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

TL;DR: The paper proposes an advanced scheduling algorithm for highly autonomous production cells considering material flow and tool lifetime. The algorithm utilizes AGVs to optimize throughput and scheduling.

TL;DR: This study proposes a hybrid multi-objective improved meme algorithm for distributed green flexible job shop scheduling, integrating process priority crossover, universal crossover, and variable neighborhood search to minimize maximum completion time and total energy consumption.

TL;DR: In this article , a dual-attention network (DAN) is proposed to exploit the complicated relationships between operations and machines to construct production-adaptive operation and machine features to support high-quality decisionmaking.

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.