Emergency medical service (EMS) systems provide medical care and transportation. While many real-world systems use multiple vehicle types to attend different call priorities, few guidelines exist about which vehicles to allocate in multitiered responses where more than one vehicle is sent per call. This paper makes recommendations for multiple-unit dispatch to multiple call priorities based on simulation optimization and heuristics. The objective is to maximize the overall expected survival probability of patients classified as "life-threatening". We assume two types of medical units and three call priorities; and that information may be updated when the medical unit arrives on-scene. First, we study the optimal dispatching policies through several examples. Numerical results show that dispatching while considering call priorities, rather than dispatching the closest units, improves EMS system effectiveness. A heuristic algorithm is developed for large-scale problems. A comparison between the heuristic and closest policy is demonstrated using real-world data.
Modern factories have been moving toward just-in-time manufacturing paradigm. Optimal resource scheduling is therefore essential to minimize manufacturing cost and product delivery delay. This paper therefore focuses on scheduling multiple unrelated parallel machines, via Pareto approach. With the proposed strategy, additional realistic concerns are addressed. Particularly, contingencies regarding product dependencies as well as machine capacity and its eligibility are also considered. Provided a jobs list, each with a distinct resource work hour capacity, this novel scheduling is aimed at minimizing manufacturing costs, while maintaining the balance of machine utilization. To this end, different computational intelligence algorithms, i.e., adaptive nearest neighbour search and modified tabu search, are employed in turn and then benchmarked and validated against combinatorial mathematical baseline, on both small and large problem sets. The experiments reported herein were made on MATLAB™ software. The resultant manufacturing plans obtained by these algorithms are thoroughly assessed and discussed.
Abstract. The integrated transportation and production lot size problems is important effect to total cost of operation system for sugar factories. In this research, we formulate a mathematic model that combines these two problems as two stage stochastic programming model. In the first stage, we determine the lot size of transportation problem and allocate a fixed number of vehicles to transport sugarcane to the mill factory. Moreover, we consider an uncertainty of machine (mill) capacities. After machine (mill) capacities realized, in the second stage we determine the production lot size and make decision to hold units of sugarcane in front of mills based on discrete random variables of machine (mill) capacities. We investigate the model using a small size problem. The results show that the optimal solutions try to choose closest fields and lower holding cost per unit (at fields) to transport sugarcane to mill factory. We show the results of comparison of our model and the worst case model (full capacity). The results show that our model provides better efficiency than the results of the worst case model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.