A cellular similarity coefficient algorithm for the design of manufacturing cells

The development and implementation of an integrated system for computer-aided process planning and cellular manufacturing design is described. A coding scheme is proposed for classifying parts according to the manufacturing processes required to produce them. The routeing data and the classification codes obtained from computer-aided process planning are then stored in a database. A heuristic algorithm has also been developed for designing manufacturing cells using the classification data stored in the process planning database. This algorithm is based on a new concept developed in this study called cellular similarity coefficient which considers the similarity between machine cells rather than individual machines, as in the case of other similarity coefficients.     

Scheduling in a cellular manufacturing system: a simulated annealing approach

The problem of scheduling in a cellular manufacturing system is considered with the objective of minimizing the sum of completion times (or total flow time) of jobs. A correct formulation of recursive equation for the flowline-based cellular manufacturing system is first proposed. Subsequently a heuristic is developed to obtain a sequence that minimizes total flow time in a flowline cell. The proposed heuristic makes use of the simulated annealing technique and is developed in two stages. A good initial heuristic seed sequence obtained in the first stage is improved upon by a proposed new variant of the simulated annealing technique wherein three different perturbation schemes have been experimented with. One of the perturbation schemes is newly proposed in this paper and is called the Adjacent interchange scheme. The proposed simulated annealing algorithm has been compared with the existing heuristics for minimizing flow time and has shown consistently good and superior solutions.     

Integrated optimisation of scheduling and ordering based on semi-finished goods delayed differentiation

The implementation of dynamic ordering policies is becoming increasingly important for the competitiveness of modern manufacturing systems. However, existing models on dynamic ordering pay little attention to production scheduling, which greatly affects the fulfilment of dynamic ordering, especially in complex manufacturing systems. Therefore, it is imperative to establish a new model which integrates both dynamic ordering and production scheduling. Accordingly, a quantitative measurement method for integration is needed. To this end, this paper proposes a semi-finished goods delayed differentiation (SFGDD) model by taking into account integration of the scheduling inventory control and dynamic ordering simultaneously. The objective of this model is to study the relationship between the shop floor inventory and the ordering control based on the semi-finished goods dynamic dispatching mechanism. In addition, the days of inventory (DOI) and a backorder penalty exponential function are developed to quantitatively measure such a relationship. To obtain the optimal results, this paper employs a heuristic genetic algorithm (HGA) with a heuristic encoding scheme to synchronise the generation and selection of inventory variables coherently. A case study on a semiconductor assembly and test manufacturing (ATM) is presented, and a significant revenue enhancement and inventory reduction are achieved accordingly.     

A heuristic for scheduling in flowshop and flowline-based manufacturing cell with multi-criteria

The problem of scheduling in flowshop and flowline-based manufacturing cell is considered with the bicriteria of minimizing makespan and total flowtime of jobs, The formulation of the scheduling problems for both the flowshop and the flowline-based manufacturing cell is first discussed. We then present the development of the proposed heuristic for flowshop scheduling. A heuristic preference relation is developed as the basis for the heuristic so that only the potential job interchanges are checked for possible improvement with respect to bicriteria, The proposed heuristic algorithm as well as the existing heuristic are evaluated in a large number of randomly generated large-sized flowshop problems. We also investigate the effectiveness of these heuristics with respect to the objective of minimizing total machine idletime. We then modify the proposed heuristic for scheduling in a cell, and evaluate its performance.     

Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach

The machine-loading problem of a flexible manufacturing system (FMS) has been recognized as one of the most important planning problems. In this research, a Genetic Algorithm (GA) based heuristic is proposed to solve the machine loading problem of a random type FMS. The objective of the loading problems is to minimize the system unbalance and maximize the throughput, satisfying the technological constraints such as availability of machining time, and tool slots. The proposed GA-based heuristic determines the part type sequence and the operation-machine allocation that guarantee the optimal solution to the problem, rather than using fixed predetermined part sequencing rules. The efficiency of the proposed heuristic has been tested on ten sample problems and the results obtained have been compared with those of existing methods.     

