The delays that outpatients have to undergo before getting their medical treatment are often excessively long. The most commonly used patterns of scheduling appointments to patients are classified into three categories: (1) Pure Block Appointment systems, (2) Individual Appointment Systems, and (3) Mixed Block-Individual Appointment Systems. Several analytical studies have been concerned with the comparison of some of these appointment systems, and their highlights are described briefly. There are many other possible ways of assigning appointments to patients; one such way, referred to as the Two-at-a-Time Appointment System, is described. The advantages of the Two-at-a-Time Appointment System over any of the commonly used ones are discussed. Finally, the steady-state waiting time distribution functions that will correspond to the Two-at-a-Time Appointment System and the Individual Appointment System, as applied to the Wilmer Outpatient Clinic, have been obtained and are compared.

The allowed safety stock level reduction and accompanying reduction in average on-shelf inventory level are evaluated, for prescribed system performance requirements, as a function of mean leadtime reduction. The general simulation technique developed for this purpose allows the treatment of the s, S periodic review inventory problem with both demands and leadtime being stochastic. Some results obtained for a military supply system, where the reduction in leadtime is achieved by resorting to airlift rather than sealift, are presented. In addition, generalizations as to the effect of certain parameters on the reductions allowable for general situations are given. Finally, a simplified means of predicting these effects by an empirical method is indicated.

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Logistics networks are constantly evolving such that new and more varied structures arise and need to be studied. Carriers are aiming for opportunities to save costs by efficient planning. Motivated by this, we define the two-region multi-depot pickup and delivery problem. A region in this setting refers to an area where customers and depots are located. We differentiate two kinds of requests depending on whether their customers are located in the same region or not. Due to geographical characteristics, direct transportation between different regions is considered inefficient and a long-distance transportation mode needs to be used to connect them. Hence, we face a complex problem where interrelated decisions are to be made. We propose a decomposition into three subproblems, which relate to well-known problems in the literature. For solving the global problem, an adaptive large neighborhood search (ALNS) algorithm is developed. The algorithm mixes operators tailored to each of the different decisions of each subproblem. We demonstrate that these operators are efficient when applied to problems of their primal nature. In an extensive computational study, we show that the proposed ALNS dominates alternative ALNS schemes, where subproblems are treated sequentially. A detailed analysis of the solution convergence is provided. The proposed approach is a powerful tool to tackle complex decision problems in large distribution networks.