A hybrid Hopfield network-genetic algorithm approach to optimal process plan selection

In the automated manufacturing environment, different sets of alternative process plans can normally be generated to manufacture each part. However, this entails considerable complexities in solving the process plan selection problem because each of these process plans demands specification of their individual and varying manufacturing costs and manufacturing resource requirements, such as machines, fixtures/jigs, and cutting tools. In this paper the problem of selecting exactly one representative from a set of alternative process plans for each part is formulated. The purpose is to minimize, for all the parts to be manufactured, the sum of both the costs of the selected process plans and the dissimilarities in their manufacturing resource requirements. The techniques of Hopfield neural network and genetic algorithm are introduced as possible approaches to solve such a problem. In particular, a hybrid Hopfield network-genetic algorithm approach is also proposed in this paper as an effective near-global optimization technique to provide a good quality solution to the process plan selection problem. The effectiveness of the proposed hybrid approach is illustrated by comparing its performance with that of some published approaches and other optimization techniques, by using several examples currently available in the literature, as well as a few randomly generated examples.     

Dynamic mutual adjustment search for supply chain operations planning co-ordination

Operational planning is an activity carried out by all manufacturing and logistical companies. Its co-ordination with supply chain partners aims at synchronising resources utilisation in order to minimise inefficiencies, such as unnecessary inventory holding, or in order to improve revenue through better resource utilisation. It is a rather complex process as partners have different objectives and information asymmetry is part of any effort to find good co-ordination solutions. Furthermore, because supply chains evolve in a dynamic and uncertain environment, once a co-ordination of operations plans is achieved, input data, such as forecasts or resources’ status, can change and affect on hand plans. These dynamic changes not only require updating the plan that is directly affected by the changes, but it also requires the adjustment of all plans that are part of the same co-ordination solution (Stadtler, H. 2009. A framework for collaborative planning and state-of-the-art. OR Spectrum, 31 (1), 5–30). Therefore, the development of a practical co-ordination approach should be capable of dealing with these dynamic changes. This paper proposes a dynamic mutual adjustment search heuristic, which can be used to co-ordinate the operations plans of two independent supply chain partners, linked by material and non-strategic information flows. Computational analysis shows that the proposed approach produces a win-win strategy in the context of two supply chain partners, and improves the results of upstream planning in each planning cycle, and also improves the fairness of revenue sharing when compared to optimal centralised planning.     

Tolerance-based process plan evaluation using Monte Carlo simulation

In the discrete part manufacturing industry, engineers develop process plans by selecting appropriate machining processes and production equipment to ensure the quality of finished components. The decisions in process planning are usually made based on personal experience and the verification of process plans is based on physical trial-and-error runs, which is costly and time-consuming. This paper proposes to verify process plans by predicting machining tolerances via Monte Carlo simulation. The basic idea is to use a set of discrete sample points to represent workpiece geometry. The changes of their spatial position are simulated and tracked as the workpiece undergoes a series of machining processes. Virtual inspections are then conducted to determine the dimensional and geometric tolerances of the machined component. Machining tolerance prediction is completed through: (1) manufacturing error synthesis, and (2) error propagation in multiple operations. In this way, engineers can quickly screen alternative process plans, spot the root error causes, and improve their decisions. Therefore, physical trial-and-error runs can be reduced, if not eliminated, resulting in significant savings in both time and costs.     

Solving constrained optimization problems with a hybrid particle swarm optimization algorithm

This article presents a particle swarm optimization algorithm for solving general constrained optimization problems. The proposed approach introduces different methods to update the particle's information, as well as the use of a double population and a special shake mechanism designed to avoid premature convergence. It also incorporates a simple constraint-handling technique. Twenty-four constrained optimization problems commonly adopted in the evolutionary optimization literature, as well as some structural optimization problems are adopted to validate the proposed approach. The results obtained by the proposed approach are compared with respect to those generated by algorithms representative of the state of the art in the area.     

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.     

Hybridizing a genetic algorithm with an artificial immune system for global optimization

This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.     

Dynamic scheduling of FMS using a real-time genetic algorithm

The paper presents a genetic algorithm capable of generating optimised production plans in flexible manufacturing systems. The ability of the system to generate alternative plans following part-flow changes and unforeseen situations is particularly stressed (dynamic scheduling). Two contrasting objectives represented by the reduction of machine idle-times, thanks to dynamic scheduling computation and the reduction of the makespan, are taken into account by the proposed system. The key-point is the real-time response obtained by an optimised evolutionary strategy capable of minimising the number of genetic operations needed to reach the optimal schedule in complex manufacturing systems.     

