Optimization of process planning with various flexibilities using an imperialist competitive algorithm

Lian, Kunlei; Zhang, Chaoyong; Shao, Xinyu; Gao, Liang

doi:10.1007/s00170-011-3527-8

Cited by 54 publications

(20 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lian et al [15] proposed an imperialist competitive algorithm (ICA) to solve IPPS problem. Lian et al [16] proposed a mathematical model for process planning problem with an objective of total cost minimization. An ICA was designed to solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Model for Multiworkshop IPPS Problem in Batch Production

Yang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Integrated Process Planning and Scheduling (IPPS) problem is an important issue in production scheduling. Actually, there exit many factors affecting scheduling results. Many types of workpieces are commonly manufactured in batch production. Moreover, due to differences among process methods, all processes of a workpiece may not be performed in the same workshop or even in the same factory. For making IPPS problem more in line with practical manufacturing, this paper addresses an IPPS problem with batches and limited vehicles (BV-IPPS). An equal batch splitting strategy is adopted. A model for BV-IPPS problem is established. Makespan is the objective to be minimized. For solving the complex problem, a particle swarm optimization (PSO) with a multilayer encoding structure is proposed. Each module of the algorithm is designed. Finally, case studies have been conducted to validate the model and algorithm.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Model for Multiworkshop IPPS Problem in Batch Production

Yang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Usually, machining costs consist of machine cost (MC), tool cost (TC), machine change cost (MCC), tool change cost (TCC) and set-up cost (SC) [4,31]. The first objective can thus be regarded as the total weighted cost (TWC) by summing up the weighted costs mentioned above; and this objective can be expressed as follows:…”

Section: Objectivesmentioning

confidence: 99%

“…In the algorithm, the genetic algorithm (GA) is treated as the main framework while SA is used as the local search method and machine cost, tool cost, machine change cost, set-up change cost as well as tool change cost were considered in a weighted sum. Later, they investigated the optimization of process planning with various flexibilities, and a novel imperialist competitive algorithm (ICA) is employed to determine promising solutions [4]. Shin et al also presented a multi-objective symbiotic evolutionary algorithm (MOSEA) for the process planning problem with various flexibilities based on network graphs [5].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

2017

Self Cite

View full text Add to dashboard Cite

Abstract:Process planning is an important function in a manufacturing system; it specifies the manufacturing requirements and details for the shop floor to convert a part from raw material to the finished form. However, considering only economical criterion with technological constraints is not enough in sustainable manufacturing practice; formerly, criteria about low carbon emission awareness have seldom been taken into account in process planning optimization. In this paper, a mathematical model that considers both machining costs reduction as well as carbon emission reduction is established for the process planning problem. However, due to various flexibilities together with complex precedence constraints between operations, the process planning problem is a non-deterministic polynomial-time (NP) hard problem. Aiming at the distinctive feature of the multi-objectives process planning optimization, we then developed a hybrid non-dominated sorting genetic algorithm (NSGA)-II to tackle this problem. A local search method that considers both the total cost criterion and the carbon emission criterion are introduced into the proposed algorithm to avoid being trapped into local optima. Moreover, the technique for order preference by similarity to an ideal solution (TOPSIS) method is also adopted to determine the best solution from the Pareto front. Experiments have been conducted using Kim's benchmark. Computational results show that process plan schemes with low carbon emission can be captured, and, more importantly, the proposed hybrid NSGA-II algorithm can obtain more promising optimal Pareto front than the plain NSGA-II algorithm. Meanwhile, according to the computational results of Kim's benchmark, we find that both of the total machining cost and carbon emission are roughly proportional to the number of operations, and a process plan with less operation may be more satisfactory. This study will draw references for the further research on green manufacturing in the process planning level.

show abstract

“…To settle the optimization of process planning with various flexibilities, Lian et al [16] utilized the ICA to find promising solutions with reasonable computational cost under the objective of minimizing total weighted sum of manufacturing cost. Shokrollahpour et al [17] and Seidgar et al [18] both exploited the ICA in assembly flow shop problem while, respectively, using the Taguchi method and neural network as their own tools in regulating the parameters.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

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

Zhang

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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.

show abstract

Optimization of process planning with various flexibilities using an imperialist competitive algorithm

Cited by 54 publications

References 34 publications

A Mathematical Model for Multiworkshop IPPS Problem in Batch Production

A Mathematical Model for Multiworkshop IPPS Problem in Batch Production

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

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

Contact Info

Product

Resources

About