Agent based e-commerce systems that react to buyers’ feedbacks - A fuzzy approach

In this paper, we have introduced an agent based e-commerce system which recommends products to buyers as per their preferences. Initially, the agent collects the buyers’ preferences in fuzzy or linguistically defined terms and based on this, presents them an ordered set of products. After obtaining the buyers’ feedbacks when they actually come across the products, the seller’s agent interacts with the buyer (buyer’s agent), revises the products preferential order and recommends either the same set of products or a new set of similar products with the revised preferential order. The buyer’s revised preferences are taken here as his/her feedback after he/she comes across with the actual products (presented products). Concepts of fuzzy logic and Fuzzy Linear Programming are used here to identify the buyer’s feedbacks on the initial presentation of the products. Our methodology also measures the degree of customers’ focus on the products which are finally recommended by the e-commerce agent. The product ranking obtained through buyers’ initial preferences is considered here as his/her subjective information and the available information from the agents’ presented products are taken as the objective information.     

Continuous learning methods in two-buyer pricing problem

This paper presents continuous learning methods in a monopoly pricing problem where the firm has uncertainty about the buyers’ preferences. The firm designs a menu of quality-price bundles and adjusts them using only local information about the buyers’ preferences. The learning methods define different paths, and we compare how much profit the firm makes on these paths, how long it takes to learn the optimal tariff, and how the buyers’ utilities change during the learning period. We also present a way to compute the optimal path in terms of discounted profit with dynamic programming and complete information. Numerical examples show that the optimal path may involve jumps where the buyer types switch from one bundle to another, and this is a property which is difficult to include in the learning methods. The learning methods have, however, the benefit that they can be generalized to pricing problems with many buyers types and qualities.     

Modeling a stochastic multi-objective supplier quota allocation problem with price-dependent ordering

One of the interesting subjects in supply chain management is supply management, which generally relates to the activities regarding suppliers such as empowerment, evaluation, partnerships and so on. A major objective of supplier evaluation involves buyers determining the optimal quota allocated to each supplier when placing an order. In this paper, we propose a multi-objective model in which purchasing cost, rejected units, and late delivered units are minimized, while the obtained total score from the supplier evaluation process is maximized. We assume that the buyer obtains multiple products from a number of predetermined suppliers. The buyer faces a stochastic demand with a probability distribution of Poisson regarding each product type. A major assumption is that the supplier prices are linearly dependent on the order size of each product. Since demand is stochastic, the buyer may incur holding and stockout costs in addition to the regular purchasing cost. We use the well-known L-1 metric method to solve the supplier evaluation problem by utilizing two meta-heuristic algorithms to solve the corresponding mathematical problems.     

Analyzing online B2B exchange markets: Asymmetric cost and incomplete information

This research applies the discriminating auction to analyze the online B2B exchange market in which a single buyer requests multiple items and several suppliers having equal capacity and asymmetric cost submit bids to compete for buyer demand. In the present model, we examine the impact of asymmetric cost and incomplete information on the participants in the market. Given the complete cost information, each supplier randomizes its price and the lower bound of the price range is determined by the highest marginal cost. In addition, the supplier with a lower marginal cost has a larger considered pricing space but ultimately has a smaller equilibrium one than others with higher marginal costs. When each supplier’s marginal cost is private information, the lowest possible price is determined by the number of suppliers and the buyer’s reservation price. Comparing these two market settings, we find whether IT is beneficial to buyers or suppliers depends on the scale of the bid process and the highest marginal cost. When the number of suppliers and the difference between the highest marginal cost and the buyer’s reservation price are sufficiently large, each supplier can gain a higher profit if the marginal costs are private information. On the contrary, when the highest marginal cost approaches the buyer’s reservation price, complete cost information benefits the suppliers.     