Optimal process plan selection in networked based manufacturing using game-theoretic approach

This paper proposes a scheme for generating optimal process plans for multi jobs in a networked based manufacturing system. Networked manufacturing offers several advantages in the current competitive atmosphere such as reducing short manufacturing cycle time and maintaining the production flexibility, thereby achieving several feasible process plans. An N-person non-co-operative game with complete information is proposed and a mathematical model has been developed to generate the payoff functions. To be part of a game, we divided the game into two sub-games such as games to address sub-game (GASG) and games to solve sub-game (GSSG) which try to interact with each other and achieve the Nash equilibrium (NE). Consequently, a hybrid dynamic-DNA (HD-DNA) based evolutionary algorithm approach has been developed for more effective solutions of the game and also for finding the perfect NE points. The objective of this game is to generate the optimal process plans to minimise the makespan. Finally, three cases having different job complexities are presented to demonstrate the feasibility of the approach. The proposed algorithm is validated and results are analysed to benefit the manufacturer.     

Computer-based informatization of manufacturing engineering activities

The paper deals with the problem of optimization of a quality inspection process structure. The problem is identified with designing an optimal arrangement of inspection operations in a production process (linkages of the quality inspection process with manufacturing and accessory processes). Three mathematical models for the cases of specialized and versatile inspection stations are formulated. It is shown that the problem can be treated as a particular case of a standard assignment problem. An optimization procedure, grounded on Balas' algorithm, is proposed. A numerical example, based on real data, is given.     

Decision-making on multi-mould maintenance in production scheduling

The reliability of a critical tool like a mould on a machine affects the productivity seriously in many manufacturing firms. In fact, its breakdown frequency is even higher than machines. The decision-making on when mould maintenance should be started become a challenging issue. In the previous study, the mould maintenance plans were integrated with the traditional production schedules in a plastics production system. It was proven that considering machine and mould maintenance in production scheduling could improve the overall reliability and productivity of the production system. However, the previous model assumed that each job contained single operation. It is not workable in other manufacturing systems such as die stamping which may contain multiple operations with multiple moulds in each job. Thus, this study models a new problem for multi-mould production-maintenance scheduling. A genetic algorithm approach is applied to minimise the makespan of all jobs in 10 hypothetical problem sets. A joint scheduling (JS) approach is proposed to decide the start times of maintenance activities during scheduling. The numerical result shows that the JS approach has a good performance in the new problem and it is sensitive to the characteristic of the setup time defined.     

An efficient algorithm for automatic machining sequence planning in milling operations

Machining sequence planning can be considered as one of the most important functions of manufacturing process planning. However, less attention has been paid to automation of this function in contemporary computer-aided process planning systems. This paper describes an efficient algorithm for automatic machining sequence planning in 2.5D milling operations. It is programmed in LISP and forms the machining sequence planning module of a CIM system. This module is integrated with a feature-based design system that determines required machining operations and parameters for each machining operation. This information is then sent to the machining sequence planning module for determining proper machining sequence plans for producing the part. The algorithm generates feasible machining sequences based on the bilateral precedence between machining operations. The algorithm results in minimized tool changes.     

Integrated approach to part scheduling and inspection policies for a job shop manufacturing system

The quality of a product greatly depends on the quality of its components. This requires that manufacturing specifications have to be met in the manufacturing environment and as a consequence inspection stations are present in many manufacturing systems and inspection policies must be adopted. One problem, which has been widely investigated, concerns the detection of the inspection points in the hypothesis that the action to be taken is known when a defective part is detected. If different jobs are to be produced, then operation scheduling becomes yet another complex problem needing to be solved. And while the problem of scheduling has received a great amount of attention from researchers, to our knowledge the interaction between the two problems has not been treated in job-shop environment. In the present paper three different control policies are preliminarily examined: they differ both in terms of the number of operations that are inspected, and with regard to the type of intervention carried out on detection of a defect. Each control policy affects the optimal inspection locations, which, in their turn, influence operation scheduling. As will be shown in the present paper, a sequential decision process based on separate optimization steps can lead to very poor final results. For this reason, an integrated approach is proposed, in an attempt to identify an optimal solution using a genetic algorithm.     

A neural network model-based open-loop optimization for the automated thermoplastic composite tow-placement system

This study demonstrates the use of on-line optimization algorithms to calculate optimum process set points for manufacturing processes such as the automated thermoplastic tow-placement (ATP) system. An in situ non-linear optimization technique based on artificial neural networks has been developed. This method is implemented in the ATP process and utilizes neural network-based process models to predict material quality as a function of process set points. The set points are computed by maximizing the throughput and maintaining a desired minimum quality. Process history can greatly affect the final part quality and, therefore, is an integral part of the optimization. The controller is validated for the highly non-linear ATP process and successfully predicts optimum processing parameters. The developed approach is applicable to many other manufacturing processes where process simulations exist and conventional control techniques are lacking.     

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.     

Computer-aided optimization of multi-pass turning operations for continuous forms on CNC lathes

The Computer Numerical Control (CNC) machine is one of the most effective production facilities used in manufacturing industry. Determining the optimal machining parameters is essential in the machining process planning since the machining parameters significantly affect production cost and quality of machined parts. Previous studies involving machining optimization of turning operations concentrated primarily on developing machining models for bar components. Machined parts on the CNC lathes, however, typically have continuous forms. In this study, we formulate an optimization model for turned parts with continuous forms. Also, a stochastic optimization method based on the simulated annealing algorithm and the pattern search is applied to solve this machining optimization problem. Finally, the applications of the developed machining model and the proposed optimization algorithm are established through the numerical examples.     

