A PSO-based approach to cell formation problems with alternative process routings

Group technology (GT) has been extensively applied to cellular manufacturing system (CMS) design for decades due to many benefits such as decreased number of part movements among cells and increased machine utilisation in cells. This paper considers cell formation problems with alternative process routings and proposes a discrete particle swarm optimisation (PSO) approach to minimise the number of exceptional parts outside machine cells. The approach contains two main steps: machine partition and part-routing assignment. Through inheritance and random search, the proposed algorithm can effectively partition machines into different cells with consideration of multiple part process routings. The computational results are compared with those obtained by using simulated annealing (SA)-based and tabu search (TS)-based algorithms. Experimental results demonstrate that the proposed algorithm can find equal or fewer exceptional elements than existing algorithms for most of the test problems selected from the literature. Moreover, the proposed algorithm is further tailed to incorporate various production factors in order to extend its applicability. Four sample cases are tested and the results suggest that the algorithm is capable of solving more practical cell formation problems.     

Cellular manufacturing systems design with routing flexibility,machine procurement,production planning and dynamic system reconfiguration

This paper investigates the problem of designing cellular manufacturing systems with multi-period production planning, dynamic system reconfiguration, operation sequence, duplicate machines, machine capacity and machine procurement. An important aspect of this problem is the introduction of routing flexibility in the system by the formation of alternate contingency process routings in addition to alternate main process routings for all part types. Contingency routings serve as backups so as to effectively address the reality of part process routing disruptions (in the main routings) owing to machine breakdowns and allow the cellular manufacturing system to operate in a continuous manner even in the event of such breakdowns. The paper also provides in-depth discussions on the trade-off between the increased flexibility obtained versus the additional cost to be incurred through the formation of contingency routings for all parts. Some sensitivity analysis is also performed on some of the model parameters. The problem is modelled and solved through a comprehensive mixed integer programming formulation. Computational results presented by solving some numerical examples show that the routing and process flexibilities can be incorporated within the cellular manufacturing system design without significant increase in the system cost.     

Cycle time analysis in reentrant robotic cells with swap ability

In this paper, reentrant robotic cells composed of a robot with swap ability for a single part type is studied. In this regard, we restrict our attention to the two-machine reentrant cell scheduling problem. In the cell, a part visits the machines at least once. To minimise the cycle time, the sequence of robot moves must be optimised. Based on our previous study on the optimality of robotic cells, we restrict our investigation to only one-unit cycles. Therefore, all one-unit cycles are generated and their cycle time formulae are developed. Initially, we determine both essential and sufficient conditions for different cycles to be optimal and present the regions of optimality when each part reenters the first machine two times. We determine optimality conditions for different cycles when each part reenters both machines two times. Finally, sensitivity analysis is performed for both cases and the influence of the parameter values in the regions of optimality for each cycle is shown.     

MOCACEF 1.0: Multiple objective capability based approach to form part-machine groups for cellular manufacturing applications

A pre-emptive goal programming formulation is developed for concurrently forming independent part/machine cells. Machine independent capability units, which are known as Resource Elements (RE), are used to define processing requirements of parts and processing capabilities of machine tools. Representation of unique and shared capability boundaries of machine tools is possible via RE, which increases the opportunity to form independent manufacturing cells and efficient utilization of them. RE-based operation sequences, processing times, capacities, demand, cell sizes, cell flexibility, load balance between cells, cell interaction, copies of each machine type in the job shop are all considered in the problem formulation. The model is solved by a specially developed tabu search algorithm.     

Machine cell formation using a mathematical model and a genetic-algorithm-based heuristic

This paper presents a comprehensive mathematical model and a genetic-algorithm-based heuristic for the formation of part families and machine cells in the design of cellular manufacturing systems. The model incorporates dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing among cells, operation cost, subcontracting cost, tool consumption cost, set-up cost and other practical constraints. To solve this model efficiently, a two-phase genetic-algorithm-based heuristic was developed. In the first phase, independent cells are formed which are relatively simple to generate. In the second phase, the solution found during the first phase is gradually improved to generate cells optimizing inter-cell movement and other cost terms of the model. A number of numerical examples of different sizes are presented to demonstrate the computational efficiency of the heuristic developed.     

