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

TL;DR: In this algorithm, new encoding and decoding schemes are designed to represent the three subproblems and transform the encoding into a feasible schedule, respectively and the proposed IGWO updates the new upper bounds of 13 difficult benchmark instances.

TL;DR: This study proposes a Q-learning-based hyper-heuristic evolutionary algorithm (QHHEA) for the distributed flexible job-shop scheduling problem with crane transportation, outperforming state-of-the-art algorithms in minimizing makespan and total energy consumption on 36 benchmark instances.

TL;DR: In this article , a mixed-integer linear programming model and three speed-up constraints are proposed to solve a dynamic scheduling problem in a U-shaped job shop robotic cell, where intermediate buffers are positioned between each pair of consecutive robots and each robotic arm has access to specific workstations based on their distance in the cell layout.

TL;DR: In this article, a reinforcement learning (RL) approach for the permutation flow shop problem with multiple lines and demand plans is presented, where actions denote the job type to be sequenced next.

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.