A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information

Both academic and corporate interest in sustainable supply chains has increased in recent years. Supplier selection process is one of the key operational tasks for sustainable supply chain management. This paper examines the problem of identifying an effective model based on sustainability principles for supplier selection operations in supply chains. Due to its multi-criteria nature, the sustainable supplier evaluation process requires an appropriate multi-criteria analysis and solution approach. The approach should also consider that decision makers might face situations such as time pressure, lack of expertise in related issue, etc., during the evaluation process. The paper develops a novel approach based on fuzzy analytic network process within multi-person decision-making schema under incomplete preference relations. The method not only makes sufficient evaluations using the provided preference information, but also maintains the consistency level of the evaluations. Finally, the paper analyzes the sustainability of a number of suppliers in a real-life problem to demonstrate the validity of the proposed evaluation model.     

Robust supply chain design under uncertain demand in agile manufacturing

This paper considers a supply chain design problem for a new market opportunity with uncertain demand in an agile manufacturing setting. We consider the integrated optimization of logistics and production costs associated with the supply chain members. These problems routinely occur in a wide variety of industries including semiconductor manufacturing, multi-tier automotive supply chains, and consumer appliances to name a few. There are two types of decision variables: binary variables for selection of companies to form the supply chain and continuous variables associated with production planning. A scenario approach is used to handle the uncertainty of demand. The formulation is a robust optimization model with three components in the objective function: expected total costs, cost variability due to demand uncertainty, and expected penalty for demand unmet at the end of the planning horizon. The increase of computational time with the numbers of echelons and members per echelon necessitates a heuristic. A heuristic based on a k-shortest path algorithm is developed by using a surrogate distance to denote the effectiveness of each member in the supply chain. The heuristic can find an optimal solution very quickly in some small- and medium-size cases. For large problems, a “good” solution with a small gap relative to our lower bound is obtained in a short computational time.     

A framework for evaluation of coordination by contracts: A case of two-level supply chains

A supply chain consists of disparate but inter-dependent members who are dependent on each other to manage various resources (such as inventory, money and information). The conflicting objectives and lack of coordination between these members may often cause uncertainties in supply and demand. Coordination may help in managing inter-dependencies and reducing uncertainties. Typically, a mechanism is required to streamline the whole supply chain and motivate all the members to be a part of the entire supply chain for sharing risks and rewards through supply chain contracts.The objective of this paper is to explore the applicability and benefits of the contracts. A two-phase framework is proposed consisting of a decision support tool and a graph-theoretic model for evaluation of coordination in a two-level supply chain. In Phase I, an analytical model is proposed based on the classical newsboy problem. Various contract mechanisms have been explored through a decision support tool based on the simulation. The effectiveness of these contracts is evaluated through a graph-theoretic model in Phase II. It is expected that by designing the contracts as per the requirements of the supply chain members as well as the whole supply chain, supply chain performance may be improved.     

Agent-based monitoring service for management of disruptive events in supply chains

Schedules of supply chains are generated with buffers to absorb the effect of disruptive events that could occur during their execution. Schedules can be systematically repaired through specific modifications within buffers by using appropriate decision models that consider the distributed nature of a supply chain. To this aim, information of disruptive events at occurrence or in advance allows decision models to make better decisions. To detect and predict disruptive events along a schedule execution, a service-oriented monitoring subsystem that uses a reference model for defining monitoring models was proposed. This subsystem offers services for collecting execution data of a schedule and environment data, and assessing them to detect/anticipate disruptive events. Because of the distributed nature and the complexity of these services functionalities, this paper presents an agent-based approach for their implementation. This technology allows dealing with supply chain monitoring by structuring monitoring subsystem functionalities as a set of autonomous entities. These entities are able to perform tailored plans created at execution time to concurrently monitor different schedules. A case study is described to try out the implemented prototype system.     

Decision support for local environmental impact assessment

Sustainability has become a key concern for consumers and industry. However, coming to understand its implications in the context of daily life can be difficult. It may be hard to come to terms with the idea that traditional activities present environmental risks and may lead to real emergencies, manifested differently depending on local conditions. Everyone will be asked to do more but the move towards sustainability may require the total transformation of current attitudes and actions. This paper considers the role of informatics in responding to, and possibly averting, environmental emergencies by making information available to decision makers, whether they are individual consumers, institutional buyers, or emergency response personnel. If all choices can be presented within a spectrum of best and worst possible impacts for sustainability, decision makers can meaningfully weigh their options. Any evaluation on this basis is presently very difficult because one needs to independently assess and integrate possibly incomplete and conflicting information from a wide variety of sources. This paper describes the use of the cogito software, which embodies an approach for data access and presentation that is expected to provide significant benefits over existing web-based tools. The paper also considers the issues related to the collection and synthesis of disparate data.     