Impact of supply base heterogeneity in electronic markets

Internet based marketplaces have enabled industrial buyers to locate suppliers from geographically diverse locations. This has resulted in increased variations in certain supplier parameters such as capacity and cost among the participating suppliers. However, the impact of this increased heterogeneity on the procurement practices are not well understood. In this paper we consider three supplier parameters that can affect the price the buyer pays and the number of suppliers that the buyer will select for award of contract. These attributes are capacity, production cost and demand for supplier’s capacity. We show how these parameters impact the price that a supplier quotes. We also show how the buyer will determine the optimum number of suppliers using a reverse auction mechanism when he does not have perfect knowledge of the suppliers’ parameters. Our model suggests that buyers need to adjust some of the input parameters while procuring capacity from a heterogeneous supply base. For instance, buyers need to pre-qualify more suppliers if the supply base has greater heterogeneity.     

Single-supplier/multiple-buyer supply chain coordination: Incorporating buyers’ expectations under vertical information sharing

We address the coordination problem in a single-supplier/multiple-buyer supply chain. The supplier wishes to coordinate the supply chain by offering quantity discounts. To obtain their complete cost information, the supplier exchanges his own cost parameters with buyers leading to vertical information sharing. The supplier thinks that the buyers, as they have access to supplier’s setup and holding cost information, may demand a portion of the anticipated coordination savings based on the partial information they hold about the cost structure of the entire supply chain. We model each buyer’s expectations based on her limited view of the entire supply chain which consists of herself and the supplier only. These expectations are then incorporated into the modeling of the supply chain, which results in a generalization of the traditional Stackelberg type models. We discuss alternative efficiency sharing mechanisms, and propose methods to design the associated discount schemes that take buyers’ expectations into account. In designing the discount schemes, we consider both price discriminatory and non-price discriminatory approaches. The study adds to the existing body of work by incorporating buyers’ expectations into a constrained Stackelberg structure, and by achieving coordination without forcing buyers to explicitly comply with the supplier’s replenishment period in choosing their order quantities. The numerical analysis of the coordination efficiency and allocation of the net savings of the proposed discount schemes shows that the supplier is still able to coordinate the supply chain with high efficiency levels, and retain a significant portion of the net savings.     

Coordinated decisions for substitutable products in a common retailer supply chain

This paper studies coordination mechanisms in a supply chain which consists of two suppliers with capacity uncertainties selling differential yet substitutable products through a common retailer who faces price-sensitive random demand of these two products. We develop in a noncompetitive setting three coordination models – revenue sharing, return policy, and combination of revenue sharing and return policy – and contrast them with a basic and uncoordinated model. We are able to establish the ordinal relationship among the retailer’s ordering and pricing decisions and analytically compare the performances between certain models when two suppliers are identical. We find that the retailer’s ordering and pricing decisions in the model with return policy in the case of identical suppliers are independent of demand or supply uncertainty. Our numerical results reveal that the performances of coordination models in the case of nonidentical suppliers resemble those in the case of identical suppliers. We find that the retailer will place a larger order quantity in models where her average cost per unit sold is smaller. We also find that product substitutability and uncertainties have different effects on chain performances.     

A deterministic,multi-item inventory model with supplier selection and imperfect quality

This paper considers the scenario of supply chain with multiple products and multiple suppliers, all of which have limited capacity. We assume that received items from suppliers are not of perfect quality. Items of imperfect quality, not necessarily defective, could be used in another inventory situation. Imperfect items are sold as a single batch, prior to receiving the next shipment, at a discounted price. The demand over a finite planning horizon is known, and an optimal procurement strategy for this multi-period horizon is to be determined. Each of products can be sourced from a set of approved suppliers, a supplier-dependent transaction cost applies for each period in which an order is placed on a supplier. A product-dependent holding cost per period applies for each product in the inventory that is carried across a period in the planning horizon. Also a maximum storage space for the buyer in each period is considered. The decision maker, the buyer, needs to decide what products to order, in what quantities, with which suppliers, and in which periods. Finally, a genetic algorithm (GA) is used to solve the model.     

