Meta-hierarchical-heuristic-mathematical- model of loading problems in flexible manufacturing system for development of an intelligent approach

Abstract

Flexible manufacturing system (FMS) promises a wide range of manufacturing benefits in terms of flexibility and productivity. These benefits are targeted by efficient production planning. Part type selection, machine grouping, deciding production ratio, resource allocation and machine loading are five identified production planning problems. Machine loading is the most identified complex problem solved with aid of computers. System up gradation and newer technology adoption are the primary needs of efficient FMS generating new scopes of research in the field. The literature review is carried and the critical analysis is being executed in the present work. This paper presents the outcomes of the mathematical modelling techniques for loading of machines in FMS's. It was also analysed that the mathematical modelling is necessary for accurate and reliable analysis for practical applications. However, excessive computations need to be avoided and heuristics have to be used for real-world problems. This paper presents the heuristics-mathematical modelling of loading problem with machine processing time as primary input. The aim of the present work is to solve a real-world machine loading problem with an objective of balancing the workload of the FMS with decreased computational time. A Matlab code is developed for the solution and the results are found most accurate and reliable as presented in the paper.

Effect of nature-inspired algorithms and hybrid dispatching rules on the performance of automatic guided vehicles in the flexible manufacturing system

“…The performance of AGVs and FMS under TEM is found to be satisfactory in terms of maximum service level, moderate flow time and minimum mean tardiness when compared with other dispatching rules. An analytical solution procedure for the loading and unloading problem of FMS with minimum computational time was proposed by Singh and Khan [34]. Simultaneous development of schedules for AGVs and FMS under different heuristic algorithms, namely clonal selection algorithm (CSA), modified memetic particle swarm optimization (MMPSO) algorithm and gray wolf optimization (GWO) algorithm, is attempted by the Chawla et al [5][6][7] and Chanda et al [9] and also optimized the AGV fleet size for the different FMS configurations.…”

Sustainable multi-objective scheduling for automatic guided vehicle and flexible manufacturing system by a grey wolf optimization algorithm

“…In order to evaluate different flexibilities of FMS Jain and Raj (2013) integrated simple additive weighting (SAW) and weighted product method (WPM) with analytical hierarchy process (AHP). Singh & Khan (2016) proposed an analytical model for the solution of loading and unloading problem in the FMS with minimum computational time. Wang et al 2016analyzed random arrival of work parts by a discrete even simulation study.In their simulation study authors applied apart launching dispatching rule-based tardiness estimation method.…”

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

“…In order to address realtime dynamic scheduling problem for welding operations and to minimize the makespan, the authors considered job quality, machine reliability and job delay along with controlling process time, sequencedependent time and job transport time in the formulated scheduling problem. The loading and unloading problems for the FMS was solved by Singh and Khan (2016). Authors developed an efficient analytical method for the solution of loading and unloading problems.…”