Optimal Descending Mechanisms for Constrained Procurement

Descending mechanisms for procurement (or, ascending mechanisms for selling) have been well‐recognized for their simplicity from the viewpoint of bidders—they require less bidder sophistication as compared to sealed‐bid mechanisms. In this study, we consider procurement under each of two types of constraints: (1) Individual/Group Capacities: limitations on the amounts that can be sourced from individual and/or subsets of suppliers, and (2) Business Rules: lower and upper bounds on the number of suppliers to source from, and on the amount that can be sourced from any single supplier. We analyze two procurement problems, one that incorporates individual/group capacities and another that incorporates business rules. In each problem, we consider a buyer who wants to procure a fixed quantity of a product from a set of suppliers, where each supplier is endowed with a privately known constant marginal cost. The buyer's objective is to minimize her total expected procurement cost. For both problems, we present descending auction mechanisms that are optimal mechanisms. We then show that these two problems belong to a larger class of mechanism design problems with constraints specified by polymatroids, for which we prove that optimal mechanisms can be implemented as descending mechanisms.     

Integrality in Stochastic Inventory Models

We study several finite‐horizon, discrete‐time, dynamic, stochastic inventory control models with integer demands: the newsvendor model, its multi‐period extension, and a single‐product, multi‐echelon assembly model. Equivalent linear programs are formulated for the corresponding stochastic dynamic programs, and integrality results are derived based on the total unimodularity of the constraint matrices. Specifically, for all these models, starting with integer inventory levels, we show that there exist optimal policies that are integral. For the most general single‐product, multi‐echelon assembly system model, integrality results are also derived for a practical alternative to stochastic dynamic programming, namely, rolling‐horizon optimization by a similar argument. We also present a different approach to prove integrality results for stochastic inventory models. This new approach is based on a generalization we propose for the one‐dimensional notion of piecewise linearity with integer breakpoints to higher dimensions. The usefulness of this new approach is illustrated by establishing the integrality of both the dynamic programming and rolling‐horizon optimization models of a two‐product capacitated stochastic inventory control system.     

A Stochastic Inventory Model With Fast‐Ship Commitments

We present a multiperiod model of a retail supply chain, consisting of a single supplier and a single retailer, in which regular replenishment occurs periodically but players have the option to support fast delivery when customers experience a stockout during a replenishment period. Because expedited shipments increase the supplier's transportation cost, and possibly production/inventory costs, the supplier typically charges a markup over and above the prevailing wholesale price for fast‐shipped items. When fast shipping is not supported, items are backordered if customers are willing to wait until the start of the next replenishment period. We characterize the retailers and the supplier's optimal stocking and production policies and then utilize our analytical framework to study how the two players respond to changes in supply chain parameters. We identify a sufficient condition such that the centralized supply chain is better off with the fast‐ship option. We find a range of markups for fast‐ship orders such that the fast‐ship option is preferred by both the supplier and the retailer in a decentralized chain. However, a markup that is a win–win for both players may not exist even when offering fast‐ship option is better for the centralized chain. Our analysis also shows that depending on how the markup is determined, greater customer participation in fast‐ship orders does not necessarily imply more profits for the two players. For some predetermined markups, the retailer's profit with the fast‐ship option is higher when more customers are willing to wait. However, the retailer may not be able to benefit from the fast‐ship option because the supplier may choose not to support the fast‐ship option when fast‐ship participation increases due to the fact that the fast‐ship participation rate adversely affects the initial order size.     

A multiobjective genetic algorithm for job shop scheduling

In this paper, a Multi Objective Genetic Algorithm (MOGA) is proposed to derive the optimal machine-wise priority dispatching rules ( pdrs ) to resolve the conflict among the contending jobs in the Giffler and Thompson (GT) procedure applied for job shop problems. The performance criterion considered is the weighed sum of the multiple objectives minimization of makespan, minimization of total idle time of machines and minimization of total tardiness. The weights assigned for combining the objectives into a scalar fitness function are not constant. They are specified randomly for each evaluation. This in turn leads to the multidirectional search in the proposed MOGA, which in turn mitigates the solution being entrapped in local minima. The applicability and usefulness of the proposed methodology for the scheduling of job shops is illustrated with 28 benchmark problems available in the open literature.     

