Performance evaluation of an automated material handling system for a wafer fab

Discrete-event simulation model was developed to evaluate the performance of an automated material handling system (AMHS) for a wafer fab with a zone control scheme avoiding all vehicle collision. The layout of this AMHS is a custom configuration. The track option contains turntables, turnouts and high-speed express lanes. The behavior of the interarrival for all stockers from the real data set was analyzed to verify the assumption of the simulation model. The results show that the underlying distributions of most stockers for interarrival times belong to the exponential or Weibull distribution. The simulation results show that the number of vehicles significantly affects the average delivery time and the average throughput. A simple one-factor response surface model is used to determine the appropriate vehicle numbers. This study was also investigated to determine the vehicle numbers in an automated guided vehicle-based intrabay material handling system.     

An integrated model and a decomposition-based approach for concurrent layout and material handling system design

This paper presents an integer programming formulation that integrates decisions concerning the layout of the resource groups on the shop floor with the design of the material handling system. The model reflects critical practical concerns, including the capacity of the material flow network and of the handling transporters, as well as the tradeoff between fixed (construction and acquisition) and variable (operational) costs. For realistic industrial cases, the size of the problem prevents the solution using explicit or implicit enumeration methods. Instead, the global model is decomposed into standard optimization problems: quadratic assignment, fixed charge capacitated network design, and non-depot distance-constrained vehicle routing. An integrated solution method, guided by a simulated annealing scheme, solves the global shop design problem. The algorithm takes advantage of the proposed decomposition and converges to a final design which is feasible with respect to all problem constraints. The method is applied to redesign the facility of a large manufacturer of radar antennas. The resulting shop configuration exhibits substantially decreased material handling effort, and requires significantly smaller investment costs compared to the existing facility.     

Multi-objective real-time dispatching for integrated delivery in a Fab using GA based simulation optimization

In a wafer fabrication Fab, the “integrated delivery”, which integrates the automated material handling system (AMHS) with processing tools to automate the material flow, is difficult to implement due to the system complexity and uncertainty. The previous dispatching studies in semiconductor manufacturing have mainly focused on the tool dispatching. Few studies have been done for analyzing combinatorial dispatching rules including lot dispatching, batch dispatching and automated guided vehicle (AGV) dispatching. To handle this problem, a GA (genetic algorithm) based simulation optimization methodology, which consists of the on-line scheduler and the off-line scheduler, is presented in this paper. The on-line scheduler is used to monitor and implement optimal combinatorial dispatching rules to the semiconductor wafer fabrication system. The off-line scheduler is employed to search for optimal combinatorial dispatching rules. In this study, the response surface methodology is adopted to optimize the GA parameters. Finally, an experimental bay of wafer fabrication Fab is constructed and numerical experiments show that the proposed approach can significantly improve the performance of the “integrated delivery system” compared with the traditional single dispatching rule approach.     

Heuristic transporter routing model for manufacturing facility design

In this study, a transporter routing problem is analyzed and adapted for use in manufacturing facility design. Given fixed facility layout and predetermined material flow paths, this study determines the minimum number of transporters required to transfer material within a given manufacturing facility with minimal handling effort. The manufacturing facility design problem is particularly complex and involves the sub-problems such as design of the material network and the transporter routing problem, which provides the fleet size and the routing of each transporter over the flow network. The problem is formulated as an integer program. To solve the problem, we used a heuristic and integrated vehicle routing model. We also developed a heuristic solution program and several tests along with an industrial example to indicate the effectiveness of this method.     

A fast two-stage hybrid meta-heuristic algorithm for robust corridor allocation problem

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before.     

Vehicle positioning in cell manufacturing systems via robust optimization

Numerous manufacturing companies are taking advantage of material handling systems due to their flexibility, reliability, safety and contribution to the increase of productivity. However, several uncertain parameters such as types of cost, availability of vehicle etc., influence the performance of the material handling system greatly. In recent years, robust optimization has proven to be an effective methodology permitting overcoming uncertainty in optimization models. Robust optimization models work well even when probabilistic knowledge of the phenomenon is incomplete. This paper thus proposes two new zero-one programming (ZOP) models for vehicle positioning in multi-cell automated manufacturing system. Uncertain parameters in these models include cost parameters, travel time between each pair of centers of cells and location of machines, average time required for performing all transports from location of machines and availability of the vehicle. Then, the robust counterpart of the proposed ZOP models is presented by using the recent extensions in robust optimization theory. Eventually, to verify the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic ZOP model for different test problems.     

