Content uploaded by Yohanes Kristianto
Author content
All content in this area was uploaded by Yohanes Kristianto
Content may be subject to copyright.
A preview of the PDF is not available
Mass customization design of engineer-to-order products using Benders’ decomposition and bi-level stochastic programming
Abstract and Figures
Leveraging product differentiation and mass production efficiency in mass customization basically entails a configure-to-order paradigm. In the engineer-to-order (ETO) business, however, companies build unique products in response to ‘foreseeable’ customer specifications. The key challenge of ETO mass customization lies in the complexity of accommodating future design changes when customers are involved in customizing design specifications. This paper proposes a two-stage, bi-level stochastic programming framework to tackle ETO mass customization. At the first stage, product platform configuration is integrated with production reconfiguration, which is formulated as a shortest path problem with resource constraints (SPPRC) to optimize production delays within the capabilities of the process platform. Benders’ decomposition algorithm is applied to solve this optimal configuration problem owing to its high computational efficiency. The second stage scrutinizes the optimal configuration resulting from the first stage for scaling optimization of design parameters (DPs) for each module. All DPs are differentiated by standard or customizable DPs. A bi-level stochastic program is implemented to leverage conflicting goals between the producer (leader) and consumer (follower) surpluses. As a result, ETO customization design is anchored with optimal values of standard DPs and optimal value ranges of customizable DPs. A case study of ship engine and power generator ETO design is presented, demonstrating the feasibility and potential of the ETO mass customization framework.
Figures - uploaded by Yohanes Kristianto
Author content
All figure content in this area was uploaded by Yohanes Kristianto
Content may be subject to copyright.
... Most of the earlier studies focused on configuration modeling and solving [10] such as product configuration knowledge [16] and conceptual modeling [17], and product configuration process [18]. For complex ETO products, a two-step strategy including product architecture configuration and module configuration is proposed in [19]. ...
... However, the two articles only describe configuration solution concepts. Kristianto et al. [18] explored the mass-customization problem of ETO products in two stages: integration configuration of the product and process platform, and module parameter value configuration. They solved the problem using Benders decomposition and double-layer stochastic programming. ...
This paper proposes a novel two-stage and multi-objective optimization design method for the configuration design of complex engineering-to-order (ETO) product under imprecise matching and other uncertainties. The goal is to support selection of the optimal technical bid solutions while meeting requirements. A new two-stage configuration design framework for complex ETO products is proposed. Stage one is product architecture configuration design, supported by an engineering characteristics design method based on constraint satisfaction problems and Bayesian networks, and stage two is physical module configuration design, where a multi-objective optimal configuration model of physical modules is developed with the goals of minimum production cost, shortest delivery time, and maximum degree of matching technical requirements under imprecise matching of technical requirements and uncertainties in such as production cost and delivery time. As for the new selection method for obtaining an optimal technical bid solution scheme, it integrates a non-dominated sorting genetic algorithm (NSGA-II) and an approximate ideal solution ranking method (TOPSIS). Our approach has been applied to the design of a technical bid solution of subway’s bogie. The results show that this approach enables bidders to quickly select the most interesting solution during a bidding process. The proposed approach aids the bidders to quickly create ETO product scheme designs, and then advise a new selection method for the bidders to quickly obtain a technical bid solution from the above product scheme designs, which has a minimum cost while meeting order requirements.
... Hu et al. [11] Summarise the concepts and technologies about DFMC, including the product family and product architecture Aggarwal et al. [12] Develop a module-based service family design for mass customisation Kristianto et al. [13] Consider the configuration and re-configuration of the product platform Wang et al. [14] Investigate the relationships between product modularity and DFMC Bonev et al. [15] Propose a formal approach for the design and customisation of the entire product family Yang and Jiao [16] Game-theoretical method to jointly decide the optimal configuration of the product family design and supply chain Marchesi and Matt [17] Consider the sustainability of mass production in the domain of product platform development Xu et al. [18] Propose a decision support model to assist manufacturers to optimise the product design process ...
... Moreover, a product ecosystem is present to quantitatively analyse the customer's perceived value based on their feelings on product variety and process variety. Kristianto et al. [13] consider the complexities of product design for mass customisation and propose a twostage optimisation model to tackle engineer-to-order mass customisation. In the first stage, the integration of the product platform configuration and reconfiguration is formulated as a shortest path problem considering resource constraints to optimise production delays within the capabilities of the process platform. ...
Abstract This survey provides a review of the fundamental approaches to design for mass customisation (DFMC), design for manufacturing (DFM) and design for supply chains (DFSC). The key term here is design while mass customisation, manufacturing and supply chain are the contexts from which the respective design objectives are derived. While these three areas of design are closely related, they have different focusses, which is reflected in the broader range of approaches proposed in the literature. The authors look at the literature through the lens of the product, process, and supply chain optimisation, with a variety of objectives ranging from improving product quality and variety while reducing costs, minimising environmental impacts and optimising supplier manufacture cooperation. In addition to the reviews of the approaches to DFMC, DFM and DFSC, recommendations on their practical system implementations are provided. While the authors acknowledge that the richness of the literature of each of the three design areas warrants a dedicated literature review, the main purpose of this survey is to pursue an integrated view on the three design issues faced by modern manufacturers and provide them and other related practitioners with a summary of representative approaches in the literature. Although it was not intended to conduct an exhaustive literature review of the literature, researchers from academia may still find the work useful by looking at the interactions of the three design areas from the perspective of joint‐decision making, which is the angle from which the literature is approached.
... They also applied the BLP to find advertising expenditures, optimal equilibrium prices, and production policies. Kristianto et al. [39] presented a two-stage bi-level stochastic model for the mass production problem and considered the manufacturer as the leader and the consumer as the follower in order to model the contradictory objectives between them. They applied the BDA to solve the proposed complex model and obtain an optimum solution. ...
... Third step: Eqs.(38) and(39) are added to the constraints of the original model. ...
... More complex approaches combine the use of the KKT conditions with other techniques. For example, in Kristianto et al. (2013) the stochastic bilevel problem is reduced to a single level using KKT conditions and is then solved using Benders' decomposition. Related approaches include (Saharidis and Ierapetritou 2009), where an algorithm for solving mixed-integer bilevel linear problems based on Benders' decomposition is presented. ...
This paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k -medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k -medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approach.
... The response to this question could depend on the environment the ETO company operates in. The solution would help understand whether the current order-fulfilment strategy supports the achievement of the desired performance outcome (Haug et al., 2009;Kristianto et al., 2013;Schoenwitz et al., 2017). Hence, in this study, the authors aim to understand the sources of differentiation between the environments that ETO companies can face and how ETO companies react to strategically fit the order-fulfilment strategy in the environment. ...
Recent empirical studies have refined our understanding of engineer-to-order (ETO) situations, supporting the existence of different order-fulfilment strategies based on the degree of customer involvement in the engineering and production activities, which differs depending on the strategic fit with the environment in which the company operates. Despite the importance of this finding, limited attempts have been made to comprehensively understand the determinants for this strategic choice in ETO companies. To overcome this gap, this study aimed to investigate the sources of differentiation between the environments that ETO companies can face and the ways of reacting to strategically fit the order-fulfilment strategy. Therefore, this research analysed the existing literature through a contingency theory lens and performed a multiple case-study research in a specific ETO sector, i.e. the machinery industry. The study identified five different order-fulfilment strategies implemented in the machinery industry to provide different product families to the market. For each strategy, the different environment characteristics were defined, and the performance outcome was measured, explaining the rationale for the positioning of the product families in different strategies. The findings of this study have two main contributions. First, the study contributes to theory by deepening and refining the analysis of contingencies for choosing different order-fulfilment strategies in the ETO context. Second, the study provides practical guidelines to ETO companies that want to adapt their order-fulfilment strategies to the unexpected or planned changes in their environment.
