Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Modern heuristic techniques for combinatorial problems
New directions on agile methods: a comparative analysis
Proceedings of the 25th International Conference on Software Engineering
User Stories Applied: For Agile Software Development
User Stories Applied: For Agile Software Development
Agile Estimating and Planning
Empirical studies of agile software development: A systematic review
Information and Software Technology
Benders decomposition, Lagrangean relaxation and metaheuristic design
Journal of Heuristics
Matheuristics: Optimization, Simulation and Control
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
A computational study of exact knapsack separation for the generalized assignment problem
Computational Optimization and Applications
An Exact Algorithm for the Two-Dimensional Strip-Packing Problem
Operations Research
Conceptual scheduling model and optimized release scheduling for agile environments
Information and Software Technology
Sprint planning optimization in agile data warehouse design
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Multi-sprint planning and smooth replanning: An optimization model
Journal of Systems and Software
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Agile methods for software development promote iterative design and implementation. Most of them divide a project into functionalities, called user stories; at each iteration, often called a sprint, a subset of user stories are developed. The sprint planning phase is critical to ensure the project success, but it is also a difficult problem because several factors impact on the optimality of a sprint plan, e.g., the estimated complexity, business value, and affinity of the user stories to be included in each sprint. In this paper we present an approach for sprint planning based on an integer linear programming model. Given the estimates made by the project team and a set of development constraints, the optimal solution of the model is a sprint plan that maximizes the business value perceived by users. Solving to optimality the model by a general-purpose MIP solver, such as IBM Ilog Cplex, takes time and for some instances even finding a feasible solution requires too large computing times for an operational use. For this reason we propose an effective Lagrangian heuristic based on a relaxation of the proposed model and some greedy and exchange algorithms. Computational results on both real and synthetic projects show the effectiveness of the proposed approach.