State Regulation of Open-Access, Common-Pool Resources.

Open-access, common-pool resources, such as many fisheries, aquifers, oil pools, and the atmosphere, often require some type of regulation of private access and use to avoid wasteful exploitation. This paper summarizes the arguments and literature associated with this problem. The historical and contemporary record of open-access resources is not a happy one, and many of the problems persist, despite large aggregate gains from resolving them. The discussion here suggests why that is the case. The paper focuses on government responses to the common pool, the private and political negotiations underlying them, and the information and transaction costs that influence the design of property rights and regulatory policies. Understanding the type of institution that emerges and its effects on the commons depends upon identifying the key parties involved, their objectives, and their political influence. Further, it requires detailed analysis of the bargaining that occurs within and across groups. The paper summarizes the open-access problem and provides case analyses of regulation of common-pool fisheries, oil reservoirs, and the atmosphere. The final section summarizes the general themes and the advantages of the New Institutional Economics (NIE) approach to analyzing the common pool.

Global supply chains of high value low volume products

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Operational Research in Education

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

A linear programming based heuristic framework for min-max regret combinatorial optimization problems with interval costs

This work deals with a class of problems under interval data uncertainty, namely interval robust-hard problems, composed of interval data min-max regret generalizations of classical NP-hard combinatorial problems modeled as 0-1 integer linear programming problems. These problems are more challenging than other interval data min-max regret problems, as solely computing the cost of any feasible solution requires solving an instance of an NP-hard problem. The state-of-the-art exact algorithms in the literature are based on the generation of a possibly exponential number of cuts. As each cut separation involves the resolution of an NP-hard classical optimization problem, the size of the instances that can be solved efficiently is relatively small. To smooth this issue, we present a modeling technique for interval robust-hard problems in the context of a heuristic framework. The heuristic obtains feasible solutions by exploring dual information of a linearly relaxed model associated with the classical optimization problem counterpart. Computational experiments for interval data min-max regret versions of the restricted shortest path problem and the set covering problem show that our heuristic is able to find optimal or near-optimal solutions and also improves the primal bounds obtained by a state-of-the-art exact algorithm and a 2-approximation procedure for interval data min-max regret problems