智能制造系统AGV小车及缓冲区容量仿真优化

具有MHS(material handling system)的智能制造系统AGV(automated guided vehicle)小车及缓冲区最大容量配置优化,属于典型的非线性整数规划问题。由于约束无法用封闭形式表达,因此较难获得问题的精确解。为此,本文提出了仿真优化的方法以获得问题的近似解。首先,对AGV小车及缓冲区最大容量配置优化问题进行了描述;其次,基于Em plant平台建立了具有MHS的智能制造系统仿真模型;然后,基于不同的优化目标,提出了3种仿真优化方法;最后,通过仿真试验对上述3种方法进行了分析与比较。分析表明,本文提出的方法及优化结果,可为企业配置AGV小车及缓冲区最大容量提供决策支持。     

Heuristics and sequence-dependent set-up jobs in flow line cells

A key operational issue in cellular manufacturing systems is the scheduling of jobs within a family at each cell. Sequence-dependent set-up times during changeovers from one job to another afford scope for the exploitation of similarities at this stage to minimize the time spent on set-ups. This paper proposes heuristics for scheduling jobs within a part family by identifying subfamilies and sequencing them to improve the use of machines within a cell and to reduce the tardiness as well as the number of tardy jobs. The proposed heuristic, when evaluated by comparison with existing benchmark heuristics, yielded encouraging results.     

Cellular manufacturing system design using grouping efficacy-based genetic algorithm

This paper presents an algorithm for the design of manufacturing cells and part families. This algorithm is suitable for arriving at a good block diagonal structure for a cellular manufacturing design problem with part machine incidence matrix as input. The objective of this algorithm is the maximisation of grouping efficacy (GE), which is one of the most widely used measures of quality for cellular configurations. Assignment of machines to cells is using genetic algorithm, and part assignment heuristic is based on an effective customised rule. A comparison of the proposed algorithm is made with seven other methods of cell formation by taking 36 problems from the literature and found that the proposed algorithm is performing much better than the others. Finally, the algorithm is extended to form configurations with good GE when there are alternative routes.     

Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system

This paper presents a similarity coefficient based approach to the problem of machine-component grouping. The proposed method incorporates relevant production data such as part type production volume, routing sequence and unit operation time in the early stages of grouping decisions for cellular manufacturing. The algorithm also suggests a methodology for evaluating alternative solutions from different algorithms on a quantitative basis using a modified version of an existing coefficient. The modified quantitative measure is a comprehensive indicator for the goodness of a grouping solution. The algorithm then identifies bottleneck machines and corresponding cell candidates for their duplication using percentage utilization in each cell as a criterion. Finally, additional constraints can be applied to determine the best grouping solution among alternative solutions generated by the algorithm. A software package has been developed to verify the implementation.     

Capability-based distributed layout approach for virtual manufacturing cells

It is shown in the literature that in highly volatile manufacturing environments functional job shops and classical cellular manufacturing systems do not perform well. Classical cellular manufacturing systems are very sensitive to changing production requirements due to their limited flexibility. In order to adapt cellular manufacturing systems to volatile manufacturing environments, the virtual cellular manufacturing concept was proposed in the 1980s by the National Bureau of Standards in USA. This concept is similar to group technology where job families are processed in manufacturing cells. The main difference between a virtual cell and the classic cell is in the dynamic nature of the virtual manufacturing cell; whereas the physical location and identity of classic cell is fixed, the virtual cell is not fixed and will vary with changing production requirements. The virtual manufacturing cell concept allows the flexible reconfiguration of shop floors in response to changing requirements. In the literature, the formation and scheduling process of virtual cells are clearly explained and researched in detail. However, the layout issue is not addressed entirely. Virtual cells are generally formed over functionally divided job shops. Forming virtual cells over a functional layout may adversely affect the performance of a virtual cellular manufacturing system. There is a need to search for different layout strategies in order to enhance the performance. The distributed layout approach may be a better alternative for virtual cellular manufacturing applications. In this research paper, a novel capability-based approach is proposed for the design of distributed layouts. A simulated annealing based heuristic algorithm is developed from the distributed layout. The proposed approach is tested with a problem with real data. An example is also shown in order to give an idea about the superiority of a capability-based distributed layout over the functional layouts in forming virtual manufacturing cells.     

A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems

The dynamic cellular facility layout problem (DCFLP) is a well-known NP-hard problem. It has been estimated that the efficient design of DCFLP reduces the manufacturing cost of products by maintaining the minimum material flow among all machines in all cells, as the material flow contributes around 10–30% of the total product cost. However, being NP hard, solving the DCFLP optimally is very difficult in reasonable time. Therefore, this article proposes a novel similarity score-based two-phase heuristic approach to solve the DCFLP optimally considering multiple products in multiple times to be manufactured in the manufacturing layout. In the first phase of the proposed heuristic, a machine–cell cluster is created based on similarity scores between machines. This is provided as an input to the second phase to minimize inter/intracell material handling costs and rearrangement costs over the entire planning period. The solution methodology of the proposed approach is demonstrated. To show the efficiency of the two-phase heuristic approach, 21 instances are generated and solved using the optimization software package LINGO. The results show that the proposed approach can optimally solve the DCFLP in reasonable time.     