Optimal constraint-based decision tree induction from itemset lattices

In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding optimal decision trees in a wide range of applications by post-processing a set of patterns: we use local patterns to construct a global model. We exploit the connection between constraints in pattern mining and constraints in decision tree induction to develop a framework for categorizing decision tree mining constraints. This framework allows us to determine which model constraints can be pushed deeply into the pattern mining process, and allows us to improve the state-of-the-art of optimal decision tree induction.     

Robust optimization to secure urban bulk water supply against extreme drought and uncertain climate change

Urban bulk water systems supply water with high reliability and, in the event of extreme drought, must avoid catastrophic economic and social collapse. In view of the deep uncertainty about future climate change, it is vital that robust solutions be found that secure urban bulk water systems against extreme drought. To tackle this challenge an approach was developed integrating: 1) a stochastic model of multi-site streamflow conditioned on future climate change scenarios; 2) Monte Carlo simulation of the urban bulk water system incorporated into a robust optimization framework and solved using a multi-objective evolutionary algorithm; and 3) a comprehensive decision space including operating rules, investment in new sources and source substitution and a drought contingency plan with multiple actions with increasingly severe economic and social impact. A case study demonstrated the feasibility of this approach for a complex urban bulk water supply system. The primary objective was to minimize the expected present worth cost arising from infrastructure investment, system operation and the social cost of “normal” and emergency restrictions. By introducing a second objective which minimizes either the difference in present worth cost between the driest and wettest future climate change scenarios or the present worth cost for driest climate scenario, the trade-off between efficiency and robustness was identified. The results show that a significant change in investment and operating strategy can occur when the decision maker expresses a stronger preference for robustness and that this depends on the adopted robustness measure. Moreover, solutions are not only impacted by the degree of uncertainty about future climate change but also by the stress imposed on the system and the range of available options.     

A web-based screening model for climate risk to water supply systems in the northeastern United States

The aim of this study is to describe the development and application of a web-based decision support tool (ViRTUE) for performing climate risk evaluations of water supply systems. The tool is designed for small-scale water utilities in the northeastern United States that may lack the resources for detailed climate change risk investigations. Development of this tool demonstrates a relatively new approach to web application development using the Shiny framework for the R programming language to create an interactive environment for stakeholders and water managers to explore climate vulnerabilities. Using a decision-scaling framework, the tool allows the user to perform a climate stress test to evaluate the performance and vulnerability to water supply shortfalls of local reservoir systems over a wide range of potential climate change scenarios using a generic systems model. Probabilities of future climate conditions derived from climate projections then help inform utility operators of impending risk.     

模糊环境下考虑零售商风险偏好的绿色供应链博弈模型

在模糊需求环境下绿色供应链运作系统中,研究由单一制造商和单一零售商组成的两级绿色供应链.首先,引入创新成本,分别建立风险中性制造商为主导者,具有中性、悲观和乐观等风险偏好的零售商为跟随者的两级绿色供应链Stackelberg博弈模型;然后,推导出最优决策的期望值和机会约束规划模型,并进行算例分析,比较产品绿色度、批发价格及零售价格与不同置信水平之间的相互影响,探讨具有不同风险偏好的零售商对模糊绿色供应链中制造商、零售商及供应链整体运作绩效的影响.研究结果表明:悲观决策模型中,伴随置信水平的增加,产品的绿色度水平、批发价格及创新成本逐步下降,零售价格、制造商、零售商及绿色供应链整体利润逐步上升;在乐观决策模型中,结论相反.     