Distressed Selling by Farmers: Model,Analysis, and Use in Policy‐Making

Among the various governmental schemes that support agriculture, support prices have been adopted by many developing countries. A support price for an agricultural crop is a guaranteed price at which a governmental entity agrees to purchase that crop from farmers. Despite this surety, a surprising practice of “distressed” selling 1 1 A 1‐minute news clip on distressed selling is available at: https://www.youtube.com/watch?v=U2lr5rBTpaU
has been widely observed in practice: Farmers sell a significant portion of their crops to outside agents at prices much lower than the support price. We build a tractable stochastic dynamic programming model that captures the salient features of the ground realities—limited as well as uncertain procurement capacity, high holding costs for the farmers, and lack of affordable credit—that conspire to induce distressed selling and, consequently, a significant loss of welfare of the farmers. Using real data on procurement under a support‐price program, we establish the accuracy of our model's prediction on the volume of distressed sales. Finally, we show how our model and its solution can serve as a simple and useful tool for policy‐makers to assess the relative impact of the improvements in the main determinants of distressed sales.     

Risk assessment of outsourcing e-procurement services: integrating SWOT analysis with a modified ANP-based fuzzy inference system

With electronic procurement solutions becoming increasingly sophisticated, many firms opt to source these services from third-party providers, effectively transferring (outsourcing) significant responsibility to these services companies. This action, however, entails certain risks, which are oftentimes difficult to assess. To guide managerial practice and to advance academic inquiry in this domain, we identify e-procurement risk factors through a strengths, weaknesses, opportunities, threats analysis, grounded in transaction cost economics (TCE), and propose a risk assessment framework based on the opinions of a group of experts. The approach taken is that of a modified analytic network process methodology, combined with a fuzzy inference system, which is versatile enough to accept the expert opinions in different input formats (such as linguistic variables and ranges). The proposed framework has the capability to aggregate expert judgements’ to estimate risk likelihoods, risk severities and risk factor indices, and derive overall risk magnitudes. The multi-method approach was motivated and is illustrated by a real-life case study of an Indian manufacturing company currently in the process of contract renewal with its existing e-procurement service provider.     

An exact algorithm for the multicriteria ordered clustering problem

In the context of multicriteria decision aid, we address the problem of regrouping alternatives into completely ordered categories based on valued preference degrees. We assume that the number of groups is fixed a priori. This will be referred to as the multicriteria ordered clustering problem. The model is based on the definition of an inconsistency matrix and only uses the ordinal properties of the pairwise preference relations. An exact algorithm is proposed to find the ordered partition and is applied as illustration to the Human Development Index.     

The Impact of Geographic Proximity on What to Buy,How to Buy,and Where to Buy: Evidence from High‐Tech Durable Goods Market*

Social contagion effects due to geographical proximity refer to the social effects wherein the behavior of an individual varies with the behavior of other individuals who are geographically close. Although the influence of such effects on consumer choices has been established in several contexts, much of the extant studies have focused on its effect on consumers’ decision of whether to buy a new product or adopt a new innovation. There has been no systematic examination of the influence of geographic proximity on other aspects of consumers’ product buying process such as what to buy (i.e., brand choice), how to buy (i.e., the channel), and where to buy (i.e., retailers). Such effects can matter significantly in high‐technology and durable goods markets and therefore, it is critical to understand the scope of these on consumers’ choice of retailers and channel as well. Drawing on literatures from word of mouth effects, ecommerce, and consumers’ perception of risk in their purchase process, we develop a set of hypotheses on the effect of geographic proximity on consumers’ choices of what to buy, how to buy, and where to buy. Leveraging a microlevel dataset of purchases of personal computers, we develop brand‐, retailer‐, and channel‐related measures of proximity effects at the individual consumer level and estimate a joint disaggregate model of the three choices that make up a product purchase process to test these hypotheses. Our results indicate a significant contagion effect on each of the three choices. Furthermore, we find evidence of a greater effect of geographic proximity on inexperienced consumers—those who are new to the product category. Our results thus help develop a holistic understanding of the influence of social contagion effects on consumers’ decision making.     

Dual Sourcing Inventory Systems: On Optimal Policies and the Value of Costless Returns

We study dual sourcing inventory systems with backordering and with stationary, stochastic demands. The two supply sources differ in their unit prices and lead times. We focus on the option of making costless returns to the cheaper, longer leadtime supplier. We show that the value of this option is zero. Our analysis leading to this result includes the derivation of several structural properties of the optimal policies for dual sourcing systems with and without the return option.     

On the Hardness of Approximating the Min-Hack Problem

We show several hardness results for the Minimum Hacking problem, which roughly can be described as the problem of finding the best way to compromise a target node given a few initial compromised nodes in a network. We give several reductions to show that Minimum Hacking is not approximable to within where δ = 1− ^c n, for any c < 1/2. We also analyze some heuristics on this problem.