Multiple Product Variants Research Articles

A cooperative coevolutionary algorithm for design of platform-based mass customized products

As a new business model, mass customization (MC) intends to enable enterprises to comply with customer requirements at mass production efficiencies. A widely advocated approach to implement MC is platform product customization (PPC). In this approach, a product variant is derived from a given product platform to satisfy customer requirements. Adaptive PPC is such a PPC mode in which the given product platform has a modular architecture where customization is achieved by swapping standard modules and/or scaling modular components to formulate multiple product variants according to market segments and customer requirements. Adaptive PPC optimization includes structural configuration and parametric optimization. This paper presents a new method, namely, a cooperative coevolutionary algorithm (CCEA), to solve the two interrelated problems of structural configuration and parametric optimization in adaptive PPC. The performance of the proposed algorithm is compared with other methods through a set of computational experiments. The results show that CCEA outperforms the existing hierarchical evolutionary approaches, especially for large-scale problems tested in the experiments. From the experiments, it is also noticed that CCEA is slow to converge at the beginning of evolutionary process. This initial slow convergence property of the method improves its searching capability and ensures a high quality solution.

A multi-criteria assessment tool for screening preliminary product platform concepts

Platform concept evaluation is a more challenging task than evaluating a single product concept since a platform must effectively support multiple product variants over a prolonged period of time. Existing platform methods develop specific criteria in depth, yet an evaluation of alternative platforms should be based on a broad set of criteria. Based on expert interviews, personal experience, and a literature search we propose a platform assessment tool consisting of 19 criteria for platform evaluation. The criteria are group into six categories: customer satisfaction, variety, after-sale, organization, flexibility, and complexity. The tool is focused on the early platform architecture phase, before proof-of-concept prototyping. However, it can also be used subsequently for platform refinement when more data becomes available. We demonstrate our platform assessment tool through an example with a cordless drill platform.

A Tandem Evolutionary Algorithm for Platform Product Customization

The increasing demand for reduction in time to market has led to new product development methodologies focusing on reuse. Platform product customization emerges as a basic idea for avoiding designing a new product completely from scratch, but reusing a product platform instead. Key dimensions of some modules of the platform can be stretched or shrunk while modules themselves can be swapped to formulate multiple product variants in a family according to specific customer requirements. This paper formulates such a problem of platform product customization as an optimization problem consistent with the manufacturer’s goal in the design of product variant(s) while satisfying customer requirements and design constraints. Optimal customization takes place at the levels of product structure and module parameters. A tandem evolutionary algorithm is proposed for identifying the optimal structural composition and the optimal parameters of the corresponding structure. The encoding schemes and genetic operators for structure and parameter optimization are designed, respectively. A case study of gantry crane customization is given to illustrate how the proposed evolutionary customization design method is used. The effectiveness of the method is also evaluated through a series of sensitivity analyses.

Modular product family design: Agent-based Pareto-optimization and quality loss function-based post-optimal analysis

The advent of mass customization and increased manufacturing competition has necessitated that many companies offer platform-oriented multiple product variants. Various design strategies such as Design for Variety and product family design have become critical in this respect. This paper provides a two-step approach to tackle the modular product family design problem. The first step performs a multi-objective optimization using a multi-agent framework to determine the Pareto-design solutions for a given module set. The proposed multi-agent framework is new and has built in flexibility to handle various constraints such as module compatibility during the optimization process. The second step performs post-optimization analysis that includes a novel application of the quality loss function to determine the optimal platform level for a related set of product families and their variants. The proposed method is applied to a product family design example to demonstrate its validity and effectiveness.

Managing Multiple Product Variants in Assembly Control with a Fuzzy Petrinet Approach

Companies with make-to-order businesses are facing increasingly dynamic movements within their sales markets. This leads to a growing variety of products and a demand for higher flexibility in order processing. Different logistical goals, process flexibility and fuzzy-expressed dimensions as well as short-term changes due to rush-orders or disturbances, have to be considered within this multiple product variant environment. This paper describes how these controversial requirements can be appropriately represented within a scheduling model, by applying a fuzzy Petri-net approach. All in all, control personnel can be efficiently supported. The described model and methods are verified within an industrial application.