Disassembly to order system under uncertainty

This paper presents a multi-criteria optimization model of a disassembly-to-order (DTO) system under uncertainty. The goal of the proposed model is to determine the best combination of the number of each product type to be taken back from the last user and/or collectors. The EOL products are then disassembled for the retrieval of reusable components and materials and resold in order to meet a certain level of demand under a variety of physical, financial and environmental constraints. The surplus components are recycled, stored for usage in subsequent periods or properly disposed. The problem is modeled as a multi-criteria decision-making problem under uncertainty, where the aspiration levels for various goals are more likely to be in the “approximately more (less) than” and/or “more (less) is better” form. We employ fuzzy goal programming technique to solve the problem. When solved, the model provides the number of EOL products to be taken back as well as the number of items reused, recycled, stored and disposed. The values of a host of other performance measures are also obtained, including total profit, materials and items sales revenues, take back cost, transportation costs as well as costs of preparation of EOL products, destructive disassembly, non-destructive disassembly, recycling, storage and disposal. A case example is presented to illustrate the model's implementation.     

Closed-loop supply chain inventory management with recovery information of reusable containers

This paper considers a closed-loop supply chain consisting of one-manufacturer and one-retailer. This supply chain provides single-kind products with reusable containers. The main purpose of this study is to explore and evaluate the value of recovery information captured by embedded sensors in the environment of internet of things. The recovery information of containers dynamically monitors recovery status and provides a reliable estimation of return quantity. The value of information is measured by the cost saving performances with full, partial or no recovery information. When the full or partial recovery information is available, the decisions are made based on the known quantities of the usable or total return flow. When no recovery information is available, the decisions are made based on the stationary distribution of the return flow. A periodic inventory model is built with uncertainties of forward and reverse flows. Then, a myopic order policy is proposed for the different levels of information utilization. Through the optimality analysis, we introduce a farsighted inventory control policy. Using the general result of Markov decision processes, the performance of heuristic policies is displayed. The farsighted policy performs better than the myopic policy. In addition, the farsighted policy helps to lessen the convex impact of utilization rate on the expected cost. Afterwards, we extend the model with the selective disposal behavior. A simulation study is used to depict sensitivity and robustness of the farsighted policy. Finally, we extend the simulation experiment with uniformly distributed in-use time for a more general applicability.     

航空公司机上周转品多基地库存优化模型

在民航业日益激烈的市场竞争环境下，航空公司对成本控制的精细化管理显得尤为重要。本文基于航空公司的实际背景，针对为旅客提供的机上周转品，建立了一个多基地库存系统的库存优化模型。该模型以系统的总库存成本最小为目标，在考虑需求、回收、调运、安全库存等因素下，决策一个订货周期内的订货及调运方案。基于最优解的分析，在成本参数满足一定条件下，可以得到系统最优的期初订货量；在此基础上，可以将该模型转化为一个网络流模型，参考最小费用最大流算法，设计了一个多项式求解算法来求解该网络流模型，进而得到各基地之间的最优调运量，并证明该算法的最优性；此外，根据期初订货量可行解的范围，设计了一个求解原库存模型的启发式算法。通过实例分析，验证了该模型与两种算法的有效性。     

Integrated retail decisions with multiple selling periods and customer segments: Optimization and insights

Integrating retail decisions on such aspects as assortment, pricing, and inventory greatly improves profitability. We examine a multi-period selling horizon where a retailer jointly optimizes assortment planning, pricing, and inventory decisions for a product line of substitutable products, in a market with multiple customer segments. Focusing on fast-moving retail products, the problem is modeled as a mixed-integer nonlinear program where demand is driven by exogenous consumer reservation prices and endogenous assortment and pricing decisions. A mixed-integer linear reformulation is developed, which enables an exact solution to large problem instances (with up to a hundred products) in manageable times. Empirical evidence is provided in support of a classical deterministic maximum-surplus consumer choice model. Computational results and managerial insights are discussed. We find that the optimal assortment and pricing decisions do not exhibit a simple, intuitive structure that could be analytically characterized, which reflects the usefulness of optimization approaches to numerically identify attractive trade-offs for the decision-maker. We also observe that suboptimal inventory policies significantly decrease profitability, which highlights the importance of integrated decision-making. Finally, we find that the seasonality of consumer preferences and supply costs present an opportunity for boosting the profit via higher inventory levels and wider assortments.     

