Nowadays in the world of mass consumption there is big demand for distributioncenters of bigger size. Managing such a center is a very complex and difficult taskregarding to the different processes and factors in a usual warehouse when we want tominimize the labor costs. Most of the workers’ working time is spent with travelingbetween source and destination points which cause deadheading. Even if a worker knowsthe structure of a warehouse well and because of that he or she can find the shortest pathbetween two points, it is still not guaranteed that there won’t be long traveling timebetween the locations of two consecutive tasks. We need optimal assignments betweentasks and workers.In the scientific literature Generalized Assignment Problem (GAP) is a wellknownproblem which deals with the assignment of m workers to n tasks consideringseveral constraints. The primary purpose of my thesis project was to choose a heuristics(genetic algorithm, tabu search or ant colony optimization) to be implemented into SAPExtended Warehouse Management (SAP EWM) by with task assignment will be moreeffective between tasks and resources.After system analysis I had to realize that due different constraints and businessdemands only 1:1 assingments are allowed in SAP EWM. Because of that I had to use adifferent and simpler approach – instead of the introduced heuristics – which could gainbetter assignments during the test phase in several cases. In the thesis I described indetails what ware the most important questions and problems which emerged during theplanning of my optimized assignment method.

This dissertation explores workforce planning in manufacturing and healthcare systems. In manufacturing systems, the existing workforce planning models often lack fidelity with respect to the mechanism of learning. Learning refers to that employees’ productivity increases as they gain more experience. Workforce scheduling in the short term has a longer term impact on organizations’ capacity. The mathematical representations of learning are usually nonlinear. This nonlinearity complicates the planning models and provides opportunities to develop solution methodologies for realistically-sized instances. This research formulates the workforce planning problem as a mixed-integer nonlinear program (MINLP) and overcomes the limitations of cur- rent solution methods. Specifically, this research develops a reformulation technique that converts the MINLP to a mixed integer linear program (MILP) and proposes several techniques to speed up the solution time of solving the MILP. In organizations that use group work, workers learn not only by individual learning but also from knowledge transferred from team members. Managers face the decision of how to pair or team workers such that organizations benefit from this transfer of learning. Using a mathematical representation that incorporates both in- dividual learning and knowledge transfer between workers, this research considers the problem of grouping workers to teams and assigning teams to sets of jobs based on workers’ learning and knowledge transfer characteristics. This study builds a Mixed- integer nonlinear programs (MINP) for parallel systems with the objective of maximizing the system throughput and propose exact and heuristic solution approaches for solving the MINLP. In healthcare systems, we focus on managing medical technicians in medical laboratories, in particular, the phlebotomists. Phlebotomists draw specimens from patients based on doctors’ orders, which arrive randomly in a day. According to the literature, optimizing scheduling and routing in hospital laboratories has not been regarded as a necessity for laboratory management. This study is motivated by a real case at University of Iowa Hospital and Clinics, where there is a team of phlebotomists that cannot fulfill doctors requests in the morning shift. The goal of this research is routing these phlebotomists to patient units such that as many orders as possible are fulfilled during the shift. The problem is a team orienteering problem with stochastic rewards and service times. This research develops an a priori approach which applies a variable neighborhood search heuristic algorithm that improves the daily performance compared to the hospital practice.

Heterogeneous Computing (HC) systems achieve high performance by networking together computing resources of diverse nature. The issues of task assignment and scheduling are critical in the design and performance of such systems. In this thesis, an auction based game theoretic framework is developed for dynamic task scheduling in HC systems. Based on the proposed game theoretic model, a new dynamic scheduling algorithm is developed that uses auction based strategies. The dynamic scheduling algorithm yields schedules with shorter completion times than static schedulers while incurring higher scheduling overhead. Thus, a second scheduling algorithm is proposed which uses an initial schedule generated with a learning automaton based algorithm, and then heuristics are used to identify windows of tasks within the application that can be rescheduled dynamically during run time.

