C.C.S. Sin

Bio: C.C.S. Sin is an academic researcher from University of Waterloo. The author has contributed to research in topics: Scheduling (computing) & Scheduling (production processes). The author has an hindex of 1, co-authored 1 publications receiving 465 citations.

Approximating Permutations with Neural Network Components for Travelling Photographer Problem

Abstract: Most of current inference techniques rely upon Bayesian inference on Probabilistic Graphical Models of observations, and does prediction and classification on observations rather well. Event understanding of machines with observation inputs needs to deal with understanding of the relationship between sets of observations, and thus there is a crucial need to build models and come up with effective data structures to accumulate and organize relationships between observations. Given a set of states probabilisitcally-related with observations, this paper attempts to fit a permutation of states to a sequence of observation tokens (The Travelling Photographer Problem). We have devised a machine learning inspired architecture for randomized approximation of state permutation, facilitating parallelization of heuristic search of permutations. Our algorithm is able to solve The Travelling Photographer Problem with very small error. We demonstrate that by mimicking components of machine learning such as normalization, dropout, lambda layer with randomized algorithm, we are able to devise an architecture which solves TPP, a permutation NP-Hard problem. Other than TPP, we are also able to provide a 2-Local improvement heuristic for the Travelling Salesman Problem (TSP) with similar ideas.

A Review of Machine Scheduling: Complexity, Algorithms and Approximability

Abstract: The scheduling of computer and manufacturing systems has been the subject of extensive research for over forty years. In addition to computers and manufacturing, scheduling theory can be applied to many areas including agriculture, hospitals and transport. The main focus is on the efficient allocation of one or more resources to activities over time. Adopting manufacturing terminology, a job consists of one or more activities, and a machine is a resource that can perform at most one activity at a time. We concentrate on deterministic machine scheduling for which it is assumed that all data that define a problem instance are known with certainty.

BISON: a fast hybrid procedure for exactly solving the one-dimensional bin packing problem

Abstract: In this paper, we consider the well-known one-dimensional bin packing problem (BPP-1), which is to pack a given set of items having different sizes into a minimum number of equal-sized bins. For solving BPP-1, an exact hybrid solution procedure, called BISON, is proposed. It favourably combines the well-known meta-strategy tabu search and a branch and bound procedure based on known and new bound arguments and a new branching scheme. Computational results indicate that BISON is very effective and outperforms existing approaches.