Multimodal Fuzzy Downstream Petroleum Supply Chain: A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex, inflexible and challenging supply chain (SC) that impacts both the national economy as well as people’s daily lives with a range of services, including transportation, heating, electricity, lubricants, as well as chemicals and petrochemicals. In the petroleum industry, supply chain management presents several challenges, especially in the logistics sector, that are not found in other industries. In addition, logistical challenges contribute significantly to the cost of oil. Uncertainty regarding customer demand and supply significantly affects SC networks. Hence, SC flexibility can be maintained by addressing uncertainty. On the other hand, in the real world, decision-making challenges are often ambiguous or vague. In some cases, measurements are incorrect owing to measurement errors, instrument faults, etc., which lead to a pentagonal fuzzy number (PFN) which is the extension of a fuzzy number. Therefore, it is necessary to develop quantitative models to optimize logistics operations and supply chain networks. This study proposed a linear programming model under an uncertain environment. The model minimizes the cost along the refineries, depots, multimode transport and demand nodes. Further developed pentagonal fuzzy optimization, an alternative approach is developed to solve the downstream supply chain using the mixed-integer linear programming (MILP) model to obtain a feasible solution to the fuzzy transportation cost problem. In this model, the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number. Furthermore, defuzzification is performed using an accuracy function. To validate the model and technique and feasibility solution, an illustrative example of the oil and gas SC is considered, providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.     

A framework for designing robust supply chains considering product development issues

The primary objective of the design for supply chain (DFSC) is the selection of an appropriate product family. Moreover, it deals with the selection of the optimal combination among the different conflicting criteria while making a trade-off between the supply chain cost, sales profit and the product design complexities. In this research, to address the DFSC issues a product platform approach has been proposed which amalgamates the component modularity as well as the function modularity in the product design. The optimisation model proposed in this paper for the product development and the supply chain design is based on a generic bill of materials (GBOM) representation. The complete framework includes vital decision-making needed for designing a robust supply chain such as locating plants to alleviate the likely dominance of production cost and market mediation cost on product variety and imparting process flexibility of the located plants. The optimisation model proposed in this paper, models the supply chain cost, sales profit and product design complexity as three criteria that altogether determine the robustness of the supply chain and the underlying product development approach. Certain parameters like process flexibility, flow types and drivers of the product variety dominance have been controlled in the design framework. To resolve the complexity of the proposed model a genetic algorithm (GA) technique has been proposed. The proposed GA adopts an arithmetic crossover, a dynamic mutation and a variable penalty strategy to produce optimal results in a very short computational time. To validate the proposed model, a simulated case study of the wiring harness supplier of an AGV manufacturer has been studied.     

Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach

Motivated by a real case of an automobile company, this study proposes a multi-objective, multi-site production planning model integrating procurement and distribution plans in a multi-echelon supply chain network with multiple suppliers, multiple manufacturing plants and multiple distribution centres. The model incorporates four important conflicting objectives simultaneously: minimisation of the total cost of logistics, maximisation of the total value of purchasing, minimisation of defective items and minimisation of late deliveries subject to some realistic constraints. Due to the imprecise/fuzzy nature of the objectives’ aspiration levels and some critical data, an interactive fuzzy goal programming formulation is first developed. Then, a novel fuzzy approach is proposed to convert the FGP model into an auxiliary crisp formulation to find an efficient compromise solution. The proposed model and solution method are validated through some numerical tests. Computational results indicate the practicality and tractability of the proposed model and also the superiority of the proposed auxiliary crisp formulation in contrast to the current alternative fuzzy approaches.     

