A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem

Abstract

Modern production systems require multiple manufacturing centers—usually distributed among different locations—where the outcomes of each center need to be assembled to generate the final product. This paper discusses the distributed assembly permutation flow‐shop scheduling problem, which consists of two stages: the first stage is composed of several production factories, each of them with a flow‐shop configuration; in the second stage, the outcomes of each flow‐shop are assembled into a final product. The goal here is to minimize the makespan of the entire manufacturing process. With this objective in mind, we present an efficient and parameter‐less algorithm that makes use of a biased‐randomized iterated local search metaheuristic. The efficiency of the proposed method is evaluated through the analysis of an extensive set of computational experiments. The results show that our algorithm offers excellent performance when compared with other state‐of‐the‐art approaches, obtaining several new best solutions.

Scheduling with day shifts and breaks

“…Further research may focus on problems with some other criteria such as minimizing total completion time (Parsa et al., 2019; Zhu et al., 2019) and maximizing the noon break period under a given deadline for all the jobs or problems in some other production environments such as flow shop (Hakeem‐Ur‐Rehman et al., 2019; Krim et al., 2019b; Ferone et al., 2020; de Siqueira et al., 2020), job shop (Benttaleb et al., 2018, 2019; Kubiak et al., 2020), and parallel machine with a single server (Arnaout, 2017; Bektur and Saraç, 2019). It is also worth considering scheduling problems in industries that are open around the clock, such as in airports, restaurants, hospitals, movie theaters, and in some retail stores, where morning, afternoon, and night shifts are involved.…”

Memetic Algorithm With Meta-Lamarckian Learning and Simplex Search for Distributed Flexible Assembly Permutation Flowshop Scheduling Problem

“…Sang et al [27] proposed an invasive weed optimization algorithm. Ferone et al [28] devised a biased-randomized iterated local search algorithm. Ochi and Driss [29] presented a bounded-search iterated greedy algorithm.…”

A Simheuristic Algorithm for Solving the Stochastic Omnichannel Vehicle Routing Problem with Pick-up and Delivery

“…In this random selection process, the higher-ranked candidates receive a higher probability of being selected. Biased-randomized algorithms have been employed for solving different COPs in transportation [40][41][42], scheduling [43,44], and facility location problems [39,45].…”