Minimizing Regular Objectives for Blocking Permutation Flow Shop Scheduling

Abstract

The objective of this work is to present and evaluate metaheuristics for the blocking permutation flow shop scheduling problem subject to regular objectives. The blocking problem is known to be NP-hard with more than two machines. We assess the difficulty level of this problem by developing two population-based meta-heuristics: Genetic Algorithm and Artificial Bee Colony algorithm. The final goal is to measure the performance of these proposed techniques and potentially contribute in possible improvements in the blocking benchmark instances. Furthermore, computational tests carried out on randomly generated test problems show that the approaches consistently yields good solutions in a moderate amount of time. Finally, an updated list of best-known solutions for the Taillard's and Ronconi and Henriques's benchmark is exposed: new best-known solutions for the blocking flow shop scheduling problem with makespan, total flow time, and total tardiness criteria are found.

An Efficient Iterated Greedy Algorithm for the Makespan Blocking Flow Shop Scheduling Problem

“…Under C max criterion, an effective Revised Artificial Immune Systems (RAIS) algorithm is proposed in [23], a three-phase algorithm presented in [24], and a Discrete Particle Swarm Optimization algorithm (DPSO) with self-adaptive diversity control was treated in [25]. Subsequently, we refer to the Memetic Algorithm (MA) in [26], the Iterated Local Search algorithm (ILS) coupled with a Variable Neighborhood Search (VNS) in [27], and the Blocking Genetic Algorithm (BGA) and Blocking Artificial Bee Colony (BABC) algorithms in [28]. Experimental results demonstrated that both of the two later proposed algorithms are more efficient in finding better solutions than all other leading techniques.…”

“…To generate the initial solution we have used the PF-NEH(x) heuristic as in [28]. However, instead of generating x solutions at the end of the heuristic we choose only the permutation with the minimum objective value.…”

“…Before testing BIG algorithm on benchmark sets, the computational experiments have been at first carried out on a set of randomly generated instances obtained following the procedure explained in Taillard. In our tests, the problem sizes are determined by varying the number of jobs and machines from 10 jobs and 3 machines to 100 jobs and 10 machines as was the case in [28].…”

“…In this subsection, we enlarge the domain of comparison and consider the BIG versus IG [21], MA [26], RAIS [23], and BGA [28] algorithms.…”