Tool Magazine Capacity Research Articles

Multi-objective hierarchical clustering for tool assignment

Due to the limited tool magazine capacities of CNC machines, time-consuming tool changeovers result in inefficient equipment utilization. This study provides a method to minimize the changeovers by optimizing the allocation of the tools to the machines. The proposed algorithm is efficient as it approaches the tool assignment task as a multi-objective hierarchical clustering problem where the products are grouped based on the similarity of the tool demands. The novelty of the goal-oriented agglomerative clustering algorithm is that it is based on the Pareto optimal selection of the merged clusters. The applicability of the method is demonstrated through an industrial case study. The tool assignment problem has also been formulated as a bin-packing optimization task, and the results of the related linear programming were used as a benchmark reference. The comparison highlighted that the proposed method provides a feasible solution for large real-life problems with low computation time.

Minimization of Number of Tool Switching Instants in Automated Manufacturing Systems

This study addresses the problem of minimizing tool switching instants in automated manufacturing systems. There exist a single machine and a group of jobs to be processed on it. Each job requires a set of tools, and due to limited tool magazine capacity, and because it is not possible to load all available tools on the machine, tools must be switched. The ultimate goal, in this framework, is to minimize the total number of tool switching instants. We provide a mathematical programming model and two constraint programming models for the problem. Because the problem is proven to be NP-hard, we develop two heuristic approaches, and compare their performance with methods described in the literature. Our analysis indicates that our constraint programming models perform relatively well in solution quality and execution time in small-sized problem instances. The performance of our greedy approach shows potential, reaching the optimal solution in 82.5% of instances. We also statistically demonstrate that the search algorithm enhances the quality of the solution obtained by the greedy heuristic, particularly in large sets. Hence, the solution approach, i.e., the greedy heuristic and the search algorithm proposed in this study is able to quickly reach near-optimal solutions, showing that the method is appropriate for manufacturing settings requiring sudden adjustments.

An iterated local search procedure for the job sequencing and tool switching problem with non-identical parallel machines

In this paper, a new generalization of the uniform job sequencing and tool switching problem is presented. It considers non-identical parallel machines, which differ in tool magazine capacity and setup times. Applications of the problem can be found in the metal working or semiconductor manufacturing industries when the jobs require different tool sets for processing. The paper provides a Mixed Integer Programming formulation for the job sequencing and tool switching problem with machine-dependent processing and tool switching times under the consideration of three conflicting objectives (minimizing the total flowtime, minimizing the makespan and minimizing the total number of tool switches). Different efficient construction heuristics and Iterated Local Search methods are proposed and evaluated, using 640 new and publicly available instances. The results of the construction heuristics, the Iterated Local Search schemes and a Mixed Integer Programming Solver are discussed. The extensive computational experiments show the merit of the perturbation strategy in order to overcome local optima. It is shown that certain methods are more or less suitable depending on the objective function.

Fast and meta heuristics for part selection in flexible manufacturing systems with controllable processing times

This study addresses a part selection problem for flexible manufacturing systems in which part processing times are controllable to optimize the total cost associated with energy consumption, operational performance, and so on. The problem is to determine the set of parts to be produced, part processing times and the number of tools for each tool type in each period of a planning horizon while satisfying processing time capacity, tool magazine capacity and tool life restrictions. The objective is to minimize the sum of part processing, earliness/tardiness, tool and subcontracting costs. Tool sharing among part types is also considered. After an integer programming model is developed, two types of solution algorithms are proposed, that is, fast heuristics useful when decision time is critical and variable neighborhood search algorithms when solution quality is important. Computational experiments were conducted on a number of test instances and the best fast heuristics are specified, together with reporting how much the variable neighborhood search algorithms improve the fast heuristics.

A new 0-1 linear programming approach and genetic algorithm for solving assignment problem in flexible manufacturing system

Flexible manufacturing system (FMS) consists of some multi-functional machines that are linked together through a material-handling system and the whole of the system is controlled by a central computer. One of the most important problems in production planning of FMS is machine–tool selection and operation allocation problem that directly influences the production costs and time. This paper presents a 0-1 integer linear programming model for the multi-objective machine–tool selection and operation allocation problem in FMS. The proposed model attempts to simultaneously minimize machining cost, material-handling cost, setup cost, and maximum machine work load time and tool life. We also consider the constraints of tool magazine capacity, tool life, and machine capacity in the problem. Due to the nondeterministic polynomial (NP)-hard nature of the derived problem, a genetic algorithm is developed to solve the model. Computational results indicate the effectiveness of the proposed algorithm for solving the problem.

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.