Fuzzy scheduling of a build-to-order supply chain

The overwhelming majority of the literature in the area of supply chain planning and scheduling considers the traditional make-to-stock (MTS) environment. However, manufacturers of assembled products such as cars, computers, furniture, etc. adopt the build-to-order supply chain (BOSC) to become agile in a mass customization process in order to meet diversified customer requirements. In this paper we propose an integrated production–distribution planning model for a multi-echelon, multi-plant and multi-product supply chain operating in a build-to-order (BTO) environment. The uncertainties associated with estimation of the various operational cost parameters are represented by fuzzy numbers. The BOSC scheduling model is thus constructed as a mixed-integer fuzzy programming (MIFP) problem with the goal of reducing the overall operating costs related to component fabrication, procurement, assembling, inspection, logistics and inventory, while improving customer satisfaction by allowing product customization and meeting delivery promise dates at each market outlet. An efficient compromise solution approach by transforming the problem into an auxiliary multi-objective linear programming model is also suggested.     

Impacts of RFID implementation on cost structure in networked manufacturing

Radio frequency identification (RFID) presents the key innovative solution to automatically capture and track the movement of material items throughout an entire supply chain. In this article, we explore influences of RFID technology implementation on cost and profit efficiencies. For this purpose a mathematical model of supply chain cost structure and functions was created to analyse the impacts. The model, which has also been verified through a case study approach, is aimed to examine the economical impact of RFID implementation at an automotive original equipment manufacturer and its suppliers. To analyse the impact of the traceability system using RFID technology, a cost reduction parameter will be used. Since in automotive supply chains the functional dependence of individual parts is changing in the upstream and downstream supply chain, only certain parts will be calculated with their complete functions and relations.     

基于PIWOA的绿色闭环供应链网络多目标模糊优化设计

针对闭环供应链网络优化设计问题,建立了一种基于Me测度的闭环供应链网络多目标优化设计模型,以此降低供应链网络设计中不确定性因素在求解时的影响。首先,针对多层级闭环供应链网络,建立以成本最小、CO₂排放量最小以及社会效益最大为目标的优化函数,并采用Me测度和三角模糊数对模型及相关约束进行模糊处理,得到不确定性闭环供应链网络优化模型;其次,在原有鲸鱼算法的基础上,引入变异收敛因子,增强其搜索能力,并将Pareto引入改进后的鲸鱼算法求解所建模型;最后,通过数值实例和仿真分析验证算法在搜索能力、时间以及优化目标函数值等方面具有较强的优势和性能。     

Uncertain supply chain network design considering carbon footprint and social factors using two-stage approach

Sustainable development has become one of the leading global issues over the period of time. Currently, implementation of sustainability in supply chain has been continuously in center of attention due to introducing stringent legislations regarding environmental pollution by various governments and increasing stakeholders’ concerns toward social injustice. Unfortunately, literature is still scarce on studies considering all three dimensions (economical, environmental and social) of sustainability for the supply chain. An effective supply chain network design (SCND) is very important to implement sustainability in supply chain. This study proposes an uncertain SCND model that minimizes the total supply chain-oriented cost and determines the opening of plants, warehouses and flow of materials across the supply chain network by considering various carbon emissions and social factors. In this study, a new AHP and fuzzy TOPSIS-based methodology is proposed to transform qualitative social factors into quantitative social index, which is subsequently used in chance-constrained SCND model with an aim at reducing negative social impact. Further, the carbon emission of supply chain is estimated by considering a composite emission that consists of raw material, production, transportation and handling emissions. In the model, a carbon emission cap is imposed on total supply chain to reduce the carbon footprint of supply chain. To solve the proposed model, a code is developed in AMPL software using a nonlinear solver SNOPT. The applicability of the proposed model is illustrated with a numerical example. The sensitivity analysis examines the effects of reducing carbon footprint cap, negative social impacts and varying probability on the total cost of the supply chain. It is observed that a stricter carbon cap over supply chain network leads to opening of more plants across the supply chain. In addition, carbon footprint of supply chain is found to be decreased in certain extent with the reduction in negative social impacts from suppliers. The carbon footprint of the supply chain is found to be reduced with increasing certainty of material supply from the suppliers. The total supply chain cost is observed to be augmented with increasing probability.     

