Bounds of Relative Regret Limit in p‐Robust Supply Chain Network Design

This research studies the p‐robust supply chain network design with uncertain demand and cost scenarios. The optimal design integrates the supplier selection together with the facility location and capacity problem. We provide a new framework to obtain the relative regret limit, which is critical in the robust supply chain design but is assumed to be a known value in the existing literature. We obtain lower and upper bounds for relative regret limit and obtain a sequence of optimal solutions for series relative regret limits between the upper and lower bounds. An algorithm for p‐robust supply chain network design is provided. A series of numerical examples are designed to find the properties of the bottleneck scenarios. A scenario with low probability and a low optimal objective function value for the scenario has a greater chance of being a bottleneck. To focus only on the influence from the relative regret, we also introduce three separate new objective functions in p‐robust design. The proposed new theories and approaches provide a sequence of options for decision makers to reduce the marketing risks effectively in supply chain network design.     

非平稳需求下考虑碳配额的供应链选址-库存模型与算法研究

针对非平稳需求下考虑碳配额的多期、多需求情景的三级供应链选址-库存问题，构建了库存策略（t，s，S）下供应链运营期望收益最大化的两阶段选址-库存随机优化模型，依据供应链企业不同着眼点下的决策流程，提出了一种三步骤的分层级启发式算法，该算法包含了选址导向和需求导向的两种子问题序贯求解模式。数值算例验证了在不同问题规模及需求类型下算法求解的有效性，同时分析了供应链网络设计、各成本占比和运营收益对不同供应链成本结构、需求不确定性与碳配额的敏感性，并给出了管理上的启示。     

Coordinated Contract Decisions in a Make‐to‐Order Manufacturing Supply Chain: A Stochastic Programming Approach

One of the important objectives of supply chain S&OP (Sales and Operations Planning) is the profitable alignment of customer demand with supply chain capabilities through the coordinated planning of sales, production, distribution, and procurement. In the make‐to‐order manufacturing context considered in this paper, sales plans cover both contract and spot sales, and procurement plans require the selection of supplier contracts. S&OP decisions also involve the allocation of capacity to support sales plans. This article studies the coordinated contract selection and capacity allocation problem, in a three‐tier manufacturing supply chain, with the objective to maximize the manufacturer's profitability. Using a modeling approach based on stochastic programming with recourse, we show how these S&OP decisions can be made taking into account economic, market, supply, and system uncertainties. The research is based on a real business case in the Oriented Strand Board (OSB) industry. The computational results show that the proposed approach provides realistic and robust solutions. For the case considered, the planning method elaborated yields significant performance improvements over the solutions obtained from the mixed integer programming model previously suggested for S&OP.     

供应链分销系统双层优化模型

分销渠道决策在整个供应链管理中非常关键,因为它直接影响着其它的市场决策. 从供 应链集成的角度出发,利用双层规划模型描述了二级分销网络优化问题,充分考虑了网络决策 部门及客户双方的自身及共同利益. 同时设计了启发式求解算法,最后用简单算例验证了模型 及其算法的有效性.     

Selection of supply portfolio under disruption risks

This paper deals with the optimal selection of supply portfolio in a make-to-order environment in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order to minimize total cost and mitigate the impact of disruption risks. The selection of suppliers and allocation of orders is based on price and quality of purchased parts and reliability of delivery. The two types of disruption scenarios are considered: scenarios with independent local disruptions of each supplier and scenarios with local and global disruptions that may result in all suppliers disruption simultaneously. The problem is formulated as a single- or bi-objective mixed integer program and a value-at-risk and conditional value-at-risk approach is applied to control the risk of supply disruptions. The proposed portfolio approach is capable of optimizing the supply portfolio by calculating value-at-risk of cost per part and minimizing expected worst-case cost per part simultaneously. Numerical examples are presented and some computational results are reported.     

不确定环境下分散控制供应链物流计划优化

供应链物流计划是供应链管理的重要内容.针对节点无限扩展的分散控制供应链物流计划问题,在提出供应链元概念的基础上,考虑供应链节点企业上下游物料价格要素的随机性,应用随机机会约束规划理论,建立了多级多节点多产品分散控制供应链一体化物流计划模型;设计了基于随机模拟的混合智能算法求解模型;并以算例仿真验证了模型和算法的有效性,...     

