Hybrid algorithm for a vendor managed inventory system in a two-echelon supply chain

In this paper we address the issue of vendor managed inventory (VMI) by considering a two-echelon single vendor/multiple buyer supply chain network. We try to find the optimal sales quantity by maximizing profit, given as a nonlinear and non-convex objective function. For such complicated combinatorial optimization problems, exact algorithms and optimization commercial software such as LINGO are inefficient, especially on practical-size problems. In this paper we develop a hybrid genetic/simulated annealing algorithm to deal with this nonlinear problem. Our results demonstrate that the proposed hybrid algorithm outperforms previous methodologies and achieves more robust solutions.     

Supply chain models for an assembly system with preprocessing of raw materials: A simple and better algorithm

Recently, Rahman and Sarker [1] formulated a nonlinear cost function to include the costs of inventories, ordering, shipping and deliveries for a manufacturing system where raw materials enter into the assembly line from two different channels. In order to find the best integer solutions to the variables, they proposed an algorithm that uses the branch and bound concept. The authors claim that their algorithm finds an optimal solution. In this paper, we present a simple and better heuristic algorithm that provides better solutions with a lower number of evaluations than Rahman and Sarker’s algorithm.     

A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge

This paper considers a two-stage distribution problem of a supply chain that is associated with a fixed charge. Two kinds of cost are involved in this problem: a continuous cost that linearly increases with the amount transported between a source and a destination, and secondly, a fixed charge, that incurs whenever there exists a transportation of a non-zero quantity between a source and a destination. The objective criterion is the minimisation of the total cost of distribution. A genetic algorithm (GA) that belongs to evolutionary search heuristics is proposed and illustrated. The proposed methodology is evaluated for its solution quality by comparing it with the approximate and lower bound solutions. Thus, the comparison reveals that the GA generates better solution than the approximation method and is capable of providing solution either equal or closer to the lower bound solution of the problem.     

Optimal selection of retailers for a manufacturing vendor in a vendor managed inventory system

A Vendor Managed Inventory (VMI) system consists of a manufacturing vendor and a number of retailers. In such a system, it is essential for the vendor to optimally determine retailer selection and other related decisions, such as the product’s replenishment cycle time and the wholesale price, in order to maximize his profit. Meanwhile, each retailer’s decisions on her willingness to enter the system and retail price are simultaneously considered in the retailer selection process. However, the above interactive decision making is complex and the available studies on interactive retailer selection are scarce. In this study, we formulate the retailer selection problem as a Stackelberg game model to help the manufacturer, as a vendor, optimally select his retailers to form a VMI system. This model is non-linear, mixed-integer, game-theoretic, and analytically intractable. Therefore, we further develop a hybrid algorithm for effectively and efficiently solving the developed model. The hybrid algorithm combines dynamic programming (DP), genetic algorithm (GA) and analytical methods. As demonstrated by our numerical studies, the optimal retailer selection can increase the manufacturer’s profit by up to 90% and the selected retailers’ profits significantly compared to non-selection strategy. The proposed hybrid algorithm can solve the model within a minute for a problem with 100 candidate retailers, whereas a pure GA has to take more than 1 h to solve a small sized problem of 20 candidate retailers achieving an objective value no worse than that obtained by the hybrid algorithm.     

A distributed coordination mechanism for supply networks with asymmetric information

The paper analyses the problem of coordination in supply networks of multiple retailers and a single supplier, where partners have asymmetric, private information of demand and costs. After stating generic requirements like distributedness, truthfulness, efficiency and budget balance, we use the apparatus of mechanism design to devise a coordination mechanism that guarantees the above properties in the network. The resulting protocol is a novel realisation of the widely used Vendor Managed Inventory (VMI) where the responsibility of planning is at the supplier. We prove that together with the required generic properties a fair sharing of risks and benefits cannot be guaranteed. We illustrate the general mechanism with a detailed discussion of a specialised version, assuming that inventory planning is done according to the newsvendor model, and explore the operation of this protocol through computational experiments.     

A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths

The competitive environment of global markets has forced many manufacturers to select the most appropriate supply chain network (SCN) for reduction of total costs and wasted time. Cost reduction and selection of the appropriate length of each period are two important factors in the competitive market that are often not addressed comprehensively by researchers. In our study, we proposed genetic algorithms (GAs) for optimising a novel mathematical model of the defective goods supply chain network (DGSCN). In the proposed model, we assumed that all imperfect-quality products are not repairable, whereas those considered as scrap are directly sold to customers at a low price. The objective of the proposed model is to minimise the costs of production, distribution, holding and backorder. In addition to minimising the costs, the model can determine the economic production quantity (EPQ), the appropriate length of each cycle (ALOEC) and the quantities of defective products, scrap products and retailer shortages using Just-In-Time logistics (JIT-L). We used the GAs and a Cplex solver with probability parameters and various dimensions for validation of the studied model in real-life situations, and we compared the outputs to demonstrate the performance of the model. Additionally, to identify the appropriate length of each cycle (ALOEC), we needed to solve the model using exact parameters and same dimensions and prefer to use Lingo for this application.     