Integer linear programming formulation of the vehicle positioning problem in automated manufacturing systems

This paper addresses the problem of vehicle location (positioning) for automated transport in fully automated manufacturing systems. This study is motivated by the complexity of vehicle control found in modern integrated circuit semiconductor fabrication facilities where the material handling network path is composed of multiple loops interconnected. In order to contribute to decrease the manufacturing lead-time of semiconductor products, we propose an integer linear program that minimizes the maximum time needed to serve a transport request. Computation experiments are based on real-life data. We discuss the practical usefulness of the mathematical model by means of a simulation experiment used to analyze the factory operational behavior.     

Solving an extended multi-row facility layout problem with fuzzy clearances using GA

Multi-row facility layout problem (MRFLP) is a class of facility layout problems, which decides upon the arrangement of facilities in some fixed numbers of rows in order to minimize material handling cost. Nowadays, according to the new layout requirements, the facility layout problems (FLPs) have many applications such as hospital layout, construction site layout planning and layout of logistics facilities. Therefore, we study an extended MRFLP, as a novel layout problem, with the following main assumptions: 1) the facilities are arranged in a two-dimensional area and without splitter rows, 2) multiple products are available, 3) distance between each pair of facilities, due to inaccurate and flexible manufacturing processes and other limitations (such as WIPs, industrial instruments, transportation lines and etc.), is considered as fuzzy number, and 4) the objective function is considered as minimizing the material handling and lost opportunity costs. To model these assumptions, a nonlinear mixed-integer programming model with fuzzy constraints is presented and then converted to a linear mixed-integer programming model. Since the developed model is an NP-hard problem, a genetic algorithm approach is suggested to find the best solutions with a minimum cost function. Additionally, three different crossover methods are compared in the proposed genetic algorithm and finally, a sensitivity analysis is performed to discuss important parameters.     

A flow allocation strategy for routing over multiple flow classes with an application to air cargo terminals

Advances of information technology have enabled the utilization of automated material handling systems in the logistics industry. The increasing costs of labor in developing countries have accelerated this trend. Major cargo terminals are now installing more and more integrated automated shipment handling systems in order to increase their operational efficiency which can be measured by the average shipping times or the facility throughput, for example. Routing is clearly an important decision category that has significant impact on the operational efficiency. In this paper, motivated by a project with one of the busiest air cargo terminals in the world, we investigate a routing optimization problem for multiple flow classes with different levels of priority. We propose a flow allocation (FA) routing strategy in which when a shipment arrives at a decision point, a set of allocation ratios will be employed to direct it to the next location. These ratios are determined by solving a mathematical model that explicitly considers the congestion effect and the characteristics of the multi-commodity network. Comprehensive simulation experiments demonstrate that the proposed FA routing strategy significantly outperforms the one currently in use.     

A revised electromagnetism-like mechanism for layout design of reconfigurable manufacturing system

The layout design problem is one of the most important issues for manufacturing system design and control. A revised electromagnetism-like mechanism (REM) is proposed in this paper for the layout design of reconfigurable manufacturing systems utilizing automated guided vehicle. First, the formal model considering both loaded and empty flows is given. Then the REM is developed to solve the proposed model. In the REM, particles are encoded discretely. The charge of a particle is calculated according the total material handling cost of the particle. In the local search procedure, variable neighbourhood search strategy based on Hamming distance is adopted. In the moving procedure, the particles are moved according to the ordering of each element. To verify the effect of the proposed method, several computation cases are carried out. The computation results show that the proposed method is able to get optimal solutions for small scale problems and near optimal solutions within limited computation time for large scale problems. This indicates that the proposed method is effective and efficient.     

Multimodal processes optimization subject to fuzzy operation time constraints: declarative modeling approach

We present an extension of the resource-constrained multi-product scheduling problem for an automated guided vehicle (AGV) served flow shop, where multiple material handling transport modes provide movement of work pieces between machining centers in the multimodal transportation network (MTN). The multimodal processes behind the multi-product production flow executed in an MTN can be seen as processes realized by using various local periodically functioning processes. The considered network of repetitively acting local transportation modes encompassing MTN’s structure provides a framework for multimodal processes scheduling treated in terms of optimization of the AGVs fleet scheduling problem subject to fuzzy operation time constraints. In the considered case, both production takt and operation execution time are described by imprecise data. The aim of the paper is to present a constraint propagation (CP) driven approach to multi-robot task allocation providing a prompt service to a set of routine queries stated in both direct and reverse way. Illustrative examples taking into account an uncertain specification of robots and workers operation time are provided.     

