Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

Abstract

Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

Shop scheduling in manufacturing environments: a review

“…6 in Section 3, the shop scheduling problem is the inner layer of the ecosystem described in this work. At this level, it is possible to identify multiple characteristics of the jobs to 2012) makespan (Michalewicz, 1996) Amin-Naseri and Afshari (2012) makespan; hybridization with local search (Goldberg, 1989) Lihong and Shengping (2012) makespan, mean flowtime Zhang and Wong (2013) makespan; hibridization with multi-agent system Lv and Qiao (2014) makespan; flowtime, machine utilization rate Zhang and Wong (2015) makespan Mohapatra et al (2015) multi-objective NSGA-II; makespan, machining cost, idle time Chaudhry and Usman (2015) makespan simulation-based multi-objective under uncertainty Xia et al (2016) makespan; hybridization with Variable Neighborhood Search (Hansen andMladenović, 2003) Luo et al (2017) multi-objective; makespan, total tardiness, total flowtime, maximum and total machine workload Zhang et al (2017) makespan Lee and Ha (2019) makespan Uslu et al (2019) makespan; hybridization with ant colony optimization Ba et al (2020) multi-objective; earliness/tardiness, maximum and total machine workload Zhang et al (2020b) be processed and the resources available on the shop floor, the workflow of the jobs while they are processed in the machines, and the mainly used performance metrics.…”

Distributed scheduling problems in intelligent manufacturing systems

“…The types of production shops include (hybrid) flow shop, parallel-machine scheduling, (flexible) job shop, and generally distributed production environments. In some studies, the distributed scheduling problems are integrated with other problems, e.g., distribution problems [35][36][37] , planning problems [38,39] , resource allocation problems [39] , and vehicle routing problems [40] . Few publications focus on real-life areas, e.g., semiconductor wafer manufacturing [41] .…”

“…To address the highly complicated distributed scheduling problems, SI and EAs have been adopted, including GA [38,39,44,51,88,[93][94][95][96][97][98][99][100][101][102][103][104] , EDA [50,81,105] , MA [106] , VNS [40,42,43,53,64,76,82,98,[107][108][109] , TS [43,55,89,110,111] , PSO [107] , ACO [35,112] , DE [42,58,61,70,113] , SS [114,115] , IGA [49, 50, 55-57, 59, 63, 80, 82, 83, 86, 89, 108, 109, 115-120] , SA [38,51,…”

An Adaptive Multi-Strategy Artificial Bee Colony Algorithm for Integrated Process Planning and Scheduling

“…Zhang et al [35] developed imperialist competitive algorithm (ICA) for the districted IPPS, and GA-based method was adapted to maintain the robustness of the plan and schedule. Wang and Song [36] presented a framework of collaborative process planning system supported by a real-time monitoring system.…”