S
Sarang Kulkarni
Researcher at Indian Institute of Technology Bombay
Publications - 5
Citations - 42
Sarang Kulkarni is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Job shop scheduling & Flow network. The author has an hindex of 2, co-authored 5 publications receiving 27 citations. Previous affiliations of Sarang Kulkarni include Monash University & National Institute of Technology Calicut.
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Journal ArticleDOI
A new formulation and a column generation-based heuristic for the multiple depot vehicle scheduling problem
Sarang Kulkarni,Sarang Kulkarni,Sarang Kulkarni,Mohan Krishnamoorthy,Mohan Krishnamoorthy,Abhiram Ranade,Andreas T. Ernst,Rahul Patil +7 more
TL;DR: A new formulation for the multiple depot vehicle scheduling problem (MDVSP) that uses assignment arcs in a multi-commodity time-space network flow formulation and proposes a solution framework that solves the linear relaxation of the MDVSP iteratively.
Journal ArticleDOI
A new two-stage heuristic for the recreational vehicle scheduling problem
Sarang Kulkarni,Sarang Kulkarni,Sarang Kulkarni,Rahul Patil,Mohan Krishnamoorthy,Andreas T. Ernst,Abhiram Ranade +6 more
TL;DR: In this paper, a new formulation motivated by inventory planning is proposed to solve the RVSP problem, which uses the assignment arcs in a network structure, which is improved by aggregating nodes and arcs.
Journal ArticleDOI
A benchmark dataset for the multiple depot vehicle scheduling problem.
Sarang Kulkarni,Sarang Kulkarni,Sarang Kulkarni,Mohan Krishnamoorthy,Mohan Krishnamoorthy,Abhiram Ranade,Andreas T. Ernst,Rahul Patil +7 more
TL;DR: The dataset has been developed to evaluate the heuristics of the MDVSP that are presented in “A new formulation and a column generation-based heuristic for the multiple depot vehicle scheduling problem” (Kulkarni et al., 2018).
Proceedings ArticleDOI
A linear programming based iterative heuristic for the recreational vehicle scheduling problem
TL;DR: An iterative construction heuristic combined with an improvement heuristic based on the solution of the LP relaxation of the problem reduces infeasibility successively at each iteration and outperforms the subgradient optimisation for most of the datasets.
Proceedings ArticleDOI
A hybrid neural network- meta heuristics approach for permutation flow shop scheduling problems
TL;DR: The objective of this study is to find a sequence of jobs for the permutation flow shop to minimize makespan and the sequence obtained using neural network is used to generate initial population for genetic algorithm (ANN-GA), genetic algorithm using Random Insertion Perturbation Scheme and Simulated Annealing.