Joint replenishment of multi retailer with variable replenishment cycle under VMI

In the recent article, Darwish and Odah (2010) develop a scheme that allows for identical replenishment cycles for all the retailers, in the context of a single vendor supplying a group of retailers under VMI partnership. This paper proposes an alternative replenishment scheme allowing for different replenishment cycles for each retailer. An example has been shown to illustrate the cost savings under the proposed model.     

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.     

A production-inventory model with probabilistic deterioration in two-echelon supply chain management

In this study, a production-inventory model is developed for a deteriorating item in a two-echelon supply chain management (SCM). An algebraical approach is applied to find the minimum cost related to this entire SCM. We consider three types of continuous probabilistic deterioration function to find the associated cost. The purpose of this study is to obtain the minimum cost with integer number of deliveries and optimum lotsize for the three different models. Some numerical examples, sensitivity analysis and graphical representation are given to illustrate the model. A numerical comparison between the three models is also given.     

A two-echelon supply chain of a returnable product with fuzzy demand

This study investigates the effects of the manufacturer’s refund on retailer’s unsold products for the two-echelon decentralized and centralized supply chains of a short life and returnable product with trapezoidal fuzzy demand, in which retailer returns the unsold and the customer’s unsatisfactory products to the manufacturer. For each returnable chain, we obtain the closed-form solution of order quantity to maximize the total expected profit of the supply chain, and confirm that demand fuzziness does indeed affect the order quantity and the members’ expected profits. We provide a number of managerial insights by comparing both chains and show that each chain is more advantageous to the members depending on certain condition. Our models are appropriate for a supply chain with a returnable product that lacks information about the demand.     

An optimal batch size for integrated production–inventory policy in a supply chain

This study presents a new integrated production–inventory policy under a finite planning horizon and a linear trend in demand. We assume that the vendor makes a single product and supplies it to a buyer with a non-periodic and just-in-time (JIT) replenishment policy in a supply chain environment. The objective is to minimize the joint total costs incurred by the vendor and the buyer. In this study, first, we develop a mathematical model and prove that it has the optimal solution. Then, we describe an explicit solution procedure for obtaining the optimal solution. Finally, we provide two numerical examples to illustrate both increasing and decreasing demands in our proposed model, and we show that the performance of the integrated consideration is better than the performance of any independent decision from either the buyer or the vendor.     

Coordination mechanism for a deteriorating item in a two-level supply chain system

The subject of the coordination between the suppliers and the buyers in multi-echelon inventory-distribution systems has been studied by many researchers. This paper considers a supply chain including a manufacturer and several buyers and assumes that the inventory items deteriorate over time and its inventory level decreases. In order to determine the order policies, coordination over the supply chain is achieved by scheduling the buyers’ delivery days and their coordination with the manufacturer’s production cycle. A mathematical model is developed and analyzed. To test the efficiency of the proposed model, two other models with the supposition of lot-sizing policies with common order cycle and independent deciding are also developed. In comparison to the other two models, the numerical results show that the synchronizing model of production and delivery cycles works better and has less total cost. In addition, in order to encourage the buyers in cooperation, a model on profit sharing is proposed that equitably shares the total savings with all the parties.     

Comparing improvement strategies for inventory inaccuracy in a two-echelon supply chain

Classical inventory theory often assumes no discrepancies between recorded inventory and actual inventory. However, inventory records are usually inaccurate due to many reasons in practice. For example, inventory shrinkage refers to the difference between booked inventory that a company should have as a result of its sales, purchasing, and manufacturing processes and actual inventory that it has on hand. This paper concerns the impact of inventory shrinkage to a supply chain and investigates different strategies to deal with inventory shrinkage. We consider a single-period two-echelon supply chain consisting of a Stackelberg manufacturer and a retailer whose inventory is subject to shrinkage errors. Our analysis is based on a single-period newsvendor model and considers the following cases of order decisions: (a) the retailer ignores the inventory errors; (b) the retailer estimates the errors; (c) the retailer shares the inventory error information with the manufacturer; and (d) RFID technology is used to reduce or eliminate the errors. The first case is the base strategy and a common practice for many supply chains, the other two represent certain non-technological strategies of the retailer when dealing with inventory inaccuracy, and the last one represents a technology improvement strategy by the manufacturer to reduce or eliminate inventory shrinkage errors. We compare these improvement strategies and derive critical tag price for RFID implementation as a technological remedy for the inventory inaccuracy problem. Conditions for the profitability of RFID adoption are discussed.     

The impact of partial information sharing in a two-echelon supply chain

We consider a simple two-echelon supply chain composed of a manufacturer and a retailer in which the demand process of the retailer is an AR(1) where the random component is a function of both sides’ information. We focus on partial information sharing under which each side informs the other of an interval in which the exact value of its own component of demand lies. These various levels of information sharing can reduce the supply chain costs.     

