Optimisation of the concurrent product and process configuration: an approach to reduce computation time with an experimental evaluation

Abstract

Concurrent configuration of a product and its associated production process is a challenging problem in customer/supplier relations dealing with customisable or configurable products. It gathers in a single model multiple choices and constraints which come simultaneously from products (choices of components or functionalities), from processes (choices of resources and quantities) and from their mutual interrelations. Considering this problem as a Constraint Satisfaction Problem (CSP), the aim of this article is to improve its optimisation, while considering multiple objectives. Using an existing evolutionary optimisation algorithm as a basis, we propose an approach that reduces the computation time required for optimisation. The idea is first to quickly compute a rough Pareto of solutions, then ask the user to select an area of interest, and finally to launch a second computation on this restricted area. After an introduction to the problem, the approach is explained and the algorithm adaptations are presented. Then various computation experiments results demonstrate that computation times are significantly reduced while keeping the optimality level.

Sustainable manufacturing through application of reconfigurable and intelligent systems in production processes: a system perspective

“…Indeed product configurators aim at overcoming the gap between customer needs and manufacturing capabilities—both in business-to-business (B2B) and business-to-consumer (B2C) relationships—by relying on different approaches: rule-based, model-based, and case-based 23 , 24 . Multiple research approaches explore the adoption of different computational algorithms for delivering a product configuration: from multi-objective frameworks 25 – 27 to fuzzy logic 28 as well as in the form of constraint satisfaction problems 29 , 30 . The challenge of mass customization and the adoption of advanced computational methods, e.g., deep learning techniques, highlight a semantic gap between customers and suppliers (especially in B2C relationships) because customers may not have enough expertise about unfamiliar products 31 .…”

Joint optimization of product configuration and process planning in Reconfigurable Manufacturing Systems

“…Some works classify customer requirements into non-negotiable and negotiable requirements [9], [10], [31]. They mapped their problem into a two steps constraint satisfaction problem.…”

Optimizing Product Configuration Problems with Multisourcing Supplier Selections under Both Carbon Cap and Carbon Tax Regulations

“…Hong et al [15] utilized the Genetic Algorithm to obtain optimal configuration given the AND-OR graph for representing a configurable product. Some recent studies concentrated on eliciting customer preferences by using the KANO approach [28], concurrent product and process configuration [29], and configuring products using online review data [30].…”