A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems

This paper presents a constraint programming (CP) methodology to deal with the scheduling of flexible manufacturing systems (FMSs). The proposed approach, which consists of both a model and a search strategy, handles several features found in industrial environments, such as limitations on number of tools in the system, lifetime of tools, as well as tool magazine capacity of machines. In addition, it tackles the problem in a integrated way by considering tool planning and allocation, machine assignment, part routing, and task timing decisions altogether in the approach. The formulation, which is able to take into account a variety of objective functions, has been successfully applied to the solution of test problems of various sizes and degrees of difficulty.

A Lagrangean relaxation based approach for the capacity allocation problem in flexible manufacturing systems

This study considers the operation assignment and capacity allocation problem in flexible manufacturing systems. A set of operations is selected to be processed and assigned to the machines together with their required tools. The purchase or usage of the required tools incurs a cost. The machines have scarce time and tool magazine capacities. The objective is to maximize the total weight of the assigned operations minus the total tooling costs. We use Lagrangean relaxation approach to obtain upper and lower bounds on the optimal objective function values. The computational experiments show that our approach provides near optimal bounds in reasonable solution times.

Capacity allocation problem in flexible manufacturing systems: branch and bound based approaches

This study considers an operation assignment and capacity allocation problem that arises in flexible manufacturing systems. The machines have limited time and tool magazine capacities and the available tools are limited. Our objective is to maximise total weight of assigned operations. We develop a branch and bound algorithm that finds the optimal solutions and a beam search algorithm that finds high quality solutions in polynomial time.

Bounding approaches for operation assignment and capacity allocation problem in flexible manufacturing systems

This study considers an operation assignment and capacity allocation problem that arises in flexible manufacturing systems. Automated machines are assumed to have scarce time and tool magazine capacities and the tools are available in limited quantities. The aim is to select a subset of operations with maximum total weight. The weight of an operation may represent its profit, processing load, relative priority. Several upper bounding procedures have been taken into account. The results of computational tests have revealed that the proposed upper bounding procedures produce satisfactory solutions in reasonable CPU times. We suggest using some of the bounds when the quality of the solutions is more important than the speed of achieving them and some others when the speed is more important than the quality.

Beam search algorithm for capacity allocation problem in flexible manufacturing systems

This study considers the operation assignment and tool allocation problem in flexible manufacturing systems. A set of operations together with their required tools are selected so as to maximize the total weight. The machines have limited time and tool magazine capacities and the tools are available in limited quantities. We develop a beam search algorithm and obtain near optimal solutions for large size problems very quickly.

The tool transporter movements problem in flexible manufacturing systems

We consider a job sequencing and tool transporter movements problem on a single flexible machine with limited tool magazine capacity. A tool transporter having limited capacity is used in transporting the tools between the machine and tool crib area. Our aim is to minimize the number of the tool transporter movements. We present several lower and upper bounds, propose a Branch-and-Bound algorithm and a Beam Search procedure, and report results from a computational experiment. We find that optimal solutions can be quickly obtained for medium-sized instances with 25 jobs and 25 tools. For large-sized problem instances, Beam Search provides high quality solutions very quickly. Finally, we address the problem of minimizing the total flow time.

Operation assignment and capacity allocation problem in automated manufacturing systems

We address an operation assignment and capacity allocation problem that arises in semiconductor industries and flexible manufacturing systems. We assume the automated machines have scarce time and tool magazine capacities and the tools are available in limited quantities. The aim is to select a subset of operations with maximum total weight. We show that the problem is NP-hard in the strong sense, develop two heuristics and a Tabu Search procedure. The results of our computational tests have revealed that our Tabu Search procedure produces near optimal solutions very quickly.

Determining production sequences for single-stage multifunctional machining systems based on the tradeoff between fixture cost, re-fixturing and tool replenishment

The main advantage of a Single-Stage Multifunctional machining System (SSMS) is its ability to combine many operations and processes, previously done on different machines in several stages, into a single stage. The flexibility and process variety these machining systems offer have ensured their widespread adoption in industry. As a result SSMSs frequently constitute the bottleneck of a manufacturing system and therefore control system throughput. Hence, it is imperative to use these resources efficiently. This study considers three possible costs and the tradeoffs between them that are pertinent to any job sequence: (i) the Tool Magazine Replenishment (TMR) cost; (ii) the Re-Fixturing (RF) cost; and (iii) the fixture cost. A job sequencing algorithm that minimizes these costs as well as the machine non-operational time, which stems from TMRs and RFs, is developed. The algorithm takes into account the tools and fixture requirements as well as the tool magazine capacity. The algorithm also recommends purchasing additional fixtures when economically justified. The algorithm is used to solve a job sequencing problem that occurs in a real industrial setting.

Capacity and tool allocation problem in flexible manufacturing systems

This study addresses an allocation problem that arises in the semiconductor industry and flexible manufacturing systems where the tools should be loaded on computer numerical controlled (CNC) machines to process a number of operations. The time and tool magazine capacities of the CNC machines and the number of available tools of each type are limited. The objective is to maximize the total weight of operation assignments. We present a mixed integer programming formulation of the problem and show that the problem is NP-hard in the strong sense. We show that the linear programming relaxation upper bound dominates the best possible Lagrangean relaxation upper bound. We develop several lower bounding procedures and a lower bounding procedure using Lagrangean relaxation approach. Our computational results show that the upper and lower bounding procedures produce near-optimal solutions in reasonable times.

The Tool-Switching Problem With Magazine Capacity and Tool Size Constraints

Previous works often simplify the tool-switching problem by using uniform tool size and magazine capacity constraints. A tool has uniform size when it requires only one slot to be stored in the magazine. The magazine capacity constraints assume that the magazine can hold all tools for each job. This paper addresses the more general case of the tool-switching problem when both uniform tool size and magazine capacity constraints are relaxed. The problem is decomposed into two cases: partial and complete job splitting. In the first case, a job is said to be partially split into subjobs if each subjob requires at least two tools which occupy less than the capacity of the magazine. In the second case, a job is said to be completely split into subjobs if each subjob requires only one tool. The problems are proven to be NP complete. An attempt is made to solve these cases while the tools are changed either concurrently with job change or not. Some promising results are obtained.

Minimization of the Number of Tool Magazine Setups on Automated Machines: A Lagrangean Decomposition Approach

This paper addresses the parts-grouping problem that arises in automated manufacturing environments where appropriate cutting tools must be loaded on Computer Numerical Control (CNC) machines to process a variety of parts. Since tool-loading times may be considerably long and reduce available machine processing times, it is important to find a mutually exclusive grouping of parts such that the total number of tools required by each group does not exceed the tool-magazine capacity and the number of groups is minimized. We present an integer programming formulation of the problem and develop a lower bounding procedure using Lagrangean decomposition. We then introduce valid inequalities to improve the quality of the lower bounds obtained from the relaxed problem. Our solution procedure for the lower bounding problem requires only one set of Lagrange multipliers, which reduces the required computational effort significantly. We also modify the lower bound solution heuristically to find an upper bound. The lower and upper bounds are then incorporated in a branch-and-bound scheme to search for an optimal solution. Our computational comparisons with the best existing solution procedures from the literature show that our procedure performs better on average, and for the majority of test problems within the scope of our experiments.

An intelligent hierarchical workstation control model for FMS

Hierarchical planning, scheduling and control in flexible manufacturing systems (FMS) provide a systematic way to effectively allocate resources along different time horizons. This paper describes the design and development of an intelligent hierarchical control model based on a proposed tool management method. The control model consists of four levels: the process plan selection, the master scheduling, the job sequencing and the control level. The model is developed to optimize the machine utilization and balance tool magazine capacity of a flexible machining workstation (FMW) in a tool-sharing environment. Problems are identified and modeled in the level of process plan selection, master scheduling, and job sequencing. A genetic-based algorithm was developed to solve the problem domains throughout the hierarchical planning and scheduling model. Fuzzy logic technique could also be incorporated into the master production schedule (MPS) level to allow for a more realistic result in the presence of uncertainty and impreciseness in order to fit the realistic nature of actual industrial environments.

Configuring flexible flowlines

Flexible machines are machines equipped to respond to a variety of jobs by the selecting appropriate tools, which are available on the machine, to match the tasks required for each incoming job. Computer Numerical Control machines (CNCs) and robots with multiple grippers are examples of such machines. However, the tool holding or tool magazine capacity usually limits the flexibility of these machines. A sequence of such machines, with directed flows, is a flexible flowline, and these are seen as a possible solution for producing large varieties of similar products. In this paper, problems related to configuring flexible flowlines are considered, with the goal of minimizing the line size without requiring any backtracking while processing parts. Several forms of the problem are considered. Scenarios where single part types are produced on identical, tool-magazine capacity limited machines are examined first. These are expanded to include restrictions on processing capabilities as constraints. Multiple-part type scenarios are then modelled, beginning with procedures addressing open magazines, finally followed by restricted machines, with tool magazine size limitations. A mathematical programming formulation for configuring such a line is detailed, and solutions for test cases presented.

Machine-tool selection and operation allocation in FMS: Solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.