P. Asokan

Optimization of multi-pass turning operations using ant colony system

This paper proposes a new optimization technique based on the ant colony algorithm for solving multi-pass turning optimization problems. The cutting process has roughing and finishing stages. The machining parameters are determined by minimizing the unit production cost, subject to various practical machining constraints. The results indicate that the proposed ant colony framework is effective compared to other techniques carried out by different researchers.

A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations

A genetic algorithm (GA) based optimization procedure has been developed to optimize grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using multi-objective function model with a weighted approach for surface grinding process. The procedure evaluates the production cost and production rate for the optimum grinding condition, subjected to constraints such as thermal damage, wheel wear parameters, machine tool stiffness and surface finish. New GA procedure is illustrated with an example and optimum results such as production cost, surface finish, metal removal rate are compared with quadratic programming techniques.

A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems

Firefly algorithm FA is a nature-inspired optimisation algorithm that can be successfully applied to continuous optimisation problems. However, lot of practical problems are formulated as discrete optimisation problems. In this paper a hybrid discrete firefly algorithm HDFA is proposed to solve the multi-objective flexible job shop scheduling problem FJSP. FJSP is an extension of the classical job shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. Three minimisation objectives - the maximum completion time, the workload of the critical machine and the total workload of all machines are considered simultaneously. This paper also proposes firefly algorithms discretisation which consists of constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. In the proposed algorithm discrete firefly algorithm DFA is combined with local search LS method to enhance the searching accuracy and information sharing among fireflies. The experimental results on the well-known benchmark instances and comparison with other recently published algorithms shows that the proposed algorithm is feasible and an effective approach for the multi-objective flexible job shop scheduling problems.

A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints

A greedy randomised adaptive search procedure (GRASP) is an iterative multi-start metaheuristic for difficult combinatorial optimisation. The GRASP iteration consists of two phases: a construction phase, in which a feasible solution is found and a local search phase, in which a local optimum in the neighbourhood of the constructed solution is sought. In this paper, a GRASP algorithm is presented to solve the flexible job-shop scheduling problem (FJSSP) with limited resource constraints. The main constraint of this scheduling problem is that each operation of a job must follow an appointed process order and each operation must be processed on an appointed machine. These constraints are used to balance between the resource limitation and machine flexibility. The model objectives are the minimisation of makespan, maximum workload and total workload. Representative benchmark problems are solved in order to test the effectiveness and efficiency of the GRASP algorithm. The computational result shows that the proposed algorithm produced better results than other authors’ algorithms.

Optimization of Surface Grinding Operations Using Particle Swarm Optimization Technique

The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. in this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques.

Concurrent optimization of assembly tolerances for quality with position control using scatter search approach

Tolerance allocation to individual parts in any assembly should be a vital design function with which both the design and manufacturing engineers are concerned. Generally design engineers prefer to have tighter tolerances to ensure the quality of their design, whereas manufacturing engineers prefer loose tolerances for ease of production and the need to be economical. This paper introduces a concurrent tolerance approach, which determines optimal product tolerances and minimizes combined manufacturing and quality related costs in the early stages of design. A non-linear multivariable optimization model is formulated here for assembly. A combinatorial optimization problem by treating cost minimization as the objective function and stack-up conditions as the constraints are solved using scatter search algorithm. In order to further explore the influence of geometric tolerances in quality as well as in the manufacturing cost, position control is included in the model. The results show how position control enhances quality and reduces cost.

A GRASP algorithm for flexible job-shop scheduling with maintenance constraints

In most realistic situations, machines may be unavailable due to maintenance, pre-schedules and so on. The availability constraints are non-fixed in that the completion time of the maintenance task is not fixed and has to be determined during the scheduling procedure. In this paper a greedy randomised adaptive search procedure (GRASP) algorithm is presented to solve the flexible job-shop scheduling problem with non-fixed availability constraints (FJSSP-nfa). The GRASP algorithm is a metaheuristic algorithm which is characterised by multiple initialisations. Basically, it operates in the following manner: first a feasible solution is obtained, which is then further improved by a local search technique. The main objective is to repeat these two phases in an iterative manner and to preserve the best found solution. Representative FJSSP-nfa benchmark problems are solved in order to test the effectiveness and efficiency of the proposed algorithm.

A GRASP algorithm for the Integration of Process Planning and Scheduling in a flexible job-shop

The Integration of Process Planning and Scheduling (IPPS) is an important research issue in achieving optimum manufacturing processes. In IPPS, vast search spaces and complex technical constraints prove to be significant barriers to the effectiveness of the processes. This paper proposes a Greedy Randomised Adaptive Search Procedures (GRASP) algorithm for the integration of process planning with production scheduling in a flexible job-shop environment. The GRASP algorithm is a metaheuristic characterised by multiple initialisations. Basically, it comprises two phases: construction phase and local search phase. For this work, the construction phase is considered through computational experiments. The performance of the presented algorithm is evaluated and compared with benchmark problem and the results demonstrate that the proposed algorithm is an effective and practical approach for the flexible job-shop.

A CAPP framework with optimized process parameters for rotational components

Process planning, as a critical stage integrating the design and manufacturing phase in a manufacturing environment, has been automated to meet the needs for higher productivity and lower production cost. Being an input to various systems such as scheduling and routing, process planning results are of great importance in the manufacturing stage. Though feature extraction and sequence optimization have been given much attention, the process parameters are rarely dealt with. This paper focuses on the development of a new generative computer aided process planning (CAPP) framework for rotational components. The developed framework includes modules for feature extraction based on CAD application programming interfaces, determination of the optimum sequence and generation of optimum process parameters. The optimization of the machining operations is achieved using the evolutionary technique. The approach resulted in the reduction and prediction of machining time and cost. The framework is demonstrated with a case study.

An artificial immune system-based algorithm to solve linear and loop layout problems in flexible manufacturing systems

The Facility Layout Problem (FLP) is concerned with the arrangement of facilities in a given location and is one of the well-explored problems in the field of combinatorial optimisation. Both linear and loop layouts are widely preferred configurations in Flexible Manufacturing Systems (FMSs) because of their materials handling flexibility and agility in accommodating new parts and processes. This paper considers both single-row and loop layout problems in FMSs. The problem is modelled as a Quadratic Assignment Problem (QAP). The Artificial Immune System (AIS) algorithm is employed to solve this problem. The effectiveness of the AIS algorithm is tested and validated through benchmarking problems. The results show that the AIS approach performs well in all test problems. In addition, the single-row and loop layout problems are compared based on the objective of minimising the materials handling cost.