Purpose The cost of pharmaceutical supply chain due to drug waste is one of the current major issues in health care. Drug waste associated with intravenous (IV) fluid form of medication is one of the crucial issues for many pharmacies. The purpose of this paper is to apply a cross-docking model to minimize the IV delivery lead time to reduce drug waste by scheduling staff in a local hospital’s inpatient pharmacy. Design/methodology/approach A mixed integer linear programming model is applied to the IV delivery system of a hospital. The parameters are selected based on the observations made in the inpatient pharmacy. Findings The result implies that cross-docking approach can be effectively applied to IV delivery system. In fact, the cross-docking optimization model employed in this case study reduces the IV delivery completion time of the inpatient pharmacy by 41 percent. Research limitations/implications The scope of this research is limited to the activities performed after IV preparation. Practical implications The application of cross-docking system in staff scheduling will be beneficial for health care organizations that aim to minimize medication waste. Originality/value The prime value of this study lies in the introduction of a cross-docking concept in an internal hospital ordering process. Cross-docking models are widely used in general supply chain systems; however, their application for specific activities inside hospitals is the novelty of this study, which can fill the research gap in terms of drug waste management within the inpatient pharmacy.

This paper presents a modeling and optimization of batch production based on layout, cutting and project scheduling problems by considering scenario planning. In order to solve the model, a novel genetic algorithm with an improvement procedure based on variable neighborhood search (VNS) is presented. Initially, the model is solved in small sizes using Lingo software and the combined genetic algorithm; then, the results are compared. Afterwards, the model is solved in large sizes by utilizing the proposed algorithm and simple genetic algorithm. The main findings of this paper show: 1) To prove the validity of the proposed method, a case study has been solved by employing the classical method (employing Lingo 11) and the results were compared to the ones developed by the proposed algorithm. Since the results are the same in both cases, the suggested algorithm is valid and able to achieve optimal and near-optimal solutions. 2) The combined genetic algorithm is more effective in achieving optimal boundaries and closer solutions in all cases compared to classical genetic algorithm. In other words, the main finding of this paper is a combined genetic algorithm to optimize batch production modeling problems, which is more efficient than the methods provided in the literature.