Economic and environmental assessment of irrigation water policies: A bioeconomic simulation study

We present a bioeconomic modeling approach that links the biophysical crop growth model CropSyst to an economic decision model at field scale. The developed model is used in conjunction with a genetic algorithm to optimize management decisions in potato production in the Broye catchment (Switzerland) in the context of different irrigation policy scenarios. More specifically, we consider the effects of water bans, water quotas, and water prices on water consumption, profitability, and the financial risks of potato production. The use of a genetic algorithm enables the direct integration of the considered decision variables as management input factors in CropSyst. We employ the farmer's certainty equivalent, measured as the expected profit margin minus a risk premium, as the objective function. Using this methodological framework allows us to consider the potential impacts of policy measures on farmers' crop management decisions due to their effects on both expected income levels and income variability.Our results show that the region's current water policy, which frequently prevents irrigation during hot and dry periods by banning water withdrawal, causes high levels of income risk for the farmer and increases the average water demand in potato production. In contrast, the implementation of an appropriate water quota could significantly decrease water consumption in potato production while allowing the farmer's certainty equivalent to remain at the same level as it is under the current irrigation water policy.     

生物质能综合利用两层通用决策支持系统的研究

在制定生物质能生产计划时,影响决策者的因素不仅包括生物质供应链本身的不同环节,而且包括生物质系统对于社会、环境和经济的影响及其在某个特定的国家内开发的困难程度。为了综合考虑上述因素,本文提出生物质能两层通用决策系统(gBEDS),其核心是数据库,包括基本的生物质信息和详细的决策信息,此外,还包括方案数据库和为新用户提供示范的案例库。在数据库的基础上,决策系统包括单元过程(UP)的模拟模块和用于优化决策的遗传算法。在图形界面的帮助下,用户可以自行定义生物质供应链,并进行环境、经济、社会或其他方面的评价;在生物质能生产全生命周期的模拟和优化模型的基础上,系统采用数据挖掘方法(模糊c均值聚类和决策树)确定最优的生物质原材料收集存储和转化工厂的地理位置。使用Matlab开发生物质供应链的生物质计划参数(例如费用和CO_2排放)的计算模型。同时,用地理信息系统(GIS)对生物能转化工厂和存储数据作可视化表达,以支持用户在智能输出的基础上做出决策。因此,gBEDS支持生物质能国家计划者,制定一种有效的生物质能生产计划并作出综合评价,地方的设计和实施者确定优化、详细的单元过程实施上述计划。日本森林废物发电的实例研究表明了上述方法的有效性和可行性。     

带有订单转保理的供应链金融的收益共享博弈模型

当作为供应商的中小企业出现了严重的财务困境问题时,急需有效融资工具和创新管理模式加以解决.订单转保理可以令资质良好的零售商为资金短缺的供应商提供融资担保,有效解决供应商的订单减少和融资难的问题.然而,分散决策的订单转保理融资模式,使得做担保的零售商的收益降低,不能明显改善供应链效率.针对此问题,研究订单转保理模式下的供应链协调模型,并重点研究由供应商与零售商组成的二级供应链的订单转保理收益共享模型.研究发现:收益共享决策模型供应链总收益小于集中决策供应链总收益,大于分散决策的供应链总收益,表明供应链成员相互合作程度越高,越有利于供应链发展,从而表明协调在供应链中发挥重要作用.然而,集中决策模型只能得到供应链最优总收益,无法得到供应商和零售商的最优收益.收益共享模型不仅能够提高产品订货量,降低产品批发价格,增加供应商和零售商的收益,而且通过最优的收益共享系数可以得到供应商和零售商的最优收益.收益共享机制能够为供应商和零售商共赢协调发展提供新的运作管理模式.     

Supply network design: Risk-averse or risk-neutral?

In this paper, we investigate a supply network design in supply chain with unreliable supply with application in the pharmaceutical industry. We consider two types of decision making policies: (1) a risk-neutral decision-making policy that is based on a cost-minimization approach and (2) a risk-averse policy wherein, rather than selecting facilities and identifying the pertinent supplier–consumer assignments that minimize the expected cost, the decision-maker uses a Conditional Value-at-Risk (CVaR) approach to measure and quantify risk and to define what comprises a worst-case scenario. The CVaR methodology allows the decision-maker to specify to what extent worst-case scenarios should be avoided and the corresponding costs associated with such a policy. After introducing the underlying optimization models, we present computational analysis and statistical analysis to compare the results of the risk-averse and risk-neutral policies. In addition, we provide several managerial insights.     

