The FMSDSS is implemented using three routines: input, analysis, and output. The input routine helps the user to select feasible FMS configurations interactively. The analysis routine consists of a generalized FMS simulation model. This routine analyses the FMS alternatives selected and provides the information required for the output analysis. The qualitative and quantitative measures of performance that have to be considered for decision making are handled by the output routine. It also combines performance measures of different units such as time and cost. The DSS is modular and hence future enhancements can be readily added.     

Design and operation of single-loop dual-rail inter-bay material handling system

Asynchronized automated material handling systems, e.g. automated electrified monorail systems and automated guided vehicle systems, have been playing important roles in manufacturing. Reliability becomes a critical issue as manufacturing enterprises are moving toward automation. In the past, reliability tissue has not been analysed explicitly for these types of systems. In this research, a new measure was proposed to specify reliability. A mixed integer programming model was developed to design flow paths for a special class of material handling systems, i.e. single-loop dual-rail systems. The model can be used to design systems with higher reliability. The model and application procedure in design and analysis were demonstrated through two case studies.     

Determination of unit load sizes in an AGV-based material handling system for an FMS

Unit load size is a key factor in an automated guided vehicle based material handling system for a flexible manufacturing system. Highlighting this aspect and its importance at the design stage, this paper presents an integer programming formulation of the problem of finding the optimal unit load size. Using an existing analytical model to decide the number of AGVs required, an algorithm based on branching and implicit enumeration and a heuristic have been developed. Revised computations due to dynamic system conditions such as changes in part mix are also possible with the proposed algorithms. The methodologies have been demonstrated using numerical examples.     

Cellular machine layout based on the Segmented Flow Topology

This paper introduces a facility layout design procedure for converting an existing manufacturing system with a predefined aisle structure to a cellular manufacturing system based on the 'segmented flow topology' (SFT) developed by Sinriech and Tanchoco. The proposed procedure is aimed at finding the best machine grouping, along with the locations of pick-up and delivery stations and machine layout for each cell based on an existing facility. The objective is to minimize the total material handling cost. In contrast to previous work in this area, the proposed design procedure takes into account both distance and material flow in forming machine clusters. In addition, a revised cost model for material handling system, which accounts for different aspects of capital and operating cost, is presented.     

Analysis of automated guided vehicle configurations in flexible manufacturing systems

Automated guided vehicle (AGV) systems complement the operation of flexible manufacturing systems (FMS) by providing integrated automated material handling that capitalizes on the system's flexibility. Previous research considering AGV systems for use in FMS installations has focused on complex control strategies to reduce the congestion problem often encountered in these systems. Recently, attention has been given to tandem system configurations that reduce congestion and simplify system control. The present study uses the simulation methodology to compare the performance of three AGV configurations under a variety of experimental conditions. The results indicate that system size, load/unload time, and machine failure rate factors have significant impacts on the operation of the systems considered. In general, with respect to due date performance, it is recommended to use the traditional configuration in small systems while using the tandem/loop configuration in larger systems. Furthermore, it is shown that the addition of the loop to the tandem configuration mitigates the sensitivity of the tandem configuration to the load/unload time factor as well as significantly improving its performance under high load/unload times. Thus, if tandem configuration is desired to reduce congestion and simplify system control, investments must be made to directly reduce the load/unload times or to construct a loop to avoid the load/unload time penalty.     

Scheduling of Automated Guided Vehicles in a Decision Making Hierarchy

Scheduling efforts made without considering the special limitations of the material handling system might lead to infeasible results. An analytical model is proposed, first, to incorporate the automated guided vehicle (AGV) system into the overall decision making hierarchy. A mathematical formulation is developed to include interaction between the AGV module and other modules in the system by considering the restrictions of the material handling system. A micro-opportunistic approach is then proposed to solve the AGV scheduling problem. Finally, the proposed method is compared with a number of dispatching rules.     

TRIP-BASED MATERIAL HANDLING SYSTEMS: THROUGHPUT CAPACITY ANALYSIS

In this paper we present a general-purpose analytical model to compute die approximate throughput capacity of a trip-based material handling system used in a manufacturing setting. A wide variety of handling systems, including freight elevators, cranes, microload automated storage/retrieval (AS/R) systems, industrial lift trucks, and automated guided vehicle (AGV) systems can be modeled as trip-based handling systems. To our knowledge, this model is one of the few analytical models that explicitly considers an empty device dispatching rule. The model is first developed for a single-device system (such as a crane) and subsequently, with a simple modification, it is extended to multiple-device systems (such as lift trucks and AGVs). Using this model one can rapidly evaluate a wide range of handling and layout alternatives for given flow data. Hence, die model would be most effective when used early in the design phase to narrow down die set of alternative handling systems and configurations prior to simulation.     

