Modelling and solving an FMS part selection problem

This paper discusses a part selection problem for a flexible manufacturing system with versatile machine tools but with no tool transportation devices. We propose a maximal network flow model with two side constraints. The model can be relaxed to either a maximal network flow problem or two independent 0-1 knapsack problems. Our computational results show that the latter relaxation significantly outperforms the former. Other factors affecting the solvability of the model are also examined. Finally, we show that our model can efficiently solve real-world part selection problems.     

A genetic algorithm for FMS part type selection and machine loading

Part type selection (PTS) and machine loading are two major problems in the production planning of flexible manufacturing systems. In this paper, we solve these problems by the use of genetic algorithms (GAs). We exploit the problem's MIP (mixed integer programming) model to make our GA more meaningful and less computation-intensive. The GA strategy is developed in three parts: solution coding, solution generation and solution recombination. In solution coding, we replace the original binary routing variables with integer variables and thus reduce the chromosome length significantly. In solution generation, the level of feasibility is the main concern. We divide the constraints into two categories: direct and indirect. The direct constraints involve only two variables each and are easily satisfied by context-dependent genes. Since the direct constraints form the major chunk of constraints, their satisfaction controls infeasibility to a large extent. The remaining indirect constraints are handled by the penalty function approach. The solution recombination involves crossover and mutation. The crossover is performed in two steps, the PTS swap followed by the routing swap, so that the feasibility level is not disturbed. With a similar intent, the mutation is allowed to operate only on selective genes. All the steps are illustrated with examples. Our GA is able to achieve optimum or near-optimum performance on a variety of objectives. A parametric study of GA factors is also carried out, indicating population size and mutation probability as influential parameters.     

Part type selection,machine loading and part type volume determination problems in FMS planning

The solution to subproblems of FMS planning problems require an integrated approach. The mathematical programming approaches, which are often followed, are not suitable for large problems. In this paper, we have proposed a three stage approach to solving part type selection, machine loading and part type volume determination problems. In contrast to the usual approach of maximizing the part types in each batch (or minimizing the number of batches), we have attempted to maximize the routeing flexibility of the batches. A heuristic has been proposed for the part type selection problem and simple mathematical programs for the other two problems. The illustrative examples show that by improving the routeing flexibility of the batches the overall system performance has improved.     

Multi-agent-based approach to solve part selection and task allocation problem in flexible manufacturing systems

Agent technology is currently being considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. A multi-agent-based approach for solving the part allocation problems in flexible manufacturing systems (FMS) is presented that can easily cope with the dynamic environment. Four agents were involved in carrying out the tasks of allocating parts on different machines: communicator, machine, part and material handling device (MHD). Upon arrival in the manufacturing facility, the part informs the communicator agent about the task requirements. The communicator agent divides the task into subtasks and sends a call-for-bids message to the machine and MHD agents. Each machine responds in accordance with its process capabilities and buffer limit. This response may be for the whole task or for one or more subtasks and it contains the price and cost details for these subtasks along with the performance index and acceptance ratio of the machine. The final allocation is made based on the objective function that includes processing and transportation costs and time. An algorithm is presented that is used by the communicator agent for allocating parts to different machines. An illustrative example is given to solve the task allocation on five machines, with each machine having different performance index and acceptance ratio.     

A due date-based approach to part type selection in flexible manufacturing systems

Nearly all of the literature on part type selection for flexible manufacturing systems has focused on maximizing throughput. We present an approach for part type selection in which meeting due dates is the primary objective. The approach is based on the idea of using information from the solution of an approximate, aggregate scheduling problem as the basis for determining release priorities. The need for the approximation and aggregation arises because the exact machine configuration (partitioning of identical machines into groups, and loading of tools) cannot be decided until the parts are selected. We develop and evaluate several different policies for setting release priorities in a context where the approximate, aggregate schedule is constructed using list scheduling (dispatching) rules. The results indicate that using information from such a schedule to set release priorities performs far better than using simpler procedures.     

Solving the machine-loading problem in a flexible manufacturing system using a combinatorial auction-based approach

The next generation manufacturing system is conceived to be intelligent enough to take decisions and automatically adjust itself to situations such as variations in production demand and machine breakdowns. The manufacturing control system must have the intelligence to ensure real time operational control by interacting with different manufacturing subsystems. One of the prominent methodologies to deal with the problem of distributed manufacturing systems is the auction-based heuristic control strategy in which various entities bid themselves, accept bids and make selection amongst bids. The present paper addresses the flexible manufacturing system machine-loading problem where job selection and operation allocation on machines are to be performed such that there is a minimization of system unbalance and a maximization of throughput. The methodology of winner determination using the combinatorial auction process is employed to solve the flexible manufacturing system machine-loading problem. In the combinatorial auction, allowing bidding on a combination of assets offers a way to enhance the efficiency of allocating the assets. The performance of the proposed approach is tested on 10 sample problems and the results thus obtained are compared with the existing models in the literature.     

