Abstract: Simulated annealing is a stochastic local search method, initially introduced for global combinatorial mono-objective optimisation problems, allowing gradual convergence to a near-optimal solution. An extended version for multiobjective optimisation has been introduced to allow a construction of near-Pareto optimal solutions by means of an archive that catches nondominated solutions while exploring the feasible domain. Although simulated annealing provides a balance between the exploration and the exploitation, multiobjective optimisation problems require a special design to achieve this balance due to many factors including the number of objective functions. Accordingly, many variants of multiobjective simulated annealing have been introduced in the literature. This paper reviews the state of the art of simulated annealing algorithm with a focus upon multiobjective optimisation field. PubDate: Thu, 23 May 2019 11:05:00 +000
Abstract: The Pareto optimality is a widely used concept for the multicriteria decision-making problems. However, this concept has a significant drawback—the set of Pareto optimal alternatives usually is large. Correspondingly, the problem of choosing a specific Pareto optimal alternative for the decision implementation is arising. This study proposes a new approach to select an “appropriate” alternative from the set of Pareto optimal alternatives. The proposed approach is based on ranking-theory methods used for ranking participants in sports tournaments. In the framework of the proposed approach, we build a special score matrix for a given multicriteria problem, which allows the use of the mentioned ranking methods and to choose the corresponding best-ranked alternative from the Pareto set as a solution of the problem. The proposed approach is particularly useful when no decision-making authority is available, or when the relative importance of various criteria has not been evaluated previously. The proposed approach is tested on an example of a materials-selection problem for a sailboat mast. PubDate: Mon, 01 Apr 2019 09:05:46 +000
Abstract: Development of credit scoring models is important for financial institutions to identify defaulters and nondefaulters when making credit granting decisions. In recent years, artificial intelligence (AI) techniques have shown successful performance in credit scoring. Support Vector Machines and metaheuristic approaches have constantly received attention from researchers in establishing new credit models. In this paper, two AI techniques are reviewed with detailed discussions on credit scoring models built from both methods since 1997 to 2018. The main discussions are based on two main aspects which are model type with issues addressed and assessment procedures. Then, together with the compilation of past experiments results on common datasets, hybrid modelling is the state-of-the-art approach for both methods. Some possible research gaps for future research are identified. PubDate: Wed, 13 Mar 2019 12:05:07 +000
Abstract: Safety is paramount in the construction industry and the fixed sprinkler and water spray systems used in firefighting involve networks of pipes of various lengths. Manufacturers of such fixed firefighting systems need to either cut the existing stocks to length—a (one-dimensional) cutting-stock problem—or lengthen the existing stocks or leftover segments through welding, a (one-dimensional) cutting-stock problem with welding. Best industry practice safety requirements allow only one weld per length of pipe. The case of a Hungarian manufacturer of fixed firefighting systems motivates this article, which argues that the cutting-stock problem with welding (with single- or multiple-size stocks) may be converted to an equivalent cutting-stock problem (with multiple-size stocks). Readily available algorithms and software may then be used to generate an optimal cutting plan for the equivalent cutting-stock problem. Subject to certain restrictions, the optimal cutting plan for the equivalent cutting-stock problem may then be converted to cutting patterns for the original cutting-stock problem with welding. PubDate: Sun, 03 Mar 2019 07:05:30 +000
Abstract: An overview of current debates and contemporary research devoted to modeling decision making processes and their facilitation directs attention to techniques based on pairwise judgments. At the core of these techniques are various judgment consistency measures which, in a sense, control the prioritization process which leads to the establishment of decision makers’ unknown preferences. If judgments expressed by decision makers were perfectly consistent (cardinally transitive), all available prioritization techniques would deliver the same solution. However, human judgments are consistently inconsistent, as it were; thus the preference estimation quality significantly varies. The scale of these variations depends, among others, on the chosen consistency measure of pairwise judgments. That is why it seems important to examine relations among various consistency measures and the preferences estimation quality. This research reveals that there are consistency measures whose performance may confuse decision makers with the quality of their ranking outcome. Thus, it introduces a measure which is directly related to the quality of the preferences estimation process. The main problem of the research is studied via Monte Carlo simulations executed in Wolfram Mathematica Software. The research results argue that although the performance of examined consistency measures deviates from the exemplary ones in relation to the estimation quality of decision makers preferences, solutions proposed in this paper can significantly improve that quality. PubDate: Sun, 03 Feb 2019 00:00:00 +000
