Z.H. Che

Supply chain network design: partner selection and production/distribution planning using a systematic model

In this paper, a novel multi-phase mathematical approach is presented for the design of a complex supply chain network. From the point of network design, customer demands, and for maximum overall utility, the important issues are to find suitable and quality companies, and to decide upon an appropriate production/distribution strategy. The proposed approach is based on the genetic algorithm (GA), the analytical hierarchy process (AHP), and the multi-attribute utility theory (MAUT) to satisfy simultaneously the preferences of the suppliers and the customers at each level in the network. A case study with a good quality solution is provided to confirm the efficiency and effectiveness of the proposed approach. Finally, to demonstrate the performance of the proposed approach, a comparative numerical experiment is performed by using the proposed approach and the common single-phase genetic algorithm (SGA). Empirical analysis results demonstrate that the proposed approach can outperform the SGA in partner selection and production/distribution planning for network design.

An integrated model for supplier selection decisions in configuration changes

Configuration change management provides a way for a manufacturer to become more competitive. Because of the short life and the large variety involved in commercial products, they must be configured accordingly. It is a task for the configuration change management. This paper presents an integrated model for modeling the change behavior of product parts, and for evaluating alternative suppliers for each part by applying fuzzy theory, T transformation technology, and genetic algorithms. The proposed model is based on the concepts of part change requirements, fuzzy performance indicators, and the integration of different attributes, to allow the part supplier selection of a specific commercial product to be explored and modeled. The application of this approach is illustrated through a case study of a TFT-LCD product for part change optimization. In terms of change performance, experimental analyses with different genetic parameters allowed the potential alternative suppliers for the product parts to be evaluated. The results of the experimental analyses show that this proposed methodology is a suitable approach and provides a quality solution for products with a complex configuration. In addition, the numerical results obtained from the new approach were compared with the results obtained by linear programming. The result shows that the proposed algorithm is reliable and robust.

Using fuzzy analytic hierarchy process and particle swarm optimisation for balanced and defective supply chain problems considering WEEE/RoHS directives

This research explored problems concerning production and delivery in a green supply chain, and constructed an optimal mathematical model to provide solutions. This model incorporates WEEE and RoHS in EU directives for the selection of green partners when establishing a supply chain. The weight of each component is calculated by fuzzy analytic hierarchy process (fuzzy AHP). Previous studies suggested that a supply chain is a balanced system, however, in actual practice, there may be processing damages or delivering losses. Thus, such a supply chain with production loss is known as a ‘defective supply chain’. This research analysed the defective supply chain system to discuss its supplier selection, production, and distribution. It developed an optimal mathematical model for both balanced and defective models, and adopted particle swarm optimisation (PSO) to obtain solutions for both models. Finally, case studies for both models with quality solutions were discussed to confirm the efficiency and effectiveness of the proposed approach.

A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA

Due to brutal business competition, new product development (NPD) has become a key factor for promoting business sustainability. To help a company determine the direction of NPD for the future, this study applies the fuzzy analytical hierarchy procedure (AHP) and fuzzy data envelopment analysis (DEA) to develop an evaluation and ranking methodology, assisting decision makers to select NPD projects with development potential and high added value. Because of the high risk characteristic of NPD, this study employs the Bayesian belief network (BBN) technology to create the risk evaluation models to assist the top managers in analyzing and measuring the NPD risks. Finally, this paper employs the development projects of the electronic extension cards as a case study for explanation and verification of significant benefits of the methodology proposed by this study.

PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding

To simplify complicated traditional cost estimation flow, this study emphasizes the cost estimation approach for plastic injection products and molds. It is expected designers and R&D specialists can consider the competitiveness of product cost in the early stage of product design to reduce product development time and cost resulting from repetitive modification. Therefore, the proposed cost estimation approach combines factor analysis (FA), particle swarm optimization (PSO) and artificial neural network with two back-propagation networks, called FAPSO-TBP. In addition, another artificial neural network estimation approach with a single back-propagation network, called FAPSO-SBP, is also established. To verify the proposed FAPSO-TBP approach, comparisons with the FAPSO-SBP and general back-propagation artificial neural network (GBP) are made. The computational results show the proposed FAPSO-TBP approach is very competitive for the product and mold cost estimation problems of plastic injection molding.

