Abstract
This chapter presents a general methodology for embodying simulation as part of a tree search procedure, as a technique for solving practical problems in combinatorial optimization. Target problems are either difficult to express as mixed integer optimization models, or have models which provide rather loose bounds; in both cases, traditional, exact methods typically fail. The idea then is to have tree search instantiating part of the variables in a systematic way, and for each particular instantiation-i.e., a node in the search tree-resort to a simulation for assigning values to the remaining variables; then, use the outcome of the simulation for evaluating that node in the tree. This method has been used with considerable success in gameplaying, but has received very limited attention as a tool for optimization. Nevertheless, it has great potential, either as a way for improving known heuristics or as an alternative to metaheuristics.We depart from repeated, randomized simulation based on problem-specific heuristics for applications in scheduling, logistics, and packing, and show how the systematic search in a tree improves the results that can be obtained. © Springer International Publishing Switzerland 2015.

Abstract

Abstract

Abstract
This paper discusses different models that can be used to construct composite indicators with both desirable and undesirable output indicators. Two approaches are considered. The first is an indirect approach, based on a traditional Data Envelopment Analysis model, requiring a prior transformation in the measurement scale of the undesirable outputs. The second is a direct approach, based on a directional distance function model. The use of a directional distance function allows for the accommodation of undesirable indicators in their original form. The main limitations of these approaches are discussed related to the data transformation in the case of the indirect approach and the possibility to obtain negative margin rates of substitution between the desirable and undesirable outputs in the case of the direct approach. These issues lead to the proposal of a new composite indicator model based on a directional distance function that overcomes the limitations associated with the existing approaches. The incorporation of information on the relative importance of individual indicators using weight restrictions is discussed. Proposed here is an enhanced formulation of weight restrictions, in the form of assurance regions type I, that reflects the relative importance of the indicators in percentage terms. The models are illustrated in the assessment of Brazilian hydropower plants and are suitable for any assessment involving the aggregation of key performance indicators whenever undesirable outputs are present.

Abstract
This chapter reports on a new solution approach for the multi-mode resource-constrained project scheduling problem (MRCPSP, MPS|jprec|Cmax). This problem type aims at the selection of a single activity mode from a set of available modes in order to construct a precedence and a (renewable and nonrenewable) resource-feasible project schedule with a minimal makespan. The problem type is known to be N P-hard and has been solved using various exact as well as (meta-)heuristic procedures. The new algorithm splits the problem type into a mode assignment and a single mode project scheduling step. The mode assignment step is solved by a satisfiability (SAT) problem solver and returns a feasible mode selection to the project scheduling step. The project scheduling step is solved using an efficient meta-heuristic procedure from literature to solve the resourceconstrained project scheduling problem (RCPSP). However, unlike many traditional meta-heuristic methods in literature to solve the MRCPSP, the new approach executes these two steps in one run, relying on a single priority list. Straightforward adaptations to the pure SAT solver by using pseudo boolean nonrenewable resourceconstraints has led to a high quality solution approach in a reasonable computational time. Computational results show that the procedure can report similar or sometimes even better solutions than found by other procedures in literature, although it often requires a higher CPU time. © Springer International Publishing Switzerland 2015.

Abstract
In this paper, an innovative bi-level model is developed for the interactions of the plug-in electric vehicle (PEV) parking lot (PL) with the upstream markets through an aggregator in the system. The preferences of the PEVs on using the PL are also considered in the study. The PEVs have a choice in participating in grid-to-vehicle or vehicle-to-grid modes with fixed or flexible amount of departed state of charge (SOC). The PL assigns various tariffs for different requirements of the PEVs. In this study, the mutual effect of the variation of PEV tariffs and the PL's behavior in the market is investigated. It is shown that the preferences of PEVs can significantly affect the PL's strategy. The changes of the equilibrium prices for PL and aggregator interaction are thoroughly analyzed in response to changes in PEV tariffs to illustrate their influence on their behavior.