A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.Plan Estatal de Investigación Científica y Técnica y de Innovación (España

STOCHASTIC MULTI-ATTRIBUTE UTILITY MODEL

In real situations, the attribute value (mostly variable)can be best represented by introducing the finite numberof attribute values level, to which the correspondingprobabilities should also be attached. Stochastic Multi-Attribute Utility Model has the ability to analyze suchstochastic multi-attribute problems. The choice of one,from the set of available options, is made by choosingthe best option based on the maximum expected utilitystructure. In this paper, we will mention some argumentsfor the development of the Stochastic Multi-AttributeUtility Model, its advantages (they are closer to reality),disadvantages (analytically difficult technique, subjectiveassessments of the values of variable attributes), aswell as the process of solving the problem

An Approach to Evaluate a Supply Chain Network

Designing the supply chain network (SCN) is the first step to creating a chain sourcing for results. The process identifies the change that will differentiate an organization from its competitors, to contact a customer with a successful value proposition, reduce costs and boost profitability. The most effective way to ensure perfect fluidity is to appoint an employee responsible for supervising the entire process. The manager will inform and coordinate the activities of the heads of different departments, from shipping to sales, focusing on communication and identification of potential problems, as well as correcting faults before they lead to disruption. This paper proposes an evaluation approach for the supply chain network design problems under uncertainty. Existing approaches to this problem are either the deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution approach integrates both features; the collective evaluation and the selection of one

TRADE-OFF BETWEEN MULTIPLE CRITERIA IN SMART HOME CONTROL SYSTEM DESIGN

The successful automation of a smart home relies on the ability of the smart home control system to organize, process, and analyze different sources of information, according to several criteria. Because of variety of key design criteria that every smart home of the future should meet, the main challenge is the trade-off between them in uncertain environment. In this paper, a problem of smart home design has been solved using the methodology based on multiplicative form of multi-attribute utility theory. Aggregated functions describing different smart home alternatives are compared using stochastic dominance principle. The aggregation of different criteria has been performed through their numerical convolution, unlike usual approach of pairwise comparison, allowing only the additive form of aggregation of individual criteria. The methodology is illustrated on the smart home controller parameter setting

A Threat Assessment Model under Uncertain Environment

Threat evaluation is extremely important to decision makers in many situations, such as military application and physical protection systems. In this paper, a new threat assessment model based on interval number to deal with the intrinsic uncertainty and imprecision in combat environment is proposed. Both objective and subjective factors are taken into consideration in the proposed model. For the objective factors, the genetic algorithm (GA) is used to search out an optimal interval number representing all the attribute values of each object. In addition, for the subjective factors, the interval Analytic Hierarchy Process (AHP) is adopted to determine each object’s threat weight according to the experience of commanders/experts. Then a discounting method is proposed to integrate the objective and subjective factors. At last, the ideal of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to obtain the threat ranking of all the objects. A real application is used to illustrate the effectiveness of the proposed model

Pairwise comparison matrix in multiple criteria decision making

The measurement scales, consistency index, inconsistency issues, missing judgment estimation and priority derivation methods have been extensively studied in the pairwise comparison matrix (PCM). Various approaches have been proposed to handle these problems, and made great contributions to the decision making. This paper reviews the literature of the main developments of the PCM. There are plenty of literature related to these issues, thus we mainly focus on the literature published in 37 peer reviewed international journals from 2010 to 2015 (searched via ISI Web of science). We attempt to analyze and classify these literatures so as to find the current hot research topics and research techniques in the PCM, and point out the future directions on the PCM. It is hoped that this paper will provide a comprehensive literature review on PCM, and act as informative summary of the main developments of the PCM for the researchers for their future research. First published online: 02 Sep 201

Optimal Budget-Constrained Sample Allocation for Selection Decisions with Multiple Uncertain Attributes

A decision-maker, when faced with a limited and fixed budget to collect data in support of a multiple attribute selection decision, must decide how many samples to observe from each alternative and attribute. This allocation decision is of particular importance when the information gained leads to uncertain estimates of the attribute values as with sample data collected from observations such as measurements, experimental evaluations, or simulation runs. For example, when the U.S. Department of Homeland Security must decide upon a radiation detection system to acquire, a number of performance attributes are of interest and must be measured in order to characterize each of the considered systems. We identified and evaluated several approaches to incorporate the uncertainty in the attribute value estimates into a normative model for a multiple attribute selection decision. Assuming an additive multiple attribute value model, we demonstrated the idea of propagating the attribute value uncertainty and describing the decision values for each alternative as probability distributions. These distributions were used to select an alternative. With the goal of maximizing the probability of correct selection we developed and evaluated, under several different sets of assumptions, procedures to allocate the fixed experimental budget across the multiple attributes and alternatives. Through a series of simulation studies, we compared the performance of these allocation procedures to the simple, but common, allocation procedure that distributed the sample budget equally across the alternatives and attributes. We found the allocation procedures that were developed based on the inclusion of decision-maker knowledge, such as knowledge of the decision model, outperformed those that neglected such information. Beginning with general knowledge of the attribute values provided by Bayesian prior distributions, and updating this knowledge with each observed sample, the sequential allocation procedure performed particularly well. These observations demonstrate that managing projects focused on a selection decision so that the decision modeling and the experimental planning are done jointly, rather than in isolation, can improve the overall selection results

Fuzzy multicriteria analysis and its applications for decision making under uncertainty

Multicriteria decision making refers to selecting or ranking alternatives from available alternatives with respect to multiple, usually conflicting criteria involving either a single decision maker or multiple decision makers. It often takes place in an environment where the information available is uncertain, subjective and imprecise. To adequately solve this decision problem, the application of fuzzy sets theory for adequately modelling the uncertainty and imprecision in multicriteria decision making has proven to be effective. Much research has been done on the development of various fuzzy multicriteria analysis approaches for effectively solving the multicriteria decision making problem, and numerous applications have been reported in the literature. In general, existing approaches can be categorized into (a) multicriteria decision making with a single decision maker and (b) multicriteria group decision making. Existing approaches, however, are not totally satisfactory due to various shortcomings that they suffer from including (a) the inability to adequately model the uncertainty and imprecision of human decision making, (b) the failure to effectively handle the requirements of decision maker(s), (c) the tedious mathematical computation required, and (d) cognitively very demanding on the decision maker(s). This research has developed four novel approaches for effectively solving the multicriteria decision making problem under uncertainty. To effectively reduce the cognitive demand on the decision maker, a pairwise comparison based approach is developed in Chapter 4 for solving the multicriteria problem under uncertainty. To adequately meet the interest of various stakeholders in the multicriteria decision making process, a decision support system (DSS) based approach is introduced in Chapter 5. In Chapter 6, a consensus oriented approach is presented in multicriteria group decision making on which a DSS is proposed for facilitating consensus building in solving the multicriteria group decision making problem. In Chapter 7, a risk-oriented approach is developed for adequately modelling the inherent risk in multicriteria group decision making with the use of the concept of ideal solutions so that the complex and unreliable process of comparing fuzzy utilities usually required in fuzzy multicriteria analysis is avoided. Empirical studies of four real fuzzy multicriteria decision making problems are presented for illustrating the applicability of the approaches developed in solving the multicriteria decision making problem. A hospital location selection problem is discussed in Chapter 8. An international distribution centre location problem is illustrated in Chapter 9. A supplier selection problem is presented in Chapter 10. A hotel location problem is discussed in Chapter 11. These studies have shown the distinct advantages of the approaches developed respectively in this research from different perspectives in solving the multicriteria decision making problem