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Assignment of a receivable arising from a business relation, bulk assignment, factoring The thesis deals with several selected questionable issues related to the assignment of a receivable arising from a business relation. I have chosen this theme because the Czech legal theory doesn't offer satisfactory answers to all of the questions that arise from this topic. The thesis seeks to analyse the selected issues and to offer reasonable solutions to the problems relating to the selected issues. The method of the thesis is based on a comprehensive analysis of the relevant Czech case law as well as the relevant literature. The realized legal conclusions are afterwards put into a broader context and critically evaluated. The thesis is composed of ten chapters. Chapter One defines aims of the thesis. Chapter Two is introductory and explains the basic concept of the assignment of a receivable under the Czech commercial law. Chapter Three contains a basic overview of the receivables which are generally assignable under the Czech commercial law including the assignment of future receivables. It also deals with the topic of another assignment of a future receivable before its origin which is in my opinion allowable. Chapter Four describes cases of prohibitions of an assignment of a receivable. The chapter is...

In this thesis, we defined a new assignment problem and named it as the artillery target assignment problem(ATAP). The artillery target assignment problem is about assigning artillery weapons to targets at different time instances while optimizing some objectives. Since decisions at a time instance may affect decisions at other time instances, solving this assignment problem is harder than the classical assignment problem. For constructing a solution approach, we defined a base case and some variations of the problem which reflects subproblems of the main problem. These sub-problems are investigated for possible solutions. For two of these sub-problems, genetic algorithm solutions with customized representations and genetic operators are developed. Experiments of these solutions and related results are presented in this thesis.

Less-than-truckload industry has a valuable potential for applications of operations research in two areas, network design and efficiency improvement within existing networks. This thesis focuses on the latter, specifically the less-than-truckload terminals where cross docking operations occur. The assignment of incoming trailers to inbound docks is one of the critical decisions that affect the performance of less-than-truckload terminals. This research reviews existing models in literature and introduces an optimal mixed integer quadratic model with the objective of generating assignments that are robust against variability in system parameters such as truck arrival and service times, terminal characteristics and trailer load content. The computational limitations of the optimal model are discussed. A dock assignment heuristic is developed to overcome the computational difficulties reported with the optimal model to solve realistic size problems. It is concluded that the heuristic is generally applicable and is robust against system variably. A dynamic dock assignment heuristic is later introduced to implement the decision process at real time. It is concluded that the dynamic dock assignment heuristic is also robust against system variability. The last part presents a case study that benchmarks the dynamic dock assignment heuristic and existing static assignments at a real terminal. The results show that the dynamic dock assignment heuristic outperforms the static assignment under system variability. Conclusions and future research areas are finally addressed in the last chapter.

Multi-robot task assignment (allocation) involves assigning robots to tasks in order to optimize the entire team’s performances. Until now, one of the most useful non-domain-specific ways to coordinate multi-robot systems is through task allocation mechanisms. This dissertation addresses the classic task assignment problems in which robots and tasks are eventually matched by forming a one-to-one mapping, and their overall performances (e.g., cost, utility, and risk) can be linearly summed. At a high level, this research emphasizes two facets of the multi-robot task assignment, including (1) novel extensions from classic assignment algorithms, and (2) completely newly designed task allocation methods with impressive new features. For the former, we first propose a strongly polynomial assignment sensitivity analysis algorithm as well as a means to measure the assignment uncertainties; after that we propose a novel method to address problems of multi-robot routing and formation morphing, the trajectories of which are obtained from projections of augmenting paths that reside in a new three-dimensional interpretation of embedded matching graphs. For the latter, we present two optimal assignment algorithms that are distributable and suitable for multi-robot task allocation problems: the first one is an anytime assignment algorithm that produces non-decreasing assignment solutions along a series of task-swapping operations, each of which updates the assignment configurations and thus can be interrupted at any moment; the second one is a new market-based algorithm with a novel pricing policy: in contrast to the buyers’ “selfish” bidding behaviors in conventional auction/market-based approaches, we employ a virtual merchant to strategically escalate market prices in order to reach a state of equilibrium that satisfies both the merchant and buyers. Both of these newly developed assignment algorithms have a strongly polynomial running time close to the benchmark algorithms but can be easily decentralized in terms of computation and communication.

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This work proposes a distributed dynamic channel assignment of multi-channel MAC protocol in Ad-Hoc networks. Major characteristics of the proposed protocol are: (a) each mobile node is equipped with two network interfaces, (b) no time synchronization is needed, (c) distribution channel assignment. The proposed protocol reduces the cost of channel negotiation by considering the property that a connection generates multiple frames for transmitting and can assign channel information faster. Compared with other multi-channel MAC protocols, the proposed algorithm allows the whole network channel allotment be stable quickly. The performance evaluation is conducted on NS-2. Simulation results show that the proposed protocol can reduce the cost of channel negotiation significantly, increase the network throughput.

Traffic assignment is the last step in the conventional four-step transportation planning model, following trip generation, trip distribution, and mode choice. It concerns selection of routes between origins and destinations on the traffic network. Traditional traffic assignment methods do not consider trip chaining behavior. Since commuters always make daily trips in the form of trip chains, meaning a traveler’s trips are sequentially made with spatial correlation, it makes sense to develop models to feature this trip chaining behavior. Network performance in congested areas depends not only on the total daily traffic volume but also on the trip distribution over the course of a day. Therefore, this research makes an effort to propose a network traffic assignment framework featuring commuters’ trip chaining behavior. Travelers make decisions on their departure time and route choices under a capacity-constrained network. The modeling framework sequentially consists of an activity origin-destination (OD) choice model and a dynamic user equilibrium (DUE) traffic assignment model. A heuristic algorithm in an iterative process is proposed. A solution tells commuters’ daily travel patterns and departure distributions. Finally, a numerical test on a simple transportation network with simulation data is provided. In the numerical test, sensitivity analysis is additionally conducted on modeling parameters.

Internationella uppdrag är en utmanande upplevelse för de individer som åker för att arbeta för sitt företag i ett annat land. En utlandsstationering omfattar tre faser i individens karriärcykel - före resan, under vistelsen och slutligen efter hemkomsten. Uppsatsen utgår från en narrativ analys och metod, där intervjuer använts som teknik för insamlande av fyra utlandsstationerades berättelser. Studien inspireras av Kohonen (2005a, 2005b, 2007, 2008) som forskat på ledares identitetsrekonstruktion under utlandsstationering. I sin forskning finner hon stöd för att identiteten för de utlandsstationerade förändras, kulturellt eller professionellt, eller både professionellt och kulturellt men det finns individer vars identitet inte förändrats. Syftet med studien är att öka vår förståelse för utlandsstationerade individers identitetskonstruktion. Resultatet visar att identiteten, sett som en process, förändras för intervjupersonerna under utlandsstationeringens tre faser. Förändringen återfinns i en kulturell identitetstyp och i en professionell identitetstyp, förändringen återfinns även i en identitetstyp som är både professionell och kulturell på samma gång. Detta innebär att både den sociala omgivningen och professionella (personlig förändring) påverkar individen lika mycket. Intervjuperso­nernas berättelser visar att en utlandsstationering innebär både självreflektion och identitetsarbete.

Zip.ca is an online DVD rental company that faces two major operational problems: calculation of the assignment of DVDs to customers every thirty minutes throughout the day and purchasing of new inventory in regular intervals. In this thesis, we model these two problems and develop algorithms to solve them. In doing so, we encounter many theoretical problems that are both applicable to Zip’s operations and intrinsically interesting problems independent of the application. First, we note that the assignment problem facing Zip is inherently in an online setting. With returns of DVDs being processed throughout the day, the dataset is constantly changing. Although the ideal solution would be to wait until the end of the day to make decisions, physical work load capacities prevent this. For this reason we discuss two online problems, online 0-1 budgeted matching and the budgeted Adwords auction. We present a 1/(2 w_max/w_min)-competitive algorithm for the online 0-1 budgeted matching problem, and prove that this is the best possible competitive ratio possible for a wide class of algorithms. We also give a (1− (S+1)/(S+e) )-competitive algorithm for the budgeted Adwords auction as the size of the bids and cost get small compared to the budgets, where S is the ratio of the highest and lowest ratios of bids to costs. We suggest a linear programming approach to solve Zip’s assignment problem. We develop an integer program that models the B-matching instance with additional constraints of concern to Zip, and prove that this integer program belongs to a larger class of integer programs that has totally unimodular constraint matrices. Thus, the assignment problem can be solved to optimality every thirty minutes. We additionally create a test environment to check daily performance, and provide real-time implementation results, showing a marked improvement over Zip’s old algorithm. We show that Zip’s purchasing problem can be modeled by the matching augmentation problem defined as follows. Given a graph with vertex capacities and costs, edge weights, and budget C, find a purchasing of additional node capacity of cost at most C that admits a B-matching of maximum weight. We give a PTAS for this problem, and then present a special case that is polynomial time solvable that still models Zip’s purchasing problem, under the assumption of uniform costs. We then extend the augmentation idea to matroids and present matroid augmentation, matroid knapsack, and matroid intersection knapsack, three NP-hard problems. We give an FPTAS for matroid knapsack by dynamic programming, PTASes for the other two, and demonstrate applications of these problems.