Shantanu Biswas

Object oriented modeling and decision support for supply chains

Numerous algorithms and tools have been deployed in supply chain modeling and problem solving. These are based on stochastic models, mathematical programming models, heuristic techniques, and simulation. Since different decision problems in supply chains entail different approaches to be used for modeling and problem solving, there is a need for a unified approach to modeling supply chains so that any required representation can be created in a rapid and flexible way. In this paper, we develop a decision support system DESSCOM (decision support for supply chains through object modeling) which enables strategic, tactical, and operational decision making in supply chains. DESSCOM has two major components: (1) DESSCOM-MODEL, a modeling infrastructure comprising a library of carefully designed generic objects for modeling supply chain elements and dynamic interactions among these elements, and (2) DESSCOM-WORKBENCH, a decision workbench that can potentially include powerful algorithmic and simulation-based solution methods for supply chain decision-making. Through DESSCOM-MODEL, faithful models of any given supply chain can be created rapidly at any desired level of abstraction. Given a supply chain decision problem to be solved, the object oriented models created at the right level of detail can be transformed into problem formulations that can then be solved using an appropriate strategy from DESSCOM-WORKBENCH. We have designed and implemented a prototype of DESSCOM. We provide a real-world case study of a liquid petroleum gas supply chain to demonstrate the use of DESSCOM to model supply chains and enable decision-making at various levels.

Iterative Dutch combinatorial auctions

The combinatorial auction problem can be modeled as a weighted set packing problem. Similarly the reverse combinatorial auction can be modeled as a weighted set covering problem. We use the set packing and set covering formulations to suggest novel iterative Dutch auction algorithms for combinatorial auction problems. We use generalized Vickrey auctions (GVA) with reserve prices in each iteration. We prove the convergence of the algorithms and show that the solutions obtained using the algorithms lie within provable worst case bounds. We conduct numerical experiments to show that in general the solutions obtained using these algorithms are much better than the theoretical bounds.

Mechanism design for sustainable virtual power plant formation

Use of renewable energy sources for electricity generation is gaining prominence due to the increasing importance of sustainable development. Many of the electricity producers using renewable energy resources have very small generating capacities and it is a challenge to integrate them with the grid. Smart grids can play an important role in facilitating integration of these small distributed electricity producers to the power grid. The concept of virtual power plants (VPPs) is one of the important approaches used in smart grid for integrating distributed energy resources. The VPP aggregates electricity generated from many distributed energy sources such as wind turbines, small hydro, etc. The energy sources within a VPP are run by their individual owners and the VPP planner helps to aggregate and deliver the power to the grid. In this paper, we model the virtual power plant (VPP) formation problem with strategic producers (suppliers) using renewable energy sources as a combinatorial auction problem. We state and prove the necessary and sufficient condition for incentive compatible and individually rational auction mechanism in the presence of strategic suppliers. We also show that the dominant strategy for a supplier in our mechanism is to bid truthfully and improve her reputation.

Mechanism Design for Allocation of Carbon Emission Reduction Units: A Study of Global Companies with Strategic Divisions and Partners

The problem addressed in this work is concerned with an important challenge faced by any green aware global company to keep its emissions within a prescribed cap. The specific problem is to allocate carbon reductions to its different divisions and supply chain partners in achieving a required target of reductions in its carbon reduction program. The problem becomes a challenging one since the divisions and supply chain partners, being autonomous, could exhibit strategic behavior. We model strategic behavior of the divisions and partners using a game theoretic approach leading to a mechanism design approach to solve this problem. While designing a mechanism for the emission reduction allocation problem, the key properties that need to be satisfied are dominant strategy incentive compatibility (DSIC), strict budget balance (SBB), and allocative efficiency (AE). Mechanism design theory has shown that it is not possible to achieve the above three properties simultaneously. We propose two solutions to the problem satisfying DSIC and AE: (1) a reverse auction protocol and (2) a forward auction protocol, while striving to keep the budget imbalance as low as possible. We compare the performance of the two protocols using a stylized, representative case study.

