A Stackelberg game theoretic model for optimizing product family architecting with supply chain consideration

Planning of an optimal product family architecture (PFA) plays a critical role in defining an organization's product platforms for product variant configuration while leveraging commonality and variety. The focus of PFA planning has been traditionally limited to the product design stage, yet with limited consideration of the downstream supply chain-related issues. Decisions of supply chain configuration have a profound impact on not only the end cost of product family fulfillment, but also how to design the architecture of module configuration within a product family. It is imperative for product family architecting to be optimized in conjunction with supply chain configuration decisions. This paper formulates joint optimization of PFA planning and supply chain configuration as a Stackelberg game. A nonlinear, mixed integer bilevel programming model is developed to deal with the leader–follower game decisions between product family architecting and supply chain configuration. The PFA decision making is represented as an upper-level optimization problem for optimal selection of the base modules and compound modules. A lower-level optimization problem copes with supply chain decisions in accordance with the upper-level decisions of product variant configuration. Consistent with the bilevel optimization model, a nested genetic algorithm is developed to derive near optimal solutions for PFA and the corresponding supply chain network. A case study of joint PFA and supply chain decisions for power transformers is reported to demonstrate the feasibility and potential of the proposed Stackelberg game theoretic joint optimization of PFA and supply chain decisions

A Stackelberg Solution to Joint Optimization Problems: A Case Study of Green Design

AbstractDesign of complex engineered systems often involves optimization of multiple competing problems that are supposed to compromise to arrive at equilibrium optima, entailing a joint optimization problem. This paper reveals the leader-follower decision structure inherent in joint optimization problems. A Stackelberg game solution is formulated to model a leader-follower joint optimization problem as a two-level optimization problem between two decision makers, implicating a mathematical program that contains sub-optimization problems as its constraints. A case study of coffee grinder green design demonstrates the potential of Stackelberg solution to joint optimization of modularity subject with conflicting goals

Leader-follower Game in VMI System with Limited Production Capacity Considering Wholesale and Retail Prices

VMI (Vendor Managed Inventory) is a widely used cooperative inventory policy in supply chains in which each enterprise has its autonomy in pricing. This paper discusses a leader-follower Stackelberg game in a VMI supply chain where the manufacturer, as a leader, produces a single product with a limited production capacity and delivers it at a wholesale price to multiple different retailers, as the followers, who then sell the product in dispersed and independent markets at retail prices. An algorithm is then developed to determine the equilibrium of the Stackelberg game. Finally, a numerical study is conducted to understand the influence of the Stackelberg equilibrium and market related parameters on the profits of the manufacturer and its retailers. Through the numerical example, our research demonstrates that: (a) the market related parameters have significant influence on the manufacturer’ and its retailers’ profits; (b) a retailer’s profit may not be necessarily lowered when it is charged with a higher inventory cost by the manufacturer; (c) the equilibrium of the Stackelberg equilibrium benefits the manufacturer.Stackelberg Game;Supply Chain;Vendor Managed Inventory

Guarantees and Limits of Preprocessing in Constraint Satisfaction and Reasoning

We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning under structural restrictions. All these problems involve two tasks: (i) identifying the structure in the input as required by the restriction, and (ii) using the identified structure to solve the reasoning task efficiently. We show that for most of the considered problems, task (i) admits a polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, in contrast to task (ii) which does not admit such a reduction to a problem kernel of polynomial size, subject to a complexity theoretic assumption. As a notable exception we show that the consistency problem for the AtMost-NValue constraint admits a polynomial kernel consisting of a quadratic number of variables and domain values. Our results provide a firm worst-case guarantees and theoretical boundaries for the performance of polynomial-time preprocessing algorithms for the considered problems.Comment: arXiv admin note: substantial text overlap with arXiv:1104.2541, arXiv:1104.556