Heavy-tailed workload distributions are commonly experienced in many areas of distributed computing. Such workloads are highly variable, where a small number of very large tasks make up a large proportion of the workload, making the load very hard to distribute effectively. Traditional task assignment policies are ineffective under these conditions as they were formulated based on the assumption of an exponentially distributed workload. Size-based task assignment policies have been proposed to handle heavy-tailed workloads, but their applications are limited by their static nature and assumption of prior knowledge of a task's service requirement. This thesis analyses existing approaches to load distribution under heavy-tailed workloads, and presents a new generalised task assignment policy that significantly improves performance for many distributed applications, by intelligently addressing the negative effects on performance that highly variable workloads cause. Many problems associated with the modelling and optimisations of systems under highly variable workloads were then addressed by a novel technique that approximated these workloads with simpler mathematical representations, without losing any of their pertinent original properties. Finally, we obtain advance queuing metrics (such as the variance of key measurements like waiting time and slowdown that are difficult to obtain analytically) through rigorous simulation.

Agents in most types of societies use information about potential partners to determine whether to form mutually beneficial partnerships. We can say that when this information is used to decide to form a partnership that one agent trusts another, and when agents work together for mutual benefit in a partnership, we refer to this as a form of cooperation. Current multi-robot teams typically have the team's goals either explicitly or implicitly encoded into each robot's utility function and are expected to cooperate and perform as designed. However, there are many situations in which robots may not be interested in full cooperation, or may not be capable of performing as expected. In addition, the control strategy for robots may be fixed with no mechanism for modifying the team structure if teammate performance deteriorates. This dissertation investigates the application of trust to multi-robot teams. This research also addresses the problem of how cooperation can be enabled through the use of incentive mechanisms. We posit a framework wherein robot teams may be formed dynamically, using models of trust. These models are used to improve performance on the team, through evolution of the team dynamics. In this context, robots learn online which of their peers are capable and trustworthy to dynamically adjust their teaming strategy. We apply this framework to multi-robot task allocation and patrolling domains and show that performance is improved when this approach is used on teams that may have poorly performing or untrustworthy members. The contributions of this dissertation include algorithms for applying performance characteristics of individual robots to task allocation, methods for monitoring performance of robot team members, and a framework for modeling trust of robot team members. This work also includes experimental results gathered using simulations and on a team of indoor mobile robots to show that the use of a trust model can improve performance on multi-robot teams in the patrolling task.

The task assignment problem is one of the fundamental combinatorial optimization problems. It has been extensively studied in operation research, management science, computer science and robotics. Task assignment problems arise in various applications of multi-robot systems (MRS), such as environmental monitoring, disaster response, extraterrestrial exploration, sensing data collection and collaborative autonomous manufacturing. In these MRS applications, there are realistic constraints on robots and tasks that must be taken into account both from the modeling perspective and the algorithmic perspective. From the modeling aspect, such constraints include (a) Task group constraints: where tasks form disjoint groups and each robot can be assigned to at most one task in each group. One example of the group constraints comes from tightly-coupled tasks, where multiple micro tasks form one tightly-coupled macro task and need multiple robots to perform each simultaneously. (b) Task deadline constraints: where tasks must be assigned to meet their deadlines. (c) Dynamically-arising tasks: where tasks arrive dynamically and the payoffs of future tasks are unknown. Such tasks arise in scenarios like searchrescue, where new victims are found dynamically. (d) Robot budget constraints: where the number of tasks each robot can perform is bounded according to the resource it possesses (e.g., energy). From the solution aspect, there is often a need for decentralized solution that are implemented on individual robots, especially when no powerful centralized controller exists or when the system needs to avoid single-point failure or be adaptive to environmental changes. Most existing algorithms either do not consider the above constraints in problem modeling, are centralized or do not provide formal performance guarantees. In this thesis, I propose methods to address these issues for two classes of problems, namely, the constrained linear assignment problem and constrained generalized assignment problem. Constrained linear assignment problem belongs to P, while constrained generalized assignment problem is NP-hard. I develop decomposition-based distributed auction algorithms with performance guarantees for both problem classes. The multi-robot assignment problem is decomposed into an optimization problem for each robot and each robot iteratively solving its own optimization problem leads to a provably good solution to the overall problem. For constrained linear assignment problem, my approaches provides an almost optimal solution. For constrained generalized assignment problem, I present a distributed algorithm that provides a solution within a constant factor of the optimal solution. I also study the online version of the task allocation problem with task group constraints. For the online problem, I prove that a repeated greedy version of my algorithm gives solution with constant factor competitive ratio. I include simulation results to evaluate the average-case performance of the proposed algorithms. I also include results on multi-robot cooperative package transport to illustrate the approach.

This thesis focuses on the development of a distributed software project planning tool that enables more than one participant to prepare the different parts of the project scope, schedule and task assignment by allowing to utilize the predefined organizational level processes. For this purpose, we discuss the need for a distributed software project planning tool, identify tool requirements and compare available tools with respect to the requirements. In addition, we evaluate the tool based on two criteria: first one is the tool&rsquo / s adequacy to meet the identified functional attributes and the second one is the validation of the tool by utilizing the data of the project schedule of a real project. This tool enables preparation of project scope, schedule and task assignments in a more effective, accurate and seamless way.

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Robots are increasingly utilized in industry for their capability to perform repetitive,complex tasks in environments unsuitable for humans. This surge in robotic applicationshas spurred research into Multi-Robot Systems (MRS), which aim to tackle complex tasksrequiring collaboration among multiple robots, thereby boosting overall efficiency. However,MRS introduces multifaceted challenges that span various domains, including robot perception,localization, task assignment, communication, and control. This dissertation delves into theintricate aspects of task assignment and path planning within MRS.The first area of focus is on multi-robot navigation, specifically addressing the limitationsinherent in current Multi-Agent Path Finding (MAPF) models. Traditional MAPF solutionstend to oversimplify, treating robots as holonomic units on grid maps. While this approachis impractical in real-world settings where robots have distinct geometries and kinematicrestrictions, it is important to note that even in its simplified form, MAPF is categorized as anNP-hard problem. The complexity inherent in MAPF becomes even more pronounced whenextending these models to non-holonomic robots, underscoring the significant computationalchallenges involved. To address these challenges, this thesis introduces a novel MAPF solverdesigned for non-holonomic, heterogeneous robots. This solver integrates the hybrid A*algorithm, accommodating kinematic constraints, with a conflict-based search (CBS) for efficientconflict resolution. A depth-first search approach in the conflict tree is utilized to accelerate theidentification of viable solutions.The second research direction explores synergizing task assignment with path-finding inMRS. While there is substantial research in both decentralized and centralized task assignmentstrategies, integrating these with path-finding remains underexplored. This dissertation evaluatesdecoupled methods for sequentially resolving task assignment and MAPF challenges. Oneproposed method combines the Hungarian algorithm and a Traveling Salesman Problem (TSP)solver for swift, albeit suboptimal, task allocation. Subsequently, robot paths are generatedindependently, under the assumption of collision-free navigation. During actual navigation, aNonlinear Model Predictive Controller (NMPC) is deployed for dynamic collision avoidance. Analternative approach seeks optimal solutions by conceptualizing task assignment as a MultipleTraveling Salesman Problem (MTSP), solved using a simulated annealing algorithm. In tandem,CBS is iteratively applied to minimize the cumulative path costs of the robots.

This contribution discusses a new control concept (compare Boden et al., 2021) that allows transport load exchange in hybrid AMHS. It focuses on vehicles able to perform load exchange dynamically by splitting transport requests into sub-tasks determined ad-hoc depending on the current system status. Unlike conventional control approaches for hybrid AMHS, our dispatching approach does not rely on high-level control rules which statically split transport tasks in advance.