A fuzzy solution approach for multi objective supplier selection

In today’s severe competitive environment the selection of appropriate suppliers is a significantly important decision for effective supply chain management. Appropriate suppliers reduce purchasing costs, decrease production lead time, increase customer satisfaction and strengthen corporate competitiveness. In this study a multiple sourcing supplier selection problem is considered as a multi objective linear programming problem. Three objective functions are minimization of costs, maximization of quality and maximization of on-time delivery respectively. In order to solve the problem, a fuzzy mathematical model and a novel solution approach are proposed to satisfy the decision maker’s aspirations for fuzzy goals. The proposed approach can be efficiently used to obtain non-dominated solutions. A numerical example is given to illustrate how the approach is utilized.     

Multiple response optimization in a fully automated FAB: an integrated tool and vehicle dispatching strategy1

In today's tight, competitive, and volatile market in semiconductor manufacturing, a successful semiconductor manufacturer has to look into multiple performance indices such as cycle time and on-time delivery. Effective dispatching mechanism is a widely used strategy to achieve these performance indices simultaneously. In the past, dispatching studies in semiconductor manufacturing have primarily focused on the level of tool dispatching. This paper presents an integrated tool and vehicle (ITV) dispatching strategy to consider multiple performance measures in a fully automated fab environment. The ITV dispatching strategy was developed using a state-dependent methodology and multiple response optimization. In order to build a simulation-based automated fab, an integrated modeling approach was proposed to automate both the manufacturing process and the automated material handling system. A case study based on a local fab is described to examine the performance impact of the ITV dispatching rule measured by cycle time, work-in-process, on-time delivery, and lot delivery time. The results of the simulation experiments and analysis show that the ITV dispatching rule is superior to the use of a static dispatching rule, consisting of an average of 15% improvement for on-time delivery and 5% for other performance measures. Furthermore, the proposed modeling framework features high-fidelity, real-time operations and re-configuration, and it can be easily used in other applications.     

基于混合方法的供应链协同计划研究

本文主要研究了供应链协同计划中的优化问题,所建数学模型中考虑了多计划期、多产品、多供应商、多制造商和多分销中心,以供应链系统总成本和总运行时间最小化为目标,采用整数规划和仿真相结合的混合方法来求解该模型。最后通过算例说明该混合方法对多目标供应链生产-分销计划模型求解的可行性和有效性。     

Effective allocation of customers to distribution centres: A multiple ant colony optimization approach

The global and competitive business environment has identified the importance of a quick and efficient service towards the customers in the past few decades. Distribution centre (DC) plays an important role in maintaining the uninterrupted flow of goods and materials between the manufacturer and customers. The performance of the supply chain network can be easily improved by an effective or balanced allocation of customers to DCs. Improper or unbalanced allocation of customers can lead to the under- or overutilization of facilities and can further deteriorate the customer service. Performance of the DC can be judged on the basis of its ability to provide the right goods, at the right time and at the right place. The lead time or transit time to deliver the goods to the customers is an important parameter for the measuring the efficiency and effectiveness of a particular DC in a supply chain. In this paper, a multiple ant colony optimization (MACO) approach is discussed in an effort to design a balanced and efficient supply chain network that maintains the best balance of transit time and customers service. The focus of this paper is on the effective allocation of the customers to the DCs with the two-fold objective of minimization of the transit time and degree of imbalance of the DCs. MACO technique is a modified form of the traditional ant colony system, where multiple ant colonies cooperate with each other to find the best possible customer allocation pattern for the DC. The proposed technique shows better performance because of its nature of considering both positive and negative feedback in search of optimum or near-optimum results. The developed algorithm based on the proposed approach is tested on a real practical problem and the results are discussed in this paper.     

Supply Chain Model for the Semiconductor Industry of Global Market

Supply chain management (SCM) is a new approach to satisfy customers of products and services via an integrated management for the whole business processes of the manufacturing from the raw material procurement to the product or service delivery to customers. With the rapid development of the information technology and global market environment the company is required to manage whole supply chain network to gain the competitiveness in business. Typically semiconductor industry is the one whose supply chain network is distributed all over the world, and its manufacturing process has the particular characteristics which has to be considered in the modeling of supply chain. In this paper we suggest the push and pull type supply chain models and their mathematical formulation for the semiconductor industry of the global market. Die bank between the front-end and back-end in the semiconductor manufacturing process is assumed to be the decoupling point by which two models are divided. Push supply chain model is based on the high throughput and the balance of the WIP flow, which takes the characteristics of re-entrant mechanism into account. Pull supply chain model is constructed considering order-based manufacturing, distribution and transportation process in order to meet customer's request appropriately. Development issues of the system and its implementation are also discussed.     

