基于数据挖掘的油田企业供应商选择

选择合适的供应商,有效控制油田企业成本,是油田企业提升盈利水平、持续健康发展的重中之重。在大数据时代下,伴随着供应链全球化的趋势日益增强,供应商的数量愈加巨大,以往对供应商的选择方式已经不适用于企业科学有效地选择供应商的需求。文章基于数据挖掘的理念,研究某油田企业现有的某一领域供应商评价指标分值,并以此作为数据样本,应用聚类分析中的K-means算法对供应商评价指标分值进行聚类分析。将供应商进行分类,挖掘供应商评价指标分值的分布特点及规律,构建适合油田企业的科学、规范、高效的供应商选择体系,摒弃传统选择方式无法避免的主观性、随机性和偶然性等弊端,为油田企业决策者挑选最合适的供应商作为战略合作伙伴提供了科学、有效的依据。     

Coordinated selection of procurement bids in finite capacity environments

Pressure to increase agility and reduce costs is pushing enterprises to dynamically select among offers from a broader range of suppliers. This process is facilitated by the adoption of web services standards. An important requirement in this context is the ability to move away from unidimensional price-based e-procurement models and develop richer solutions that are capable of capturing other important attributes in the selection of supplier bids. Research on the evaluation and selection of supplier bids (“winner determination”) has traditionally ignored the temporal and finite capacity constraints under which manufacturers and service providers often operate. We consider the problem faced by a firm that procures multiple key components or services from a number of possible suppliers. Bids submitted by suppliers include both a price and a delivery date. The firm has to select a combination of supplier bids that will maximize its overall profit. Profit is determined by the revenue generated by the products (or services) sold by the firm, the costs of the components (or services) it acquires as well as late delivery penalties it incurs if it fails to deliver its products/services in time to its own customers. We provide a formal model of this important class of problems, discuss its complexity and introduce rules that can be used to efficiently prune the resulting search space. We proceed to show that our model can be characterized as a pseudo-early/tardy scheduling problem and use this observation to build an efficient heuristic search procedure. Computational results show that our heuristic procedure typically yields solutions that are within a few percent from the optimum. They further indicate that taking into account the manufacturer/service provider’s capacity can significantly improve its bottom line.     

Order allocation for multiple supply-demand networks within a cluster

Manufacturers outside an industry cluster and suppliers within cluster form a multiple sourcing supply-demand network. Two different order allocation strategies, the production capacity-based strategy and the production load equilibrium-based strategy, are studied in this paper. The respective order allocation models of multiple manufacturers versus multiple suppliers are proposed. Considering the uncertainty of demand and enterprises’ production capacity, the discrete event system simulation is used to verify that the production load equilibrium-based strategy can not only guarantee the order’s on-time delivery from the perspective of the available manufacturing resources, but also lead to promote the whole supplier group’s operation level, so as to realize the optimization of the entire supply chain.     

网络联盟企业中的供应商选择方法

传统上,企业对供应商的选择主要是根据他们的报价。随着Ｉｎｔｅｒｎｅｔ和后勤技术的发展,企业可供选择的供应商越来越多,因此,需要对供应商有一个综合的评估方法,本文采用层次分析法建立了选择供商的数学模型,并在Ｍａｔｌａｂ环境下开发了一个供应商选择系统。     

Applying fuzzy mathematical programming approach to optimize a multiple supply network in uncertain condition with comparative analysis

Supply chain design problems have recently raised a lot of interest since the opportunity of an integrated management of the supply chain can reduce the propagation of undesirable events through the network and can affect decisively the profitability of the members. Often uncertainties may be associated with demand and relevant costs. In most of the existing models uncertainties are treated as randomness and are handled by appealing to probability theory. Here, we propose a fuzzy mathematical programming model for a supply chain which considers multiple depots, multiple vehicles, multiple products, multiple customers, and different time periods. In this work not only demand and cost but also decision variables are considered to be fuzzy. We apply two ranking functions for solving the model. The aim of the fuzzy mathematical program is to select the appropriate depots among candidate depots, the allocation of orders to depots and vehicles, also the allocation of the returning vehicles to depots, to minimize the total costs. To validate the model some numerical experiments are worked out and a comparative analysis is investigated. Also, a regression model is considered to analyze the applied fuzzy ranking methods.     

