Mixed-integer linear programming and constraint programming formulations for solving distributed flexible job shop scheduling problem

TL;DR: An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop minimizes transportation costs by optimizing the number of AGVs, assigning tasks to AGVs, and determining the optimal sequence of tasks for each AGV.

TL;DR: The objective of this paper is to introduce a new DRL method for solving the flexible job-shop scheduling problem, particularly for large instances, based on the use of heterogeneous graph neural networks to a more informative graph representation of the problem.

TL;DR: This formulation of discrete linear programming seems, however, to involve considerably fewer variables than two other recent proposals and on these grounds may be worth some computer experimentation.

TL;DR: The goal of this paper is to develop models and methods that use complementary strengths of Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) techniques to solve problems that are otherwise intractable if solved using either of the two methods.

TL;DR: A mathematical model and heuristic approaches for flexible job shop scheduling problems (FJSP) are considered and it is concluded that the hierarchical algorithms have better performance than integrated algorithms and the algorithm which use tabu search and simulated annealing heuristics for assignment and sequencing problems consecutively is more suitable than the other algorithms.

TL;DR: After an exhaustive computational and statistical analysis it can be concluded that the proposed method shows an excellent performance overcoming the rest of the evaluated methods in a comprehensive benchmark set of instances.