An integrated approach to solving the process plan selection problem in an automated manufacturing system

Selection of a process plan is a crucial decision making problem encountered in manufacturing systems due to the presence of several alternative process plans arising out of availability of several machines, tools, fixtures etc. capable of performing the same operations of the part. Because of its vital impact on the performance of the manufacturing system, several researchers have addressed the plan selection problem in recent years. Although functional integration plays a significant role in the development of current manufacturing systems, many of the functions in manufacturing systems have been developed without a sense of integration. Therefore, it becomes important to emphasize the integration of functions rather than the individual development of the function itself. This paper attempts to address the plan selection problem taking into account the similarity measures among the process plans of the parts. Four algorithms have been developed to integrate the several segments of the process plan selection problem. Application of these algorithms ensures considerable computational simplicity in yielding the feasible process plans of the parts.     

An integrated decision aid extension to material selection problem

Choice of an adequate material in design process is one of the critical tasks for the relevant decision-makers. At this insight, the consistency of the decisions is extremely depending on the relevance of adapted techniques to the nature of different problem cases. This paper proposed an integrated decision aid (IDEA) to match the suitable techniques with different problem cases based on the following six dimensions: (i) the type of the decision problem, (ii) the size of the problem, (iii) selection of the preference techniques by decision-makers, (iv) decision-makers’ preference structure, (v) the necessity for the use of relative importance, (vi) the nature of performance values. Furthermore, the implementation procedure of the proposed IDEA for a material selection problem is demonstrated with the previously cited applications from material science literature. Hereafter, it is expected that the IDEA provides great advantages and encouragements to researchers/practitioners in order to prevent excessive time consuming, probable misapplications, and the other challenging issues in multiple criteria analysis of material selection problems.     

Machine loading problem of FMS: A fuzzy-based heuristic approach

The art and science of simulating complex manufacturing systems is rapidly changing. A great deal of attention is being devoted to the possibilities of bringing artificial intelligence (AI) and expert systems (ES) technology into simulation methodology. Such systems will hopefully allow models to be quickly developed, validated and run with as much of the necessary expertise as possible built into the software. This paper addresses: (a) the motivation and need for developing such systems, (b) the nature of such systems, (c) the potential benefits of this technology over existing approaches and (d) the current state-of-the-art as it applies to simulation.     

A genetic algorithm approach to the integrated inventory-distribution problem

We introduce a new genetic algorithm (GA) approach for the integrated inventory distribution problem (IIDP). We present the developed genetic representation and use a randomized version of a previously developed construction heuristic to generate the initial random population. We design suitable crossover and mutation operators for the GA improvement phase. The comparison of results shows the significance of the designed GA over the construction heuristic and demonstrates the capability of reaching solutions within 20% of the optimum on sets of randomly generated test problems.     

Solving resource contention problem in FMS using Petri nets and a rule-based approach

The present work focuses on the development of a shop floor controller (SFC) using Petri nets and a rule-based approach. An FMS consisting of three workstations with in-process buffers and two robots is taken as a case study. Stochastic Petri nets are used to model various activities in the manufacturing system. A new concept of P-Levels is introduced for the solution of problems of resource contention and circular wait by assigning priorities to the claimant parts in a multibatch manufacturing system. A case study also shows the capabilities of the developed system controller with relevant results. Various possibilities about the number of batches processed simultaneously are considered for the example case.     

An integrated approach to product design, materials selection and cost estimation

An integrated approach to the interrelated activities of product design, materials selection and cost estimation is proposed. The wide range of engineering materials is first narrowed to a limited number of candidates using design limitations and performance requirements. Each of the candidate materials is used to develop an optimum design which is then used in cost estimation. An optimization technique, such as benefit-cost analysis, is used to select the optimum design-material combination. A case study is presented to illustrate the use of the integrated approach.     

A GA-based approach to machining operation sequencing for prismatic parts

This paper presents a study of using a genetic algorithm (GA) method to select the machining operation sequence for prismatic parts. Four types of process planning rules including precedence rules, clustering rules, adjacent order rules and optimization rules are considered and are encompassed quantitatively in the fitness calculations for alternative operation sequences. The impact of variations of the rules on the result of operation sequencing and that of GA parameters on the solution efficiency are discussed through analysis of examples and experiments. The proposed genetic algorithm proves effective for machining operation sequencing of prismatic parts, by incorporating various production environment considerations into process planning.     