Pricing Decisions During Inter‐Generational Product Transition

How should companies price products during an inter‐generational transition? High uncertainty in a new product introduction often leads to extreme cases of demand and supply mismatches. Pricing is an effective tool to either prevent or alleviate these problems. We study the optimal pricing decisions in the context of a product transition in which a new‐generation product replaces an old one. We formulate the dynamic pricing problem and derive the optimal prices for both the old and new products. Our analysis sheds light on the pattern of the optimal prices for the two products during the transition and on how product replacement, along with several other dynamics including substitution, external competition, scarcity, and inventory, affect the optimal prices. We also determine the optimal initial inventory for each product and discuss a heuristic method.     

Distributing a Product Line in a Decentralized Supply Chain

Although there is a rich literature on single product distribution in decentralized supply chains, the incentive problems that arise in distributing a product line have largely not been investigated. In practice, most manufacturers distribute a line of products with different features and qualities and not just a single product. Consider a manufacturer who distributes a product line through competing downstream retailers. In this setting, we investigate how and why the retailers' price and inventory decisions deviate from the centrally optimal decisions. Due to substitution between different product variants, as well as between different retailers, the incentive problems associated with distributing a product line are more complicated than that of distributing a single product. We characterize retailers' incentive distortions under a residual‐claimancy contract, and construct contracts that achieve channel coordination. We show that retail price floors or inventory buybacks, appropriately tailored to each product variant, are among the contracts that can achieve coordination. Using numerical simulations, we demonstrate how the optimal contract terms (such as wholesale prices and buyback prices) for each variant are influenced by the parameters of an underlying consumer choice model.     

A WTP‐Choice Model: Empirical Validation,Competitive and Centralized Pricing

Willingness To Pay (WTP) of customers plays an anchoring role in pricing. This study proposes a new choice model based on WTP, incorporating sequential decision making, where the products with positive utility of purchase are considered in the order of customer preference. We compare WTP‐choice model with the commonly used (multinomial) Logit model with respect to the underlying choice process, information requirement, and independence of irrelevant alternatives. Using WTP‐choice model, we find and compare equilibrium and centrally optimal prices and profits without considering inventory availability. In addition, we compare equilibrium prices and profits in two contexts: without considering inventory availability and under lost sales. One of the interesting results with WTP‐choice model is the “loose coupling” of retailers in competition; prices are not coupled but profits are. That is, each retailer should charge the monopoly price as the collection of these prices constitute an equilibrium but each retailer's profit depends on other retailers' prices. Loose coupling fails with dependence of WTPs or dependence of preference on prices. Also, we show that competition among retailers facing dependent WTPs can cause price cycles under some conditions. We consider real‐life data on sales of yogurt, ketchup, candy melt, and tuna, and check if a version of WTP‐choice model (with uniform, triangle, or shifted exponential WTP distribution), standard or mixed Logit model fits better and predicts the sales better. These empirical tests establish that WTP‐choice model compares well and should be considered as a legitimate alternative to Logit models for studying pricing for products with low price and high frequency of purchase.     