... Secondly, the upper objective and the lower objective can form their own optimal values through game theory. e mathematical description of the bi-level programming model is as follows [17]: ...
The optimal configuration of manufacturing resources in the cloud manufacturing environment has always been the focus of research on various advanced manufacturing systems. Aiming at the problem of manufacturing resources optimization configuration for middle and lower batch customization enterprises in cloud manufacturing environment, this paper gives a bi-level programming model for manufacturing resources optimization configuration in cloud manufacturing environment which fully considers customer satisfaction and enterprise customization economic benefits. The method firstly identifies the relationship between customer demands and customer satisfaction through questionnaires and quantifies the Kano model effectively. Then, it uses Quality Function Deployment (QFD) to transform customer demand characteristics into engineering characteristics and integrates the qualitative and quantitative results of the Kano model. Next, the method establishes enterprise economic benefits function according to the factors of order quantity and input cost. Furthermore, a comprehensive nonlinear bi-level programming model is established based on cost, time, and quality constraints. The model is solved by intelligent algorithm. Finally, the validity and feasibility of the model are verified by model simulation of actual orders of an enterprise. This method effectively realizes the optimal configuration of manufacturing resources in the cloud manufacturing environment, while maximizing the interests of both suppliers and demanders.
... The bilevel programming [24] model is a hierarchical model with a master-slave hierarchical structure. In the bilevel programming model, the upper and lower decisionmakers have their own objective functions and constraints. ...
Discrete manufacturing products are often assembled from multiple parts through a series of discrete processes. How to effectively configure resources in a discrete manufacturing environment is an important research topic worthy of attention. Based on an in-depth analysis of the discrete manufacturing operation model and the manufacturing resource allocation process, this paper fully considers the uncertainty factors of the manufacturing resource customers and the interests of the manufacturing resource suppliers and proposes a bilevel planning model under a fuzzy environment that comprehensively considers the customers’ expectation bias and the suppliers’ profit maximization. The method firstly uses a language phrase to collect the language evaluation of the customers and suppliers for manufacturing tasks and uses a trapezoidal fuzzy number to convert the language evaluation phrase into a value that can be calculated. Then, we use the prospect theory to optimize the constraint indicators based on the language evaluation of customers and suppliers. Next, the bilevel planning model for optimal configuration of manufacturing resources in discrete manufacturing environment is established under the consideration of the respective interests of both the customers and the suppliers, and the fast nondominated sorting genetic algorithm (NSGA-II) is used to solve the model. Finally, an example is given to verify the validity and feasibility of the model.
Product configuration design combines the existing configurable components of enterprises to quickly form a new product that meets customised requirements. However, the representation, maintenance, and update of the configuration knowledge, and the mismatch problem restrict the rationality and efficiency of existing configuration design methods. In this paper, a recommendation model is developed for customised product configuration design, which takes personalised customer requirements and product component information as input and outputs a ranked list of component instances. It consists of two sub-models: a retrieval sub-model and a ranking sub-model. The retrieval sub-model selects a set of component instance candidates from all possible candidates, and then the ranking sub-model ranks them and selects the best possible candidate. To boost the ranking sub-model performance, we propose a novel interacting network, DualAdap, to extract meaningful low-order, high-order, and adaptive-order cross features. Based on learned cross features, the ranking sub-model computes the adoption scores of all candidates and then selects the best possible candidate according to adoption scores. The configuration design of the elevator traction machine is taken as a case study. Results verify that our recommendation model can identify similar component instances and then accurately pick out the best possible candidate that meets customer requirements.