Approximately Efficient Iterative Mechanisms for Combinatorial Exchanges

Combinatorial exchanges are double sided marketplaceswith multiple sellers and multiple buyers trading with thehelp of combinatorial bids. The allocation and other associated problems in such exchanges are known to be among the hardest to solve among all economic mechanisms. In this paper, we develop computationally efficient iterative auction mechanisms for solving combinatorial exchanges. Our mechanisms satisfy Individual rationality (IR) and budget-nonnegativity (BN) properties. We also show that the exchange problem can be reduced to combinatorial auction problem when either the buyers or the sellers are single minded. Our numerical experiments show that our algorithm produces good quality solutions and is computationally efficient.

Carbon footprint optimization: game theoretic problems and solutions

We discuss four problems that we have identified under the umbrella of carbon economics problems: carbon credit allocation (CCA), carbon credit buying (CCB), carbon credit selling (CCS), and carbon credit exchange (CCE). Because of the strategic nature of the players involved in these problems, game theory and mechanism design provides a natural way of formulating and solving these problems. We then focus on a particular CCA problem, the carbon emission reduction problem, where the countries or global industries are trying to reduce their carbon footprint at minimum cost. We briefly describe solutions to the above problem.

An iterative auction mechanism for combinatorial exchanges

Combinatorial exchanges are double sided marketplaces with multiple sellers and multiple buyers trading with the help of combinatorial bids. The allocation and other associated problems in such exchanges are known to be among the hardest to solve among all economic mechanisms. In this paper, we develop computationally efficient iterative auction mechanisms for solving combinatorial exchanges. Our mechanisms satisfy Individual-rationality (IR) and budget-nonnegativity (BN) properties. We also show that our method is bounded and convergent. Our numerical experiments show that our algorithm produces good quality solutions and is computationally efficient.

Incentive compatible green procurement using scoring rules

Green or sustainable procurement is critical to any supply chain in the modern era. In this paper, we address the issue of selection of suppliers in order to ensure that the procurement process in a manufacturing or service supply chain selects suppliers so as to minimize carbon emissions. The specific problem we address pertains to that of an orchestrator or a procurement planner who wishes to put together a green procurement network consisting of strategic suppliers. Our approach decomposes the problem into two stages. In Stage 1 (information elicitation), the orchestrator uses a green budget to offer appropriate incentives to the suppliers to report their carbon emissions accurately. The incentives are determined using an approach based on proper scoring rules. Having obtained emissions data in Stage 1, the orchestrator identifies a pool of suppliers in Stage 2 (green supplier selection) to minimize the quantum of carbon emissions of the procurement process. The paper focuses on Stage 1 of the problem and develops an incentive compatible mechanism for elicitation of emission estimates. We illustrate the proposed mechanism with a stylized example and also with detailed simulation results.

Mechanism design for green, truthful procurement auctions

Auction based mechanisms have become popular in industrial procurement settings. These mechanisms minimize the cost of procurement and at the same time achieve desirable properties such as truthful bidding by the suppliers. In this paper, we investigate the design of truthful procurement auctions taking into account an additional important issue namely carbon emissions. In particular, we focus on the following procurement problem: A buyer wishes to source multiple units of a homogeneous item from several competing suppliers who offer volume discount bids and who also provide emission curves that specify the cost of emissions as a function of volume of supply. We assume that emission curves are reported truthfully since that information is easily verifiable through standard sources. First we formulate the volume discount procurement auction problem with emission constraints under the assumption that the suppliers are honest (that is they report production costs truthfully). Next we describe a mechanism design formulation for green procurement with strategic suppliers. Our numerical experimentation shows that emission constraints can significantly alter sourcing decisions and affect the procurement costs dramatically. To the best of our knowledge, this is the first effort in explicitly taking into account carbon emissions in planning procurement auctions.

Optimal Combinatorial Auction for Initial Commitment Decision Problem in Virtual Organizations

We present an optimal combinatorial auction mechanism for the initial commitment decision problem (ICDP) in virtual organizations for rational agents. ICDP determines how a virtual organization (VO) planner can allocate tasks to supplier agents forming a virtual organization. We take into consideration the reputation of agents in the auction formulation. The reputation of agents is formed over time by their behavior of completing the tasks assigned to them. This is very important, since some of the agents (in real time) might be assigned other (more profitable) tasks in addition to the VO tasks and they can decide not to complete the tasks allocated to them by the VO planner.