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Research Summary Safe harbor laws are designed to redirect young victims of commercial sexual exploitation away from justice system involvement by prohibiting their arrest and prosecution as criminals. A quasi‐experimental design was used to compare prostitution‐related crime and sex abuse maltreatment trends at the county level in states that have implemented safe harbor laws with a comparison group of counties in states that have not implemented safe harbor laws. Uniform Crime Reports (UCR) were used to measure prostitution‐related crime trends, while National Child Abuse and Neglect Data System (NCANDS) data were used to measure sex abuse maltreatment trends. We used a multilevel Poisson regression model to analyze the change in prostitution‐related crime and sex abuse maltreatment trends in treatment and comparison counties over an 11‐year observation period (2005–15). Policy Implications Overall, the findings provide a striking perspective into the current U.S. landscape of dealing with the commercial sexual exploitation of juveniles. To that end, the decline in the number of overall and juvenile arrests across all prostitution‐related offenses suggests that safe harbor laws were effective in redirecting young victims away from system involvement. But the systematic provision of treatment services envisioned to go to these young victims has, to date, not become a reality. Nevertheless, these findings offer policymakers a foundation of evidence that can be used to engage intelligently and knowledgeably with regard to the current state of U.S. policy.

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This paper discusses the use of the finite capacity planning model as a basis for a job-shop scheduling system. The suitability of this approach for constructing short term schedules with very short lead times is examined. This paper introduces a rule-based scheduling system whose operation, unlike previous monolithic schedulers, is based around distinct though interlinked processes. Scheduling herein is defined as the selection of a set of orders for manufacture, and the allocation of processing time to each on an acyclic network of processing resources. The objectives are, firstly, compliance with all customer deadlines and, secondly, the efficient utilisation of available machine time and prevailing machine setups.Key words : Finite capacity scheduling, rule-based systems. INTRODUCTIONProduction scheduling can be seen as forming a link between the areas of Computer Aided Design (CAD) andComputer Aided Manufacture (CAM), whose objectives are the implementation of all managerial directives relating to the manufacturing domain. Taking a more algorithmic approach, scheduling may be thought of as the problem of constructing a master production schedule (MPS) from a set of pending orders and a set of resource allocations. Early scheduling systems used priority dispatch rules 1 , forming a deterministic method whereby orders are not scheduled until certain constraints have been fulfilled. Later heuristic systems (e.g. XCON & ISIS 2) were intended more for medium or long term planning, rather than short term scheduling.Much work has also gone into the examination of Flexible Manufacturing Systems (FMS) -domains with multiple machines connected to an automated transportation system. This, however, only covers a fraction of manufacturing domains. This paper describes the development of a scheduling system for use in a manufacturing domain which is viewed as a directed graph, with nodes representing processing machines and edges between nodes representing the movement of material between these machines. Each order has a predetermined "route" which is a list of nodes to be visited by that order, the object of the scheduling system being to produce a schedule at very short notice (within 5 minutes) to suit the current factory status. The most important feature of this system is that orders which fall into a category called "urgent orders" must be manufactured promptly.Other aims are the efficient use of machine setups and maximum utilisation of the available resources. SCHEDULINGMany different systems have been developed to solve a variety of scheduling problems. The solution described in this paper differs from existing systems in many ways, and most of these differences are due to the need for reactive 3 scheduling.The much referenced XCON, which proved the applicability of expert system techniques to solving real world problems, would not prove a good model on which to base this project. XCON configured the