ABMS for Inventory Control

To provide a general overview of the applicability of ABMS specifically for

inventory control, this section summarizes several articles on ABMS relevant to

rather provide several examples of recent research in the area of ABMS for inventory

control.

Ito and Abadi propose an agent based model for a warehouse system composed of

three subsystems; agent based communication system, agent based material handling

system, and agent based inventory planning and control system. Warehouse systems take

care of fluctuation and uncertainty of demands from customers, and provide just-in-time

delivery of materials. That is because inventory avoids shortages, but at the cost of capital

investment, operation and maintenance, material handling, and insurance. The model,

written in Java, utilizes master agents and subagents including customer, supplier, order,

inventory, product, supplier-order, and automatic-guided vehicle (AGV) agents. With

further study proposed by the authors, the model will provide a mechanism for

autonomous setting of parameters to determine the order points or order-up-to-level point

of products based on the history of customer orders and supplier lead times. Furthermore,

the model will provide a mechanism for effective job-allocation to AGVs and scheduling

jobs of each AGV. (Ito and Abadi 2002)

Li and Li consider a multi-location inventory system with several retailers who

share one supplier. The model, built using the Anylogic software, considers demand lead-

time, replenishment lead-time, and transshipment lead-time. Also the model does not

employ a central agency to decide transshipments, and retailers make their decisions

separately. Running the model led to emergent transshipments happening between

retailers when in-hand inventory and pipeline stock are not enough to meet the demand.

Furthermore, optimal inventory policies were found by considering holding, ordering,

Chen, Zhou, and Hu propose an agent-oriented Petri net model for an inventory-

scheduling model, with focus on the problems of analysis and modeling of multi-agent

systems. Petri net aims at researching the organization structure and dynamic behavior of

a system, with an eye on all possible state changes and the relation of the change in the

system. The proposed agent-oriented Petri net model is applied in modeling the inventory

scheduling of supply system. (Chen et al. 2008)

Jirong et al. propose a 4-level multi-agent system model for supply chain

inventory with a decision-making model for every enterprise agent in the supply chain.

This modeling technique was selected due to the dynamic nonlinear complexity of supply

chain inventory systems. The simulation study is conducted for the influence of lead time

and information sharing among the four agent types; retailer, wholesaler, distributor, and

manufacturer. Results confirmed that the information sharing strategy effectively

decreases the variation amplitudes of inventory of each enterprise in the supply chain.

That is, the bullwhip effect is diminished when enterprises in the supply chain share

information. (Jirong et al. 2008)

Jiang and Sheng propose a reinforcement learning algorithm combined with case-

base reasoning in a multi-agent supply chain system. Reinforcement learning is an

approach to machine intelligence that learns to achieve the given goal by trial-and-error

iterations with its environment. This is done by combining dynamic programming and

supervised learning. Recent research in this area tends to focus on mathematical or

analytical models, such as Bayesian approach, Utility Function Method, fuzzy set

the article was programmed under Java2 Development Kit (JDK) 1.5 to study the

problem of dynamic inventory control for satisfying target service level in supply chain

with nonstationary customer demand. (Jiang and Sheng 2009)

Cao et al. describe a simulation-based inventory management tool developed for

the IBM Enterprise Server Group. IBM’s supply chain involves expensive components

with high inventory carrying cost, extensive tests for components for high quality

requirements, multi-tier suppliers with long lead time, and high customer service levels

requiring complex product configuration and quick order response time. The fabrication

stage is a build-to-plan process, while the fulfillment stage is a make-to-order process.

Thus, the stages together form a hybrid process structure combined with inherent

randomness in the process that pose tremendous challenges to inventory management,

particularly in terms of financial and operational impacts. To model impact of

randomness in parameters like lead times, yields and component usage rates, the authors

developed a simulation tool with Java. With inventory costs and Days-of-Supply profiles

as outputs, the simulation tool provides decision support at an operational level. That is,

the model provides the capability to project the future inventory performance for selected

high-dollar parts in IBM Enterprise Server Manufacturing. (Cao et al. 2003)

Sirivunnabood and Kumara used an agent based simulation model to determine

appropriate risk mitigation strategies for a supply chain network under supplier risks.

Implemented in Java on the Java Agent Development (JADE) platform, the model

consists of supplier agents, plant agents, warehouse agents, customer agents, and a

controller agent. Unexpected events were randomly generated to mimic the risks that

inventories were the two risk mitigation strategies tested for four types of risks, which

were depicted by frequency and duration. (Sirivunnabood and Kumara 2009)

Krishnamurthy et al. consider a new inventory control technique for large-scale

supply chains, which considers stochastic transport delays, manufacturing times, and

repair times and probabilistic characterization of part repair success. Because stochastic

disturbances enter at both ends of a bidirectional supply chain and the necessity for

overly simplified assumptions, optimization techniques for inventory control for

bidirectional stochastic supply chains are computationally intractable. For this reason the

paper provides an agent based simulation model of aircraft supply chain involving

multiple original equipment manufacturers (OEMs), depots, bases, squadrons, and planes.

ABMS was used to avoid explicitly modeling inventory dynamics for each site and

formulating complex coupling signals between the sites. With an adaptive feature, the

model can adjust stock levels with the objective of reducing excess inventory and

maintaining or increasing mission capability of aircraft. The simulation was written in

Python language. Output from the model can be used to determine the number of parts of

each part type that each site should order from its associated supplier site, and the number

of parts of each part type to start manufacturing. (Krishnamurthy et al. 2008)

While ABMS is applicable to supply chains, as depicted in this section and the

previous section, there must be consideration of efficiency in implementing ABMS for

large supply chains. For ABMS to be truly helpful in analyzing large supply chains there

must be a wide range of fidelity within a single model to analyze questions at different

interest, it is recommended to instill the concept of variable resolution in developing

agent based simulation models.