Machine loading and part type selection in flexible manufacturing systems

This paper presents a new approach to the loading problem in flexible manufacturing systems. It focuses on the existence of alternatives routes for each part type. Also, the optimal number of copies of each tool type to be loaded into each tool magazine is directly determined. Thus, the decision variables are the routing mix and the tool allocation. The loading objective is to balance machine workloads. Constraints on the number of available tools and on tool magazine capacities can be imposed. The problem is modelled as a mixed-integer linear program. Also, an extension of the model is formulated that includes part type selection.     

An integrated approach to tolerance synthesis,process selection and machining parameter optimization problems

Tolerance synthesis, process selection and machining parameter optimization have been recognized as key issues to ensure product quality and reduce production cost. Although the three issues are closely interrelated, they are rarely addressed simultaneously. This often leads to inconsistent and conflicting decisions. This paper reports an integrated approach for simultaneously addressing these issues subject to their common constraints and considers both tangible and intangible cost criteria. Most commonly used machining processes such as milling, turning, drilling, reaming, boring and grinding have been taken into account. Particular attention has also been paid to multiple quality characteristics. Two example problems, one requiring rotational machining, the other involving planar machining, are solved to demonstrate the application of the proposed approach.     

A competitive neural network approach to multi-objective FMS scheduling

The main contribution of this paper is the development of a multi-objective FMS scheduler which is designed to maximally satisfy the desired values of multiple objectives set by the operator. For each production interval, a decision rule for each decision variable is chosen by the FMS scheduler. A competitive neural network is applied to present fast but good decision rules to the operator. A unique feature of the FMS scheduler is that the competitive neural network generates the next decision rules based on the current decision rules, system status and performance measures. A commercial FMS is simulated to prove the effectiveness of the FMS scheduler. The result shows that the FMS scheduler can successfully satisfy multiple objectives.     

A process planning-orientated approach to part family formation problem in group technology applications

The paper reports on constrained hierarchical clustering applied to similarity analysis, the grouping of process plans and, consequently, the parts manufactured in accordance with them. It proposes solutions to some important problems such as: determining a metric of process plan similarity evaluation; the study of a cluster-creating technique with two process plans and a rule for singling out the standard plan; the determination of a logical threshold value for partitioning into families. Validation of the method creates a family set. The ‘strong forms’ of this set constitute definitive partition into families of the process plan set.     

A combined goal programming—AHP approach to maintenance selection problem

This paper presents a ‘Lexicographic’ Goal Programming (LGP) approach to define the best strategies for the maintenance of critical centrifugal pumps in an oil refinery.For each pump failure mode, the model allows to take into account the maintenance policy burden in terms of inspection or repair and in terms of the manpower involved, linking them to efficiency-risk aspects quantified as in FMECA methodology through the use of the classic parameters occurrence (O), severity (S) and detectability (D), evaluated through an adequate application of the Analytic Hierarchy Process (AHP) technique.An extended presentation of the data and results of the case analysed is proposed in order to show the characteristics and performance of this approach.     

Tool selection for optimal part production: a Lagrangian relaxation approach

This paper extends previous work on implementation problems associated with a flexible system that produces flat sheet-metal parts with interior holes. The paper makes four main contributions. First, we formulate the problem of selecting tooling and design standards to minimize the cost of producing parts as an optimization model. Second, we develop a projected subgradient algorithm for the Lagrangian relaxation of the problem by using the model's special structure to develop relationships between the Lagrangian multipliers. Third, we demonstrate that the algorithm produces close to optimal solutions (duality gap less than 2%) very quickly on a number of problems derived using a substantial data set obtained from a Chicago area firm. Fourth, an important variant of the traditional repair kit problem is shown to be a special case of the tool selection problem.     

A utility-driven approach to supplier evaluation and selection: empirical validation of an integrated solution framework

Supplier evaluation and selection (SES) problems have long been studied, leading to the development of a wide range of individual and hybrid models for solving them. However, the lack of widespread diffusion of existing SES models in the industry points to a need for simpler models that can systematically evaluate both qualitative and quantitative attributes of potential suppliers while enhancing the flexibility decision-makers need to account for relevant situational factors. Furthermore, empirical validations of existing models in SES have been few and far between. With a view to addressing these issues, this paper proposes an integrated solution framework that can be used to evaluate both tangible and intangible attributes of potential suppliers. The proposed framework combines three individual methods, namely the fuzzy analytic hierarchy process, fuzzy complex proportional assessment and fuzzy linear programming. The framework is validated through application in a Turkish textile company. The results generated using the proposed framework is compared with the actual historical data collected from the company. Additionally, a feasibility assessment is conducted on the sample supplier selection criteria employed, as well as assessment of the results generated using the proposed model.