Scatter search algorithm for supplier selection and order lot sizing under multiple price discount environment

Supplier selection is one the most important elements of supply chain management. This function involves evaluation of many factors such as, cost of parts/materials, size of order, quality, and delivery performance. Therefore, this problem is categorized as multi-criteria decision making problems. Different approaches have been applied in order to assess and select the suppliers when suppliers offer discount on the unit price. In practical conditions, buyers may face a situation where different types of discount may be offered by candidate suppliers. None of the previous studies have considered different discount schemes simultaneously. In this article a mathematical model is introduced which consider different types of discount (all-unit cost, incremental discount, and total business volume discount) through multi-objective formulation for single item purchasing problem. In addition, constraints such as suppliers’ capacity and demand are taken into consideration in the model. Due to the complexity of the problem a proposed scatter search algorithm (SSA) is presented to solve this problem. Finally several sample problems have been solved by the proposed SSA and the exact (branch and bound) method. The results illustrate slight relative errors to compare with reasonable saving in computational times.     

An uncertain mathematical model to maximize profit of the defective goods supply chain by selecting appropriate suppliers

This study presents an uncertain mathematical model for maximizing profit of the defective goods supply chain during uncertain situations by using selection of appropriate suppliers, just-in-time (JIT) logistic philosophy and minimizing total costs including costs of production, shipping, holding, defective goods, scrap goods, shortage in retailers. The selections of suppliers are based on three criteria, namely, quality, JIT delivery, and level of responsibility, which have important roles in the production of perfect goods by manufacturers. In each criterion, two kinds of indicators are considered by manufacturers. The first indicator is the importance of the weighted factor of each criterion for each manufacturer, and the second is weighted factor of each supplier with respect to each criterion. The proposed mathematical model with uncertain parameters and the probability of occurring in various scenarios is investigated. The model is studied in ten scenarios and the average amount is calculated. The mentioned mathematical model is solved by the averages of the parameters using CPLEX.12 solver and Expert Choice software. The findings of the model are maximum profit, amounts of economic production quantity, defective goods, scrap goods, and amounts of products that should be exchanged among the nodes of the supply chain. To achieve maximum benefit, the model can select the appropriate suppliers. The results obtained demonstrate the validity and efficiency of the proposed uncertain mathematical model.     

信息系统硬件资源需求测算方法

信息系统硬件资源作为支撑企业信息业务的基础设施,其容量的准确测算对于信息系统的建设、运行以及扩容具有重要意义;目前信息系统硬件资源容量测算方法主要有TPC,SPEC,SAP基准测试,Linpack以及RPE2等;研究表明,这些方法有一定的通用性,但在反映系统运行的实际性能指标等方面仍有不足,导致企业在信息系统建设过程中普遍缺乏合适的硬件资源容量评估与配置方法,造成信息化建设成本增高或资源浪费;为解决这一问题,通过对实际信息系统进行性能测试,基于实测数据,运用拟合方法和灰色关联方法对系统的硬件资源容量的关键参数进行分析,建立硬件资源容量评估与测算模型,从而对资源需求和性能指标进行较为准确的量化测算;算例表明,所建立的模型正确有效,具有一定的工程应用价值;运用该方法结合企业信息系统实际,建立适合企业自身特点的测算标准,帮助企业在硬件资源配置中明确需求,确定选型,降低采购成本,达到避免浪费的同时满足业务需求的目标。     

Supplier selection model with contingency planning for supplier failures

We consider the optimal allocation of demand across a set of suppliers given the risk of supplier failures. We assume items sourced are used in multiple facilities and can be purchased from multiple suppliers with different cost and reliability characteristics. Suppliers have production flexibility that allows them to deliver a contingency quantity in case other suppliers fail. Costs considered include supplier fixed costs and variable costs per unit, while failure to deliver to a demand point results in a particular financial loss. The model utilizes the decision tree approach to consider all the possible states of nature when one or more suppliers fail, as well as expand the traditional transportation problem. Unlike other supplier selection models, this model considers contingency planning in the decision process, minimizing the total network costs. This results in a base allocation to one or more of the available suppliers and a state of nature specific delivery contingency plan from the suppliers to each demand point. A numerical example, as well as sensitivity analysis, is presented to illustrate the model and provide insights.     

Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing

The coordinated supplier selection and customer order scheduling in the presence of supply chain disruption risks is studied for single and multiple sourcing strategies. Given a set of customer orders for products, the decision maker needs to select a single supplier or a subset of suppliers for purchasing parts required to complete the customer orders, and schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The suppliers are located in different geographic regions and the supplies are subject to different types of disruptions: to random local disruptions of each supplier individually, to random regional disruptions of all suppliers in the same region simultaneously and to random global disruptions of all suppliers simultaneously. For any combination of suppliers hit by different types of disruptions, a formula for calculating the corresponding disruption probability is developed. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional value-at-risk as a risk measure. The problem objective is either to minimize expected worst-case cost or to maximize expected worst-case customer service level, i.e., the expected worst-case fraction of customer orders filled on or before their due dates. The risk-averse solutions that optimize worst-case performance of a supply chain under disruptions risks are compared for the two sourcing strategies and the two objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Autonomous negotiation for resource allocation of multiple sectors in the TFT-LCD manufacturing industry

In a global TFT-LCD manufacturing enterprise, a central planning sector promises orders to maximize profit, while the production sector is responsible for minimizing costs and meeting delivery deadline. The two sectors frequently struggle with different preferences. The contradictory objectives raise conflict between the two sectors when developing an entire resource allocation plan for the enterprise. This study presents a novel negotiation framework and develops a mutually acceptable resource allocation plan via autonomous negotiation between sectors with different preferences. A mathematical model considering the major characters of the TFT-LCD industry is formulated. Individual sectors can employ preferred negotiation tactics to achieve their objectives with an acceptable level of trade-off. This study examines various negotiation tactics and compares the proposed decentralized model with a centralized solution. Negotiation experiments demonstrate a good resource allocation plan over a short time and the conflict between sectors can be resolved efficiently. The proposed autonomous negotiation facilitates smart operations management.     

Multi-period part selection and loading problems in flexible manufacturing systems

We consider multi-period part selection and loading problems in flexible manufacturing systems with the objective of minimizing subcontracting costs. The part selection problem is to select sets of part types and to determine their quantities to be produced during the upcoming planning horizon while satisfying due dates of all orders for the parts, and the loading problem involves allocation of operations and required tools to machines. Production demands should be satisfied for periods through subcontracting if production demands cannot be satisfied by the system due to machine capacity or tool magazine capacity constraints. For the part selection and loading problems, we develop three iterative algorithms, called the forward algorithm, the backward algorithm and the capacity approximation algorithm, that solve the part selection and loading problems iteratively for each period. To compare the three algorithms, a series of computational experiments is done on randomly generated test problems.     

A particle swarm optimization algorithm for the multiple-level warehouse layout design problem

Warehouse operation and management is one of the essential parts of manufacturing and service operations. The warehouse layout problem is a key to warehouse operations. Generally, warehouse layout design models attempt to optimize different objectives such as the orientation of storage racks, the allocation of space among competing uses, the number of cranes, the overall configuration of the facility, etc. The warehousing strategies can be classified as distribution-type, production-type and contract-type warehouse strategies. In this study, a distribution-type warehouse considered that various type products are collected from different suppliers for storing in the warehouse for a determined period and for delivery to different customers. The aim of the study is to design a multiple-level warehouse shelf configuration which minimizes the annual carrying costs. The turnover rates of the products are classified and they are considered while putting/picking them to/from shelves regarding the distances between the shelves and docks. Since proposed mathematical model was shown to be NP-hard, a particle swarm optimization algorithm (PSO) as a novel heuristic was developed for determining the optimal layout.     

