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Emergency medical services (EMS) are among the most important services in any society due to their role in saving people's lives and reducing morbidities. The location of ambulance stations and the allocation of ambulances to the stations is an important planning problem for any EMS system to ensure adequate coverage while minimising the response time. This study considers a mixed-integer programming model that determines the ambulance locations by considering the time of day variations in demand. The presented model also considers heterogeneous performance measures based on survival function and coverage for different patient types with varying levels of urgency. A memetic algorithm based-approach that applies a mixed chromosome representation for solutions is proposed to solve the problem. Our computational results indicate that neglecting time-dependent variation of demand can underestimate the number of ambulances required by up to 15% during peak demand. We also demonstrate the effectiveness of the proposed solution approach in providing good quality solutions within a reasonable time. [ABSTRACT FROM AUTHOR]

This study considers a two-echelon system in which a vendor produces a product at a finite production rate and supplies it to several buyers facing independent normally-distributed demands. The product is delivered to the buyers using a set of different speed vehicles with identical capacity and different operating costs. The issues of lead time reduction and the service level constraint on the buyers have been incorporated in the model. A model is formulated to determine the optimal production–inventory policy, vehicle routes and vehicle type for each route by minimising the related production, inventory, lead time crashing and transportation costs of the system while satisfying the service level constraint on each buyer. A coordinated two-phase iterative approach is proposed to solve the model. Finally, a numerical example is included to describe the solution approach and illustrate the results. [ABSTRACT FROM PUBLISHER]

In this work, we study an integrated inventory-transportation problem in a supply chain consisting of region-bound warehouses located in different regions. The supply chain deals with multiple items that compete for storage space and transportation capacity with multi-modal transportation considering regional capacity constraint for each mode of transportation. The objective is to determine an optimal storage and transportation plan to satisfy the demand of all regions without shortages for known procurement plan for all items. The problem is formulated as a mixed integer programming (MIP) model for minimizing the total costs over a finite planning horizon. An MIP-based fix-and-optimize (F&O) heuristic with several decomposition schemes is proposed to solve the problem efficiently. The performance of the decomposition schemes is investigated against the structure of the sub-problems obtained. To enhance the performance, F&O is crossbred with two metaheuristics – genetic algorithm (GA) and iterated local search (ILS) separately, which lead to hybrid heuristic approach. Extensive numerical experiments are carried out to analyze the performance of the proposed solution methodology by randomly generating several problem instances built using data collected from the Indian Public Distribution System. The proposed solution approach is found to be computationally efficient and effective, and outperforming state of the art MIP solver Cplex for practical size problem instances. Also, the hybridization of F&O heuristic with GA and ILS boosts its performance although with a justified increase in the computational time. [ABSTRACT FROM AUTHOR]

This paper presents a production-inventory model in a single-vendor single-buyer system for decaying items which deplete with constant decay rate. It is assumed that the lead time demand follows a normal distribution and shortages are backordered. Also, buyer's lead time can be reduced at an added cost. A model has been formulated to find the optimal order quantity, lead time and the number of shipments from the vendor to the buyer during one production cycle while minimising the total expected cost per unit time of the vendor-buyer integrated system. It is often difficult to estimate the stock-out cost in inventory systems. Therefore, instead of having a stock-out term in the objective function, a service level constraint is included in the model. Due to mathematical complexities involved, it is difficult to establish the solutions analytically. An iterative procedure is developed to find the optimal solution using LINGO 10, and a numerical example is given to illustrate the results of the proposed model. Furthermore, the effects of key parameters are also studied to provide the insight into the structure of the solution. In addition, the savings of buyer and vendor are investigated from implementation of joint optimisation model over the model in which they minimise their own cost independently. [ABSTRACT FROM AUTHOR]