Allocation of Returned Products among Different Recovery Options through an Opportunity Cost–Based Dynamic Approach*

In a make‐to‐order product recovery environment, we consider the allocation decision for returned products decision under stochastic demand of a firm with three options: refurbishing to resell, parts harvesting, and recycling. We formulate the problem as a multiperiod Markov decision process (MDP) and present a linear programming (LP) approximation that provides an upper bound on the optimal objective function value of the MDP model. We then present two solution approaches to the MDP using the LP solution: a static approach that uses the LP solution directly and a dynamic approach that adopts a revenue management perspective and employs bid‐price controls technique where the LP is resolved after each demand arrival. We calculate the bid prices based on the shadow price interpretation of the dual variables for the inventory constraints and accept a demand if the marginal value is higher than the bid price. Since the need for solving the LP at each demand arrival requires a very efficient solution procedure, we present a transportation problem formulation of the LP via variable redefinitions and develop a one‐pass optimal solution procedure for it. We carry out an extensive numerical analysis to compare the two approaches and find that the dynamic approach provides better performance in all of the tested scenarios. Furthermore, the solutions obtained are within 2% of the upper bound on the optimal objective function value of the MDP model.     

Understanding multi-methodology: Evaluating the perceived impact of mixing methods for group budgetary decisions

This paper reports on the design and implementation of a multi-methodology intervention intended to support a budget prioritisation decision by a multi-organisational group tasked with tackling the problem of teenage pregnancy in an English borough. The intervention approach involved the combined use of cognitive/causal mapping and a multi-criteria decision analysis method to develop and prioritise a number of projects aimed at alleviating issues associated with teenage pregnancy. The paper describes and discusses the process of developing and applying the intervention approach, and provides an evaluation of its perceived impact by the client group. Drawing on the rich seam of data gathered over the course of our work with the multi-organisational group, we explore the varying degrees of impact that the mapping and multi-criteria evaluation methods achieved during and after the intervention. Analysis of the intervention data suggests that both methods were perceived to facilitate a different way of thinking and learning. In addition, we find that the mapping and multi-criteria evaluation methods successfully addressed participants' democratic and rational concerns. However, despite these reported positive effects, the full recommendations of the intervention were not implemented. An attempt is made to explain this outcome in terms of both the multi-organisational context within which the intervention took place and the nature of the group task. Directions for further research are then proposed.     

考虑策略性消费者的生鲜农产品定价和库存决策

消费者的策略性行为使零售商的生鲜农产品的定价和库存决策面临更大挑战。本文基于报童模型,综合考虑消费者的策略性行为,对生鲜农产品价值下降进行离散化处理。刻画策略性消费者的决策行为,构建零售商的单阶段和两阶段定价及库存决策模型,分析了产品价值剩余率对消费者行为、零售商最优定价、最优库存水平以及零售商利润的影响机理。研究发现,在单阶段模型中零售商最优价格和最优库存水平均随产品价值剩余率的递增而递增;而在两阶段模型中,第二阶段最优价格随价值剩余率的变化趋势可能存在阈值。     

An optimal product-reconfiguration policy due to order changes

Abstract

It is not an uncommon problem that a finished product cannot be delivered to its customer due to order change. In many real-life applications, such as rack-mounted computer products, it is possible to reconfigure a product by altering the combination of its components. Therefore, an inventory item, resulting from a cancelled order, can be handled in one of two ways: (1) keep the product in storage for a future order; or (2) send the product back to the manufacturing Door for reconfiguration in accordance with a new order A wise decision can be made by evaluating the trade-off between the inventory cost and the conversion cost. This paper presents an optimization procedure for achieving the minimum-cost solution. The problem is formulated by a quadratic programming model. Under a fairly general condition, the problem can be converted into a standard capacitated transportation problem and, therefore, can be solved efficiently. The cost structure, problem formulation, and solution technique arc discussed.     

Joint product assortment,inventory and price optimization to attract loyal and non-loyal customers

