Responsible tourism in Indonesia: multiple choices, open ended answers

Peer Reviewe

Responsible tourism in Indonesia: multiple choices, open ended answers

Peer Reviewe

Using Simulation to Assess the Opportunities of Dynamic Waste Collection

In this paper, we illustrate the use of discrete event simulation to evaluate how dynamic planning methodologies can be best applied for the collection of waste from underground containers. We present a case study that took place at the waste collection company Twente Milieu, located in The Netherlands. Even though the underground containers are already equipped with motion sensors, the planning of container emptying’s is still based on static cyclic schedules. It is expected that the use of a dynamic planning methodology, that employs sensor information, will result in a more efficient collection process with respect to customer satisfaction, profits, and CO2 emissions. In this research we use simulation to (i) evaluate the current planning methodology, (ii) evaluate various dynamic planning possibilities, (iii) quantify the benefits of switching to a dynamic collection process, and (iv) quantify the benefits of investing in fill‐level sensors. After simulating all scenarios, we conclude that major improvements can be achieved, both with respect to logistical costs as well as customer satisfaction

Formally Verified Space-Safety for Program Transformations

Existing work on compilers has often primarily concerned itself with preserving behavior, but programs have other facets besides their observable behavior. We expect that the performance of our code is preserved and bettered by the compiler, not made worse. Unfortunately, that\u27s exactly what sometimes occurs in modern optimizing compilers. Poor representations or incorrect optimizations may preserve the correct behavior, but push that program into a different complexity class entirely. We\u27ve seen such blowups like this occurring in practice, and many transformations have pitfalls which can cause issues. Even when a program is not dramatically worsened, it can cause the program to use more resources than expected, causing issues in resource-constrained environments, and increasing garbage-collection pauses. While several researchers have noticed potential issues, there have been a relative dearth of proofs for space-safety, and none at all concerning non-local optimizations. This work expands upon existing notions of space-safety, allowing them to be used to reason about long-running programs with both input and output, while ensuring that the program maintains some temporal locality of space costs. In addition, this work includes new proof techniques which can handle more dramatic shifts in the program and heap structure than existing methods, as well as more frequent garbage collection. The results are formalized in Coq, including a proof of space-safety for lifting data up in scope, which increases sharing and saves duplicate work, but may also catastrophically increase space usage, if done incorrectly

Collecting More Garbage

We present a method, adapted to polymorphically typed functional languages, to detect and collect more garbage than existing GCs. It can be applied to strict or lazy higher order languages and to several garbage collection schemes. Our GC exploits the information on utility of arguments provided by polymorphic types of functions. It is able to detect garbage that is still referenced from the stack and may collect useless parts of otherwise useful data structures. We show how to partially collect shared data structures and to extend the type system to infer more precise information. We also present how this technique can plug several common forms of space leaks