Data management of green product development with generic modularized product architecture

Many manufacturers are facing a complex situation in the mixed production environment, in which green and non-green products are fabricated simultaneously. They are losing competitiveness as a downstream supplier due to lacking of a cost-effective approach to managing product variations compliant with different green directives. This paper presents a methodology based on generic modularized product architecture that facilitates data management of green product development. The four-level architecture allows one unified representation for multiple product models. An option control mechanism enables a quick generation of their BOMs (bills of material). A procedure consisting of seven steps is proposed to accomplish this. PDM functions are implemented to demonstrate the effectiveness of the methodology using a real LCD TV family as an example. This work complements the past studies on green product development, which mainly tackled the problem from design, process, and supply chain improvement. In contrast, from a management perspective, the proposed methodology provides a simple but useful tool for small-to-medium-sized enterprises (SMEs) to perform green product development in an economical manner.     

Industry 4.0 enabling manufacturing competitiveness: Delivery performance improvement based on theory of constraints

Due date control (DDC) can be regarded as one of the competitive abilities of an enterprise. Based on advanced connected technologies implemented in Industry 4.0, DDC can collect processing information accurately and in real time to be integrated with production systems. It is an intelligent event-driven feedback control that can be designed to control process plans. In this study, a work-in-process alert system framework is developed based on a machine-to-machine communication approach. Subsequently, a manufacturer can monitor and perform DDC for each process based on the buffer control of the theory of constraints. The latter is a methodology for identifying the most important limiting factor, such as constraints, that prevents the attainment of a goal and then systematically improves that constraint until it is no longer the limiting factor. Furthermore, using hybrid strategies of Industry 3.5, a material planner can collaborate with suppliers to improve delivery performance. This approach is effective in semiconductor manufacturing.     

Multi-objective decision-making methodology to create an optimal design chain partner combination

In today’s highly competitive business environment, many companies adopt the time-to-market strategy to obtain a competitive advantage. To reduce the time and cost of product development and to employ global product development resources, design chain partner evaluation and selection has become a crucial issue. Thus, establishing an optimal design chain partner combination has received significant attention because it has a far-reaching effect on the results of product development. With this perspective, this paper develops an integrated decision-making methodology to assist enterprises as they create an optimal design chain partner combination. First, this study establishes the framework and evaluation models of the criteria for the different roles of design chain partners, including system integration, functional module development and software and component development. Then, this paper applies a weight-restricted DEA (data envelopment analysis) approach to create the models for performance analysis of design chain partners to acquire the performance value of each candidate and select the efficient design chain partners. Moreover, this paper employs the multi-objective performance evaluation model proposed in this paper to analyze the synthesized performance of design chain combinations. Moreover, this research uses a multi-objective genetic algorithm (GA) to search efficiently for the optimal design chain partner combination to minimize product development cost and time and maximize product reliability. Finally, this study employs a derivative new product development project for a digital TV box as a case study to illustrate the efficacy of the proposed methodology.     

Structured methodology for supplier selection and evaluation in a supply chain

Supply chain management (SCM) is one of the most important competitive strategies used by modern enterprises. The main aim of supply chain management is to integrate various suppliers to satisfy market demand. Meanwhile, supplier selection and evaluation plays an important role in establishing an effective supply chain. Traditional supplier selection and evaluation methods focus on the requirements of single enterprises, and fail to consider the entire supply chain. Therefore, this study proposes a structured methodology for supplier selection and evaluation based on the supply chain integration architecture.In developing the methodology for supplier selection and evaluation in a supply chain, enterprise competitive strategy is first identified using strengths weaknesses opportunities threats (SWOT) analysis. Based on the competitive strategy, the criteria and indicators of supplier selection are chosen to establish the supplier selection framework. Subsequently, potential suppliers are screened through data envelopment analysis (DEA). Technique for order preference by similarity to ideal solution (TOPSIS), a multi-attribute decision-making (MADA) method is adapted to rank potential suppliers. Finally, the Taiwanese textile industry is used to illustrate the application and feasibility of the proposed methodology.This study facilitates the improvement of collaborator relationships and the management of potential suppliers to help increase product development capability and quality, reduce product lifecycle time and cost, and thus increase product marketability.     

