5 research outputs found
A Truthful Mechanism for the Generalized Assignment Problem
We propose a truthful-in-expectation, -approximation mechanism for a
strategic variant of the generalized assignment problem (GAP). In GAP, a set of
items has to be optimally assigned to a set of bins without exceeding the
capacity of any singular bin. In the strategic variant of the problem we study,
values for assigning items to bins are the private information of bidders and
the mechanism should provide bidders with incentives to truthfully report their
values. The approximation ratio of the mechanism is a significant improvement
over the approximation ratio of the existing truthful mechanism for GAP.
The proposed mechanism comprises a novel convex optimization program as the
allocation rule as well as an appropriate payment rule. To implement the convex
program in polynomial time, we propose a fractional local search algorithm
which approximates the optimal solution within an arbitrarily small error
leading to an approximately truthful-in-expectation mechanism. The presented
algorithm improves upon the existing optimization algorithms for GAP in terms
of simplicity and runtime while the approximation ratio closely matches the
best approximation ratio given for GAP when all inputs are publicly known.Comment: 18 pages, Earlier version accepted at WINE 201
Design of multiunit electronic exchanges through decomposition
In this paper, we exploit the idea of decomposition to match buyers and sellers in an electronic exchange for trading large volumes of homogeneous goods, where the buyers and sellers specify marginal-decreasing piecewise constant price curves to capture volume discounts. Such exchanges are relevant for automated trading in many e-business applications. The problem of determining winners and Vickrey prices in such exchanges is known to have a worst-case complexity equal to that of as many as (1+m+n) NP-hard problems, where m is the number of buyers and n is the number of sellers. Our method proposes the overall exchange problem to be solved as two separate and simpler problems: 1) forward auction and 2) reverse auction, which turns out to be generalized knapsack problems. In the proposed approach, we first determine the quantity of units to be traded between the sellers and the buyers using fast heuristics developed by us. Next, we solve a forward auction and a reverse auction using fully polynomial time approximation schemes available in the literature. The proposed approach has worst-case polynomial time complexity and our experimentation shows that the approach produces good quality solutions to the problem
Constraint programming for random testing of a trading system
Financial markets use complex computer trading systems whose failures can
cause serious economic damage, making reliability a major concern. Automated
random testing has been shown to be useful in nding defects in these systems,
but its inherent test oracle problem (automatic generation of the expected system
output) is a drawback that has typically prevented its application on a larger scale.
Two main tasks have been carried out in this thesis as a solution to the test
oracle problem. First, an independent model of a real trading system based on
constraint programming, a method for solving combinatorial problems, has been
created. Then, the model has been integrated as a true test oracle in automated random
tests. The test oracle maintains the expected state of an order book throughout
a sequence of random trade order actions, and provides the expected output of every
auction triggered in the order book by generating a corresponding constraint
program that is solved with the aid of a constraint programming system.
Constraint programming has allowed the development of an inexpensive, yet
reliable test oracle. In 500 random test cases, the test oracle has detected two
system failures. These failures correspond to defects that had been present for
several years without being discovered neither by less complete oracles nor by the
application of more systematic testing approaches.
The main contributions of this thesis are: (1) empirical evidence of both the
suitability of applying constraint programming to solve the test oracle problem and
the e ectiveness of true test oracles in random testing, and (2) a rst attempt, as
far as the author is aware, to model a non-theoretical continuous double auction
using constraint programming.Castañeda Lozano, R. (2010). Constraint programming for random testing of a trading system. http://hdl.handle.net/10251/8928.Archivo delegad
Best matching processes in distributed systems
The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individualsâfrom clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehiclesâroutes, suppliersâretailers, employeesâdepartments, and productsâautomated guided vehiclesâstorage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory.
The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies