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

TL;DR: A multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs is formulated and it is proved that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem.

TL;DR: In this article , the authors present a review of the literature about shop scheduling problems in manufacturing systems, revealing the concepts and methodologies that most impact the usage of scheduling theory in manufacturing environments.

TL;DR: Flexible job shop scheduling problem (FJSP) is a complex scheduling problem that involves intricate relationships between operations and machines. This article proposes a novel end-to-end learning framework that leverages self-attention models and DRL to address FJSP. The proposed approach outperforms traditional methods and achieves results comparable to exact methods in certain cases.

TL;DR: Zhang et al. as mentioned in this paper proposed a new multi-case fitness scheme, which creates multiple cases from a single scheduling simulation, thus achieving a better balance between the number of cases for lexicase selection and evaluation efficiency.

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.