Optimal production plans and shipment schedules in a supply-chain system with multiple suppliers and multiple buyers

This research addresses an optimal policy for production and procurement in a supply-chain system with multiple non-competing suppliers, a manufacturer and multiple non-identical buyers. The manufacturer procures raw materials from suppliers, converts them to finished products and ships the products to each buyer at a fixed-interval of time over a finite planning horizon. The demand of finished product is given by buyers and the shipment size to each buyer is fixed. The problem is to determine the production start time, the initial and ending inventory, the cycle beginning and ending time, the number of orders of raw materials in each cycle, and the number of cycles for a finite planning horizon so as to minimize the system cost. A surrogate network representation of the problem developed to obtain an efficient, optimal solution to determine the production cycle and cycle costs with predetermined shipment schedules in the planning horizon. This research prescribes optimal policies for a multi-stage production and procurements for all shipments scheduled over the planning horizon. Numerical examples are also provided to illustrate the system.     

Management Adopts Operational Research: Session Report

Quantitative methods are derived to assist buyers purchasing commodities in fluctuating price markets. Demand is known whilst price is a stochastic variable which may contain trends or seasonal fluctuations. The essential feature of the problem is that the buyer has many opportunities to make a purchase.Mathematical models are formulated to describe particular commodity buying problems. The optimal purchasing policy is derived by using dynamic programming. It consists of a set of discrete price breaks at each buying opportunity together with the associated stock levels the buyer should aim to achieve at each price break with his purchase at this opportunity. The price breaks are dependent on the probability density functions of future prices and the number of future buying opportunities. Recurrence relations are derived to calculate these price breaks. The case of restrictions on the purchase quantity at each price offer, either because of supply limitations or by the buyer as a policy decision, and price discounts are also considered.A case study illustrating the techniques is given and the methods are extended to purchasing for a blending problem with substitutable commodities.     

To reveal or not to reveal? Strategic disclosure of private information in negotiation

Within the bargaining literature, it is widely held that negotiators should never reveal information that will lead to disclosure of their reservation prices. We analyze a simple bargaining and search model in which the informed buyer can choose to reveal his cost of searching for an outside price (which determines his reservation price) to the uninformed seller. We demonstrate that buyers can be made better off by revealing their search cost. More interestingly, we also find that, depending on the assumed distribution of search costs, sometimes buyers with relatively low search costs should reveal their private information whereas in other cases buyers with relatively high search costs should do so. We then test our model experimentally and find that subjects’ behavior is not entirely consistent with theoretical predictions. In general, bargainers’ behavior is better explained by a bounded rationality model similar to “fictitious play”.     

基于等量订货的可替代易变质产品库存策略研究

零售商常常根据不同"年龄"的易变质产品质量的差异而制定不同的价格,从而会带来不同的顾客需求.而当某个"年龄"的产品缺货时,顾客可能会购买其他"年龄"的产品作为替代.在上述背景下,本文考虑某个销售季节内多周期等量订货策略,分别在单周期和多周期情况下找到了零售商的最优订货量.     

Optimal capacity for substitutable products under operational postponement

We consider a monopolist producing two substitutable products with one flexible (shared) capacity. The demand of each product is a linear function of the prices of both products, and is subject to an additive shock. We study the impact of two key drivers, namely the degree of substitution between the products and the level of operational postponement, on the optimal capacity and the resulting expected profit. We show that the relationship between the optimal capacity and the degree of product substitution is not impacted by the different postponement strategies the firm can utilize or by the different settings (forced clearance versus holdback) considered in the previous literature. On the other hand, how capacity is affected by postponement critically depends on how closely substitutable the products are. In particular, we show that the well-known result that operational postponement and capacity are strategic complements in a single-product setting (Van Mieghem and Dada, 1999) no longer holds in our setting, because the two substitutable products are now linked through consumer-driven substitution, which the firm can influence through pricing. In particular, capacity and operational postponement (in the form of quantity postponement) can be either strategic substitutes or strategic complements, and this depends on both the firm’s cost structure and the degree of substitution between the products. We also study the impact of forced clearance on the firm’s expected profit and find that clearance deteriorates the firm’s earnings more when the products it offers are highly differentiated.     