Integrated planning of loaded and empty container movements

Efficiently planning drayage operations is an important task for transportation companies since these operations constitute a large part of the cost of an intermodal transport. In this paper, a full truckload vehicle routing problem for transporting loaded and empty containers in drayage operations is studied. For empty container transports, either the origin or the destination is not predefined. The problem is formulated as an asymmetric multiple vehicle Travelling Salesman Problem with Time Windows (am-TSPTW). Two solution approaches are proposed: a sequential and an integrated approach. For both approaches, a single- and a two-phase deterministic annealing algorithm are presented. Results show that the proposed algorithms are able to find good quality solutions in a small amount of computation time. The integrated approach clearly outperforms the sequential one and the results confirm the advantage of using a two-phase algorithm for vehicle routing problems with hierarchical objectives. Finally, it is shown that the proposed integrated solution method improves previous results on a similar problem.     

A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design

A number of multi-objective evolutionary algorithms have been proposed in recent years and many of them have been used to solve engineering design optimization problems. However, designs need to be robust for real-life implementation, i.e. performance should not degrade substantially under expected variations in the variable values or operating conditions. Solutions of constrained robust design optimization problems should not be too close to the constraint boundaries so that they remain feasible under expected variations. A robust design optimization problem is far more computationally expensive than a design optimization problem as neighbourhood assessments of every solution are required to compute the performance variance and to ensure neighbourhood feasibility. A framework for robust design optimization using a surrogate model for neighbourhood assessments is introduced in this article. The robust design optimization problem is modelled as a multi-objective optimization problem with the aim of simultaneously maximizing performance and minimizing performance variance. A modified constraint-handling scheme is implemented to deal with neighbourhood feasibility. A radial basis function (RBF) network is used as a surrogate model and the accuracy of this model is maintained via periodic retraining. In addition to using surrogates to reduce computational time, the algorithm has been implemented on multiple processors using a master–slave topology. The preliminary results of two constrained robust design optimization problems indicate that substantial savings in the actual number of function evaluations are possible while maintaining an acceptable level of solution quality.     

Integrated group technology,cell formation,process planning,and production planning with application to the emergency room

In this paper an integrated approach for the formation of parts and machine families in group technology is developed. The integrated approach is used to solve cell formation, process planning, and production planning simultaneously. The given information is part processing sequence, part production volume, part alternative processing plans, and part processing times. The approach is used to determine the machine-part cells and part processing plans, while the total intercell part flow is minimized. Also, the convergence of the algorithm is investigated. The approach goes across and beyond the group technology methods by considering sequencing, production planning, process planning, and part-machine cellular information simultaneously. Two methods are investigated: exact (optimal) and heuristic. The approach first solves an integer programming problem to find processing plans and then uses a procedure to form the machine-part cells. The proposed approach solves the problem iteratively until a set of plans for machine-part cell formations is obtained with minimal intercell part flow or interflow cost. An example is presented to explain the developed approach. Experimental results are also provided. An extension of the approach for solving the operations planning of an emergency room is also covered. In this extension of the approach, the application of cell formation provides a solution to efficiently managing patients and utilizing resources. By grouping patients by their needed medical procedures, time and resource efficiency is accomplished. An application to ER of University Hospitals of Case Western Reserve University is given.     

Good operations practice supervisor profiles in cell-centric manufacturing

The selection and implementation of good operations practice cannot be undertaken in isolation; it must consider the enterprise context. The aim of this paper is to describe a robust process for the development of specific environment good operations practice role profiles for supervisors, and illustrate this through a case study within a complex cell-centric manufacturing environment. The approach identifies the activities undertaken by a cell leader and team leader in a given manufacturing organization, and also by a good operations practice cell leader and team leader in a relevant external organization. Then via a survey of operations managers and functional managers, those activities that a cell leader and team leader should do within the given manufacturing organization are identified. The approach enables the contextual adoption of good operations practice, develops supervisor role profiles which allows them to be proficient within their task domains, contributes to attaining agreement amongst operational and functional management, and has applications across cell-centric manufacturing. The potentially significant impacts available across cell-centric manufacturing are illustrated by the finding that following this examination of operations practice in the case study company two-thirds were classified as requiring change.     

A comparative study of the operations strategies of globally- and domestically-oriented New Zealand manufacturing firms

There has been much written on why and how firms become involved in entering international markets. There is less material on the production/operations actions taken by such firms. This paper investigates the differences in competitive capabilities and manufacturing performance improvement plans between globally-oriented and domestically-oriented firms. It uses data collected from 140 New Zealand manufacturing companies. The results indicate that both groups emphasize the same competitive capabilities, but the globally-oriented group is placing more emphasis on some action plans involving quality assurance, improving existing processes and design for manufacture. Their most favoured action plans are similar to those of firms in most advanced manufacturing countries which participate in the Manufacturing Futures Project.