Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation

Abstract

This paper proposes a genetic algorithm (GA) to find the pseudo-optimum of integrated process planning and scheduling (IPPS) problems. IPPS is a combinatorial optimization problem of the NP-complete class that aims to solve both process planning and scheduling simultaneously. The complexity of IPPS is very high because it reflects various flexibilities and constraints under flexible manufacturing environments. To cope with it, existing metaheuristics for IPPS have excluded some flexibilities and constraints from consideration or have built a complex structured algorithm. Particularly, GAs have been forced to construct multiple chromosomes to account for various flexibilities, which complicates algorithm procedures and degrades performance. The proposed new integrated chromosome representation makes it possible to incorporate various flexibilities into a single string. This enables the adaptation of a simple and typical GA procedure and previously developed genetic operators. Experiments on a set of benchmark problems showed that the proposed GA improved makespan by an average of 17% against the recently developed metaheuristics for IPPS in much shorter computation times.

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.…”

A Production Scheduling Method for Resolving Multiple Equipment Operation Conflicts in Lithium Battery Mills

“…Decoding of chromosomes means reducing the values of individual genes in chromosomes to meaningful solutions of the problem under model constraints. The decoding process in this paper is improved on the basis of the decoding approach of [31]. Firstly, all chromosomes are decoded according to the general decoding method to obtain the semi-active solution, and the top 30% chromosomes with high adaptation in the population are decoded by insertion: when arranging the processing moment of each order, the order start time slice is inserted into the earliest equipment vacant processable time slice under satisfying the rolling process constraint, and the new coding order is decoded in turn to obtain the new scheduling solution, and the old coding order is replaced by the new coding order.…”

Solving Integrated Process Planning, Dynamic Scheduling, and Due Date Assignment Using Metaheuristic Algorithms

“…Meissner and Aurich [107] applied a cyberphysical system for IPPS. Sustainable IPPS problem was solved by Lee and Ha [108] using a standard GA. Yin et al [109] used two competing agents for the integrated production, inventory, and batch delivery scheduling and due date assignment. Mor [110] studied common SWDDA with the focus on minmax objective functions.…”