This research aimed to explain the complementary relationship between the marketing intelligence system and mass customization strategies which is to take advantage of the opportunities available to business organizations through a field case study at the GASN cement factory and Tasluja cement factory in Sulaymaniyah Governorate. And to achieve these goals, the study demonstrated through the theoretical aspect to put forward some of what it is mentioned in both marketing intelligence and mass customization system topics, relying on not a few Arab and foreign resources. As - for the practical side, the two researchers relied on a questionnaire which included a set of common dimensions between the marketing intelligence system and mass customization system, and then the responses of the research sample were analyzed through the statistical software (SPSS) for data analysis. The main hypothesis of the research was the existence of a positive and significant relationship between the marketing intelligence systems and mass customization system. The research reached to a number of conclusions. The most important conclusion among these was that there is a significant positive relationship between the two variables, where information technology contributes to supporting the marketing intelligence system by collecting, analyzing, storing and publishing data and converting it into information to help organizations in general and the organization under search in making appropriate decisions to maintain their competitive position in the market. The marketing intelligence system gives a clear vision for the production department to take advantage of existing opportunities in the current market or in the potential markets. Where as the most important suggestions presented by the researchers , are: The necessity of using the latest information technology, such as using computers and servers for the purpose of maintaining; publishing and constantly updating information; moreover to analyzing the market through the information provided by the system for the purpose of production department service and predicting mass customization system.
Manufacturing companies in the 21st Century will face unpredictable, high-frequency market changes driven by global competition. To stay competitive, these companies must possess new types of manufacturing systems that are cost-effective and very responsive to all these market changes. Reconfigurability, an engineering technology that deals with costeffective, quick reactions to market changes, is needed. Reconfigurable manufacturing systems (RMS), whose components are reconfigurable machines and reconfigurable controllers, as well as methodologies for their systematic design and rapid ramp-up, are the cornerstones of this new manufacturing paradigm.
Tougher competitive situations have led to increasing attention being paid to customer satisfaction, of which timely and customized services are the key concepts. As the product life cycle becomes shortened, high product quality becomes necessary for survival. Markets become highly diversified and global, and continuous and unexpected change become the key factors for success. The need for a method of rapidly and cost-effectively developing products, production facilities and supporting software, including design, process planning and shop floor control system has led to the concept of agile manufacturing. Agile manufacturing can be defined as the capability to survive and prosper in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services. This article details the key concepts and enablers of agile manufacturing. The key enablers of agile manufacturing include: (i) virtual enterprise formation tools/metrics; (ii) physically distributed manufacturing architecture and teams; (iii) rapid partnership formation tools/metrics; (iv) concurrent engineering; (v) integrated product/production/business information system; (vi) rapid prototyping tools; and (vii) electronic commerce. A conceptual framework for the development of an agile manufacturing system and future research directions are presented in this paper. This framework takes into account the customization and system integration with the help of business process redesign, legal issues, concurrent engineering, computer-integrated manufacturing, cost management, total quality management and information technology.
In this paper we focus on scale-based product families derived from scalable product platforms that can be exploited from both a functional and a manufacturing standpoint to increase the potential benefits of having a common platform. While many companies have been successful with scalable product platforms, few, if any, have instituted a systematic approach to design (i) the product platform and (ii) the corresponding family of products which are scaled around the product platform. Accordingly, in this paper we address the following question: How can a scalable product platform and its resulting product family be efficiently and effectively modeled, analyzed, and designed?
After a comprehensive review of the literature we introduce the Product Platform Concept Exploration Method (PPCEM) which has been developed to facilitate the design of a family of products based on a scalable product platform. By way of illustration, we design a family of ten universal electric motors that are scaled around a common motor platform to realize a variety of torque requirements. Further, to mitigate the paucity of examples in this domain, we provide detailed information for the universal electric motor example to enable other researchers to benchmark their methods with this example. We evaluate the effectiveness of the PPCEM by comparing the family of motors obtained using the PPCEM to a group of individually optimized motors. We evaluate the efficiency of the PPCEM by comparing the computational expense of designing the family of motors using the PPCEM to that of optimizing each motor individually. We conclude that the PPCEM provides an efficient and effective means to design a scalable product platform and corresponding product family, promoting increased commonality within the product family with minimal performance tradeoff.
