Abstract
Rising energy prices, increasing maintenance costs, and strict environmental regimes have augmented the already existing pressure on the contemporary manufacturing environment. Although the decentralization of supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirement and enhancing the process planning and scheduling functions are the predominant approaches still needed to be addressed. Therefore, this paper aims to address this issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption, and increased service utilization rate, interoperability, and reliability. To execute the approach initially, text-mining-based supervised machine-learning models, namely Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machines (SVM) were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming (MILP) model. We then proposed a Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem for 10 different instances, along with a comparison of the results with a Non-Dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the feasibility of the approach.
Highlights
Increasing competition, coupled with advancing computing technologies and the advent of decentralization in the supply chain, has led to the attainment of a shorter product life cycle, reducing production costs and responding to customer demands with greater flexibility
For most of the instances, the proposed Hybrid Multi-Objective Moth Flame Optimization algorithm (HMFO) gives better makespan and energy consumption values when compared with existing Simulated Annealing (SA)-Genetic Algorithm (GA) makespan and energy consumption values
And 11, it can be confirmed that the values marked in bold are the best values for showing the superiority of the proposed HMFO algorithm over the Non-Dominated Sorting Genetic algorithm (NSGA). Advancements in technology, such as information and communication technologies (ICT), have changed the traditional manufacturing systems practices. This is especially true for a distributed manufacturing system due to its ability to cater to its needs such as
Summary
Increasing competition, coupled with advancing computing technologies and the advent of decentralization in the supply chain, has led to the attainment of a shorter product life cycle, reducing production costs and responding to customer demands with greater flexibility. Manufacturing units are leaning toward a distributed manufacturing environment far from the traditional approach of promptly manufacturing products [1] This involves multiple processes consisting of classification of manufacturing units, assignment of tasks as per product category on the basis of requirements, and information exchange within various units of an enterprise and between firms. All these together represent parameters of a compound scenario needed to be refined. Irrespective of any kind of manufacturing system, the prominent functions are process planning and scheduling
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