The economics of greenhouse gas accumulation: A simulation approach

This article investigates efficient policies against global warming in the case of multiple greenhouse gases. In a dynamic optimization model conditions for an efficient combination of greenhouse gases are derived. The model is empirically specified and adapted to a simulation approach. By various simulation runs, the economics of greenhouse gas accumulation are illuminated; and in particular, it is shown that a CO2-policy alone would most likely lead to an allocation far from efficiency. These results indicate, that policy measures against global warming should allow for substituting between different greenhouse gases. Such a policy would mainly affect the agricultural sector because livestock and intensive farming techniques contribute significantly to the emission of greenhouse gases.

Constrained Optimization in Random Simulation:Efficient Global Optimization and Karush-Kuhn-Tucker Conditions

We develop a novel method for solving constrained optimization problems in random (or stochastic) simulation; i.e., our method minimizes the goal output subject to one or more output constraints and input constraints. Our method is indeed novel, as it combines the Karush-Kuhn-Tucker (KKT) conditions with the popular algorithm called "effciient global optimization" (EGO), which is also known as "Bayesian optimization" and is related to “active learning". Originally, EGO solves non-constrained optimization problems in deterministic simulation; EGO is a sequential algorithm that uses Kriging (or Gaussian process) metamodeling of the underlying simulation model, treating the simulation as a black box. Though there are many variants of EGO - for these non-constrained deterministic problems and for variants of these problems - none of these EGO-variants use the KKT conditions - even though these conditions are well-known (first-order necessary) optimality conditions in white-box problems. Because the simulation is random, we apply stochastic Kriging. Furthermore, we allow for variance heterogeneity and apply a popular sample allocation rule to determine the number of replicated simulation outputs for selected combinations of simulation inputs. Moreover, our algorithm can take advantage of parallel computing. We numerically compare the performance of our algorithm and the popular proprietary OptQuest algorithm, in two familiar examples (namely, a mathematical toy example and a practical inventory system with a service-level constraint); we conclude that our algorithm is more efficient (requires fewer expensive simulation runs) and effective (gives better estimates of the true global optimum)

Analysis of the effect of parameter variation on a dynamic cost function for distributed energy resources : a DER-CAM case study

Abstract: This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function, which optimizes the allocation of distributed energy resources for a mid-rise apartment building. This is achieved by comparison of parameter optimization results for both a high- and low-level optimizer respectively. The optimization process is carried out using the following approach: (1) a two-objective function is constructed with one objective function similar to that of the high-level optimizer (DER-CAM); (2) an evolutionary algorithm (EA) with modified selection capability is used to optimize the two-objective function problem in (1) for 4 selected cases of DER utilization previously optimized in DER-CAM. (3) the optimization results of the low-level optimizer are compared with the outcome of DER-CAM optimization for the 4 selected cases. This is done to establish the capability of DER-CAM as an effective tool for optimal distributed energy resource allocation. Results obtained demonstrate the effect of load shifting and solar photovoltaic (PV) panels with power exporting capability on the optimization of the cost function. The Pareto-based MOEA approach has also proved to be effective in observing the interactions between objective function parameters. Mean inverted generational distance (MIGD) values obtained over 10 runs for each of the 4 cases considered show that a DER combination of PV panel, battery storage, heat pump and load shifting outperforms the other strategies in 70% of the total simulation runs

Conflicts and resolutions in managing water allocation at the watershed scale

Multiple runs of a river basin model produced information about water allocation under different users’ priorities, creating a set of allocation scenarios as possible decision alternatives. To identify the most desired scenario that will, expectedly, be more readily accepted and implemented, involvement of stakeholders and reaching the consensus among them in evaluating scenarios are essential. This article describes methodology for integrating multi-criteria optimization as an efficient tool for the evaluation of scenarios in a group context, with river basin simulation-optimization models. Methodology was developed within the scope of the bilateral project Serbia–Portugal, and it consisted of five phases: defining the preference schemes of allocation, running the ACQUANET model, evaluating the criteria and strategies with analytic hierarchy process, aggregation and initial search for consensus in subgroups, and obtaining the final consensus converged result (best management strategy). The approach was tested on the water allocation problem in the Nadela watershed in Vojvodina Province in Serbia, with participation of 23 stakeholders. Promising results recommended the approach for the testing in different conditions in the area near Bragança in northeast Portugal (Sabor watershed).info:eu-repo/semantics/publishedVersio