In this paper, we analyze the emerging retail practice of carrying a combined product assortment consisting of both regular “standard” products and more fashionable and short-lived “special” products. The purpose of this practice is to increase store traffic by attracting heterogeneous classes of customers, which drives up sales of standard products due to the potential cross-selling effect. Customers who are primarily attracted by special products will also buy some standard products. In this context, we analyze three decisions that are crucial for a retailer׳s commercial success: the product assortment, the inventory levels and the pricing. We propose an optimization model and an iterative heuristic to analyze the trade-offs between the combined product assortment, the inventory level and the price per product when there is limited shelf space. Using numerical experiments, we show that our heuristic can be trusted and that its accuracy improves when the number of products increases. Our findings indicate that to attract more customers for standard products, a retailer may benefit from carrying low priced special products which, if considered in isolation, would be non-profitable. As the cross-selling effect decreases, a retailer should focus more on the standard assortment by increasing its size and decreasing the prices. However, introducing special products and ignoring the cross-selling effect may decrease a retailer׳s profitability. We show that the introduction of special products involves more than just choosing the right specials for non-loyal customers but impacts the global assortment planning, the standard products and the products pricing.     

A NUMERICAL ANALYSIS OF CAPACITATED POSTPONEMENT

Customer satisfaction can be achieved by providing rapid delivery of a wide variety of products. High levels of product variety require correspondingly high levels of inventory of each item to quickly respond to customer demand. Delayed product differentiation has been identified as a strategy to reduce final product inventories while providing the required customer service levels. However, it is done so at the cost of devoting large production capacities to the differentiation stage. We study the impact of this postponement capacity on the ability to achieve the benefits of delayed product differentiation. We examine a single‐period capacitated inventory model and consider a manufacturing system that produces a single item that is finished into multiple products. After assembly, some amount of the common generic item is completed as non‐postponed products, whereas some of the common item is kept as in‐process inventory, thereby postponing the commitment to a specific product. The non‐postponed finished‐goods inventory is used first to meet demand. Demand in excess of this inventory is met, if possible, through the completion of the common items. Our results indicate that a relatively small amount of postponement capacity is needed to achieve all of the benefits of completely delaying product differentiation for all customer demand. This important result will permit many firms to adopt this delaying strategy who previously thought it to be either technologically impossible or prohibitively expensive to do so.     

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.     

Dynamic Pricing to Minimize Maximum Regret

We consider a dynamic pricing problem that involves selling a given inventory of a single product over a short, two‐period selling season. There is insufficient time to replenish inventory during this season, hence sales are made entirely from inventory. The demand for the product is a stochastic, nonincreasing function of price. We assume interval uncertainty for demand, that is, knowledge of upper and lower bounds but not a probability distribution, with no correlation between the two periods. We minimize the maximum total regret over the two periods that results from the pricing decisions. We consider a dynamic model where the decision maker chooses the price for each period contingent on the remaining inventory at the beginning of the period, and a static model where the decision maker chooses the prices for both periods at the beginning of the first period. Both models can be solved by a polynomial time algorithm that solves systems of linear inequalities. Our computational study demonstrates that the prices generated by both our models are insensitive to errors in estimating the demand intervals. Our dynamic model outperforms our static model and two classical approaches that do not use demand probability distributions, when evaluated by maximum regret, average relative regret, variability, and risk measures. Further, our dynamic model generates a total expected revenue which closely approximates that of a maximum expected revenue approach which requires demand probability distributions.     

Responsive Pricing Newsvendor Networks with Discretionary Commonality

Discretionary commonality is a form of operational flexibility used in multi‐product manufacturing environments. Consider a case where a firm produces and sells two products. Without discretionary commonality, each product is made through a unique combination of input and production capacity. With discretionary commonality, one of the inputs could be used for producing both products, and one of the production capacities could be used to process different inputs for producing one of the products. In the latter case, the manager can decide, upon the realization of uncertainty, not only the quantities for different products (outputs) but also the means of transforming inputs into outputs. The objective of this study is to understand how the firm's value, its inventory levels for inputs and capacity levels for resources are affected by the demand characteristics and market conditions. In pursuing this research, we extend Van Mieghem and Rudi ( 2002 )'s newsvendor network model to allow for the modeling of product interdependence, demand functions, random shocks, and firm's ex post pricing decision. Applying the general framework to the network with discretionary commonality, we discover that inventory and capacity management can be quite different compared to a network where commonality is non‐discretionary. Among other results, we find that as the degree of product substitution increases, the relative need for discretionary commonality increases; as the market correlation increases, while the firm's value may increase for complementary products, the discretionary common input decreases but the dedicated input increases. Numerical study shows that discretionary flexibility and responsive pricing are strategic substitutes.     