A beam search-based algorithm and evaluation of scheduling approaches for flexible manufacturing systems

This paper presents a new algorithm for the flexible manufacturing system (FMS) scheduling problem. The proposed algorithm is a heuristic based on filtered beam search. It considers finite buffer capacity, routing and sequence flexibilities and generates machine and automated guided vehicle (AGV) schedules for a given scheduling period. A new deadlock resolution mechanism is also developed as an integral part of the proposed algorithm. The performance of the algorithm is compared with several machine and AGV dispatching rules using mean flow time, mean tardiness and makespan criteria. It is also used to examine the effects of scheduling factors (i.e., machine and AGV load levels, routing and sequence flexibilities, etc.) on the system performance. The results indicate that the proposed scheduling algorithm yields considerable improvements in system performance over dispatching rules under a wide variety of experimental conditions.     

The value of the shortest loop covering all work centers in a manufacturing facility layout

In this study we develop mathematical models to design circular material flow systems. We first develop a tight formulation to find the shortest loop covering all work centers within a manufacturing facility layout. The shortest loop is an attractive solution for most types of conveyors and power-and-free systems, where the length of the flow path is the major driver of the total cost. We develop a primal as well as a dual graph formulation and discuss their one-to-one correspondence in node-edge as well as in connectivity constraints. Our solution times outperform other optimization models available for the facility layout shortest loop design problem. We then approach trip-based material handling, such as automated guided vehicle systems, where the total loaded and empty trip distance is the major driver of the total cost. The problem in these systems evolves into concurrent design of the loop, pickup and dropoff station, and the empty vehicle dispatching policies. On the foundation of the shortest loop model, we propose a decomposition heuristic for design of trip-based flow systems. Computational results indicate that the heuristic provides high quality and robust solutions.     

A robust cell formation approach for varying product demands

In cellular manufacturing environments, manufacturing cells are generally formed based on deterministic product demands. In this paper, we consider a system configuration problem with product demands expressed in a number of probabilistic scenarios. An optimization model integrating cell formation and part allocation is developed to generate a robust system configuration to minimize machine cost and expected inter-cell material handling cost. A two-stage Tabu search based heuristic algorithm is developed to find the optimal or near optimal solutions to the NP-hard problem. Numerical examples show that this model leads to an appropriate compromise between system configuration costs and expected material handling costs to meet the varying product demands. These example problems also show that the proposed algorithm is effective and computationally efficient for small or medium size problems.     

A simulation study of multiple-load-carrying automated guided vehicles in a flexible manufacturing system

Although the technology exists for more advanced applications of automated guided vehicles in flexible manufacturing systems, the current employment of these vehicles in material handling generally subscribes to a simple mode of operation : single-load-carrying capacity for each vehicle and unidirectional traffic on each route of the system. Through a simulation programme, this study investigates the effect of several key factors related to the automated guided vehicles on the overall performance of a flexible manufacturing system. These are the number of pallets allowed in the system, the number of vehicles used and the carrying capacity of each and the input and output queue capacities of the machining stations; finally bidirectional traffic is allowed in some routes. The results show that there is a strong interaction among these factors and reveal their combined effects on the throughput from a small flexible manufacturing system. Upon the user's request, the simulation program also provides the animated colour graphics of the system to view developments concurrently under the selected decision values through time.     

Optimal solution for the flow path design problem of a balanced unidirectional AGV system

An optimal flow path layout (FPL) design method is introduced as a handy tool for an automated guided vehicle (AGV) system planing stage. The problem is analysed and formulated by linear mixed-integer programming. A procedure based on the branch-and-bound depth-first search technique is proposed to solve the FPL problem. The procedure is implemented as an efficient computer program and yields an optimal solution in a small number of iterations. Using the transportation model for calculating the required and optimal flow of empty vehicles, system balance is achieved. Finally, two examples are given. A simple illustrative example is discussed to demonstrate the procedure, and a realistic FPL problem with 23 nodes, 66 arcs and nine pick-up/delivery stations is solved.     