A simheuristic algorithm for solving an integrated resource allocation and scheduling problem

Modern companies have to face challenging configuration issues in their manufacturing chains. One of these challenges is related to the integrated allocation and scheduling of resources such as machines, workers, energy, etc. These integrated optimization problems are difficult to solve, but they can be even more challenging when real-life uncertainty is considered. In this paper, we study an integrated allocation and scheduling optimization problem with stochastic processing times. A simheuristic algorithm is proposed in order to effectively solve this integrated and stochastic problem. Our approach relies on the hybridization of simulation with a metaheuristic to deal with the stochastic version of the allocation-scheduling problem. A series of numerical experiments contribute to illustrate the efficiency of our methodology as well as their potential applications in real-life enterprise settings

VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared

Autonomic State Management for Optimistic Simulation Platforms

We present the design and implementation of an autonomic state manager (ASM) tailored for integration within optimistic parallel discrete event simulation (PDES) environments based on the C programming language and the executable and linkable format (ELF), and developed for execution on x8664 architectures. With ASM, the state of any logical process (LP), namely the individual (concurrent) simulation unit being part of the simulation model, is allowed to be scattered on dynamically allocated memory chunks managed via standard API (e.g., malloc/free). Also, the application programmer is not required to provide any serialization/deserialization module in order to take a checkpoint of the LP state, or to restore it in case a causality error occurs during the optimistic run, or to provide indications on which portions of the state are updated by event processing, so to allow incremental checkpointing. All these tasks are handled by ASM in a fully transparent manner via (A) runtime identification (with chunk-level granularity) of the memory map associated with the LP state, and (B) runtime tracking of the memory updates occurring within chunks belonging to the dynamic memory map. The co-existence of the incremental and non-incremental log/restore modes is achieved via dual versions of the same application code, transparently generated by ASM via compile/link time facilities. Also, the dynamic selection of the best suited log/restore mode is actuated by ASM on the basis of an innovative modeling/optimization approach which takes into account stability of each operating mode with respect to variations of the model/environmental execution parameters

Evaluation of planning policies for marshalling track allocation using simulation

Planning the operational procedures in a railway marshalling yard is a complex problem. When a train arrives at a marshalling yard, it is uncoupled on an arrival yard and then its cars are rolled to a classification yard. All cars should eventually be rolled to the classification track that has been assigned to the train they’re supposed to depart with. However, there is normally not enough capacity to compound all trains at once. In Sweden, cars arriving before a track has been assigned to their train can be stored on separate tracks called mixing tracks. All cars on mixing tracks will be pulled back to the arrival yard, and then rolled to the classification yard again to allow for reclassification. Today all procedures are planned by experienced dispatchers, but there are no documented strategies or guidelines for efficient manual planning. The aim of this paper is to examine operational planning strategies that could help dispatchers find a feasible marshalling schedule that minimizes unnecessary mixing. In order to achieve this goal, two different online planning strategies have been tested using deterministic and stochastic simulation. The Hallsberg marshalling yard was used as a case study, and was simulated for the time period between December 2010 and May 2011. The first tested strategy simply assigns tracks to trains on a first come-first served basis, while the second strategy uses time limits to determine when tracks should be assigned to departing trains. The online planning algorithms have been compared with an offline optimized track allocation. The results from both the deterministic and the stochastic simulation show that the optimized allocation is better than all online strategies and that the second strategy with a time limit of 32 hours is the best online method