37,773 research outputs found
The regional model for Mediterranean agriculture
AgriPoliS is a multi-agent mixed integer linear programming (MIP) model, spatially explicit, developed in C++ language and suitable for long-term sim- ulations of agricultural policies. Beyond the mixed integer programming core, the model main feature is the interaction among a set of heterogeneous farm- ers and between them and the environment in which they operate. In this paper we describe an extension of the model allowing AgriPoliS to deal with typical characters of the Mediterranean agriculture. In particular AgriPoliS was extended to allow a generic number of products and soil types, included perennial crops and products with quality differentiation. Furthermore, it can explicitly take into account irrigation.Mediterranean Agriculture; Common Agricultural Policy; Agent-based Models
Decomposition and Mean-Field Approach to Mixed Integer Optimal Compensation Problems
Mixed integer optimal compensation deals with optimization problems with integer- and real-valued control variables to compensate disturbances in dynamic systems. The mixed integer nature of controls could lead to intractability in problems of large dimensions. To address this challenge, we introduce a decomposition method which turns the original n-dimensional optimization problem into n independent scalar problems of lot sizing form. Each of these problems can be viewed as a two-player zero-sum game, which introduces some element of conservatism. Each scalar problem is then reformulated as a shortest path one and solved through linear programming over a receding horizon, a step that mirrors a standard procedure in mixed integer programming. We apply the decomposition method to a mean-field coupled multi-agent system problem, where each agent seeks to compensate a combination of an exogenous signal and the local state average. We discuss a large population mean-field type of approximation and extend our study to opinion dynamics in social networks as a special case of interest
Interaction between intelligent agent strategies for real-time transportation planning
In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to dfferent collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents other jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a multi-agent system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead policies instead of a myopic policy (10-20%) and (ii) the joint effect of two look-ahead policies is larger than the effect of an individual policy. To provide an indication of the savings that might be realized with a central solution methodology, we benchmark our results against an integer programming approach
Mixed Integer Linear Programming For Exact Finite-Horizon Planning In Decentralized Pomdps
We consider the problem of finding an n-agent joint-policy for the optimal
finite-horizon control of a decentralized Pomdp (Dec-Pomdp). This is a problem
of very high complexity (NEXP-hard in n >= 2). In this paper, we propose a new
mathematical programming approach for the problem. Our approach is based on two
ideas: First, we represent each agent's policy in the sequence-form and not in
the tree-form, thereby obtaining a very compact representation of the set of
joint-policies. Second, using this compact representation, we solve this
problem as an instance of combinatorial optimization for which we formulate a
mixed integer linear program (MILP). The optimal solution of the MILP directly
yields an optimal joint-policy for the Dec-Pomdp. Computational experience
shows that formulating and solving the MILP requires significantly less time to
solve benchmark Dec-Pomdp problems than existing algorithms. For example, the
multi-agent tiger problem for horizon 4 is solved in 72 secs with the MILP
whereas existing algorithms require several hours to solve it
Mixed integer optimal compensation: Decompositions and mean-field approximations
Mixed integer optimal compensation deals with optimizing integer- and real-valued control variables to compensate disturbances in dynamic systems. The mixed integer nature of controls might be a cause of intractability for instances of larger dimensions. To tackle this issue, we propose a decomposition method which turns the original n-dimensional problem into n independent scalar problems of lot sizing form. Each scalar problem is then reformulated as a shortest path one and solved through linear programming over a receding horizon. This last reformulation step mirrors a standard procedure in mixed integer programming. We apply the decomposition method to a mean-field coupled multi-agent system problem, where each agent seeks to compensate a combination of the exogenous signal and the local state average. We discuss a large population mean-field type of approximation as well as the application of predictive control methods. © 2012 AACC American Automatic Control Council)
Multi-Agent Reach-Avoid Games: Two Attackers Versus One Defender and Mixed Integer Programming
We propose a hybrid approach that combines Hamilton-Jacobi (HJ) reachability
and mixed-integer optimization for solving a reach-avoid game with multiple
attackers and defenders. The reach-avoid game is an important problem with
potential applications in air traffic control and multi-agent motion planning;
however, solving this game for many attackers and defenders is intractable due
to the adversarial nature of the agents and the high problem dimensionality. In
this paper, we first propose an HJ reachability-based method for solving the
reach-avoid game in which 2 attackers are playing against 1 defender; we derive
the numerically convergent optimal winning sets for the two sides in
environments with obstacles. Utilizing this result and previous results for the
1 vs. 1 game, we further propose solving the general multi-agent reach-avoid
game by determining the defender assignments that can maximize the number of
attackers captured via a Mixed Integer Program (MIP). Our method generalizes
previous state-of-the-art results and is especially useful when there are fewer
defenders than attackers. We validate our theoretical results in numerical
simulations
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