Integrated supply,production and distribution scheduling under disruption risks

This paper presents a bi-objective stochastic mixed integer programming approach for a joint selection of suppliers and scheduling of production and distribution in a multi-echelon supply chain subject to local and regional disruption risks. Two conflicting problem objectives are minimization of cost and maximization of service level. The three shipping methods are considered for distribution of products: batch shipping with a single shipment of different customer orders, batch shipping with multiple shipments of different customer orders and individual shipping of each customer order immediately after its completion. The stochastic combinatorial optimization problem is formulated as a time-indexed mixed integer program with the weighted-sum aggregation of the two objective functions. The supply portfolio is determined by binary selection and fractional allocation variables while time-indexed assignment variables determine the production and distribution schedules. The problem formulation incorporates supply–production, production–distribution and supply–distribution coordinating constraints to efficiently coordinate supply, production and distribution schedules. Numerical examples modelled after an electronics supply chain and computational results are presented and some managerial insights are reported. The findings indicate that for all shipping methods, the service-oriented supply portfolio is more diversified than the cost-oriented portfolio and the more cost-oriented decision-making, the more delayed the expected supply, production and distribution schedules.     

The impact of operations anticipations on the quality of stochastic location-allocation models

It is now a common practice to use optimization models, such as location-allocation models, to support the design of supply chain networks (SCN). The value creation potential of a SCN design must be evaluated in terms of capital expenditures, but also of the operating revenues and expenses incurred during the planning horizon considered. The design model used should therefore be formulated to anticipate these revenues and expenses (relatively) accurately over the planning horizon. In classical location-allocation models, the aggregate flow and throughput variables used yield very crude anticipations. It was never shown that they lead to the best SCN design that one should expect. This paper draws on the stochastic multi-period location-transportation problem (SMLTP) for studying the impact of various types of operations anticipations on the quality of the SCN designs obtained. Since accurate anticipations yield more complex models, solvability is also an issue. Several alternative SCN design models based on more detailed anticipations than the ones embedded in classical location-allocation models are proposed and tested. Accuracy-solvability trade-offs are explored and recommendations are made on the modeling strategy to use to get better SCN designs.     

Closed-loop supply chain configuration for new and reconditioned products: An integrated optimization model

Closed-Loop Supply Chain Management (CLSCM) is considered as a strategic response to the call for corporate sustainability while further expanding the scope of value creation to include product reconstruction. The Closed-Loop Supply Chain (CLSC) performance is directly related to the CLSC network design. The CLSC network design, with long-term and strategic connotations, involves selection of an integrated network of partner organizations to be engaged on one hand in the forward supply chain processes relevant to families of existing and new products and also involved in reverse supply chain activities relevant to reconstruction of the returned products. At the tactical level, Closed-Loop Supply Chain Configuration (CLSCC) attempts to address issues pertinent to launch of a new product and its reconstruction. The CLSC network design is well studied in the current literature, but addressing the CLSCC is neglected. To study the CLSCC problem we: (a) develop an integrated optimization model for problem; (b) present a real-world case study of a battery manufacturer; (c) based on the case study, we conduct a comprehensive set of computational experiments followed by a series of what-if analyses to compare profitability of the Forward Supply Chain Configuration (FSCC) versus the CLSCC; and (c) discuss the key observations and managerial implications drawn from the computational experiments, applicable to other real-world instances. The significant outcomes of the study suggest that: (i) performance of the firm׳s base case integrated CLSCC model is significantly better than the current supply chain model (ii) the sales-price ratio of new battery is found to be negatively related with the maximum acquiring price of used batteries; (iii) combination of sales price ratios of new and reconditioned batteries determines the total net profit for a given return rate. Finally, important managerial insights and scope for future research are discussed.     

Quantifying the effectiveness of VMI and integrated inventory management in a supply chain with uncertain lead-times and uncertain demands

This article considers the inventory management problem in a supply chain with uncertain replenishment lead-times and uncertain demands. The optimal integrated inventory management (IIM) policy is developed using stochastic dynamic programming theory. The IIM policy is contrasted with two pull-type vendor-managed inventory policies (VMI-1 and VMI-2) and a traditional retailer-managed inventory policy (RMI). Computational results show that in such stochastic supply chains, IIM performs about 23, 15, and 3% better than the optimised RMI, VMI-1 and VMI-2 policies, respectively, while two VMI policies are about 8 and 20% better than the best RMI. The basestock-based VMI-2 is a very good form of VMI. The ANOVA analysis reveals that the replenishment lead-times have the largest effect on the relative performance between IIM and other policies. Numerical examples demonstrated that the IIM policy has good structural properties and can be characterised by a set of switching curves.     

基于随机产出与随机需求的农产品供应链风险共担合同

本文采用单位价格补贴的风险共担机制,分析了农产品供应链中随机产出和随机需求下供应商-制造商采取不同的风险共担合同对农资投入、供应商、制造商以及整个供应链的利润的影响。文中的风险共担合同包括风险无共担合同、需求风险共担合同、产出风险共担合同、产出-需求风险共担合同,结合算例,结论得出:需求风险分担可以使供应链利润增加;产出风险分担可以增加供应商的利润收益,而赋予制造商控制供应商农资投入积极性的权利;产出-需求共担合同既能增加供应链及各成员的利润收益,同时还使得双方相互制约、相互协调,促使供应链良好发展。最后,结合非线性补贴的风险分担形式对上述合同的有效性进行了验证,研究表明,不同的补贴形式只在量上对决策存在影响,而本质上的结论并没有变化。     

Designing an intervention strategy for public-interest goods: The California electric vehicle market case

We study the intervention problem for public-interest goods. Public-interest goods are known as goods with positive externalities, allowing the consumer as well as others who do not pay for them benefit from the consumption. Health related goods, such as vaccines, or products with less carbon emissions are well known examples. We consider a supply chain for such a product. Generally, wider adoption or usage of such goods is ensured by the intervention of a central authority in their supply chain. We explore the problem for a setting composed of a retailer and a central authority. The main goal of the central authority is to design and fund an intervention scheme so that decisions of the channel are in line with the good of society, specified as a social welfare function. We propose two intervention tools applied simultaneously: (1) investing in demand-increasing strategies, which affects the level of the stochastic demand in the market; and (2) rebates that affect revenue per unit received by the retailer. We introduce a model that determines a utility maximizing intervention scheme and further investigate the model. We also present two decentralized approaches as benchmarks. Finally, we conduct a case study for California׳s electric vehicle market and validate our findings by a detailed analysis of the results, including comparisons with the current practice.     

Supply chain configuration for diffusion of new products: An integrated optimization approach

We develop an integrated/hybrid optimization model for configuring new products’ supply chains while explicitly considering the impact of demand dynamics during new products’ diffusion. The hybrid model simultaneously determines optimal production/sales plan and supply chain configuration. The production and sales plan provides decisions on the optimal timing to launch a new product, as well as the production and sales quantity in each planning period. The supply chain configuration provides optimal selection of options and safety stock level kept at each supply chain function. Extensive computational experiments on randomly generated testbed problems indicate that the hybrid modeling and solution approach significantly outperforms non-hybrid alternative modeling and solution approaches under various diffusion and supply chain topologies. We provide insights on optimal production/sales plan and supply chain configuration for new products during their diffusion process. Also, managerial implications relevant to effectiveness of the hybrid approach are discussed.     

Performance Metric Portfolios: A Framework and Empirical Analysis

The widespread adoption of supply chain management principles suggests that managers recognize the importance of evaluating operational decisions holistically. However, it is often difficult to link specific operational practices to strategic level outcomes and in turn to corporate financial results. This presents problems for both managers and academic researchers attempting to justify the often high cost of operational improvement initiatives in terms of objective accounting metrics. This study provides evidence that it is possible to demonstrate linkages between carefully chosen portfolios of tactical, strategic, and financial metrics. Survey data from 118 manufacturers are used to evaluate hypotheses linking multilevel metrics of performance across three well‐established strategic foci. We present portfolios of metrics drawn from the literature and from the Supply Chain Counciľs supply‐chain operations reference model and related design and customer chain models. Our analysis suggests that metric portfolios in which tactical metrics are designed to match strategic‐level metrics, based on alignment with a specific strategic focus, provide clearer mechanisms for understanding performance linkages.     

具有遗憾值约束的鲁棒供应链网络设计模型研究

考虑不确定性环境,研究战略层次的供应链网络鲁棒设计问题,目标是设计参数发生摄动时,供应链性能能够保持稳健性。基于鲁棒解的定义,建立从上游供应商选择到下游设施选址-需求分配的供应链网络设计鲁棒优化模型;提出确定遗憾值限定系数上限和下限的方法,允许决策者调节鲁棒水平,选择多种供应链网络结构;通过模型分解与协调,设计了供应链节点配置的禁忌搜索算法。算例的计算结果表明了禁忌搜索算法具有良好的收敛特性,以及在处理大规模问题上的优越性;同时也反映了利用鲁棒优化模型进行供应链网络设计,可以有效规避投资风险。     

Cooperative Advertising and Pricing in a Dynamic Stochastic Supply Chain: Feedback Stackelberg Strategies

Cooperative (co‐op) advertising is an important instrument for aligning manufacturer and retailer decisions in supply chains. In this, the manufacturer announces a co‐op advertising policy, i.e., a participation rate that specifies the percentage of the retailer's advertising expenditure that it will provide. In addition, it also announces the wholesale price. In response, the retailer chooses its optimal advertising and pricing policies. We model this supply chain problem as a stochastic Stackelberg differential game whose dynamics follows Sethi's stochastic sales‐advertising model. We obtain the condition when offering co‐op advertising is optimal for the manufacturer. We provide in feedback form the optimal advertising and pricing policies for the manufacturer and the retailer. We contrast the results with the advertising and price decisions of the vertically integrated channel, and suggest a method for coordinating the channel.     

Strategic and operational decision-making in expanding supply chains for LNG as a fuel

The European Union aims for a 40% reduction in greenhouse gas emissions by 2030, compared to 1990 levels, and recognizes the opportunities of Liquefied Natural Gas (LNG) as an alternative fuel for transportation to reach this goal. The lack of a mature supply chain for LNG as a fuel results in a need to invest in new (satellite) terminals, bunker barges and tanker trucks. This network design problem can be defined as a Two-Echelon Capacitated Location Routing Problem with Split Deliveries (2E-CLRPSP). An important feature of this problem is that direct deliveries are allowed from terminals, which makes the problem much harder to solve than the existing location routing literature suggests. In this paper, we improve the performance of a hybrid exact algorithm and apply our algorithm to a real-world network design problem related to the expansion of the European supply chain for LNG as a fuel. We show that satellite terminals and bunker barges become an interesting option when demand for LNG grows and occurs further away from the import terminal. In those situations, the large investments associated with LNG satellites and bunker barges are offset by reductions in operational costs of the LNG tanker trucks.     

服务质量影响市场需求下资金约束型网购供应链协调研究

高质量发展服务业已经写入国家战略，其重要性在以顾客需求为导向的网购市场经济中尤为突出。企业服务质量直接决定顾客的购物体验。在供应链意图改善顾客体验但零售商受资金约束的背景下，考虑服务质量对市场需求的影响，设计回购契约与"基于目标销售量的信用契约"组合，构建资金约束型网购供应链协调决策模型，并对其进行求解以获得供应链最优经营策略。研究结果显示，运用最优策略不仅有助于缓解零售商的资金压力，而且能激励其提高服务质量，改善顾客购物体验；所得契约设计不仅能实现网购供应链协调，而且能实现供应链总利润在节点企业之间的任意分配，提高了契约的操作性，保障了最优策略的有效性。     

On service degrade at a discount: Capacity, demand pooling, and optimal discounting

In this paper we study the capacity investment decisions and operational strategies of a firm providing two-class services facing uncertain demands. The capacity decisions of the resources are made before demands are observed. Each service can be implemented by its corresponding resource. Should a mismatch between the capacity and the actual demand for the services arise, the low-class resource can be used as a substitute for the high-class service. We introduce an operational strategy called degrade-at-a-discount, where a price discount is offered to motivate customers to accept a lower class service when their original choice is out of capacity. By formulating the problem as a one-period, two-stage stochastic problem, we analyze how to set up the optimal capacity and the optimal discount. We also conduct a comprehensive numerical study to show the benefits of the degrade-at-a-discount strategy.     

Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing

This paper presents a stochastic mixed integer programming approach to integrated supplier selection and customer order scheduling in the presence of supply chain disruption risks, for a single or dual sourcing strategy. The suppliers are assumed to be located in two different geographical regions: in the producer's region (domestic suppliers) and outside the producer's region (foreign suppliers). The supplies are subject to independent random local disruptions that are uniquely associated with a particular supplier and to random semi-global (regional) disruptions that may result in disruption of all suppliers in the same geographical region simultaneously. The domestic suppliers are relatively reliable but more expensive, while the foreign suppliers offer competitive prices, however material flows from these suppliers are more exposed to unexpected disruptions. Given a set of customer orders for products, the decision maker needs to decide which single supplier or which two different suppliers, one from each region, to select for purchasing parts required to complete the customer orders and how to schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The problem objective is either to minimize total cost or to maximize customer service level. The obtained combinatorial stochastic optimization problem will be formulated as a mixed integer program with conditional value-at-risk as a risk measure. The risk-neutral and risk-averse solutions that optimize, respectively average and worst-case performance of a supply chain are compared for a single and dual sourcing strategy and for the two different objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.