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

TL;DR: This study addresses joint scheduling of parallel machines and AGVs with sequence-dependent setup times in a matrix workshop, proposing a mixed-integer linear programming model and an efficient discrete artificial bee colony algorithm to minimize makespan.

TL;DR: This paper jointly schedules AGVs and parallel machines in an automated electrode foil production factory, proposing a mixed-integer linear programming model and a discrete gray wolf optimization algorithm to minimize makespan, improving manufacturing system efficiency.

TL;DR: This paper proposes a population-based iterated greedy algorithm for the multi-AGV scheduling problem with unloading safety detection, incorporating a hyper-heuristic, two-stage destruction strategy, and local search to minimize total cost in a matrix manufacturing workshop.

TL;DR: This study proposes an improved artificial bee colony algorithm (IABC) for distributed permutation flowshop scheduling problems with peak power consumption, outperforming seven state-of-the-art algorithms with an average relative percentage increase of 1.

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.