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

TL;DR: In this article , an adaptive flexible job shop rescheduling problem with real-time order acceptance (ROA) and condition-based preventive maintenance (CBPM) is addressed, and a multi-objective optimization model is developed for the concerned problem.

TL;DR: In this paper, a discrete version of the spotted hyena optimizer (DSHO) is proposed for solving the distributed job shop scheduling problem (DJSP), where a workload-based facility order mechanism and a greedy heuristic approach are combined with the DSHO algorithm.

TL;DR: In this article , a secure and energy-efficient communication multi-objective optimization problem (SECMOP) was formulated to circumvent the effects of the known and unknown eavesdroppers and minimize the propulsion energy consumption of UAVs, by optimizing the hovering positions and excitation current weights of the UAV and the scheduling for communicating with the remote ground users.

TL;DR: This paper addresses the distributed flexible job shop scheduling problem (DFJSP) with minimizing energy consumption and proposes an efficient hybrid shuffled frog-leaping algorithm (HSFLA) for solving DFJSP, particularly for large-sized problems.

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.