Integration of closed loop distribution supply chain network and 3PRLP selection for the case of battery recycling

The increased use of lead acid batteries in automotive vehicles will increase the demand for lead and to meet this increasing demand used batteries are identified as a most important source of lead through recycling. Since recycled lead is a costly commodity, the market potential for reclaiming the secondary lead from the used batteries has been growing. For this purpose, a multi-echelon, multi-product closed loop distribution supply chain (CLDSC) network is designed and integrated with the selection process of best third-party reverse logistics provider (3PRLP) to achieve cost efficiency and delivery schedules in reverse logistics. A fuzzy multi-criteria decision-making (FMCDM) model based on the VIKOR method is used for the selection of best 3PRLP. To optimise the proposed network, a mixed integer linear programming model is developed and solved using LINGO 8.0 optimisation software package for the case of battery recycling and decisions are made regarding raw material procurement from the supplier, production, distribution and amount of reclaimed/recycled material. Since the main aim of this study is to optimise the CLDSC network, the supply chain cost of the forward distribution supply chain network is found and compared with the total supply chain cost of the CLDSC network and the results show that a cost reduction can be achieved in the case of CLDSC network. Finally, model experiments for varying rate of returns are conducted to study the percentage of cost savings.     

Fuzzy QFD for supply chain management with reliability consideration

Although many products are made through several tiers of supply chains, a systematic way of handling reliability issues in a various product planning stage has drawn attention, only recently, in the context of supply chain management (SCM). The main objective of this paper is to develop a fuzzy quality function deployment (QFD) model in order to convey fuzzy relationship between customers needs and design specification for reliability in the context of SCM. A fuzzy multi criteria decision-making procedure is proposed and is applied to find a set of optimal solution with respect to the performance of the reliability test needed in CRT design. It is expected that the proposed approach can make significant contributions on the following areas: effectively communicating with technical personnel and users; developing relatively error-free reliability review system; and creating consistent and complete documentation for design for reliability.     

A multi-phase model for product part change problems

The parts of a product may be changed over the product life cycle for the purpose of enhancing profits, that is to say, product part change has a direct impact on production efficiency and customer satisfaction. The analysis of product part change can provide producers with related components/part suppliers by analysing the product's configuration. Before performing the physical assembly of a specific product, selected part suppliers are connected and have to verify their ability to assemble. This paper proposes an assessment model consisting of three major phases: (1) analysis of the assembly relationship among the parts; (2) preliminary selection of the appropriate parts and their suppliers; and (3) solution selection for supplier association. The proposed model solves the product part change problem for a given related quality level, operation and cost data. The liaison graph, fuzzy theory and value engineering approaches are employed in phases 1 and 2 to analyse the product's configuration and assembly relationships, to deal with production data and to carry out the preliminary analysis. To solve the solution selection model of phase 3, a genetic algorithm based heuristic approach was developed. Finally, the configuration of the stand module of TFT-LCD is used as an example to demonstrate the application and working of the proposed approach. The results of the illustrated example are used to demonstrate its efficiency and effectiveness in solving the part change problem.     

A mathematical programming approach to supply chain models with fuzzy parameters

Supply chain (SC) models play an important role in supply chain management (SCM) for reducing costs and finding better ways to create and deliver value to customers. An approach to deriving the membership function of the fuzzy minimum total cost of the multi-product, multi-echelon, and multi-period SC model with fuzzy parameters is proposed in this article. On the basis of α-cut representation and the extension principle, a pair of mathematical programs are formulated to calculate the lower and upper bounds of the fuzzy minimum total cost at possibility level α. The membership function of the fuzzy minimum total cost is constructed by enumerating different values of α. To demonstrate the validity of the proposed procedure, a four-echelon five-period SC model with fuzzy parameters is solved successfully. Since the objective value is expressed by membership functions rather than by crisp values, they completely conserve the fuzziness of input information when some of the SC data are ambiguous. Thus the proposed approach can represent SCs with fuzzy parameters more accurately, and more information is provided for designing SCs in real-world applications.     