Many companies are using product families and platform-based product development to reduce costs and time-to-market while increasing product variety and customization. Multi-objective optimization is increasingly becoming a powerful tool to support product platform and product family design. In this paper, a genetic algorithm-based optimization method for product family design is suggested, and its application is demonstrated using a family of universal electric motors. Using an appropriate representation for the design variables and by adopting a suitable formulation for the genetic algorithm, a one-stage approach for product family design can be realized that requires no a priori platform decision-making, eliminating the need for higher-level problem-specific domain knowledge. Optimizing product platforms using multi-objective algorithms gives the designer a Pareto solution set, which can be used to make better decisions based on the trade-offs present across different objectives. Two Non-Dominated Sorting Genetic Algorithms, namely, NSGA-II and ε-NSGA-II, are described, and their performance is compared. Implementation challenges associated with the use of these algorithms are also discussed. Comparison of the results with existing benchmark designs suggests that the proposed multi-objective genetic algorithms perform better than conventional single-objective optimization techniques, while providing designers with more information to support decision making during product family design.
Simultaneous design of multiple products has become essential for manufacturing firms to expand design optimization basis. Various viewpoints on this issue have been provided from investigation of practices, but their outcomes still stay on descriptive stages. This paper discusses product variety design under modular architecture and module commonalization toward a computational methodology. It reviews the module based product variety with a mixture of views from customer's needs, functions, manufacturing modules and hierarchical representation of systems. Based on the consequent framework, this paper also mathematically formulates the problem of product variety design as a 0-1 integer-programming problem in a systematically abstract form, and develops an optimization algorithm based on a simulated annealing technique. These discussion and optimization method are applied to a virtual design problem of television receiver circuits to show their validity and promise.
A novel technique that addresses the solution of the general nonlinear bilevel programming problem to global optimality is presented. Global optimality is guaranteed for problems that involve twice differentiable nonlinear functions as long as the linear independence constraint qualification condition holds for the inner problem constraints. The approach is based on the relaxation of the feasible region by convex underestimation, embedded in a branch and bound framework utilizing the basic principles of the deterministic global optimization algorithm, αBB [2, 4, 5, 11]. Epsilon global optimality in a finite number of iterations is theoretically guaranteed. Computational studies on several literature problems are reported.
This paper examines first the far-reaching consequences of unifying bills of material and routings on the basic functions of production planning and control, i.e.: (a) On the structure of the production database and the opportunity for significant simplification; (b) On the mid-term planning of materials and capacities (traditional MRP and CRP software modules) and the chances for simultaneous planning of materials and capacities; (c) On the opportunity to support the extended enterprise and lean production concepts through better planning of the supply chain; (d) On shop floor control and the chances for reducing the volume of transactions and apply more effective scheduling strategies. In parallel, the paper proposes a new production planning and control (PPC) concept which capitalizes on the above consequences. This concept has led to the development of a PPC software system that uses the traditional and widely accepted MRP processing logic, thus avoiding the intricacies of the OPT system and being much more readily accepted by manufacturing personnel.
High-variety production like mass customization is facing the challenge of effective variety management, which needs to deal with numerous variants of both product and process in order to accommodate diverse customer requirements. To utilize commonality underlying product diversity and process variation, it has been widely accepted as a practice to develop product families, in which a set of similar variants share common product and process structures and variety differentiates within these common structures. Based on such variety implication, this paper proposes a data structure, called generic Bill-of-Materials-and-Operations (BOMO), by unifying Bills- of-Materials (BOM) and routing data into a single set in order to synchronize multiple perspectives on variety such as customer ordering, product engineering, and operations planning. A generic structure is accordingly developed for characterizing variety effectively. The merits of the generic BOMO for integrated product and production data management are detailed in terms of order processing, engi neering change control, production job planning, cost accounting, as well as integrated material and capacity planning. An implementa tion of the proposed generic BOMO methodology in customized souvenir clock manufacturing is also reported.