模糊智能决策树模型与应用研究

决策问题是计算智能最核心的问题之一.基于模糊数学理论建立了一个普适的模糊决策树模型;用节点刻画决策前提和控制信息,用树上的边形式化推理规则;并在节点和边上定义合理的模糊决策算子,进行多级综合决策.工程决策考虑不同方案的成本、可行性和收益,将这些信息进行融合作为决策方案优劣的测度;建立加权模糊智能决策模型,并给出了基于该...     

The valuation of RFID investment using fuzzy real option

Net present value (NPV) and return on investment (ROI) are commonly used to evaluate investment in new technologies. Sometimes, however, measuring the value of investment in new IT becomes very difficult due to its wide scope of application coupled with embedded options in its adoption. Therefore, comprehensive but easily understandable methodologies are needed to solve the complicated problems resulting from the complexity of new technologies. This paper employs a real option analysis to evaluate RFID adoption in the supply chain. Real options analysis should be a better way to evaluate a disruptive technology like RFID. However, the pure (probabilistic) real option rule characterizes the present value of expected cash flows and the expected costs by a single number, which is not realistic in many cases. To solve the problem, this paper considers the real option rule in a more realistic setting, namely, when the present values of expected cash flows and expected costs are estimated by trapezoidal fuzzy numbers. Specifically, it drew out their means and variance and presented a method of calculating fuzzy real options through numerical value examples of RFID investment assuming the current value of expected cash flow and investment costs using trapezoid fuzzy number fuzzy real options. Since advanced information technology such as RFID has very high risk and options such as change, extension, delay and withdrawal, etc., investment valuation using the real options technique should be done, and in the process, in a more realistic and practical approach, the fuzzy real options model presented in this study is judged to be useful.     

Solving a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm

This study presents a stochastic demand multi-product supplier selection model with service level and budget constraints using Genetic Algorithm. Recently, much attention has been given to stochastic demand due to uncertainty in the real world. Conflicting objectives also exist between profit, service level and resource utilization. In this study, the relationship between the expected profit and the number of trials as well as between the expected profit and the combination of mutation and crossover rates are investigated to identify better parameter values to efficiently run the Genetic Algorithm. Pareto optimal solutions and return on investment are analyzed to provide decision makers with the alternative options of achieving the proper budget and service level. The results show that the optimal value for the return on investment and the expected profit are obtained with a certain budget and service level constraint.     

Optimal apparel supplier selection with forecast updates under carbon emission taxation scheme

Carbon emission tax is an important measure for sustainable supply chain management. This paper studies an optimal supplier selection problem in the fashion apparel supply chain in the presence of carbon emission tax. We consider the scenario in which there are multiple suppliers in the market. In the basic model, each supplier offers a supply lead time and a wholesale pricing contract to the fashion retail buyer. For the fashion retail buyer, the supplier which offers a shorter lead time allows it to postpone the ordering decision with updated and better forecast, and also a smaller carbon tax. However, the wholesale price is usually larger. We propose a two-phase optimal supplier selection scheme in which phase one filters the inferior suppliers and phase two helps to select the best supplier among the set of non-inferior suppliers by multi-stage stochastic dynamic programming. The impacts brought by different formats of carbon emission tax are explored. Finally, we examine an extended model in which there is a local supplier who offers a buyback contract and accepts product returns. Insights from the analysis are discussed.     

A global human walking model with real-time kinematic personification

This paper presents a human walking model built from experimental data based on a wide range of normalized velocities. The model is structured on two levels. On the first level, global spatial and temporal characteristics (normalized length and step duration) are generated. On the second level, a set of parameterized trajectories produce both the position of the body in space and the internal body configuration. This is performed for a standard structure and an average configuration of the human body.The experimental context corresponding to the model is extended by allowing a continuous variation of global spatial and temporal parameters according to the motion rendition expected by the animator. The model is based on a simple kinematic approach designed to keep the intrinsic dynamic characteristics of the experimental model. Such an approach also allows a personification of the walking action in an interactive real-time context in most cases. A correction automata of such motion is then proposed.     

产出和需求不确定下三级供应链契约协调模型

研究基于供应商和制造商随机产出以及零售商随机需求的单一供应商、制造商和零售商组成的三级供应链契约协调问题, 构建并分析集中和分散条件下供应链系统的最优决策模型, 证明随机产出和需求下运用风险共担契约可以使分散期望利润达到集中决策的水平. 在数值算例中, 通过模型和契约参数的分析, 阐述了风险共担契约协调的有效性.