Multiple decoupling point paradigms in a global supply chain syndrome: a relational analysis

A responsive and flexible supply chain in the global product value chain set-up links the manufacturer with various inter-company entities, such as suppliers, distributors, contract manufacturers and customers to deal with volatile demand. It is imperative and prudent to look at the overall product chain rather than focusing on any single supply chain entity. From a supply chain perspective, it is crucial that these individual entities are lean and agile (leagile) themselves, as well as the overall product value chain. As a result of the global nature of the product value chain, the concept of the decoupling point has undergone a paradigm shift. Through the literature review the authors establish three different types of decoupling point existing in product value chains, namely: (1) product structure decoupling point (PSDP), bill of materials decoupling; (2) supply structure decoupling point (SSDP), supply network decoupling; and (3) demand transfer decoupling point (DTDP), associated with information sharing. It is argued that such decoupling points are already an established and accepted phenomena, but have not been comprehensively discussed until now. It is proposed that, in the global supply chain, PSDP and SSDP are elemental decoupling points and DTDP is a logical decoupling, which is a function of PSDP and SSDP. It is proposed that DTDP swings between PSDP and SSDP and the swing direction is a critical piece of supply chain information that helps identify distortion. A multidimensional scaling (MDS) method is proposed to discover the relationship of DTDP with SSDP and PSDP using interpretation matrices and stress coefficients. Considering the supply chain as a fuzzy system, a combined effect of MDS over fuzzy applications has been used to determine the extent of swing of DTDP in the particular application of the supply chain.     

Multi-objective mixed integer programming and an application in a pharmaceutical supply chain

Multi-objective integer linear and/or mixed integer linear programming (MOILP/MOMILP) are very useful for many areas of application as any model that incorporates discrete phenomena requires the consideration of integer variables. However, the research on the methods for the general multi-objective integer/mixed integer model has been scant when compared to multi-objective linear programming with continuous variables. In this paper, an MOMILP is proposed, which integrates various conflicting objectives. We give importance to the imprecise nature of some of the critical factors used in the modelling that can influence the effectiveness of the model. The uncertainty and the hesitation arising from estimating such imprecise parameters are represented by intuitionistic fuzzy numbers. The MOMILP model with intuitionistic fuzzy parameters is first converted into a crisp MOMILP model, using appropriate defuzzification strategies. Thereafter, the MOMILP is transformed into a single objective problem to yield a compromise solution with an acceptable degree of satisfaction, using suitable scalarisation techniques such as the gamma-connective technique and the minimum bounded sum operator technique. The proposed solution method is applied to several test problems and a multi-objective pharmaceutical supply chain management model with self generated random data.     

An analysis of manufacturers’ supply and demand uncertainty based on the dynamic customisation degree

Diverse demands regarding products are common; however, manufacturers usually cannot respond immediately to meet such changes upon demand, and thus, customer satisfaction tends to be reduced. Notebook computer manufacturers adopt a production mode of mass customisation; hence, a certain degree of dynamic customisation measurements, inherent in different supply chain models, allow manufacturers to evaluate costs and profits in advance. The application of the model, as proposed in this study, indicates that the most important factor of the customisation degree is product price. The dynamic customisation degree is adjusted based on monitoring indicators, which requires less total cost and produces greater accuracy in forecast results regarding the prediction model of customer demands. This study develops a dynamic customisation model for total product profits, inventory cost of semi-manufactured products, shortage costs and buffer inventory costs, which are affected by the degree of dynamic customisation of the products. It also analyses the supply and demand uncertainties of the Direct Shipment of the Manufactured Model, as well as the Door-to-Door Direct Shipment of the End User Model in the notebook computer industry, as the criteria with respect to a firm's customisation degree, costs, and profits in different supply chain mode operations.     