Design of cellular manufacturing system with quasi-dynamic dual resource using multi-objective GA

A new concept is presented in this paper of quasi-dynamic cell formation for the design of a cellular manufacturing system, based on analysing the fact that static and dynamic cell formation could not reflect the real situation of a modern cellular manufacturing system. Further, workforce resources are integrated into quasi-dynamic cell formation and thus a quasi-dynamic dual-resource cell-formation problem is proposed. For solving this problem, this paper first establishes a non-linear mixed integer programming model, where inter-cell and intra-cell material cost, machine relocation cost, worker operation time, loss in batch quality and worker salary are to be minimised. Then, a multi-objective GA is developed to solve this model. Finally, a real life case study is conducted to validate the proposed model and algorithm. The actual operation results show that the case enterprise significantly decreases its material handling cost and workforce number and obviously increases its product quality after carrying out the obtained scheme.     

Determination of optimal AGV fleet size for an FMS

The required number of AGVs necessary to perform a given level of material handling task in an FMS environment is determined using analytical and simulation modelling. The analytical method involves consideration of load handling time, empty travel time, and waiting and blocking time. Load handling time is computed from given system parameters. Determination of empty vehicle travel is difficult due to the inherent randomness of an FMS. Several research studies for this purpose are discussed and a new model is proposed. It entails formulation of a mixed integer programme with an objective of minimizing empty trips. The constraints are in the form of upper and lower bounds placed on the total number of empty trips starting from or ending at a load transfer station. The phenomena of vehicle waiting and blocking are also discussed. The cumulative impact of these three time estimates are then translated into an initial estimate of AGV fleet size as predicted by individual models. The method is applied to an illustrative example. Finally, simulation methodology is used to validate the initial estimates of fleet size. The results indicate that the different models either under-estimate or over-estimate the actual number of vehicles required in the system. The proposed model, though under-estimates the minimum AGV requirement, yet provides results which are close to the simulation results. Hence, it can be used as an analytical tool prior to the simulation phase of AGVS design.     

A neural algorithm for finding the shortest flow path for an automated guided vehicle system

The automated guided vehicle (AGV)system is emerging as the dominant technology to maximize the flexibility of material handling, while increasing the overall productivity of manufacturing operations. This paper presents a new way of finding the shortest flow path for an AGV system on a specific routing structure. An optimal solution of the system is determined by using an approach based on the Hopfield neural network with the simulated annealing (SA) procedure. In other words, the proposed approach reduces the total cost of an AGV delivery path from one workstation to another on the shop floor. By changing the temperature of the two-stage SA, a solution can be found that avoids potential collisions between AGVs. Both the flow path and the potential collision, which are major problems in AGV systems, may be solved simultaneously by the proposed neural network approach. Other advantages offered by the proposed method are its simplicity compared with operations research (OR)methods and a decreased number of needed AGVs. The performance of the approach is also investigated.     

Performance evaluation of tandem and conventional AGV systems using generalized stochastic Petri nets

This paper investigates the different policies and concepts followed in the traffic management of automated guided vehicle (AGV) systems, and develops the controls for automatically eliminating potential vehicle conflicts in an AGV system. The planning of the AGV system is performed in such a way that there are no conflicts or deadlocks for the vehicles using stochastic Petri nets (SPNs). The major effort is devoted to determining the benefits of the tandem AGV control in comparison with the conventional AGV control method. SPNs have been used to model the different designs of flexible manufacturing systems (FMSs) and with different policies for the movement of material, vehicle path control, inventory planning and tool control. The SPN model is solved and the performance of the system can be evaluated. In this study, the effort is directed to model an FMS with two different types of AGV traffic management methods, namely the conventional and tandem AGV control. A SPN program is used which takes the FMS Petri net model as the input and evaluates the different properties of the Petri net. Finally the performance measures are obtained, which helps in evaluating and comparing the two different AGV traffic management methods.     

A price-directed decomposition approach for solving large-scale capacitated part-routing problems

We consider the problem of planning the production steps of several parts through a manufacturing system with both process and routing flexibilities. The problem is formulated as a network flow-based linear programming model which seeks to minimise the total material handling, production, and outsourcing costs subject to satisfying all the part demands and not exceeding any of the machine capacity limits. We develop a price-directed decomposition-based approach that exploits the special structure of the model in order to solve it. An extensive computation experiment is carried out in order to gain some insights into the impacts of flexibility in the manufacturing system on the optimal decision and cost, and to test the efficiency of the procedure in handling large scale problems.