Abstract: Operating room (OR) surgery scheduling is a challenging combinatorial optimization problem that determines the operation start time of every surgery to be performed in different surgical groups, as well as the resources assigned to each surgery over a schedule period. One of the main challenges in health care systems is to deliver the highest quality of care at the lowest cost. In real-life situations, there is significant uncertainty in several of the activities involved in the delivery of surgical care, including the duration of the surgical procedures. This paper tackles the operating room surgery scheduling problem with uncertain surgery durations, where uncertainty in surgery durations is represented by means of fuzzy numbers. The problem can be considered as a Fuzzy Flexible Job-shop Scheduling Problem (FFJSP) due to similarities between operating room surgery scheduling with uncertain surgery durations and a multi-resource constraint flexible job-shop scheduling problem with uncertain processing times. This research handles both the advanced and allocation scheduling problems simultaneously and provides an Ant Colony Optimization (ACO) metaheuristic algorithm which utilized a two-level ant graph to integrate sequencing jobs and allocating resources at the same time. To assess the performance of the proposed method, a computational study on five test surgery cases is presented, considering both deterministic and fuzzy surgery durations to enhance the significance of the study. The results of this experiment demonstrated the effectiveness of the proposed metaheuristic algorithm. PubDate: Mon, 31 Dec 2018 00:00:00 +000
Abstract: Supplier selection is one of the intricate decisions of managers in modern business era. There are different methods and techniques for supplier selection. Data envelopment analysis (DEA) is a popular decision-making method that can be used for this purpose. In this paper, a new dynamic DEA approach is proposed which is capable of evaluating the suppliers in consecutive periods based on their inputs, outputs, and the relationships between the periods classified as desirable relationships, undesirable relationships, and free relationships with positive and negative natures. To this aim various social, economic, and environmental criteria are taken into account. A new method for constructing an ideal decision-making unit (DMU) is proposed in this paper which differs from the existing ones in the literature according to its capability of considering periods with unit efficiencies which do not necessarily belong to a unique DMU. Furthermore, the new ideal DMU has the required ability to rank the suppliers with the same efficiency ratio. In the concerned problem, the supplier that has unit efficiency in each period is selected to construct an ideal supplier. Since it is possible to have more than one supplier with unit efficiency in each period, the ideal supplier can be made with different scenarios with a given probability. To deal with such uncertain condition, a new robust dynamic DEA model is elaborated based on a scenario-based robust optimization approach. Computational results indicate that the proposed robust optimization approach can evaluate and rank the suppliers with unit efficiencies which could not be ranked previously. Furthermore, the proposed ideal DMU can be appropriately used as a benchmark for other DMUs to adjust the probable improvement plans. PubDate: Sun, 09 Dec 2018 00:00:00 +000
Abstract: The paper illustrates the development of an evaluation model for supporting the decision-making process related to an urban regeneration intervention. In particular, the study proposes an original multi-methodological approach, which combines SWOT Analysis, Stakeholders Analysis and PROMETHEE method for the evaluation of alternative renewal strategies of an urban area in Northern Italy. The article also describes the work carried out within an experts’ panel that has been organized for validating the structuring of the decision problem and for evaluating the criteria of the model. PubDate: Wed, 14 Nov 2018 06:25:17 +000
Abstract: One of the core complexities involved in evaluating decision alternatives in the area of public decision-making is to deal with conflicts. The stakeholders affected by and involved in the decision often have conflicting preferences regarding the actions under consideration. For an executive authority, these differences of opinion can be problematic, during both implementation and communication, even though the decision is rational with respect to an attribute set perceived to represent social welfare. It is therefore important to involve the stakeholders in the process and to get an understanding of their preferences. Otherwise, the stakeholder disagreement can lead to costly conflicts. One way of approaching this problem is to provide means for comprehensive, yet effective stakeholder preference elicitation methods, where the stakeholders can state their preferences with respect to actions part of the current agenda of a government. In this paper we contribute two supporting methods: (i) an application of the cardinal ranking (CAR) method for preference elicitation for conflict evaluations and (ii) two conflict indices for measuring stakeholder conflicts. The application of the CAR method utilizes a do nothing alternative to differentiate between positive and negative actions. The elicited preferences can then be used as input to the two conflict indices indicating the level of conflict within a stakeholder group or between two stakeholder groups. The contributed methods are demonstrated in a real-life example carried out in the municipality of Upplands Väsby, Sweden. We show how a questionnaire can be used to elicit preferences with CAR and how the indices can be used to semantically describe the level of consensus and conflict regarding a certain attribute. As such, we show how the methods can provide decision aid in the clarification of controversies. PubDate: Thu, 27 Sep 2018 08:37:32 +000
Abstract: The optimization computation is an essential transversal branch of operations research which is primordial in many technical fields: transport, finance, networks, energy, learning, etc. In fact, it aims to minimize the resource consumption and maximize the generated profits. This work provides a new method for cost optimization which can be applied either on path optimization for graphs or on binary constraint reduction for Constraint Satisfaction Problem (CSP). It is about the computing of the “transitive closure of a given binary relation with respect to a property.” Thus, this paper introduces the mathematical background for the transitive closure of binary relations. Then, it gives the algorithms for computing the closure of a binary relation according to another one. The elaborated algorithms are shown to be polynomial. Since this technique is of great interest, we show its applications in some important industrial fields. PubDate: Thu, 23 Aug 2018 08:26:17 +000
