Mitigating the Cost of Anarchy in Supply Chain Systems

In a decentralized two-stage supply chain where a supplier serves a retailer who, in turn, serves end customers, operations decisions based on local incentives often lead to suboptimal system performance. Operating decisions based on local incentives may in such cases lead to a degree of system disorder or anarchy, wherein one party's decisions put the other party and/or the system at a disadvantage. While models and mechanisms for such problem classes have been considered in the literature, little work to date has considered such problems under nonstationary demands and fixed replenishment order costs. This paper models such two-stage problems as a class of Stackelberg games where the supplier announces a set of time-phased ordering costs to the retailer over a discrete time horizon of finite length, and the retailer then creates an order plan, which then serves as the supplier's demand. We provide metrics for characterizing the degree of efficiency (and anarchy) associated with a solution, and provide a set of easily understood and implemented mechanisms that can increase this efficiency and reduce the negative impacts of anarchic decisions

Integrated market selection and production planning: complexity and solution approaches

Emphasis on effective demand management is becoming increasingly recognized as an important factor in operations performance. Operations models that account for supply costs and constraints as well as a supplier's ability to in°uence demand characteristics can lead to an improved match between supply and demand. This paper presents a new class of optimization models that allow a supplier to select, from a set of potential markets, those markets that provide maximum profit when production/procurement economies of scale exist in the supply process. The resulting optimization problem we study possesses an interesting structure and we show that although the general problem is NP-complete, a number of relevant and practical special cases can be solved in polynomial time. We also provide a computationally very effcient and intuitively attractive heuristic solution procedure that performs extremely well on a large number of test instances

Integrated market selection and production planning: Complexity and solution approaches

Emphasis on effective demand management is becoming increasingly recognized as an important factor in operations performance. Operations models that account for supply costs and constraints as well as a supplier's ability to influence demand characteristics can lead to an improved match between supply and demand. This paper presents a class of optimization models that allow a supplier to select, from a set of potential markets, those markets that provide maximum profit when production/procurement economies of scale exist in the supply process. The resulting optimization problem we study possesses an interesting structure and we show that although the general problem is NP -complete, a number of relevant and practical special cases can be solved in polynomial time. We also provide a computationally very efficient and intuitively attractive heuristic solution procedure that performs extremely well on a large number of test instances

Allocating Procurement to Capacitated Suppliers with Concave Quantity Discounts

We consider a procurement problem where suppliers offer concave quantity discounts. The resulting continuous knapsack problem involves the minimization of a sum of separable concave functions. We identify polynomially solvable special cases of this NP-hard problem, and provide a fully polynomial-time approximation scheme for the general problem