Determining the operator-machine assignment for machine interference problem and an empirical study in semiconductor test facility

Semiconductor is a capital-intensive industry in which equipment costs account for more than seventy percentage of the capital investment in semiconductor test facilities. In an industrial investigation, the machine interference may be 10% of machine time. Hence, there is a need to assign an appropriate number of machines to the operators to minimize machine interference time or labor cost. This paper aims to develop an effective methodology to determine the optimal assignment relationships between the test machines and the operators for different product mixes to enhance utilization for the optimal system performance. In particular, we employed response surface methodology and genetic algorithms to explore alternative assignment rations and thus identify well-performed assignment alternatives for the test machines and operators in various decision contexts with simulation. An empirical study with real data collected was conducted in a semiconductor test facility to validate this approach. The results have shown the validity of the proposed approach in real settings. Indeed, the developed approach has been implemented on line.     

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.     

Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach

Supply chain management allows modern enterprises to relax their own capacities and produce in a more flexible manner for diversified consumer demands. However, for an enterprise with divergent supply chain (DSC) and multiple product lines, to plan the production allocation for higher competitive advantage in the risky global market is a challenging problem. The existing literature still has not address this problem very well. This paper is aimed to treat this problem by using an integrated approach of activity based costing (ABC) and management, five forces analysis, risk and value-at-risk analysis, decision making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and fuzzy goal programming (FGP). The proposed model can effectively incorporate the key factors of precise costing, managerial constraints, competitive advantage analysis, and risk management into DSC forecasting and multi-objective production planning. A case study of a consumer-oriented cell phone DSC is also presented. The sensitivity analysis shows that identifying and relaxing crucial constraints can play an important role in DSC planning for higher competitive advantage and lower risk.     

Joint optimization of production planning and supplier selection incorporating customer flexibility: an improved genetic approach

Efficient and effective production planning and supplier selection are important decisions for manufacturing industries in a highly competitive supply chain, in particular, when customers are willing to accept products with less desirable product attributes (e.g., color, material) for economic reasons. Yet these two decision making problems have traditionally been studied separately due to their inherent complexity. This paper attempts to solve optimally the challenging joint optimization problem of production planning and supplier selection, considering customer flexibility for a manufacturer producing multiple products to satisfy customers’ demands. This integrated problem has been formulated as a new mixed integer programming model. The objective is to maximize the manufacturer’s total profit subject to various operating constraints of the supply chain. Due to the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, an improved genetic approach is proposed to locate near-optimal solutions. This approach differs from a canonical genetic algorithm in three aspects, i.e., a new selection method to reduce the chance of premature convergence and two problem-specific repair heuristics to guarantee the feasibility of the solutions. The computational results of applying the proposed approach to solve a set of randomly generated test problems clearly demonstrate its excellent performance.     

Integrated cost optimization in a two-stage,automotive supply chain

The efficiency of the automotive supply chain is crucial for ensuring the competitiveness of the automotive industry, which represents one of the most significant manufacturing sectors. We model the integrated production and transportation planning problem of a Tier-1 automotive supplier while taking into account realistic conditions such as sequence-dependent setups on multiple injection molding machines operating in parallel, auxiliary resource assignments of overhead cranes, and multiple types of costs. Finished parts go to the integrated supply chain׳s second stage, transportation, for subsequent delivery by capacitated vehicles to multiple distribution centers for meeting predefined due date requirements. We develop a mixed-integer, linear programming model of the problem, and then present a hybrid simulated annealing algorithm (HSAA), including a constructive heuristic. Our proposed HSAA employs an effective encoding-decoding strategy to solve the NP-hard problem to near optimality in a timely manner. Computational results demonstrate the promising performance of the proposed solution approach.     

An integrated fuzzy-lp approach for a supplier selection problem in supply chain management

In supply chain management process, the firm select best supplier takes the competitive advantage to other companies. Thus, supplier selection is an important issue and with the multiple criteria decision-making approach, the supplier selection problem includes both tangible and intangible factors. This paper is aimed to present an integrated fuzzy and linear programming approach to the problem. Firstly, linguistic values expressed in trapezoidal fuzzy numbers are applied to assess weights and ratings of supplier selection criteria. Then a hierarchy multiple model based on fuzzy set theory is expressed and fuzzy positive and negative ideal solutions are used to find each supplier’s closeness coefficient. Finally, a linear programming model based on the coefficients of suppliers, buyer’s budgeting, suppliers’ quality and capacity constraints is developed and order quantities assigned to each supplier according to the linear programming model. The integrated model is illustrated by an example in a textile firm.     