Abstract: This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods. PubDate: Tue, 14 Aug 2018 00:00:00 +000
Abstract: We propose modelling for a facilities localization problem in the context of multimode transportation. The applicative goal is to locate service facilities such as schools or hospitals while optimizing the different transportation modes to these facilities. We formalize the School Problem and solve it first exactly using an adapted -constraint multiobjective method. Because of the size of the instances considered, we have also explored the use of heuristic methods based on evolutionary multiobjective frameworks, namely, NSGA2 and a modified version of PAES. Those methods are mixed with an original local search technique to provide better results. Numerical comparisons of solutions sets quality are made using the hypervolume metric. Based on the results for test-cases that can be solved exactly, efficient implementation for PAES and NSGA2 allows execution times comparison for large instances. Results show good performances for the heuristic approaches as compared to the exact algorithm for small test-cases. Approximate methods present a scalable behavior on largest problem instances. A master/slave parallelization scheme also helps to reduce execution times significantly for the modified PAES approach. PubDate: Thu, 07 Jun 2018 07:58:00 +000
Abstract: We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems. PubDate: Thu, 07 Jun 2018 00:00:00 +000
Abstract: Decisions on transport plans and projects involve relevant public investments and may also determine radical changes in users’ costs. Unfortunately, it is not rare that—especially at the strategic planning stage—decisions on alternative projects or scenarios are made on a qualitative basis or, at best, by setting some indicators and verifying how much they reach the politically decided targets (e.g., “increasing the use of bicycles by 10%”). In order to reduce subjectivity, a more quantitative and comprehensive approach to the evaluation is needed. A Cost-Benefit Analysis is a tool commonly used to assess public expenditure, but its application to mobility plans introduces further practical and theoretical complexities. In this paper, we will thus try to contribute to the topic of the assessment of both sustainable mobility transport plans and infrastructure projects by presenting the operative application of a CBA methodology that is, at the same time, theoretically coherent and rich in outputs to support the decision-maker. Moreover, we will discuss the possible use of GIS software in order to provide to the decision-makers a clear and immediate “picture” of the effects on the network linked to different scenarios. The structure is as follows. Firstly, we discuss the complexities involved in the evaluation of plans with respect to a single infrastructure. Secondly, we introduce the available approaches for the assessment of consumer surplus, namely, the Rule of Half and the logsum function method, which allow the perfect integration between CBA and transport models. Thirdly, we present, through some operative case studies, the methodologies applied to the assessment and the network effects visualization of the urban mobility plan and new infrastructures. Finally, we underline how we can make the results more understandable to politicians, policy-makers, stakeholders, and citizens and in general improve the transparency and the awareness of the choices. PubDate: Wed, 23 May 2018 10:59:11 +000
Abstract: This paper presents a review pertaining to assignment problem within the education domain, besides looking into the applications of the present research trend, developments, and publications. Assignment problem arises in diverse situations, where one needs to determine an optimal way to assign subjects to subjects in the best possible way. With that, this paper classified assignment problems into two, which are timetabling problem and allocation problem. The timetabling problem is further classified into examination, course, and school timetabling problems, while the allocation problem is divided into student-project allocation, new student allocation, and space allocation problems. Furthermore, the constraints, which are of hard and soft constraints, involved in the said problems are briefly elaborated. In addition, this paper presents various approaches to address various types of assignment problem. Moreover, direction and potential paths of problem solving based on the latest trend of approaches are also highlighted. As such, this review summarizes and records a comprehensive survey regarding assignment problem within education domain, which enhances one’s understanding concerning the varied types of assignment problems, along with various approaches that serve as solution. PubDate: Thu, 17 May 2018 00:00:00 +000
Abstract: System dynamics methodology has been used in many fields of study which include supply chain, project management and performance, and procurement process. The said methodology enables the researchers to identify and study the impact of the variables or factors on the outcome of the model they developed. In this paper, we showed the use of system dynamics methodology in studying the behavior of procurement process that is totally different from those mentioned in previous studies. By using a typical procurement process employed by a telecommunication company as a case study, we proposed a new one (i.e., procurement process) and developed a procurement model where we discovered that the number of days involved in completing the whole procurement process depends heavily upon the scenarios we created (especially those that exceed two months), and suggested future research undertakings. PubDate: Sun, 22 Apr 2018 00:00:00 +000
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