A fuzzy goal-programming model of machine-tool selection and operation allocation problem in FMS: a quick converging simulated annealing-based approach

Abstract

A fuzzy goalprogramming model of machine-tool selection and operation allocation problem in FMS: a quick converging simulated annealing-based approach, Fuzzy set theory has been widely accepted in modelling of some of the vague phenomena and relationships that are non-stochastic in nature. The problem of machine-tool selection and operation allocations in a flexible manufacturing system usually involves parameters that are non-deterministic and imprecise in nature. This paper adopts a fuzzy goal-programming model having multiple conflicting objectives and constraints pertaining to the machine-tool selection and operation allocation problem, and a new random search optimization methodology termed Quick Converging Simulated Annealing (QCSA) is being used to resolve the underlying issues. The main feature of the proposed QCSA algorithm is that it outperforms genetic algorithm and simulated annealing approaches as far as convergence to the near optimal solution is concerned. Moreover, it is also capable of eluding local optima. Extensive experiments are performed on a problem involving real-life complexities, and some of the computational results are reported to validate the efficacy of the proposed algorithm.

Multi task selection including part mix, tool allocation and process plans in CNC machining centers using new binary PSO

“…This group corresponds to series "sento" [23] and "weing" [7]. This group contains 10 instances with known optima, where dimension varies between 28 and 105.…”

“…Recently a constrained programming based approach is proposed in [15] for dealing scheduling based problems, by considering tool planning and allocation, machine assignment, part routing, and task timing decisions altogether in the approach. Modern heuristic methods such as hybridized simulated annealing-tabu search technique [23], hybrid hopfield network-genetic algorithm [25] and ant colony optimization [6] inspired from some natural phenomenon have also been exploited to solve such NP-hard problems. The main advantage of these methods comes with the randomness that extends their applicability to a wide range of problems.…”

Biogeography-based optimisation for flexible manufacturing system scheduling problem

“…These approaches are implemented in the form of heuristic or metaheuristic algorithms. Mishra et al (2006) extended the work of Atmani and Lashkari (1998) and Rai, Kameshwaran, and Tiwari (2002) and proposed heuristic-based approach namely a quick convergence simulated annealing (SA) to solve a fuzzy goal programming model of FMS machine-tool selection and operation allocation problem. Low, Yip, and Wu (2006) extended a MILP formulation to FMS operation scheduling that aims at minimising a multiple objective such as mean flow time, mean tardiness and mean machine idle time while taking set-up time into account.…”

Performance evaluation of flexible manufacturing systems under uncertain and dynamic situations

“…If for operation j O k  where E [RD jk ] > S jk (P), the part type j will be delayed by Z jk, J = E [RD jk ] -S jk,J (P S ) where Z jk,J is the delay in processing of part type j due to breakdowns during the processing of operation k. The first objective function defined above in equation (14) includes the parameter Y n that is defined below in equation (25) as,…”