In this paper, we consider a permutation flowshop scheduling problem with the total flow time as the schedule performance measure. A proactive-reactive approach is designed to simultaneously deal with stochastic disruptions (e.g., machine breakdowns) and dynamic events (e.g., newly arriving jobs and delay in job availability). In the proactive stage, the stochastic machine breakdown is hedged against the construction of a robust and stable baseline schedule. This schedule is either optimized by incorporating uncertainty into two surrogate measures or obtained by simulation. Robustness is measured by the expected schedule performance, while stability is measured by the aggregation of dissatisfactions of manager, shopfloor operator, and customers using the prospect theory. In the reactive stage, we assume that the stochastic and dynamic disruptions concurrently occur. Unlike the simple right-shifting method, a more effective rescheduling approach is proposed to balance the realized schedule performance with stability. A common issue in these two stages is the conflict between objectives. Thus, we propose a hybridization strategy that successfully enhances the classic Non-dominated Sorting Genetic Algorithm (NSGA-II and the hybridized algorithm outperforms NSGA-II, multiobjective evolutionary algorithm based on decomposition, and multiobjective memetic algorithms designed for deterministic scheduling problems. Finally, extensive computational studies on the Taillard flowshop benchmark instances are conducted to illustrate the effectiveness of the proposed proactive-reactive approach and the algorithm hybridization strategy.

Rapid changes in production environments have motivated researchers and industrial manufacturers to coordinate the production and distribution in supply chain management. This paper aims to address the permutation flow shop scheduling problem with batch delivery to multiple customers. In this problem, products are first manufactured in a permutation flow shop, and subsequently delivered to multiple customers in batches. To optimize the tradeoff between customer service and distribution cost, the objective of this paper is to minimize the total cost of tardiness and batch delivery. To deal with such optimization problem, two simple heuristics and a novel meta-heuristic (GA-TVNS) are developed to determine integrated production and distribution schedules. GA-TVNS hybridizes genetic algorithm and variable neighborhood search (VNS) to provide better exploration and exploitation in the search space. Moreover, to improve the local search of VNS, two new learning-based neighborhood structures are designed based on the classical school learning process of teaching-learning-based optimization. Computation experiments on both small-sized and large-sized test problems indicate that GA-TVNS performs the best among all the compared scheduling algorithms.