基于讨价还价博弈的ATO供应链零部件生产及补货策略

通过建立ATO供应链中最终产品制造商与其两个供应商间的讨价还价合作模型,研究了ATO供应链的零部件生产补货策略和相应的利润分配策略,并探讨了供应链中各合作主体议价能力对最优解的影响。研究发现,ATO供应链应随着最终产品销售价格的提高而增加零部件产量,随最终产品组装成本或零部件生产成本的提高而减少零部件产量;随着最终产品制造商对其中一个供应商议价能力的增强,该供应商零部件的转移价格及其利润会降低,而另外一个供应商的零部件转移价格和利润会提高;若最终产品制造商比另外一个供应商的议价能力强,其期望利润会得到提高,反之,则会降低。     

基于直觉模糊关系的供应链风险评估

针对供应链风险评判过程中的复杂模糊关系,借助直觉模糊理论,将犹豫度引入到具体的风险评判过程中,构建基于直觉模糊关系的供应链风险评判决策模型,并详细阐述了该方法的算法步骤,最后通过具体算例对该模型的实际应用进行了一定的分析.结果表明,该模型有望在实际中得到更加广泛的应用.     

Integrating production-transportation planning decision with fuzzy multiple goals in supply chains

This work presents a novel fuzzy multi-objective linear programming (f-MOLP) model for solving integrated production-transportation planning decision (PTPD) problems in supply chains in a fuzzy environment. The proposed model attempts to simultaneously minimise total production and transportation costs, total number of rejected items, and total delivery time with reference to available capacities, labor level, quota flexibility, and budget constraints at each source, as well as forecast demand and warehouse space at each destination. An industrial case demonstrates that the proposed f-MOLP model achieves an efficient compromise solution and overall decision maker satisfaction with determined goal values. Additionally, the proposed model provides a systematic framework that facilitates decision makers to interactively modify the fuzzy data and parameters until a satisfactory solution is obtained. Overall, the f-MOLP model offers a practical method for solving PTPD problems with fuzzy multiple goals, and can effectively improve producer–distributor relationships within a supply chain.     

A hybrid optimisation approach to configure a supply chain for new product diffusion: a case study of multiple-sourcing strategy

Configuring a supply chain for a new product is a challenging task due to the lack of historical demand data and the dynamic/uncertain nature of the new product diffusion process. An integrated supply chain configuration (SCC) and new product diffusion (NPD) model is developed to explicitly account for the impact of demand dynamics during a new product's diffusion on an optimal supply chain configuration. Our hybrid NPD-SCC model allows a manufacturer to source from multiple suppliers, vendors or modes for its supply chain entities. Such a multiple-sourcing approach not only helps the manufacturer to diversify its pool of suppliers and maintain bargaining power, but also builds redundancy into the supply chain to hedge against potential demand surge and supply disruption during the new product life cycle. Through a case study and a comprehensive computational study, we find that although the single-sourcing solution is able to achieve lower unit-manufacturing cost (UMC), the multiple-sourcing approach is superior to single-sourcing on the overall supply chain performance in the environment with random supply disruptions. By building-in redundancy as multiple suppliers and modes, the resultant supply chain has less chance of being disrupted and achieves higher overall profit on average. We also draw several other managerial insights closing the gap between some supply chain operations and marketing strategies.     

An integrated inventory model for a single vendor and single buyer with order-processing cost reduction and process mean

Co-ordination in a supply chain is an effective way to improve channel performance. This paper considers a supply chain in which a vendor supplies a product to a buyer. In this paper, we develop a model that integrates the single vendor single buyer problem with order-processing cost reduction and process mean. A solution procedure is provided to determine the optimal order policy. Finally, a numerical example is presented to illustrate the solution procedure.     

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective,multi-site supply-chain planning problem

The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.