This paper investigates the integrated inventory and transportation planning under flexible vehicle constraint. To offer better services at lower prices, more and more companies turn to outsource transportation functions to other professional service providers, namely 3rd party logistics companies. Under these vehicle rental arrangements, the number of vehicles is a decision variable instead of...

Project planner’s main goal is to complete project with minimal time as well as minimal cost. Much effort (e.g. resources which demand cost) is required to complete the project in shorter time. There is a trade-off relationship between activity duration and its resources. This study employed a GA based multi-objective approach for time-cost tradeoff using resources-time tradeoff as input. Combi...

Maintenance strategies are typically implemented by optimizing only the cost whilst the reliability of facility performance is neglected. This study proposes a novel algorithm using multi-objective particle swarm optimization (MOPSO) technique to evaluate the cost-reliability tradeoff in a flexible maintenance strategy based on non-dominant solutions. Moreover, a probabilistic model for regress...

Proactive Learning is a generalized form of active learning with multiple oracles exhibiting different reliabilities (label noise) and costs. We propose a general approach for Proactive Learning that explicitly addresses the cost vs. reliability tradeoff for oracle and instance selection. We formulate the problem in the PAC learning framework with bounded noise, and transform it into realizable...

One of the challenging problems in circuit implementations is finding the best state assignment for implementing a synchronous sequential circuit which are also represented as Finite State Machines. This problem, commonly known as State Assignment Problem (S.A.P.), has been studied extensively because of its importance in reducing the cost of implementation of circuits. The previous work on thi...

Massive classification, a classification task defined over a vast number of classes (hundreds of thousands or even millions), has become an essential part of many real-world systems, such as face recognition. Existing methods, including the deep networks that achieved remarkable success in recent years, were mostly devised for problems with a moderate number of classes. They would meet with sub...

In distributed storage systems reliability is achieved through redundancy stored at different nodes in the network. Then a data collector can reconstruct source information even though some nodes fail. To maintain reliability, an autonomous and efficient protocol should be used to repair the failed node. The repair process causes traffic and consequently transmission cost in the network. Recent...

Caching algorithms are usually described by the eviction method and analyzed using a metric of hit probability. Since contents have different importance (e.g. popularity), the utility of a high hit probability, and the cost of transmission can vary across contents. In this paper, we consider timer-based (TTL) policies across a cache network, where contents have differentiated timers over which ...

Deep Entity Matching (EM) is one of the core research topics in data integration. Typical existing works construct EM models by training deep neural networks (DNNs) based on samples with onehot labels. However, these sharp supervision signals labels harm generalization models, causing them to overfit and perform badly unseen datasets. To solve this problem, we first propose that challenge a wel...

Large-scale maintenance in industrial plants requires the entire shutdown of production units for disassembly, comprehensive inspection and renewal. It is an important process but causes high out-of-service cost. Therefore a good schedule for a shutdown and and an analysis of possible associated risks are crucial for the manufacturer. We derive models and algorithms for shutdown scheduling that...