An Optimal Batch Size for a Production System Operating Under a Fixed-Quantity,Periodic Delivery Policy

A manufacturing system which procures raw materials from suppliers and processes them to convert to finished products is considered here. This paper develops an ordering policy for raw materials to meet the requirements of a production facility which, in turn, must deliver finished products demanded by outside buyers at fixed interval points in time. First, a general cost model is developed considering both supplier (of raw material) and buyer (of finished products) sides. This model is used to determine an optimal ordering policy for procurement of raw materials, and the manufacturing batch size to minimize the total cost for meeting equal shipments of the finished products, at fixed intervals, to the buyers. The total cost is found to be a piece-wise convex cost function. An interval that contains the optimal solution is first determined followed by an optimization technique to identify the exact solution from this interval.     

The scheduling of deliveries in a production-distribution system with multiple buyers

In a real production and distribution business environment with one supplier and multiple heterogeneous buyers, the differences in buyers’ ordering cycles have influence on production arrangements. Consequently, the average inventory level (AIL) at the supplier’s end is affected by both the production policy and the ordering policy, typically by the scheduling of deliveries. Consequently, the average inventory holding cost is most deeply affected. In this paper, it is proposed that the scheduling of deliveries be formulated as a decision problem to determine the time point at which deliveries are made to buyers in order to minimize the supplier’s average inventory. A formulation of the average inventory level (AIL) in a production cycle at the supplier’s end using a lot-for-lot policy is developed. Under the lot-for-lot policy, the scheduling of deliveries (SP) is formulated as a nonlinear programming model used to determine the first delivery point for each buyer with an objective to minimize the sum of the product of the individual demand quantity and the first delivery time for each buyer. Thus, the SP model determines not only the sequence of the first deliveries to individual buyers, but also the time when the deliveries are made. An iterative heuristic procedure (IHP) is developed to solve the SP model assuming a given sequence of buyers. Six sequence rules are considered and evaluated via simulation.     

Strategic capability investments and competition for supply contracts

Suppliers often make proactive investments to strategically position themselves to win contracts with a large buyer. Such investments reduce the suppliers’ variable costs of serving the buyer’s demand. We show that an auction mechanism does not always benefit the buyer, the supply chain, or the society. We identify scenarios where the buyer can implement the supply chain and socially optimal solution by committing to a bilateral relationship with fair reimbursement, and forgoing the benefits of competition altogether. We explore the role of commitment by the buyer (to a procurement mechanism) and by the suppliers (to an investment level) by analyzing different timing games under symmetric and asymmetric information about suppliers’ types. We show that it never benefits anyone for the suppliers to commit first. Equilibrium investments and cost structures depend upon the buyer’s bargaining power (opportunity cost). However, the winning supplier’s investments are almost always below the supply chain optimal level.     

Single-vendor multi-buyer integrated production-inventory model with controllable lead time and service level constraints

This paper presents an integrated production-inventory model where a vendor produces an item in a batch production environment and supplies it to a set of buyers. The buyer level demand is assumed to be independent normally distributed and lead time of every buyer can be reduced at an added crash cost. The buyers review their inventory using continuous review policy, and the unsatisfied demand at the buyers is completely backordered. A model is formulated to minimize the joint total expected cost of the vendor–buyers system to determine the optimal production-inventory policy. Since it is often difficult to estimate the stock-out cost in inventory systems, and so instead of having stock-out cost component in the objective function, a service level constraint (SLC) corresponding to each buyer is included in the model. A Lagrangian multiplier technique based algorithmic approach is proposed, which evaluates a very limited number of combinations of lead time of the buyers to find simultaneously the optimal lead time, order quantity and safety factor of the buyers and the number of shipments between the vendor and the buyers in a production cycle. Finally, a numerical example and effects of the key parameters are included to illustrate the results of the proposed model.     

Exact algorithms for procurement problems under a total quantity discount structure

In this paper, we study the procurement problem faced by a buyer who needs to purchase a variety of goods from suppliers applying a so-called total quantity discount policy. This policy implies that every supplier announces a number of volume intervals and that the volume interval in which the total amount ordered lies determines the discount. Moreover, the discounted prices apply to all goods bought from the supplier, not only to those goods exceeding the volume threshold. We refer to this cost-minimization problem as the total quantity discount (TQD) problem. We give a mathematical formulation for this problem and argue that not only it is NP-hard, but also that there exists no polynomial-time approximation algorithm with a constant ratio (unless P = NP). Apart from the basic form of the TQD problem, we describe four variants. In a first variant, the market share that one or more suppliers can obtain is constrained. Another variant allows the buyer to procure more goods than strictly needed, in order to reach a lower total cost. We also consider a setting where the buyer needs to pay a disposal cost for the extra goods bought. In a third variant, the number of winning suppliers is limited, both in general and per product. Finally, we investigate a multi-period variant, where the buyer not only needs to decide what goods to buy from what supplier, but also when to do this, while considering the inventory costs. We show that the TQD problem and its variants can be solved by solving a series of min-cost flow problems. Finally, we investigate the performance of three exact algorithms (min-cost flow based branch-and-bound, linear programming based branch-and-bound, and branch-and-cut) on randomly generated instances involving 50 suppliers and 100 goods. It turns out that even the large instances of the basic problem are solved to optimality within a limited amount of time. However, we find that different algorithms perform best in terms of computation time for different variants.     

Nash game model for optimizing market strategies,configuration of platform products in a Vendor Managed Inventory (VMI) supply chain for a product family

This paper discusses how a manufacturer and its retailers interact with each other to optimize their product marketing strategies, platform product configuration and inventory policies in a VMI (Vendor Managed Inventory) supply chain. The manufacturer procures raw materials from multiple suppliers to produce a family of products sold to multiple retailers. Multiple types of products are substitutable each other to end customers. The manufacturer makes its decision on raw materials’ procurement, platform product configuration, product replenishment policies to retailers with VMI, price discount rate, and advertising investment to maximize its profit. Retailers in turn consider the optimal local advertising investments and retail prices to maximize their profits. This problem is modeled as a dual simultaneous non-cooperative game (as a dual Nash game) model with two sub-games. One is between the retailers serving in competing retail markets and the other is between the manufacturer and the retailers. This paper combines analytical, iterative and GA (genetic algorithm) methods to develop a game solution algorithm to find the Nash equilibrium. A numerical example is conducted to test the proposed model and algorithm, and gain managerial implications.     

The impact of cost uncertainty on the location of a distribution center

The location of a distribution center (DC) is a key consideration for the design of supply chain networks. When deciding on it, firms usually allow for transportation costs, but not supplier prices. We consider simultaneously the location of a DC and the choice of suppliers offering different, possibly random, prices for a single product. A buying firm attempts to minimize the sum of the price charged by a chosen supplier, and inbound and outbound transportation costs. No costs are incurred for switching suppliers. We first derive a closed-form optimal location for the case of a demand-populated unit line between two suppliers offering deterministic prices. We then let one of the two suppliers offer a random price. If the price follows a symmetric and unimodal distribution, the optimal location is closer to the supplier with a lower mean price. We also show the dominance of high variability: the buyer can decrease the total cost more for higher price variability for any location. The dominance result holds for normal, uniform, and gamma distributions. We propose an extended model with more than two suppliers on a plane and show that the dominance result still holds. From numerical examples for a line and a plane, we observe that an optimal location gets closer to the center of gravity of demands as the variability of any supplier’s price increases.