Single sourcing versus dual sourcing under conditions of learning

This paper considers a buyer who has to decide whether to select a single or two sources of supply for a homogeneous product. The production processes of the suppliers are subject to learning effects, which reduce the production costs and increase the production capacities of the suppliers. This, in turn, enables the suppliers to reduce the sales price, which results in lower acquisition costs at the buyer. As the supplier selection decision influences the individual production quantity of a supplier, the learning effect has to be considered when deciding how many and which suppliers to select. Since the effect of learning on the supplier selection problem has not been investigated in the literature, this paper addresses this limitation and derives models for continuous learning and when learning plateaus. Numerical results indicate that the supplier selection decision can comprehensively influence the learning process for the suppliers and therewith the total costs of the system under study. The results also show that it is not necessarily optimal solely to select the supplier with the highest learning rate.     

钢铁企业生产资源平衡计划系统分析与设计

针对市场需求剧烈变动环境下,钢铁企业如何利用有限产能、平衡资源分配、优化产品组合的问题,提出了通过生产资源平衡计划系统来解决的方法.在系统功能需求分析的基础上,通过数据流图、实体关系图、用例图及时序图构建钢铁生产资源平衡计划系统模型,详细描述系统的设计目标、业务流程及子模块协作机制.该系统以基于数学优化和智能计算的优化计算引擎为核心,综合考虑盈利指标、市场需求、生产能力等因素进行资源平衡,为生产与销售提供决策支持.     

Integrated Inventory Control and Facility Location Decisions in a Multi-Echelon Supply Chain Network with Hubs

This paper develops mathematical models to coordinate facility location and inventory control for a four-echelon supply chain network consisting of multiple suppliers, warehouses, hubs and retailers. The hubs help in reducing transportation costs by consolidating products from multiple warehouses and directing the larger shipments to the retailer. The integrated models studied in this paper simultaneously determines three types of decisions: (i) facility location—the number and location of warehouses and hubs, (ii) allocation—assignment of suppliers to located warehouses and retailers to located warehouses via the location hubs, and (iii) inventory control decisions at each located warehouse. The goal is to minimize the facility location, transportation and the inventory costs. A mixed integer nonlinear programming formulation is first presented. The nonlinear integer programming formulation is then transformed into a conic mixed integer program and a novel and compact conic mixed integer programming formulation. Computational runs are conducted using commercial solvers to compare the performance of the different formulations. The compact conic mixed integer programming formulation was found to significantly outperform the other formulations by achieving significant computational savings. The results demonstrate that large scale instances of certain multi-echelon supply chain network design problems can be solved using commercial solvers through intelligent reformulation of the model.     

Analytical study on multi-product production planning with outsourcing

This paper studies a problem on multi-product capacitated production planning with outsourcing. The context of the problem is about an enterprise that manufactures multiple products in multiple periods for stochastic demands. Manufacturers usually have two alternative modes for the production: one is to outsource parts from outside suppliers and then assemble them; the other is to in-house manufacture parts and then assemble them. Each mode has its relative merits. In addition, the capacity constraint by in-house manufacturing is also taken into account. This paper investigates how to balance the trade-off between the two modes. An analytical approach is proposed to study the optimal decision on the above two modes for all products during each planning period. Some findings are drawn out from this analytical study. Numerical experiments show the significant cost reduction can be obtained using the proposed decision model.     

An intelligent supplier management tool for benchmarking suppliers in outsource manufacturing

A global corporation's supply chain usually consists of enterprises and manufacturers that are graphically dispersed around the world, whereby each company is involved in a wide variety of supply chain activities such as order fulfillment, international procurement, acquisition of information technology, manufacturing, and customer service. However, selecting suppliers based on accumulated experience is not both effective and scientific due to subjective judgment and lack of systematic analysis. Therefore, continuously tracking and benchmarking performance of suppliers and forming an appropriate supplier selection mechanism is one of the crucial activities in supply chain management. This paper presents an intelligent supplier management tool (ISMT) using the case-based reasoning (CBR) and neural network (NN) techniques to select and benchmark suppliers. The development of ISMT and how the CBR and NN techniques are used in benchmarking suppliers during the process of new product development in Honeywell Consumer Products (Hong Kong) Limited are presented.