The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO) algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front) maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The performance assessment is done by using the inverted generational distance (IGD) measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

Co-op advertising is an interactive relationship between manufacturer and retailer(s) supply chain and makes up the majority of marketing budget in many product lines for manufacturers and retailers. This paper considers pricing and co-op advertising decisions in two-stage supply chain and develops a monopolistic retailer and duopolistic retailer & apos; s model. In these models, the manufacturer and the retailers play the Nash, Manufacturer-Stackelberg and cooperative game to make optimal pricing and co-op advertising decisions. A bargaining model is utilized for determine the best pricing and co-op advertising scheme for achieving full coordination in the supply chain.

Electrochemical micromachining (EMM) appears to be a very promising micromachining process for having higher machining rate, better precision and control, reliability, flexibility, environmental acceptability, and capability of machining a wide range of materials. It permits machining of chemically resistant materials, like titanium, copper alloys, super alloys and stainless steel to be used in biomedical, electronic, micro-electromechanical system and nano-electromechanical system applications. Therefore, the optimal use of an EMM process for achieving enhanced machining rate and improved profile accuracy demands selection of its various machining parameters. Various optimization tools, primarily Derringer’s desirability function approach have been employed by the past researchers for deriving the best parametric settings of EMM processes, which inherently lead to sub-optimal or near optimal solutions. In this paper, an attempt is made to apply an almost new optimization tool, i.e. differential search algorithm (DSA) for parametric optimization of three EMM processes. A comparative study of optimization performance between DSA, genetic algorithm and desirability function approach proves the wide acceptability of DSA as a global optimization tool.

In future electricity industry transferring high quality of power is essential. In this case, using Flexible AC Transmission System (FACTS) devices is inevitable. FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. Therefore, finding the optimal locations for these devices in power networks is necessary. There are several varieties of FACTS devices with different characteristics, deployed for different purposes. Imperialist Competitive (IC) algorithm is a recently developed optimization technique, applied in power systems. IC algorithm is a new heuristic approach for global optimization searches based on the concept of imperialistic competition. In this paper, an IEEE 4-bus system is deployed as a case study in order to demonstrate the results of this novel approach using MATLAB.

In recent decays, there has been an extensive improvement in technology and knowledge; hence, human societies have started to fortify their urban environment against the natural disasters in order to diminish the context of vulnerability. Local administrators as well as government officials are thinking about new options for disaster management programs within their territories. Planning to set up local disaster management facilities and stock pre-positioning of relief items can keep an urban area prepared for a natural disaster. In this paper, based on a real-world case study for a municipal district in Tehran, a multi-objective mathematical model is developed for the location-distribution problem. The proposed model considers the role of demand in an urban area, which might be affected by neighbor wards. Integrating decision-making process for a disaster helps to improve a better relief operation during response phase of disaster management cycle. In the proposed approach, a proactive damage estimation method is used to estimate demands for the district based on worst-case scenario of earthquake in Tehran. Since such model is designed for an entire urban district, it is considered to be a large-scale mixed integer problem and hence, a genetic algorithm is developed to solve the model.

Inventory management is considered as major concerns of every organization. In inventory holding, many steps are taken by managers that result a cost involved in this row. This cost may not be constant in nature during time horizon in which perishable stock is held. To investigate on such a case, this study proposes an optimization of inventory model where items deteriorate in stock conditions. To generalize the decaying conditions based on location of warehouse and conditions of storing, the rate of deterioration follows the Weibull distribution function. The demand of fresh item is declining with time exponentially (because no item can always sustain top place in the list of consumers’ choice practically e.g. FMCG). Shortages are allowed and backlogged, partially. Conditions for global optimality and uniqueness of the solutions are derived, separately. The results of some numerical instances are analyzed under various conditions.

The purpose of this study is to solve a complex multi-product four-layer capacitated location-routing problem (LRP) in which two specific constraints are taken into account: 1) plants have limited production capacity, and 2) central depots have limited capacity for storing and transshipping products. The LRP represents a multi-product four-layer distribution network that consists of plants, central depots, regional depots, and customers. A heuristic algorithm is developed to solve the four-layer LRP. The heuristic uses GRASP (Greedy Randomized Adaptive Search Procedure) and two probabilistic tabu search strategies of intensification and diversification to tackle the problem. Results show that the heuristic solves the problem effectively.

Optimization of a multi-response dynamic system aims at finding out a setting combination of input controllable factors that would result in optimum values for all response variables at all signal levels. In real life situation, often the multiple responses are found to be correlated. The main advantage of PCA-based approaches is that it takes into account the correlation among the multiple responses. Two PCA-based approaches that are commonly used for optimization of multiple responses in dynamic system are PCA-based technique for order preference by similarity to ideal solution (TOPSIS) and PCA-based multiple criteria evaluation of the grey relational model (MCE-GRM). This paper presents a new PCA-based approach, called PCA-based utility theory (UT) approach, for optimization of multiple dynamic responses and compares its optimization performance with other existing PCA-based approaches. The results show that the proposed PCA-based UT method is superior to the other PCA-based approaches.

This paper deals with the problem of grouping a set of objects into clusters. The objective is to minimize the sum of squared distances between objects and centroids. This problem is important because of its applications in different areas. In prior literature on this problem, attributes of objects have often been assumed to be crisp numbers. However, since in many realistic situations object attributes may be vague and should better be represented by fuzzy numbers, we are interested in the generalization of the minimum sum-of-squares clustering problem with the attributes being fuzzy numbers. Specifically, we consider the case where an object attribute is a triangular fuzzy number. The problem is first formulated as a fuzzy nonlinear binary integer programming problem based on a newly proposed dissimilarity measure, and then solved by developing and demonstrating a problem-specific ant colony optimization algorithm. The proposed algorithm is evaluated by computational experiments.

In this paper, we explored an economic production quantity model (EPQ) model for finite production rate and deteriorating items with time-dependent trapezoidal demand. The objective of the model under study is to determine the optimal production run-time as well as the number of production cycle in order to maximize the profit. Numerical example is also given to illustrate the model and sensitivity analyses regarding various parameters are performed to study their effects on the optimal policy.