A genetic algorithm-based model for solving multi-period supplier selection problem with assembly sequence

Under fierce market competition, only products that can meet market demands timely and are competitive can enjoy advantages in the market. Production planning is important in enhancing product competitiveness by effectively reducing both production cost and time. To complete the planning task, a better assembly sequence that includes selecting suitable part suppliers and satisfying the multi-period demands should be designed. In this paper, a mathematical model is presented for dealing with this planning problem, and its objective is to minimise the value of integrated criteria. A hybrid heuristic algorithm, which involves guided genetic algorithm combined with Pareto genetic algorithm, known as Guided-Pareto genetic algorithm (Gu-PGA), is developed for solving the addressed problem. Finally, experiments are conducted to validate the proposed algorithm. The results demonstrate that the Gu-PGA is more effective in solving the multi-period supplier selection problem.

A particle swarm optimization algorithm for solving unbalanced supply chain planning problems

This paper focuses on developing a decision methodology for the production and distribution planning of a multi-echelon unbalanced supply chain. In the supply chain system discussed here, multiple products, production loss, transportation loss, quantity discount, production capacity, and starting-operation quantity are considered simultaneously, and the system pattern is ascertained with based on appropriate partners and suitable transportation quantities. To make a quality decision in supply chain planning, we first propose an optimization mathematical model which integrates cost and time criteria. Then, a particle swarm optimization (PSO) solving method is proposed for obtaining acceptable results is called MEDPSO. The MEDPSO introduces the maximum possible quantity strategy into the basic procedure of PSO to generate the initial feasible population in a timely fashion and provides an exchange and disturbance mechanism to prevent particle lapse into the local solution. Finally, one case and two simulated supply chain structures are proposed to illustrate the effectiveness of the MEDPSO method by comparing the results of classical GA and PSO in solving multi-echelon unbalanced supply chain planning problems with quantity discount.

A dispatching rule for photolithography scheduling with an on-line rework strategy

Generally speaking, wafer fabrication factories define the photolithography area as the dispatching center of the entire factory. To establish a set of operative dispatching rules in the photolithography area while taking into consideration the rework of defective products would assist in coordinating and balancing the workload of the entire production line. Furthermore, it would help to enhance both the productivity and efficiency of the wafer fabrication, reduce the on-line WIP stock, shorten the production cycle time, and satisfy the requirements of customers regarding production due time and product quality. This research uses on-line rework as the basis for bringing the factor of reworking of a batch process into the dispatching rule for measurement. It then develops the dispatching rule (Rw-DR) which includes the rework strategy. In addition, this research focuses on the batch with high finished proportion in the photolithography area for finding a way to complete the manufacturing procedure faster, lighten the machine workload of the waiting line, and at the same time increase the output quantity.

A three-phase integrated model for product configuration change problems

Requirements of engineers or customers may result in product configuration change with product life cycle; effective management of product configuration can actually enhance productivity and customer satisfaction. This study aims to develop a three-phase evaluation model incorporating fuzzy theory, value engineering and multi-criterion to find optimal strategies for product configuration change, so as to select suitable combination of parts suppliers. Genetic algorithm was used to solve the issue concerned with part change in a short time with part quality, cost, time and reliability as evaluation parameters. Finally, as a case study, the display module of a notebook was analyzed, the results indicate that the evaluation can be effectively applied in a large-scale product configuration change.

Clustering and selecting suppliers based on simulated annealing algorithms

This study proposes two optimization mathematical models for the clustering and selection of suppliers. Model 1 performs an analysis of supplier clusters, according to customer demand attributes, including production cost, product quality and production time. Model 2 uses the supplier cluster obtained in Model 1 to determine the appropriate supplier combinations. The study additionally proposes a two-phase method to solve the two mathematical models. Phase 1 integrates k-means and a simulated annealing algorithm with the Taguchi method (TKSA) to solve for Model 1. Phase 2 uses an analytic hierarchy process (AHP) for Model 2 to weight every factor and then uses a simulated annealing algorithm with the Taguchi method (ATSA) to solve for Model 2. Finally, a case study is performed, using parts supplier segmentation and an evaluation process, which compares different heuristic methods. The results show that TKSA+ATSA provides a quality solution for this problem.