Time-dependent optimization of a multi-item uncertain supply chain network: A hybrid approximation algorithm

We consider the uncertain least cost shipping problem. The input is a multi-item supply chain network with time-evolving uncertain costs and capacities. Exploiting the operational law of uncertainty theory, a mathematical model of the problem is established and the indeterminacy factors are tackled. We use the scaling idea together with transformation approach and uncertainty programming to develop a hybrid algorithm to optimize and obtain the uncertainty distribution of the total shipping cost. We analyze the practical performance of the algorithm and present an illustrative example.     

The simplified solution algorithm for an integrated supplier-buyer inventory model with two-part trade credit in a supply chain system

Ho et al. [Ho, C.H., Ouyang, L.Y., Su, C.H., 2008. Optimal pricing, shipment and payment policy for an integrated supplier-buyer inventory model with two-part trade credit, European Journal of Operational Research 187, 496-510] discussed the integrated inventory model with two-part trade credit and presented an algorithm to solve it. Basically, Ho et al.’s inventory model is correct and interesting. However, this paper indicates that the solution algorithm described in Ho et al. (2008) can be simplified further. So, this paper can not only derive the optimally closed-form formulations for the optimal numbers of shipments but also develop different algorithms to improve those in Ho et al. (2008). Numerical examples illustrate that the algorithm to locate the optimal solution is rather accurate and rapid.     

A Stackelberg game and its improvement in a VMI system with a manufacturing vendor

Vendor managed inventory (VMI) is an inventory management strategy to let a vendor manage his retailers’ inventories, which makes the vendor have the opportunity to obtain some inventory and market-related information of his retailers. This paper discusses how the vendor can take advantage of this information for increasing his own profit by using a Stackelberg game in a VMI system. The vendor here is a manufacturer who procures raw materials to produce a finished product and supplies it at the same wholesale price to multiple retailers. The retailers then sell the product in independent markets at retail prices. Solution procedures are developed to find the Stackelberg game equilibrium that each enterprise is not willing to deviate from for maximizing his own profit. The equilibrium makes the manufacturer benefited, and the retailers’ profits maximized. The equilibrium can then be improved for further benefiting the manufacturer and his retailers if the retailers are willing to cooperate with the manufacturer by using a cooperative contract. Finally, a numerical example and the corresponding sensitivity analysis are given to illustrate that: (1) the manufacturer can benefit from his leadership, and monopolize the added profit of the VMI system in some cases; (2) The manufacturer can further improve his own profit, and then the retailers’ profits by the cooperative contract, as compared to the Stackelberg equilibrium; (3) market and raw material related parameters have significant influence on every enterprise’s net profit.     

An optimal solution to a three echelon supply chain network with multi-product and multi-period

Recently, Kadadevaramath et al. (2012) [1] presented a mathematical model for optimizing a three echelon supply chain network. Their model is an integer linear programming (ILP) model. In order to solve it, they developed five algorithms; four of them are based on a particle swarm optimization (PSO) method and the other is a genetic algorithm (GA). In this paper, we develop a more general mathematical model that contains the model developed by Kadadevaramath et al. (2012) [1]. Furthermore, we show that all instances proved in Kadadevaramath et al. (2012) [1] can easily be solved optimally by any integer linear programming solver.     

Multiple-buyer multiple-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead-time: A harmony search algorithm

In this paper, a multi-buyer multi-vendor supply chain problem is considered in which there are several products, each buyer has limited capacity to purchase products, and each vendor has warehouse limitation to store products. In this chain, the demand of each product is stochastic and follows a uniform distribution. The lead-time of receiving products from a vendor to a buyer is assumed to vary linearly with respect to the order quantity of the buyer and the production rate of the vendor. For each product, a fraction of the shortage is backordered and the rest are lost. The ordered product quantities are placed in multiple of pre-defined packets and there are service rate constraints for the buyers. The goal is to determine the reorder points, the safety stocks, and the numbers of shipments and packets in each shipment of the products such that the total cost of the supply chain is minimized. We show that the model of this problem is of an integer nonlinear programming type and in order to solve it a harmony search algorithm is employed. To validate the solution and to compare the performance of the proposed algorithm, a genetic algorithm is utilized as well. A numerical illustration and sensitivity analysis are given at the end to show the applicability of the proposed methodology in real-world supply chain problems.     

Risk pooling strategy in a multi-echelon supply chain with price-sensitive demand

This paper studies a nonstationary inventory and pricing problem. We consider a two-echelon supply chain with one supplier and two retailers, in which the supplier carries all inventory to supply the retailers. Both the reserved and pooled inventory systems are analyzed. Results with normally distributed demands are compared. Assuming the random demand at each retailer is price-sensitive, we further consider the cases when the retailers have and do not have service level requirements. We start with analyzing inventory and pricing strategies for the supplier in a one-period scenario. Then we extend our analysis to both the backlogging and lost-sale scenarios in an infinite planning horizon. The first author’s research is sponsored by Grant No. 70502009 and No. 70432001 of the Chinese National Natural Science Foundation and the second author’s research is sponsored by Grant #W911NF-04-D-0003 of the US Army Research Office and Grant #DMI-0553310 of the US National Science Foundation.