A scatter search approach with dispatching rules for a joint decision of cell formation and parts scheduling in batches

A joint decision of cell formation and parts scheduling is addressed for a cellular manufacturing system where each type of machine and part may have multiple numbers and parts must require processing and transferring in batches. The joint decision problem is not only to assign batches and associated machine groups to cells, but also to sequence the processing of batches on each machine in order to minimise the total tardiness penalty cost. A nonlinear mixed integer programming mathematical model is proposed to formulate the problem. The proposed model, within nonlinear terms and integer variables, is difficult to solve efficiently for real size problems. To solve the model for practical purposes, a scatter search approach with dispatching rules is proposed, which considers two different combination methods and two improvement methods to further expand the conceptual framework and implementation of the scatter search so as to better fit the addressed problem. This scatter search approach interactively uses a combined dispatching rule to solve a scheduling sub-problem corresponding to each integer solution visited in the search process. A computational study is performed on a set of test problems with various dimensions, and computational results demonstrate the effectiveness of the proposed approach.     

Automatic clustering for generalised cell formation using a hybrid particle swarm optimisation

This paper considers the cell formation (CF) problem in which parts have alternative process routings and the number of machine cells is not known a priori. Very few studies address these two practical issues at the same time. This paper proposes an automatic clustering approach based on a hybrid particle swarm optimisation (PSO) algorithm that can automatically evolve the number and cluster centres of machine cells for a generalised CF problem. In the proposed approach, a solution representation, comprising an integer number and a set of real numbers, is adopted to encode the number of cells and machine cluster centres, respectively. Besides, a discrete PSO algorithm is utilised to search for the number of machine cells, and a continuous PSO algorithm is employed to perform machine clustering. Effectiveness of the proposed approach has been demonstrated for test problems selected from the literature and those generated in this study. The experimental results indicate that the proposed approach is capable of solving the generalised machine CF problem without predetermination of the number of cells.     

Grouping and placement of machine cells

We consider a real-world machine grouping and layout problem in which the objective is not only to identify machine cells and corresponding part families but also to determine a near-optimal layout of machines within each cell and the cells themselves. The three-stage approach developed incorporates materials flow considerations while determining machine groups, considers alternative process plans, exploits part routing information and allows the user to perform sensitivity analysis. The menu-driven, interactive program has been applied to four test problems and an industrial problem. Our experience with applying the approach to the industrial problem is also reported.     

Dynamic routing and the performance of automated manufacturing cells

There has been a considerable amount of research done on the “cell formation” problem, in which machining cells are designed to process a family of components. More recently, it has been suggested that machining cells should be designed so that they take advantage of the flexibility for processing parts that have alternate, or multiple machine routing possibilities. It is argued that such flexibility will improve machine utilization as well as other measures of cell performance and may reduce the need for centralized cell loading and scheduling algorithms. Unfortunately, if the cell is automated, routing flexibility requirements can create a complex control problem for the cell controller. In this paper we implement a cell controller designed to handle the requirements of the flexible routing of parts and compare the performance of the cell to the case in which each part has only one routing. We find that significant improvements occur when the cell design is capable of processing parts with flexible routings.     

Dynamic scheduling for complex engineer-to-order products

The current paper considers dynamic production scheduling for manufacturing systems producing products with deep and complex product structures and complicated process routings. It is assumed that manufacturing and assembly processing times are deterministic. Dynamic scheduling problems may be either incremental (where the schedule for incoming orders does not affect the schedule for existing orders) or regenerative (where a new schedule is produced for both new and existing orders). In both situations, a common objective is to minimize total costs (the sum of work-in-progress holding costs, product earliness and tardiness costs). In this research, heuristic and evolutionary-strategy-based methods have been developed to solve incremental and regenerative scheduling problems. Case studies using industrial data from a company that produces complex products in low volume demonstrate the effectiveness of the methods. Evolution strategy (ES) provides better results than the heuristic method, but this is at the expense of significantly longer computation times. It was found that performing regenerative planning is better than incremental planning when there is high interaction between the new orders and the existing orders.     

Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing

This paper deals with the design and loading of Cellular Reconfigurable Manufacturing Systems in the presence of alternative routing and multiple time periods. These systems consist of multiple reconfigurable machining cells, each of which has Reconfigurable Machine Tools and Computer Numerical Control (CNC) machines. Each reconfigurable machine has a library of feasible auxiliary machine modules for achieving particular operational capabilities, while each CNC machine has an automatic tool changer and a tool magazine of a limited capacity. The proposed approach consists of two phases: the machine cell design phase which involves the grouping of machines into machine cells, and the cell loading phase that determines the routing mix and the tool and module allocation. In this paper, the cell design problem is modelled as an Integer Linear Programming formulation, considering the multiple process plans of each part type as if they were separate part types. Once the manufacturing cells are formed, a Mixed Integer Linear Programming model is developed for the cell loading problem, considering multi-period demands for the part types, and minimising transportation and holding costs while keeping the machine and cell utilisations in each period, and the system utilisation across periods, approximately balanced. An illustrative problem and experimental results are presented.     

An agent-based negotiation approach to integrate process planning and scheduling

This paper presents the development of an agent-based negotiation approach to integrate process planning and scheduling (IPPS) in a job shop kind of flexible manufacturing environment. The agent-based system comprises two types of agents, part agents and machine agents, to represent parts and machines respectively. For each part, all feasible manufacturing processes and routings are recorded as alternative process plans. Similarly, alternative machines for an operation are also considered. With regard to the scheduling requirements and the alternative process plans of a part, the proposed agent-based IPPS system aims to specify the process routing and to assign the manufacturing resources effectively. To establish task allocations, the part and machine agents have to engage in bidding. Bids are evaluated in accordance with a currency function which considers an agent's multi-objectives and IPPS parameters. A negotiation protocol is developed for negotiations between the part agents and the machine agents. The protocol is modified from the contract net protocol to cater for the multiple-task and many-to-many negotiations in this paper. An agent-based framework is established to simulate the proposed IPPS approach. Experiments are conducted to evaluate the performance of the proposed system. The performance measures, including makespan and flowtime, are compared with those of a search technique based on a co-evolutionary algorithm.     

The machine loading and tool allocation problem in a flexible manufacturing system

In this paper the machine loading and tool allocation problem of an FMS is discussed, A mathematical model is developed to determine the routings of parts through the machines and to allocate appropriate cutting tools to each machine to achieve minimum overall machining cost. Computational experience with this model is presented under various system and operation parameter values. Computational refinements based on lagrangean relaxation are also discussed.     

Efficient algorithm for cell formation with sequence data,machine replications and alternative process routings

Cell formation is an important problem in the design of a cellular manufacturing system. Despite a large number of papers on cell formation being published, only a handful incorporate operation sequence in intercell move calculations and consider alternative process routings, cell size, production volume and allocating units of identical machines into different cells. Modelling the above factors makes the cell formation problem complex but more realistic. The paper develops a model and solution methodology for a problem of cell formation to minimize the sum of costs of intercell moves, machine investment and machine operating costs considering all the factors mentioned above. An algorithm comprised of simulated annealing and local search heuristics has been developed to solve the model. A limited comparison of the proposed algorithm with an optimal solution generated by complete enumeration of small problems indicates that the algorithm produces a solution of excellent quality. Large problems with 100 parts and 50 machine types are efficiently solved using the algorithm.     

Formation of independent flow-line cells based on operation requirements and machine capabilities

In this paper we study the generalized grouping problem of cellular manufacturing. We propose an operation-sequence-based method for forming flow-line manufacturing cells. Process planning in the form of selection of the machine for each operation is included in the problem formulation. Input requirements include the set of operation requirements for each part type, and operation capabilities for all available machine types. The objective is to find the minimum-cost set of flow-line cells that is capable of producing the desired part mix. A similarity coefficient based on the longest common operation subsequence between part types is defined and used to group parts into independent, flow-line families. An algorithm is developed for finding a composite operation supersequence for each family. Given machine options for each operation in this sequence, the optimal machine sequence and capacity for each cell is then found by solving a shortest path problem on an augmented graph. The method is shown to be efficient and computational results are reported.     