一种多目标不等面积设施布局问题的启发式算法

多目标不等面积设施布局问题（UA-FLP）是将一些不等面积设施放置在车间内进行布局,要求优化多个目标并满足一定的限制条件。以物料搬运成本最小和非物流关系强度最大来建立生产车间的多目标优化模型,并提出一种启发式算法进行求解。算法采用启发式布局更新策略更新构型,通过结合基于自适应步长梯度法的局部搜索机制和启发式设施变形策略来处理设施之间的干涉性约束。为了得到问题的Pareto最优解集,提出了基于Pareto优化的局部搜索和基于小生境技术的全局优化方法。通过两个典型算例对算法性能进行测试,实验结果表明,所提出的启发式算法是求解多目标UA-FLP的有效方法。     

Towards measuring the effectiveness of a facilities layout

Performance measurement models are essential to support various decision making problems that may arise during life cycle of a facilities layout. Available models are only suitable for early stages in the design phase of life cycle. However, measurement models have a great consequence in other phases also such as production planning, control and when modification to be incorporated due to the changes in market demand, which happens very often in today's global competition. In addition, the existing models have considered only material handling cost as the performance measurement factor. Nevertheless, the empty travel of material handling equipment, layout flexibility and area utilisation have a significant contribution towards the layout effectiveness. It is therefore necessary to have a measurement model to determine the facilities layout's effectiveness by considering all significant factors. A measurement model considering a set of three layout effectiveness factors—facilities layout flexibility (FLF), productive area utilisation (PAU) and closeness gap (CG)—is developed in our research. The proposed model will enable the decision-maker of a manufacturing enterprise to analyse a layout in three different aspects, based on which they can make decision towards productivity improvement. This paper mainly discusses about the measurement of the CG. The CG is developed with respect to the objective of bringing closer the highly interactive facilities/departments. The CG presented in this paper extends other related works by incorporating numerous aspects of layout that include empty travel of material handling equipment, information flow, personnel flow and equipment flow.     

Deadlock Resolution in Automated Manufacturing Systems With Robots

An automated manufacturing system (AMS) contains a number of versatile machines (or workstations), buffers, and an automated material handling system (MHS). The MHS can be an automated guide vehicle (AGV) system, and/or a system that consists of multiple robots. Deadlock resolution in AMS is an important issue. For the AMS with an AGV system as MHS, the problems of deadlock resolution for part processing process and AGV system as an integrated system has been studied. It is shown that AGVs can serve as both material handling devices and central buffers at the same time to help resolve deadlocks. For AMS with robots as MHS, the existing work treated the robots just as material handling devices and showed that the robots had contribution to deadlock. In this paper, such AMS is modeled by resource-oriented Petri nets. Contrary to the existing work, it is shown that the robots have no contribution to deadlock by adopting such nets to control AMS. More interestingly, they can be used to resolve deadlock by serving as temporary part storage devices. A new deadlock control policy is proposed by treating robots as both material handling devices and buffers. The new policy outperforms the existing ones.     

A robust model for multi-floor layout problem

Multi-floor layout problem (MFLP) can be categorized as one of the most crucial basic problems in the real world especially in the field of plant layout design. Specially, the aim of this problem is to find the locations of each department, storage, and elevator in the floors of a multi-floor building and without overlapping to minimize the total material handling cost. In order to consider practical issues in theory and presenting an applicable problem, for the first time in the published literature for MFLP, we develop a robust mathematical model indiscrete and uncertain environment in which some parameters can be changed in predetermined intervals. In fact, one of the most considerable factors is material flow among departments and storages which is assumed to have uncertain values rather than fixed amounts. In this study, we develop a Mixed Integer Programming (MIP) robust model for a new form of MFLP, we called MFDLP where the cellar is considered to contain main storages and upper floors in which departments will be located in predetermined locations. Moreover, there is an elevator set for transferring materials between floors and should be located without overlapping with departments and storages. Finally, the presented model is tested and assessed on some problems and its efficiency is shown.     

