In many scheduling studies, researchers consider the processing times of jobs as constant numbers. This assumption sometimes is at odds with practical manufacturing process due to several sources of uncertainties arising from real-life situations. Examples are the changing working environments, machine breakdowns, tool quality variations and unavailability, and so on. In light of the phenomenon of scenario-dependent processing times existing in many applications, this paper proposes to incorporate scenario-dependent processing times into a two-machine flow-shop environment with the objective of minimizing the total completion time. The problem under consideration is never explored. To solve it, we first derive a lower bound and two optimality properties to enhance the searching efficiency of a branch-and-bound method. Then, we propose 12 simple heuristics and their corresponding counterparts improved by a pairwise interchange method. Furthermore, we set proposed 12 simple heuristics as the 12 initial seeds to design 12 variants of a cloud theory-based simulated annealing (CSA) algorithm. Finally, we conduct simulations and report the performances of the proposed branch-and-bound method, the 12 heuristics, and the 12 variants of CSA algorithm.

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In this paper, we propose a novel tabu search (NTS) algorithm that improves the efficiencies of picking goods of automated guided vehicles (AGVs) in an automatic warehouse by solving the conflicts that happen when multiple AGVs work at the same time. Relocation and exchanging operations are designed for the neighborhood searching process based on each pickup-point’s location in the warehouse, along with the initial solution generation and the termination condition in the proposed algorithm. The experimental results show that the tabu search algorithm can effectively optimize the order of pickup points, which could further reduce the total travel distance and improve the efficiencies of AGVs in automatic warehouses.

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