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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Articles 1 - 9 of 9
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Distributing Complementary Resources Across Multiple Periods With Stochastic Demand, Shih-Fen Cheng, John Tajan, Hoong Chuin Lau
Distributing Complementary Resources Across Multiple Periods With Stochastic Demand, Shih-Fen Cheng, John Tajan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we evaluate whether the robustness of a market mechanism that allocates complementary resources could be improved through the aggregation of time periods in which resources are consumed. In particular, we study a multi-round combinatorial auction that is built on a general equilibrium framework. We adopt the general equilibrium framework and the particular combinatorial auction design from the literature, and we investigate the benefits and the limitation of time-period aggregation when demand-side uncertainties are introduced. By using simulation experiments, we show that under stochastic conditions the performance variation of the process decreases as the time frame length (time …
Spreadsheet Data Resampling For Monte-Carlo Simulation, Thin Yin Leong, Wee Leong Lee
Spreadsheet Data Resampling For Monte-Carlo Simulation, Thin Yin Leong, Wee Leong Lee
Research Collection School Of Computing and Information Systems
The pervasiveness of spreadsheets software resulted in its increased application as a simulation tool for business analysis. Random values generation supporting such evaluations using spreadsheets are simple and yet powerful. However, the typical approach to Monte-Carlo simulations, which is what simulations with stochasticity are called, requires significant amount of time to be spent on data collection, data collation, and distribution function fitting. In fact, the latter can be overwhelming for undergraduate students to learn and do properly in a short time. Resampling eliminates both the need to fit distributions to the sample data, and to perform the ensuing tests of …
Determining The Number Of Bp Neural Network Hidden Layer Units, Huayu Shen, Zhaoxia Wang, Chengyao Gao, Juan Qin, Fubin Yao, Wei Xu
Determining The Number Of Bp Neural Network Hidden Layer Units, Huayu Shen, Zhaoxia Wang, Chengyao Gao, Juan Qin, Fubin Yao, Wei Xu
Research Collection School Of Computing and Information Systems
This paper proposed an improved method to contrapose the problem which is difficult to determine the number of BP neural network hidden layer units. it is proved that the method is efficeient in reducing the frequency of the test through experients, and improves the efficiency of determining the best number of hidden units, which is more valuable in the applications.
Generating Robust Schedules Subject To Resource And Duration Uncertainties, Na Fu, Hoong Chuin Lau, Fei Xiao
Generating Robust Schedules Subject To Resource And Duration Uncertainties, Na Fu, Hoong Chuin Lau, Fei Xiao
Research Collection School Of Computing and Information Systems
We consider the Resource-Constrained Project Scheduling Problem with minimal and maximal time lags under resource and duration uncertainties. To manage resource uncertainties, we build upon the work of Lambrechts et al 2007 and develop a method to analyze the effect of resource breakdowns on activity durations. We then extend the robust local search framework of Lau et al 2007 with additional considerations on the impact of unexpected resource breakdowns to the project makespan, so that partial order schedules (POS) can absorb both resource and duration uncertainties. Experiments show that our proposed model is capable of addressing the uncertainty of resources, …
A Heuristic Method For Job-Shop Scheduling With An Infinite Wait Buffer: From One-Machine To Multi-Machine Problems, Z. J. Zhao, J. Kim, M. Luo, Hoong Chuin Lau, S. S. Ge
A Heuristic Method For Job-Shop Scheduling With An Infinite Wait Buffer: From One-Machine To Multi-Machine Problems, Z. J. Zhao, J. Kim, M. Luo, Hoong Chuin Lau, S. S. Ge
Research Collection School Of Computing and Information Systems
Through empirical comparison of classical job shop problems (JSP) with multi-machine consideration, we find that the objective to minimize the sum of weighted tardiness has a better wait property compared with the objective to minimize the makespan. Further, we test the proposed Iterative Minimization Micro-model (IMM) heuristic method with the mixed integer programming (MIP) solution by CPLEX. For multi-machine problems, the IMM heuristic method is faster and achieves a better solution. Finally, for a large problem instance with 409 jobs and 30 types of machines, IMM-heuristic method is compared with ProModel and we find that the heuristic method is slightly …
Dynamic Allocation Of Airline Check-In Counters: A Queueing Optimisation Approach, Mahmut Parlar, Sharafali Moosa
Dynamic Allocation Of Airline Check-In Counters: A Queueing Optimisation Approach, Mahmut Parlar, Sharafali Moosa
Research Collection Lee Kong Chian School Of Business
This paper was motivated by an observation in an international airport with regard to allocation of resources for check-in counters. In an exclusive check-in counter system, each flight has a dedicated number of counters that will be open until at least a half-hour before the scheduled departure of that flight. Currently, in many of the airports around the world, the decision to open or close check-in counters is done on an ad hoc basis by human schedulers. In doing so, the schedulers are almost always forced to perform a balancing act in meeting the quality of service stipulated by the …
Linear Relaxation Techniques For Task Management In Uncertain Settings, Pradeep Varakantham, Stephen F. Smith
Linear Relaxation Techniques For Task Management In Uncertain Settings, Pradeep Varakantham, Stephen F. Smith
Research Collection School Of Computing and Information Systems
In this paper, we consider the problem of assisting a busy user in managing her workload of pending tasks. We assume that our user is typically oversubscribed, and is invariably juggling multiple concurrent streams of tasks (or work flows) of varying importance and urgency. There is uncertainty with respect to the duration of a pending task as well as the amount of follow-on work that may be generated as a result of executing the task. The user’s goal is to be as productive as possible; i.e., to execute tasks that realize the maximum cumulative payoff. This is achieved by enabling …
Electric Elves: What Went Wrong And Why, Milind Tambe, Emma Bowring, Jonathan Pearce, Pradeep Reddy Varakantham, Paul Scerri, David V. Pynadath
Electric Elves: What Went Wrong And Why, Milind Tambe, Emma Bowring, Jonathan Pearce, Pradeep Reddy Varakantham, Paul Scerri, David V. Pynadath
Research Collection School Of Computing and Information Systems
Software personal assistants continue to be a topic of significant research interest. This article outlines some of the important lessons learned from a successfully-deployed team of personal assistant agents (Electric Elves) in an office environment. In the Electric Elves project, a team of almost a dozen personal assistant agents were continually active for seven months. Each elf (agent) represented one person and assisted in daily activities in an actual office environment. This project led to several important observations about privacy, adjustable autonomy, and social norms in office environments. In addition to outlining some of the key lessons learned we outline …
The Oil Drilling Model And Iterative Deepening Genetic Annealing Algorithm For The Traveling Salesman Problem, Hoong Chuin Lau, Fei Xiao
The Oil Drilling Model And Iterative Deepening Genetic Annealing Algorithm For The Traveling Salesman Problem, Hoong Chuin Lau, Fei Xiao
Research Collection School Of Computing and Information Systems
In this work, we liken the solving of combinatorial optimization problems under a prescribed computational budget as hunting for oil in an unexplored ground. Using this generic model, we instantiate an iterative deepening genetic annealing (IDGA) algorithm, which is a variant of memetic algorithms. Computational results on the traveling salesman problem show that IDGA is more effective than standard genetic algorithms or simulated annealing algorithms or a straightforward hybrid of them. Our model is readily applicable to solve other combinatorial optimization problems.