Simultaneous scheduling of machines and tools in a multi machine FMS with alternative routing using symbiotic organisms search algorithm

This paper deals with simultaneous scheduling of machines and tools with alternate machines in a multi machine flexible manufacturing system (FMS) to minimize makespan (MS). Only one copy of each type of tools is made available due to economic restrictions and the tools are stored in a central tool magazine (CTM) that shares with and serves for several machines. The problem is to select machines from alternate machines for job-operations, allocation of tools to job-operations and job-operations’ sequencing on machines for MS minimization. This paper presents a nonlinear mixed integer programming (MIP) formulation to model the combined scheduling of machines and tools with alternate machines and a symbiotic organisms search algorithm (SOSA) built on the symbiotic interaction strategies that organisms employ to continue to exist in the ecosystem for solving the scheduling of machines and tools with alternate machines. The results have been tabulated, analyzed. It is observed that there is a reduction in MS when the alternate machines are considered for job-operation.

Learning the Parametric Transfer Function of Unitary Operations for Real-Time Evaluation of Manufacturing Processes Involving Operations Sequencing

For better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The last contains the solution of the model describing the system under study, for any choice of the selected parameters. These surrogate models, also known as meta-models, virtual charts or computational vademecum, in the context of model order reduction, were successfully employed in a variety of industrial applications. However, they remain confronted to a major difficulty when the number of parameters grows exponentially. Thus, processes involving trajectories or sequencing entail a combinatorial exposition (curse of dimensionality) not only due to the number of possible combinations, but due to the number of parameters needed to describe the process. The present paper proposes a promising route for circumventing, or at least alleviating that difficulty. The proposed technique consists of a parametric transfer function that, as soon as it is learned, allows for, from a given state, inferring the new state after the application of a unitary operation, defined as a step in the sequenced process. Thus, any sequencing can be evaluated almost in real time by chaining that unitary transfer function, whose output becomes the input of the next operation. The benefits and potential of such a technique are illustrated on a problem of industrial relevance, the one concerning the induced deformation on a structural part when printing on it a series of stiffeners.

An Improved Immune Algorithms for Solving Flexible Manufacturing System Distributed Production Scheduling Problem Subjects to Machine Maintenance

Competitiveness and rapid expansion of flexible manufacturing system (FMS) as one of the industrial alternatives has attracted many practitioners’ and academicians’ interest. Recent globalization events have further encouraged FMS development into distributed, self-reliant units of production center. The flexible manufacturing system in distributed system (FMSDS) considers multi-factory environments, where jobs are processed by a system of FMSs. FMSDS problems deal with the allocation of jobs to factories, independent assignment of job operation to the machines, and operations sequencing on the machine. Additionally, in many previous studies, impact of maintenance as one of the core parts of production scheduling has been neglected. This significantly affects the overall performance of the production scheduling. As such, maintenance has been considered in this paper as part of the production scheduling. The objective of this paper is to minimize the global makespan over all the factories. This paper proposes an Improved Immune Algorithm (IIA) to solve the FMSDS problem. Antibody encoding adoption explicitly represents the information of factory, job, and maintenance, whilst a greedy decoding procedure exploits flexibility and determines the job routing. Rather than s traditional mutation operator, an improvised mutation operator is used to improve the solutions by refining the most promising individuals of each generation. The proposed approach has been compared with other algorithms and obtained satisfactory results, where the algorithm performance has been tested with several parameter tunings.

A Cooperative Simulated Annealing Algorithm for the Optimization of Process Planning

Process planning is an essential component of computer aided process planning (CAPP), which involves operations selection from design features and operations sequencing of these selected operations. It makes process planning a complex combinatorial optimization problem to conduct of these two steps simultaneously. In this paper, we propose a cooperative simulated annealing (CoSA) approach for the process planning problem to minimize total manufacturing cost. The proposed CoSA algorithm employed a novel optimization strategy different from all the existing approaches in the literature. Simulated annealing was utilized to optimize the four components of a process plan individually and sequentially. The approach is tested on two parts from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

Automation of Process Planning for Boring of Turned Components With Arbitrary Internal Geometry From a Semi-Finished Stock

This paper describes a novel method for automated process planning for boring of turned components with arbitrary internal geometry from a semi-finished stock. Earlier work has been reported on process planning for boring of components with monotonic internal geometry made from bar stock. This paper addresses the more general problem of process planning of parts with nonmonotonic internal geometry from arbitrary given the initial geometry, i.e., from a casting or from a semi-finished stock. With the algorithms developed, we are able to achieve full automation of all aspects of the process plan, including operations sequencing, parameter selection, numerical control (NC) code generation, etc. Thus, it becomes possible to go from design to NC code in a fully automated fashion. In the present work we focus on a tightly defined part family, which results in very simple but robust automation algorithms. This is in contrast to much of the reported work on automated process planning, which generally targets broad part families, leading to complex algorithms that fall short of complete design-to-NC automation.

Automation of Process Planning for Rough Boring of Turned Components With Arbitrary Internal Geometry From a Semi-Finished Stock

This paper describes a novel method for automated process planning for rough boring of turned components with arbitrary internal geometry from a semi-finished stock. Earlier work has been reported on process planning for boring of components with monotonic internal geometry made from bar stock. This paper addresses the more general problem of process planning of parts with non-monotonic internal feature list from arbitrary given initial geometry, i.e., from a casting or from a semi-finished stock. With the algorithms developed, we are able to achieve full automation of all aspects of the process plan, including operations sequencing, parameter selection, NC code generation, etc. Thus, it becomes possible to go from design to NC code in a fully automated fashion. In the present work we focus on a tightly defined part family, which results in very simple but robust automation algorithms. This is in contrast to much of the reported work on automated process planning, which generally targets broad part families, leading to complex algorithms that fall short of complete design-to-NC automation.