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

TL;DR: The results show that the SLDSSA algorithm can provide competitive results to the comparative algorithms, effectively solve job shop scheduling problems and deal with the interference caused by dynamic events.

TL;DR: This paper develops a deep reinforcement learning algorithm for dynamic distributed job shop scheduling with transfers, modeling the problem as a Markov decision process and employing five DRLs to minimize mean tardiness under random job arrivals and transportation constraints.

TL;DR: This paper proposes a constraint programming-based iterated greedy algorithm for the open shop scheduling problem with sequence-dependent processing times, outperforming exact models and existing approximate procedures, and effectively solving large-sized instances with reasonable computational effort.

TL;DR: In this article , the authors propose a real-time scheduling and control system for flexible job shop JIT production, which enables jobs to go smoothly between shops on a production line by completing jobs in the upstream shop just in time (JIT) for the downstream shop.

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.