COMPONENT COMMONALITY: MODELS WITH PRODUCT‐SPECIFIC SERVICE CONSTRAINTS*

Common components are used extensively for reasons including product postponement and expediting new product development. We consider a two‐stage assemble‐to‐order system with two products having uniformly distributed demand, one common component, and product‐specific components. We develop optimization models in which the cost‐minimizing inventory of the components must be determined and allocated to products in order to meet product‐specific service level constraints. We compare two different commonality models based on whether or not the products are prioritized. A distinctive feature of our study is the use of product‐specific service levels. We compare our results with models using aggregate service levels.     

DEMAND FULFILLMENT RATES IN AN ASSEMBLETO- ORDER SYSTEM WITH MULTIPLE PRODUCTS AND DEPENDENT DEMANDS

This paper presents a model for evaluating product fulfillment rates in an assemble-to-order system with a two-level bill-of-material structure (products and components). Inventories are kept at the component level and are used to support service levels to product demands; each component is managed under a periodically reviewed, decentralized order-up-to policy. Product demands are correlated in a given period but are independent across periods. We develop the demand fulfillment rate for each product when demands are filled based on a first-come-first-served discipline with limited priorities for different products within a period. We show that the fulfillment rate can be expressed as a multivariate normal probability when the product demands follow a multivariate normal distribution. We then use a numerical example to discuss computational issues and bounds associated with the evaluation of the demand fulfillment rates. Finally, we consider a model for minimizing the total inventory investment subject to given product demand fulfillment rates.     

Multi-criteria decision support and evaluation of strategies for nuclear remediation management

Environmental emergency situations can differ in many ways, for instance according to their causes and the dimension of their impacts. Yet, they share the characteristic of sudden onset and the necessity for a coherent and effective emergency management. In this paper we consider decision support in the event of a nuclear or radiological accident in Europe. RODOS, an acronym for real-time on-line decision support system, is a decision support system designed to provide support from the early phases through to the medium and long-term phases. This work highlights the role of multi-criteria decision analysis (MCDA) within RODOS in ensuring the transparency of decision processes within emergency and remediation management. Special emphasis is placed on the evaluation of alternative remediation or countermeasure strategies using the multi-criteria decision support tool Web-HIPRE in scenario focused decision making workshops involving different stakeholder and expert groups. Decision support is enhanced by a module that generates natural language explanations to facilitate the understanding of the evaluation process, therefore contributing to the direct involvement of the decision makers, with the aim of increasing their confidence in the results of the analyses carried out, forming an audit trail for the decision making process and improving the acceptability of the system as a whole.     

逆向供应链中电子废弃产品回收定价和补贴策略研究

随着废弃电子产品的大量涌现和社会环保意识的增强,废弃电子产品的回收处理成为社会关注的热点问题之一。在现有的文献研究中,更多的是从逆向供应链系统化的视角关注价格或税收政策对回收商或处理商的运营效率、积极性的影响,逆向供应链激励的关键问题即如何确定回收价格和补贴方面的研究却较少见。本文基于社会福利最大化的视角,建立斯塔克伯格博弈模型（社会福利模型）来确定逆向供应链分散管理中的回收价格和社会最优补贴费,该模型可以检验分散管理系统（环保局作为领导者、MIS和回收商作为追随者）中回收逆向供应链的回收价格和补贴的激励效应。通过社会福利模型和资金平衡模型的比较,以及比较案例的数值演算,结果发现：达到市场最优销售数量和向顾客提供回收废弃电子产品的最优奖金的状况下,销售商和回收商处于平衡状态;社会福利模型中窒息价格、零奖金对应的基准收集数量、相对于奖金的收集量敏感度等因素对社会福利的影响均优于资金平衡模型。