Solving the tool switching problem with memetic algorithms

Abstract

The tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to be completed), as well as the tools to be loaded/unloaded at each step to process these jobs, such that the total number of tool switches is minimized. Different exact and heuristic methods have been defined to deal with this problem. In this work, we focus on memetic approaches to this problem. To this end, we have considered a number of variants of three different local search techniques (hill climbing, tabu search, and simulated annealing), and embedded them in a permutational evolutionary algorithm. It is shown that the memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).

Hybrid method with CS and BRKGA applied to the minimization of tool switches problem

“…The last approach provides better solutions than those found by the Beam Search and Tabu Search. Also, Amaya et al [15] combine a Genetic Algorithm with three different local search heuristics: hill climbing, Tabu Search and Simulated Annealing. The memetic algorithm with hill climbing found the best results.…”

Nonpermutation flow line scheduling by ant colony optimization

“…Biologically inspired general-purpose optimization algorithms are capable of dealing with large job-size problems and with the exponential increase in the solution search space with the number of machines and jobs. Examples of metaheuristics include taboo search, simulated annealing, genetic algorithms (Elbeltagi et al, 2005), and memetic algorithms (Amaya et al, 2012). Despite their successful performance, in the extensive reviews by Ruiz and Maroto (2005) and by Ribas et al (2010), ant colony or pheromone-based systems are not present.…”

A Simple and Effective Hybrid Genetic Search for the Job Sequencing and Tool Switching Problem

“…Hybrid methods combining genetic algorithms (GA) with other search procedures can be found in (Ahmadi et al, 2018;Amaya et al, 2008Amaya et al, , 2011Amaya et al, , 1305Amaya et al, , 2012Amaya et al, , 2010Chaves et al, 2016). Amaya et al (1305Amaya et al ( , 2008Amaya et al ( , 2012 combined GA with local search-based procedures such as hill climbing, simulated annealing and tabu search, leading to hybrid methods which are also known under the name of memetic algorithms. Amaya et al (2011Amaya et al ( , 1305Amaya et al ( , 2010 combined GA with a multi-agent approach or cross-entropy methods.…”

“…We also refer to Calmels (2019) for a comprehensive literature review and classification of SSP variants. Tang and Denardo (1988a) 1988 Exact methods + heuristics Tang and Denardo (1988a) Bard (1988) 1988 Exact methods + heuristics Bard (1988) Crama et al (1994 1994 Heuristics Most of the aforementioned GA-based implementations use traditional parent-selection strategies (Whitley, 2019) such as roulette wheel (Ahmadi et al, 2018) and binary tournament (Amaya et al, 2008(Amaya et al, , 2011(Amaya et al, , 2012. Survivor selections are solely based on individual objective values, typically obtained with KTNS (Ahmadi et al, 2018;Amaya et al, 2008Amaya et al, , 2011Amaya et al, , 1305Amaya et al, , 2012Chaves et al, 2016).…”

“…Ahmadi et al ( 2018) also take into account a chromosome similarity function to remove individuals from the population. The crossover operators applied are parameterized uniform crossover (Chaves et al, 2016), uniform cycle crossover (Amaya et al, 2012), alternating position (APX) (Amaya et al, 2008(Amaya et al, , 2011 and partially mapped (PMX) (Ahmadi et al, 2018). Finally, although some authors have developed local search procedures based on problem-specific metrics such as 0-blocks and 1-blocks (Crama et al, 1994), ties arising during move evaluations are generally handled arbitrarily (Amaya et al, 2011(Amaya et al, , 2012.…”