IFSJSP: A novel methodology for the Job-Shop Scheduling Problem based on intuitionistic fuzzy sets

The Job-Shop Scheduling Problem (JSP) is an important concern in advanced manufacturing systems. In real applications, uncertainties exist practically everywhere in the JSP, ranging from engineering design to product manufacturing, product operating conditions and maintenance. A variety of approaches have been proposed to handle the uncertain information. Among them, the Intuitionistic Fuzzy Sets (IFS) is a novel tool with the ability to handle vague information and is widely used in many fields. This paper develops a method to address the JSP under an uncertain environment based on IFSs. Another contribution of this paper is to put forward a generalised (or extended) IFS to process the additive operation and to compare the operation between two IFSs. The methodology is illustrated using a three-step procedure. First, a transformation is constructed to convert the uncertain information in the JSP into the corresponding IFS. Secondly, a novel addition operation between two IFSs is proposed that is suitable for the JSP. Then a novel comparison operation on two IFSs is presented. Finally, a procedure is constructed using the chromosome of an operation-based representation and a genetic algorithm. Two examples are used to demonstrate the efficiency of the proposed method. In addition, a comparison between the results of the proposed IFSJSP and other existing approaches demonstrates that IFSJSP significantly outperforms other existing methods.     

Designing integrated cellular manufacturing systems with tactical decisions

In this paper, we aim to design cellular manufacturing systems that optimize the performance of a manufacturing system subject to the optimization aspects of production planning. Consequently, the demand for each part – one of the production planning features – plays a vital role in determining the part families and the formation of machine cells in each period. In our study, holding and backorder costs follow a probabilistic structure, and they are described by a set of stochastic scenarios. In this model, five objective functions are employed: one of them minimizes the expected total holding and backorder costs in order to evaluate the risk in the model. The aim of this model is to select and optimize the assignment of parts and machines to different cells as well as the number of each produced part in each period. A new heuristic algorithm based on the optimization method is established to yield the best solution for this complicated mathematical formulation. Further, the performance of the proposed algorithm is verified using certain test problems in which the obtained results are compared with those obtained using the branch-and-bound algorithm and heuristic procedures.     

Continuous-time algorithms for scheduling a single machine with sequence-dependent setup times and time window constraints in coordinated chains

In this paper we address the problem of selecting and scheduling several jobs on a single machine with sequence-dependent setup times and strictly enforced time window constraints on the start time of each job. We use short-term production targets to coordinate decentralised local schedulers and to make the objectives of specific areas in line with the chain objectives by maintaining a desired work in process profile in manufacturing environments. The existing literature in this domain is based on discrete-time approaches. We depart from prior approaches by considering continuous time. We introduce a two-step mathematical programming model based on disjunctive constraints to solve small problems to optimality, and propose an insertion-based heuristic to solve large-scale instances. We provide several variations of the insertion heuristic based on different score functions. The primary objective of these approaches is to maximise the total defined score for jobs while satisfying production targets for families of jobs in each shift. Further, our models minimise the maximum completion time of all selected jobs. The effectiveness, efficiency, and robustness of the proposed algorithms are analysed and compared with the existing literature.     

A heuristic for designing manufacturing focus units with resource considerations

This paper presents a model of the plant-within-a-plant (PWP) design problem and demonstrates a heuristic for analysing the problem. Although the benefits of a manufacturing focus have been articulated in the literature, methods for implementation with consideration for resource requirements have not been developed previously. In this study, we discuss the importance of including resource considerations and propose a methodology that can help managers arrive at a facility design with a high degree of focus and minimum resource needs. A heuristic is developed that incorporates the concept of order-winning criteria and volume into the focus design. The heuristic not only recognises the effects of conflicting manufacturing tasks, but also considers resource costs and material flows between PWP units. Experimental results show that the proposed methodology offers managers the opportunity to generate and assess alternative PWP designs, which are otherwise unavailable. Overall, this research provides an analytical framework for further research in focused manufacturing.     

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.     

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.     

A HIERARCHICAL DIVISIVE CLUSTERING METHOD FOR MACHINE-COMPONENT GROUPING PROBLEMS

This paper proposes a new approach based on graph theory for the machine-component grouping problem in a cellular manufacturing system. The objective is to form components into part families such that the degree of interrelations is high among components within the same part family and low between components of different part families.

Since finding an optimal solution through total enumeration is prohibitive in terms of time and efforts even for problems with a moderate number of components, a heuristic algorithm is proposed. The algorithm is hierarchical and divisive in nature and illustrated with numerical examples.     

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.     

A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.