AN OPERATION-SEQUENCE-BASED APPROACH IN DESIGNING A FLOW LINE INDEPENDENT-CELL SYSTEM WITH MACHINE CAPACITY INCORPORATED

This paper considers a problem of designing a flow line independent-cell system where each machine can treat multiple production operations and at most two machines of each machine type can be installed in the same cell. The objective is to minimize the total system cost including machine cost and material handling cost subject to each cell capacity. The problem is characterized as NP-hard. Therefore, in order to find a good solution efficiently, this paper proposes two greedy-type heuristic algorithms including a single-combining algorithm and a double-combining algorithm. Both the algorithms are derived by using the process of combining the cells and are tested for their efficiencies with various numerical problems.     

Applying the network flow model to evaluate an FMC's throughput

An increasingly competitive environment and the changing customers' preference have forced manufacturers to find ways to increase productivity. The cellular manufacturing system has proved to be an alternative for improving manufacturing efficiency and increasing productivity. A major problem in implementing an FMC is how to evaluate its total throughput efficiently. In this paper, we apply a network flow model to select different machines in an FMC. This network flow model can be used in a processing environment with multiple available routings and obtain the maximum total throughput. In addition, two numerical examples are demonstrated to illustrate the use of this model. The results show that the network flow model established in this paper provides a simple and powerful tool when evaluating different machine configurations with limited information available. That is, only the operating routings and operating time are required when using the network flow model.     

Towards a psychologically consistent cellular manufacturing system

In cellular manufacturing systems (CMSs), an operator plays an important role. Because operators work for long-time periods in a production area, an increase in job satisfaction and system productivity occurs if the consistency of operators’ personal characteristics are considered in the design of CMSs. In a CMS, a cell formation problem (CFP) focuses on grouping and allocating machines, part families and operators to manufacturing cells. This paper considers a decision-making style (DMS) as an operator’s personal characteristic index in a CFP for designing a psychologically consistent CMS. DMS influences not only the interaction between two operators, but also the work that operator does on a machine. Hence, this paper develops a novel multi-objective mathematical model for the CFP considering consistency between each two operators in each cell and consistency between operator and his/her assigned machine(s). Because of possibility of a change in the primary DMS of a person to the backup one, this paper tackles this issue by applying a probabilistic procedure. Two hybrid meta-heuristic algorithms are developed for the large-sized test problems. In addition, the PROMETHEE-II method is applied to select the best Pareto solution. Finally, a real case study is presented to show the applicability of the developed approach.     

Duplication Process in Machine Cells Formation in Group Technology

Duplication in the machine cells formation process is the assignment of one machine type to more than one cell. It is done to reduce the number of intercellular moves. While the existing machine cells formation models employ the duplication process to reduce the machine cells interdependence, none of tem considers its economic consequences. This paper presents a cost-based duplication procedure which uses the duplication cost and the associated reduction in intercellular material handling cost as a basis for decision making in the duplication process.     

Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems

This article presents a fuzzy goal programming-based approach for solving a multi-objective mathematical model of cell formation problem and production planning in a dynamic virtual cellular manufacturing system. In a dynamic environment, the product mix and part demand change over a planning horizon decomposed into several time periods. Thus, the cell formation done for one period may be no longer efficient for subsequent periods and hence reconfiguration of cells is required. Due to the variation of demand and necessity of reconfiguration of cells, the virtual cellular manufacturing (VCM) concept has been proposed by researchers to utilise the benefits of cellular manufacturing without reconfiguration charges. In a VCM system, machines, parts and workers are temporarily grouped for one period during which machines and workers of a group dedicatedly serve the parts of that group. The only difference of VCM with a real CM is that machines of the same group are not necessarily brought to a physical proximity in VCM. The virtual cells are created periodically depending on changes in demand volumes and mix, as new parts accumulate during a planning horizon. The major advantage of the proposed model is the consideration of demand and part mix variation over a multi-period planning horizon with worker flexibility. The aim is to minimise holding cost, backorder cost and exceptional elements in a cubic space of machine–part–worker incidence matrix. To illustrate the applicability of the proposed model, an example has been solved and computational results are presented.