Terrorism as Method in Nepali Maoist Insurgency, 1996–2016

During the period 1996–2006, the Communist Party of Nepal (Maoist) waged overt people’s war to seize state power and institute a new order that realized the party’s understanding of ‘New Democracy’ as posited by Mao Tse-tung. Contextual shifts led to a crucial strategic turning point in September 2005, when the Maoists agreed to a united front with estranged legal parties to oust the monarchy and establish a republic. Though touted as acceptance of political reintegration, the move was tactical rather than strategic. The party had no intention of supporting a parliamentary version of democracy and thus, 2006–2016, engaged in a covert effort to seize power. Central to this effort was the paramilitary Young Communist League (YCL), the members of which responded to inflammatory party verbiage and exhortations with attacks upon rival political actors. These attacks, academically and legally, were terrorism and offered a salient illustration of intra-state unrestricted warfare. Ultimately, organizational, national, and regional circumstances caused the main Maoist movement to move decisively away from its covert approach. By that time, however, radical splinters had embraced the use of terrorism against rival political actors, creating a situation whereby local politics is yet a dangerous endeavor in certain areas and at certain times.     

Expressions for item fill rates in periodic inventory systems

We examine the problem of estimating the item fill rate in a periodic inventory system. We show that the traditional expressions for line item fill rate, found in many operations management textbooks, perform well for high fill rates (above 90%), but they consistently underestimate the true fill rate. The problem of underestimation becomes significant as the fill rate falls below 90% and is greatly amplified in cases with very low fill rates (below 50%). We review other more accurate expressions for fill rate, discussing their relative merits. We then develop an exact fill rate expression that is robust for both high and low fill rates. We compare the new expression to others found in the literature via an extensive set of simulation experiments using data that reflect actual inventory systems found at Hewlett-Packard. We also examine the robustness of the expressions to violations in the underlying assumptions. Finally, we develop an alternative fill rate expression that is robust for cases of high demand variability where product returns are allowed. © 1995 John Wiley & Sons, Inc.     

An extension of the (szwarc) truck assignment problem

In this journal in 1967. Szware presented an algorithm for the optimal routing of a common vehicle fleet between m sources and n sinks with p different types of commodities. The main premise of the formulation is that a truck may carry only one commodity at a time and must deliver the entire load to one demand area. This eliminates the problem of routing vehicles between sources or between sinks and limits the problem to the routing of loaded trucks between sources and sinks and empty trucks making the return trip. Szwarc considered only the transportation aspect of the problem (i. e., no intermediate points) and presented a very efficient algorithm for solution of the case he described. If the total supply is greater than the total demand, Szwarc shows that the problem is equivalent to a (mp + n) by (np + m) Hitchcock transportation problem. Digital computer codes for this algorithm require rapid access storage for a matrix of size (mp + n) by (np + m); therefore, computer storage required grows proportionally to p². This paper offers an extension of his work to a more general form: a transshipment network with capacity constraints on all arcs and facilities. The problem is shown to be solvable directly by Fulkerson's out-of-kilter algorithm. Digital computer codes for this formulation require rapid access storage proportional to p instead of p². Computational results indicate that, in addition to handling the extensions, the out-of-kilter algorithm is more efficient in the solution of the original problem when there is a mad, rate number of commodities and a computer of limited storage capacity.