Generalized Assignment for Multi-Robot Systems via Distributed Branch-And-Price

In this paper, we consider a network of agents that has to self-assign a set of tasks while respecting resource constraints. One possible formulation is the Generalized Assignment Problem, where the goal is to find a maximum payoff while satisfying capability constraints. We propose a purely distributed branch-and-price algorithm to solve this problem in a cooperative fashion. Inspired by classical (centralized) branch-and-price schemes, in the proposed algorithm each agent locally solves small linear programs, generates columns by solving simple knapsack problems, and communicates to its neighbors a fixed number of basic columns. We prove finite-time convergence of the algorithm to an optimal solution of the problem. Then, we apply the proposed scheme to a generalized assignment scenario in which a team of robots has to serve a set of tasks. We implement the proposed algorithm in a ROS testbed and provide experiments for a team of heterogeneous robots solving the assignment problem

A two-phase method for operating room scheduling

In this paper, a two-phase method and a computational program to perform surgery scheduling for operating rooms in a hospital are presented. In Phase I, surgeries are assigned to operating rooms using a generalized assignment model. In Phase II, a sequence for performing surgeries in each room is established. In the proposed method, Phase I maximizes doctors’ room preferences, taking into account the time available in each room, and Phase II establishes a schedule of surgeries in the rooms, according to doctors’ preferences for periods of day. The program was written in Visual Basic for Microsoft Excel and was tested as a surgery scheduling tool at St. Lydia Hospital, Ribeirão Preto, Brazil

Air Force Specialty Code Assignment Optimization

Each year, the Air Force Personnel Center determines which career field newly commissioned officers will serve under during their time in the Air Force. The career fields are assigned while considering five priorities, dictated by Headquarters Air Force, Manpower and Personnel: target number of cadets, education requirements, average cadet percentile, cadet source of commissioning, and cadet preference. A mixed-integer linear program with elasticized constraints is developed to generate cadet assignments according to these priorities. Each elasticized constraint carries an associated reward and penalty, which is used to dictate the importance of the constraint within the model. A subsequent analysis is conducted on historical data to display the interaction of the constraints and the impact of the rewards and penalties on the model results. The new formulation can generate a feasible set of assignments using the elasticized constraints in instances where the cadet and AFSC data would cause infeasibility in the original assignments model. It also provides users and decision makers with the ability to identify trade-offs between goals and prioritize each constraint

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 individuals—from 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, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage 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

Deep Learning for Bipartite Assignment Problems

A recurring problem in autonomy is the optimal assignment of agents to tasks. Often, such assignments cannot be computed efficiently. Therefore, the existing literature tends to focus on the development of handcrafted heuristics that exploit the structure of a particular assignment problem. These heuristics can find near-optimal assignments in real-time. However, if the problem specification changes slightly, a previously derived heuristic may not longer be applicable. Instead of manually deriving a heuristic for each assignment problem, this thesis considers a deep learning approach. Given a problem description, deep learning can be used to find near-optimal heuristics with minimal human input. The main contribution of this thesis is a deep learning architecture called Deep Bipartite Assignments (DBA), which can automatically learn heuristics for a large class of assignment problems. The effectiveness of DBA is demonstrated on two NP-Hard problems: the weapon-target assignment problem and the multi-resource generalised assignment problem. Without any expert domain knowledge, DBA is competitive with strong, handcrafted baselines.Thesis (MPhil) -- University of Adelaide, School of Electrical and Electronic Engineering, 201

Gestión eficiente de pequeños comerciantes en redes de e-commerce: problemas, modelos y optimización

El presente trabajo aborda un problema de toma de decisión dentro del diseño de la red logística en el contexto de un e-commerce muy i mportante de Latinoamérica. Si bien estas plataformas tradicionalmente se focalizan en grandes vendedores, este trabajo se centra en explorar el segmento denominado long-tail para la gestión logística de las ventas de pequeños comerciantes mediante centros de consolidación; usualmente tercerizados a través de comercios alternativos con capacidad ociosa. Partiendo de datos reales provistos por l a empresa, se realizaron distintos ejercicios con el fin de responder preguntas relevantes de negocio relacionadas con el diseño y l a operación de l a red de este segmento particular. Las decisiones son formuladas mediante modelos de programación l ineal entera, en particular relacionados con problemas de asignación generalizada (GAPs) y abordadas utilizando algoritmos específicos para su resolución. Los resultados obtenidos muestran una mejora sustancial comparada con el procedimiento actual de la plataforma. Las soluciones encontradas muestran reducciones en las distancias recorridas del orden del 50%, implicando una mejora tanto en los costos como en la experiencia de los actores involucrados.This paper addresses a decision-making problem within the design of the l ogistics network in the context of a very i mportant e-commerce i n Latin America. Although these platforms traditionally focus on l arge sellers, this work focuses on exploring the segment called long-tail for the l ogistics management of small merchant sales through consolidation centers, usually outsourced through alternative businesses with i dle capacity. Based on real data provided by the company, different exercises were carried out i n order to answer relevant business questions related to the design and operation of the network for this particular segment. Decisions are formulated using i nteger l inear programming models, particularly related to generalized allocation problems (GAPs), and addressed using specific algorithms for their resolution. The results obtained show a substantial improvement compared to the current platform procedure. The solutions found show reductions in distances traveled of the order of 50%, i mplying an improvement both in costs and in the experience of the actors involved

Proposição de um modelo de alocação baseado em competências : um estudo sobre o problema da designação generalizada aplicado a equipes de prestação de serviços

Essa dissertação aborda o desenvolvimento e a aplicação de um modelo para a solução do problema de alocação de equipes baseado em competências. Fundamentado no tradicional problema da alocação generalizada, inicialmente introduzido por Ross e Soland (1975), este trabalho propõe um método heurístico considerando três dimensões (trabalhador, região e competência). Considerando-se uma prestadora de serviços cuja demanda é variável segundo a sua região de origem, a quantidade de atendimentos necessários e o perfil de competências exigido para cada atendimento. Supondo ser inerente ao serviço que os trabalhadores possam realizar tanto atendimentos locais quanto em outras regiões e que é desejável priorizar atendimentos com profissionais locais. O objetivo do modelo é determinar a melhor região para a alocação individual de um grupo de profissionais, com base em seu perfil de competências e no comportamento da demanda por essas competências. O modelo desenvolvido é aplicado em uma empresa prestadora de serviços de segurança e saúde no trabalho. Quatro cenários são avaliados comparativamente, sendo um desses o ambiente original e os demais resultantes de três simulações com o método realizadas sob considerações distintas. Os resultados indicam que o modelo proposto possui significativa convergência com a solução ótima, assim como uma potencial contribuição a estratégia de atendimento da empresa.This dissertation addresses the development and the implementation of a method for solving the competency-based team allocation problem. Based on the traditional generalized assignment problem, initially introduced by Ross and Soland (1975), this paper proposes a heuristic method considering three dimensions (worker, region and competence). Considering a service provider whose demand varies according to its location (region), the amount of needed assistance and the expertise profile required for each service demanded. Assuming it is inherent to the service that workers can provide assistance both locally and remotely, and that it is desirable to prioritize local professionals for service assignment. The method’s main objective is to determine the best region for the individual assignment of a group of professionals, based on their competency profile and the demand behavior for those competencies. The developed method is applied in a company that provides occupational health and safety services. Four scenarios are comparatively evaluated, one of them being the original environment and the others resulting from three simulations with the method performed under different considerations. The outcomes indicate that the proposed method has significant convergence to the optimal solution, as well as a potential contribution to the company's service strategy