Assignment problem

About: Assignment problem is a research topic. Over the lifetime, 7588 publications have been published within this topic receiving 172820 citations. The topic is also known as: marriage problem.

Pareto-Optimization for Scheduling of Crude Oil Operations in Refinery via Genetic Algorithm

Abstract: With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems.

A faster path-based algorithm for traffic assignment

Abstract: This paper takes a fresh look at the arguments against path-enumeration algorithms for the traffic assignment problem and provides the results of a gradient projection method The motivation behind the research is the orders of magnitude improvement in the availability of computer storage over the last decade Faster assignment algorithms are necessary for real-time traffic assignment under several of the proposed Advanced Traffic Management System (ATMS) strategies, and path-based solutions are preferred Our results show that gradient projection converges in 1/10 fewer iterations than the conventional Frank-Wolfe algorithm The computation time improvement is of the same order for small networks, but reduces as the network size increases We discuss the computer implementation issues carefully, and provide schemes to achieve a 10-fold speed-up for larger networks also We have used the algorithm for networks of up to 2000 nodes on a typical computer work station, and we discuss certain data structures to save storage and solve the assignment problem for even a 5000 node network

A unified framework and algorithm for (T/F/C)DMA channel assignment in wireless networks

Abstract: Channel assignment problems in the time, frequency and code domains have thus far been studied separately. Exploiting the similarity of "constraints" that characterize assignments within and across these domains, we introduce the first unified framework for the study of assignment problems. Our framework identifies eleven atomic constraints underlying most current and potential assignment problems, and characterizes a problem as a combination of these constraints. Based on this framework, we present a unified algorithm for efficient (T/F/C)DMA channel assignments to nodes or to inter-nodal links in a (multihop) wireless network. The algorithm is parametrized to allow for tradeoff-selectable use as three different variants called random (RAND) ordering, minimum neighbors first (MNF), and progressive minimum neighbors first (PMNF). Using theoretical analysis, we show that the worst-case performance guarantee of PMNF is an order of magnitude better than that of the traditional RAND and MNF for most networks. We also experimentally study the relative performance for one node and one link assignment problem. We observe that PMNF performs the best, and that a larger fraction of unidirectional links degrades the performance in general.

FSA-based link assignment and routing in low-earth orbit satellite networks

Abstract: We propose a new framework for the link assignment (i.e., topological design) problem that arises from the use of intersatellite links (ISL's) in low-earth orbit (LEO) satellite networks. In the proposed framework, we model an LEO satellite network as a finite state automaton (FSA), where each state corresponds to an equal-length interval in the system period of the LEO satellite network. This FSA-based framework allows the link assignment problem in LEO satellite networks to be treated as a set of link assignment problems in fixed topology networks. Within this framework, we study various link assignment and routing schemes. In particular, both regular link assignment and link assignment optimized by simulated annealing are considered. For each link assignment, both static and dynamic routing schemes are considered. Our simulation results show that the optimized link assignment combined with static routing achieves the best performance in terms of both newly initiated call blocking probability and ongoing call blocking probability. The results also show that when the link assignment is the same, static routing gives better performance than dynamic routing since the latter requires a substantial amount of time to stabilize its routing table after a state transition.

Maximum likelihood method for probabilistic multihypothesis tracking

Abstract: In a multi-target multi-measurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. In this paper, a strictly probabilistic approach to the measurement-to-track assignment problem is taken. Measurements are not assigned to tracks as in traditional multi-hypothesis tracking (MHT) algorithms; instead, the probability that each measurement belongs to each track is estimated using a maximum likelihood algorithm derived by the method of Expectation-Maximization. These measurement-to-track probability estimates are intrinsic to the multi-target tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. Unlike MHT algorithms, the PMHT algorithm does not maintain explicit hypothesis lists. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning.