In the present work, apart from operating on the structure of a conventional genetic algorithm (GA), a heuristic which uses techniques like differential mutation probability, elitism and local search is used to produce near optimal solutions for large machine loading problems with less computational intensity. Two variants of the machine loading problem are analysed in the present work: single batch model and the multiple batch models. The sensitivity of the problem with respect to the tool capacity constraint is evaluated to find that moderately restricted problems requiring greater computational resources in comparison to lesser restricted and tightly restricted class of problems. The performance of various dispatching rules was compared to infer that the least slack principle fares better than the other tested dispatching rules. It is observed from the results, that the proposed heuristic is efficient in handling large and complex machine loading problems. © 2015 Inderscience Enterprises Ltd.

G. L. Samuel

Department of Mechanical Engineering

G Srinivasan

Department of Management Studies

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Optimisation of a machine loading problem using a genetic algorithm–based heuristic

International Journal of Productivity and Quality Management

In today's unstable economic conditions, achieving a sustained throughput is the prime objective of the companies. In this paper, an attempt is made on implementing the integrated concept of theory of constraints (TOC), lean and Six Sigma to get the maximum throughput which is the primary goal of TOC. An empirical model is derived to predict the organisational goal. Dynamic smart goal tracking system is developed in the proposed model. The goal of each employee or the sub-team's goal is aligned to team's overall goal with weighted ranking. Path finding to the strategic goal is decided with next high priority goal on an iterative mode. This approach aligns employee goals in line to accomplishing the organisation priority goal throughput. Vision of the organisation to sub-team gets clarity on what to be achieved. This system thinking approach, TOC philosophy enables to achieve the organisation goal in a better way.

Integration of lean, Six Sigma and theory of constraints for productivity improvement of mining industry

International Journal of Manufacturing Technology and Management

Memetic algorithm for non-identical parallel machines scheduling problem with earliness and tardiness penalties

A loading problem in a modern FMS is the allocation of a job from the job pool maintaining the flexibility of the system, reducing the system imbalance and thereby maximizing the throughput. In this context a heuristic has been developed. Ten sample problems have been tested with the proposed heuristic. A comparison has been made with the existing methods and it is found that the results are encouraging and indicate significant improvement. The Petri net model for the problem attempted by the proposed heuristic has been constructed to delineate its graphical representation and subsequent validation. © 1997 Taylor &amp; Francis Group, LLC.

International Journal of Production Research

A heuristic solution approach to the machine loading problem of an FMS and its Petri net model

The machine-loading problem of a flexible manufacturing system (FMS) has been recognized as one of the most important planning problems. In this research, a Genetic Algorithm (GA) based heuristic is proposed to solve the machine loading problem of a random type FMS. The objective of the loading problems is to minimize the system unbalance and maximize the throughput, satisfying the technological constraints such as availability of machining time, and tool slots. The proposed GA-based heuristic determines the part type sequence and the operation-machine allocation that guarantee the optimal solution to the problem, rather than using fixed predetermined part sequencing rules. The efficiency of the proposed heuristic has been tested on ten sample problems and the results obtained have been compared with those of existing methods.

Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach

This paper develops a modelling framework that addresses the machine loading problem of FMSs. A generic 0-1 mixed integer programming formulation that models the loading problem, with the minimization of system unbalance and maximization of throughput as the bi-criterion objective, has been proposed. The constraints considered are availability of tool slots and time on machines. Two heuristic methods are discussed to solve the problem. In heuristic 1, sequence generation is carried out using the fixed part sequencing rules. In heuristic 2, tabu-search-based methodology has been adopted to perturb the part sequences obtained from heuristic one and then reallocation of operations on machines are performed keeping in view the above objectives and constraints. The proposed methodology has been tested on ten problems representing three types of FMSs: Small, medium and large. Exhaustive computational experiments reveal that the proposed methodology consistently offers good results for all the test problems.

Journal	International Journal of Productivity and Quality Management
Publisher	Inderscience Enterprises Ltd.
ISSN	17466474
Open Access	Yes