Optimization based e-sourcing

International audienc

Optimal buying from online retailers offering total value discounts

International audienceDiscounts based on the total value of purchase is a common business practice among the online retailers. The discounts offered could be in the form of rebates, cash backs, gift vouchers, or reward points. The comparison shopping bots provide only price information for individual products but do not consider the total value discounts that could be accrued by buying more products from the same retailer. In this paper, we consider a buyer interested in buying M different items from N retailers who offer total value discounts. The buyer is faced with an optimization problem of choosing the retailers and the items to buy from them to minimize the total buying cost taking into account the total value discounts offered by the retailers. We propose an integer programming formulation for the problem, which can be integrated with the current comparison shopping search engines. As this problem needs to be solved online, we propose a linear programming based heuristic to obtain a nearoptimal solution that could be used with the commercial solvers to accelerate the solution time

Design and Analysis of Value Creation Networks

There are many diverse domains like academic collaboration, service industry, and movies, where a group of agents are involved in a set of activities through interactions or collaborations to create value. The end result of the value creation process is two pronged: firstly, there is a cumulative value created due to the interactions and secondly, a network that captures the pattern of historical interactions between the agents. In this paper we summarize our efforts towards design and analysis of value creation networks: 1) network representation of interactions and value creations, 2) identify contribution of a node based on values created from various activities, and 3) ranking nodes based on structural properties of interactions and the resulting values. To highlight the efficacy of our proposed algorithms, we present results on IMDB and services industry data

Branch-and-Cut Algorithms for Winner Determination in Discount Auctions

Discount auction is a procurement mechanism for buying M indivisible heterogeneous items. The bidders are suppliers and a bid consists of individual cost for each of the items and a nondecreasing discount function defined over the number of items. The winner determination problem faced by the buyer is to determine the winning suppliers and their corresponding winning items that minimizes the total procurement cost, subject to the supply, demand, and discount constraints. We show that this problem is NP-hard upon reduction from the set covering problem. An integer programming formulation is presented and valid inequalities are derived, which serve as cuts to the linear relaxation. A suite of branch-and-cut algorithms are developed with different cut addition techniques and branching strategies. The performance of the proposed algorithms for different problem types are studied with extensive computational experiments

Discount Auctions for Procuring Hetergeneous Items

Advents of business over Internet have given rise to a number of innovative trading mechanisms. In this work we propose a new auction mechanism, called as discount auctions, for procuring heterogeneous items. The buyer, who is the auctioneer, has a unit demand for M distinct items. The suppliers, who are the bidders, specify individual costs for each of the items. In addition, a supplier also specifies a discount function: a non-decreasing function over the number of items. This discount bid, in essence, conveys the individual costs for each of the items and the discount that can be availed based on the number of items bought. The winner determination problem faced by the buyer is to choose the optimal set of winning suppliers and their respective winning items such that the total cost of procurement is minimized. First we show that this problem is NP-hard upon reduction from the set covering problem. Next we propose two exact algorithms to solve the problem to optimality. The first one is a branch-and-bound algorithm, called as branch-on-supply (BoS), which does not use mathematical programming formulation but rather exploits the embedded network structure. The second is a suite of branch-and-cut algorithms. We derive valid inequalities to the integer programming formulation, which serve as cuts for the LP relaxation. A heuristic branching technique, called as branch-on-price (BoP), is proposed that branches on the current price of an item, which is partially supplied by more than one supplier. The design philosophies of the above are different in the sense that BoS searches for the optimal number of items from the suppliers, whereas BoP searches for the optimal price of the items. We compare the performance of these algorithms with extensive computational experiments

Integrated Operations (Re-)Scheduling from Mine to Ship

Mining companies have complex supply chains that start from the mining location and stretch thousands of kilometers to the end customer in a different country and continent. The logistics of moving the materials from mines to ship is composed of series of optimization problems like berth allocation, ship scheduling, stockyard scheduling, and rail scheduling, which are individually NP-hard. In this paper, we present a scheduling application, called as IBM Optimization: Mine to Ship, for end-to-end integrated operations scheduling. The application is built on IBM ILOG ODM Enterprise with advanced features like rescheduling under deviations and disturbances, and maintenance scheduling. The modeling and computational complexity of integrated scheduling optimization is tamed using hybrid optimization technique that leverages mathematical programming and constraint programming. The application will benefit the mining companies with increased resource usage, higher throughput, reduced cost of operations, and higher revenue