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

TL;DR: An adaptive discrete whale swarm algorithm (ADWSA) is proposed in this paper that outperforms the other algorithms in terms of finding the best solution to the distributed job-shop scheduling problem considering limited AGVs.

TL;DR: In this paper , a hybrid flexible flow shop scheduling problem with realistic features, such as job-dependent and sequence-dependent setup times, due dates, and waiting policies, is considered.

TL;DR: A dimension-aware gaining-sharing knowledge algorithm (DGSK) is presented to address the distributed hybrid flowshop scheduling problem with resource-dependent processing times (DHFSP-RDPT). The DGSK improves the performance by solving the problem in a multidimensional space and using a gain-sharing mechanism.

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.