COORDINATION OF LEADER-FOLLOWER MULTI-AGENT SYSTEM WITH TIME-VARYING OBJECTIVE FUNCTION

This thesis aims to introduce a new framework for the distributed control of multi-agent systems with adjustable swarm control objectives. Our goal is twofold: 1) to provide an overview to how time-varying objectives in the control of autonomous systems may be applied to the distributed control of multi-agent systems with variable autonomy level, and 2) to introduce a framework to incorporate the proposed concept to fundamental swarm behaviors such as aggregation and leader tracking. Leader-follower multi-agent systems are considered in this study, and a general form of time-dependent artificial potential function is proposed to describe the varying objectives of the system in the case of complete information exchange. Using Lyapunov methods, the stability and boundedness of the agents\u27 trajectories under single order and higher order dynamics are analyzed. Illustrative numerical simulations are presented to demonstrate the validity of our results. Then, we extend these results for multi-agent systems with limited information exchange and switching communication topology. The first steps of the realization of an experimental framework have been made with the ultimate goal of verifying the simulation results in practice

The Application of Memetic Algorithms for Forearm Crutch Design: A Case Study

Product design has normally been performed by teams, each with expertise in a specific discipline such as material, structural, and electrical systems. Traditionally, each team would use its member\u27s experience and knowledge to develop the design sequentially. Collaborative design decisions explore the use of optimization methods to solve the design problem incorporating a number of disciplines simultaneously. It is known that such optimized product design is superior to the design found by optimizing each discipline sequentially due to the fact that it enables the exploitation of the interactions between the disciplines. In this paper, a bi-level decentralized framework based on Memetic Algorithm (MA) is proposed for collaborative design decision making using forearm crutch as the case. Two major decisions are considered: the weight and the strength. We introduce two design agents for each of the decisions. At the system level, one additional agent termed facilitator agent is created. Its main function is to locate the optimal solution for the system objective function which is derived from the Pareto concept. Thus to Pareto optimum for both weight and strength is obtained. It is demonstrated that the proposed model can converge to Pareto solutions

Difference games and policy evaluation: A conceptual framework

Economics

Leader-follower Game in VMI System with Limited Production Capacity Considering Wholesale and Retail Prices

VMI (Vendor Managed Inventory) is a widely used cooperative inventory policy in supply chains in which each enterprise has its autonomy in pricing. This paper discusses a leader-follower Stackelberg game in a VMI supply chain where the manufacturer, as a leader, produces a single product with a limited production capacity and delivers it at a wholesale price to multiple different retailers, as the followers, who then sell the product in dispersed and independent markets at retail prices. An algorithm is then developed to determine the equilibrium of the Stackelberg game. Finally, a numerical study is conducted to understand the influence of the Stackelberg equilibrium and market related parameters on the profits of the manufacturer and its retailers. Through the numerical example, our research demonstrates that: (a) the market related parameters have significant influence on the manufacturer’ and its retailers’ profits; (b) a retailer’s profit ma

Game theoretic optimization for product line evolution

Product line planning aims at optimal planning of product variety. In addition, the traditional product line planning problem develops new product lines based on product attributes without considering existing product lines. However, in reality, almost all new product lines evolve from existing product lines, which leads to the product line evolution problem. Product line evolution involves trade-offs between the marketing perspective and engineering perspective. The marketing concern focuses on maximizing utility for customers; the engineering concern focuses on minimizing engineering cost. Utility represents satisfaction experienced by the customers of a product. Engineering cost is the total cost involved in the process of the development of a product line. These two goals are in conflict since the high utility requires high-end product attributes which could increase the engineering cost and vice versa. Rather than aggregating both problems as one single level optimization problem, the marketing and engineering concerns entail a non-collaborative game per se. This research investigates a game-theoretic approach to the product line evolution problem. A leader-follower joint optimization model is developed to leverage conflicting goals of marketing and engineering concerns within a coherent framework of game theoretic optimization. To solve the joint optimization model efficiently, a bi-level nested genetic algorithm is developed. A case study of smart watch product line evolution is reported to illustrate the feasibility and potential of the proposed approach.M.S