Setting order promising times in a supply chain network using hybrid simulation-analytical approach: An industrial case study

Supply chain management (SCM) involves the management of material and information flow among the members of chain such as vendors, manufacturing plants and distribution centers. The main processes in supply chain are production planning, control, distribution and logistics. Effective management of all these processes provides the manufacturing companies great advantages in time based competition. In order to gain a competitive edge, firms must be able to deliver their products fast and on-time. This can be managed by better production planning, scheduling and due-date setting. This paper suggests a hybrid approach for setting realistic order due dates for a produce-to-order manufacturing company operating within a supply chain environment. The proposed hybrid approach combines mathematical and simulation modeling to bring together the advantages of both approaches.     

An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design

This paper addresses an evaluation of new heuristics solution procedures for the location of cross-docks and distribution centers in supply chain network design. The model is characterized by multiple product families, a central manufacturing plant site, multiple cross-docking and distribution center sites, and retail outlets which demand multiple units of several commodities. This paper describes two heuristics that generate globally feasible, near optimal distribution system design and utilization strategies utilizing the simulated annealing (SA) methodology. This study makes two important contributions. First, we continue the study of location planning for the cross-dock and distribution center supply chain network design problem. Second, we systematically evaluate the computational performance of this network design location model under more sophisticated heuristic control parameter settings to better understand interaction effects among the various factors comprising our experimental design, and present convergence results. The central idea of the paper is to evaluate the impact of geometric control mechanism vis-a-vis more sophisticated ones on solution time, quality, and convergence for two new heuristics. Our results suggest that integrating traditional simulated annealing with TABU search is recommended for this supply chain network design and location problem.     

An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design

In respond to new market requirements and competitive positioning of manufacturing companies selecting optimal machines that are consistent with manufacturing goals is of crucial importance. As it involves multiple conflicting criteria and inherent ambiguity and vagueness, election of a suitable machine can be regarded as a fuzzy multi-criteria decision making problem. In this study, for the first time in the literature, an integrated approach consisting of fuzzy simple multiattribute rating technique (SMART) approach and fuzzy weighted axiomatic design (FWAD) approach is proposed to determining the optimal continuous fluid bed tea dryer for a privately owned tea plant operating in Turkey. The weights of the evaluation criteria are calculated via fuzzy SMART and then FWAD is utilized to rank competing machine alternatives in terms of their overall performance. In the FWAD application phase, five experts have determined functional requirements (FRs) and have rated alternatives. Therefore, individual fuzzy opinions were required to be aggregated in order to set up a group consensus. A group decision analysis, referred to as the least squares distance method is used to aggregating the ratings of FRs and alternatives. It is concluded that the proposed hybrid methodology is a robust decision support tool for ranking machine alternatives under fuzzy environment and furthermore, it can be exploited for other fuzzy decision making problems, as well.     

从复杂性的角度分析供应链管理问题

供应链是企业适应市场环境变化而产生的运作模式,也是企业之间竞争与协同发展的产物。供应链是由多个企业组成的复杂供需网络,因此它属于开放的复杂巨系统的范畴,而处理开放的复杂巨系统的有效方法是综合集成研讨体系。该文首先介绍了复杂性的研究情况;分析了供应链的复杂性;指出供应链是一个开放的复杂巨系统;提出用综合集成研讨厅体系的思想研究供应链管理问题,为供应链管理问题的研究提供了一个新的思路。     

Effects of uncertainty on a tire closed-loop supply chain network

In a closed-loop supply chain (CLSC) network, there are both forward and reverse supply chains. In this research, a tire remanufacturing CLSC network is designed and optimized based on tire recovery options. The objective of the optimization model is to maximize the total profit. The optimization model includes multiple products, suppliers, plants, retailers, demand markets, and drop-off depots. The application of the model is discussed based on a realistic network in Toronto, Canada using map. In addition, a new decision tree-based methodology is provided to calculate the net present value of the problem in multiple periods under different sources of uncertainty such as demand and returns. Furthermore, the discount cash flow is considered in the methodology as a novel innovative approach. This methodology can be applied in comparing the profitability of different design options for CLSCs.