Solving the design of distributed layout problem using forecast windows: A hybrid algorithm approach

In today’s competitive environment, manufacturing facilities have to be more responsive to the frequent changes in product mix and demand by realigning their organizational structure for minimizing material handling cost. However, manufacturing firms are reluctant to modify the layout as it leads to operation disruption and excess rearrangement cost. In this paper, we present an alternative approach for designing a multi-period layout (i.e., distributed layout) that maintains a tradeoff between re-layout cost and cost of excess material handling. Obtaining an optimal solution to distributed layout problem is generally a difficult task, owing to larger size of quadratic assignment problem. In order to overcome the aforementioned drawback, a meta-heuristic, named ‘CSO-DLP’ (Clonal Symbiotic Operated-Distributed Layout Planning) is developed for designing a distributed layout that jointly determines the arrangement of department and flow allocation among them. It inherits its trait from Symbiotic algorithm and Clonal algorithm. In addition to these; the concept of ‘forecast window’ is used, which evaluates the layout for varying number of periods at a given time. The proposed meta-heuristic is applied on a benchmark dataset and the effect of system parameters, such as rearrangement cost, department disintegration, and duplication are investigated and benchmarked in this paper.     

A genetic algorithm with the heuristic procedure to solve the multi-line layout problem

The paper presents a genetic algorithm-based meta-heuristic to solve the facility layout problem (FLP) in a manufacturing system, where the material flow pattern of the multi-line layout is considered with the multi-products. The matrix encoding technique has been used for the chromosomes under the objective of minimizing the total material handling cost. The proposed algorithm produces a table with the descending order of the data corresponding to the input values of the flow and cost data. The generated table is used to create a schematic representation of the facilities, which in turn is utilized to heuristically generate the initial population of the chromosomes and to handle the heuristic crossover and mutation operators. The efficiency of the proposed algorithm has been proved through solving the two examples with the total cost less than the other genetic algorithms, CRAFT algorithm, and entropy-based algorithm.     

Dynamic vehicle allocation control for automated material handling system in semiconductor manufacturing

The current study examines the dynamic vehicle allocation problems of the automated material handling system (AMHS) in semiconductor manufacturing. With the uncertainty involved in wafer lot movement, dynamically allocating vehicles to each intrabay is very difficult. The cycle time and overall tool productivity of the wafer lots are affected when a vehicle takes too long to arrive. In the current study, a Markov decision model is developed to study the vehicle allocation control problem in the AMHS. The objective is to minimize the sum of the expected long-run average transport job waiting cost. An interesting exhaustive structure in the optimal vehicle allocation control is found in accordance with the Markov decision model. Based on this exhaustive structure, an efficient algorithm is then developed to solve the vehicle allocation control problem numerically. The performance of the proposed method is verified by a simulation study. Compared with other methods, the proposed method can significantly reduce the waiting cost of wafer lots for AMHS vehicle transportation.     

Facility layout using weighted association rule-based data mining algorithms: Evaluation with simulation

Facility layout has considerable effects on the operational productivity and efficiency of a facility because of its direct effect on material handling costs. The objective of this study is to propose new weighted association rule-based data mining approaches for facility layout problem. Classic association rule-based approaches assume that each item has the same level of significance. On the other hand, in weighted association rule-based approaches, each item is assigned a weight according to its significance with respect to some user defined criteria. In this study, different weighted association rule-based data mining approaches, namely MINWAL(O), MINWAL(W), WARM and BWARM, are applied to facility layout problem. To confirm the viability of the proposed approaches, two case studies are presented. The approaches are compared in terms of general performance criteria for the facility layout problems using simulation. This is the first study that applies weighted association rule-based data mining approaches to facility layout problem. To address the needs in practice, “demand”, “part handling factor” and “efficiency of material handling equipment” are used as the weighting criteria. Then, this study differs from the previous works in that it considers the three key location factors together.     

Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems

Facility layout design (FLD) has a very important effect on the performance of a manufacturing system. The concept of FLD is usually considered as a multiobjective problem. For this reason, a layout generation and its evaluation are often challenging and time consuming due to their inherent multiple objectives in nature and their data collection process. In addition, an effective facility layout evaluation procedure necessitates the consideration of qualitative criteria, e.g., flexibility in volume and variety and quality related to the product and production, as well as quantitative criteria such as material handling cost, adjacency score, shape ratio, and material handling vehicle utilization in the decision process. This paper presents a decision-making methodology based on data envelopment analysis (DEA), which uses both quantitative and qualitative criteria, for evaluating FLD. The criteria that are to be minimized are viewed as inputs whereas the criteria to be maximized are considered as outputs. A computer-aided layout-planning tool, VisFactory, is adopted to facilitate the layout alternative design process as well as to collect quantitative data by using exact and vague data by means of fuzzy set theory. Analytic hierarchy process (AHP) is then applied to collect qualitative data related to quality and flexibility. The DEA methodology is used to solve the layout design problem by simultaneously considering both the quantitative and qualitative data. The purposed integrated procedure is applied to a real data set of a case study, which consists of 19 FLDs provided of the plastic profile production system.