The problem of generating a train schedule for a single-track railway system is addressed in this paper. A three stage scheduling is proposed to reduce the total train tardiness. We derived an appropriate job-shop scheduling algorithm called DR-algorithm. In the first stage, by determining appropriate weights of the dispatching rules, a pre-schedule is constructed. In the second stage, on the basis of the pre-schedule, the departure times of the trains are modified to reduce the number of conflicts in using railway sections by different trains. In the third stage, a train speed control helps the scheduler to change the trains’ speeds in order to reduce the train tardiness and to reach other objectives. The factual train schedule is based on the modified train speeds and on the modified departure times of the trains. The experimental running of the DR-algorithm on the benchmark instances showed this algorithm can solve train scheduling problems in a close to optimal way. In particular, the total train tardiness was reduced about 20% due to controlling train speeds and the departure times of the trains.

The objective of the study is to assess the performance of multilayer coated carbide insert in the machining of hardened AISI D2 steel (53 HRC) using Taguchi design of experiment. The experiment was designed based on Taguchi L27 orthogonal array to predict surface roughness. The S/N ratio and optimum parametric condition are analysed. The analysis of variance has also been carried out to predict the significant factors affecting surface roughness. Based on Taguchi S/N ratio and ANOVA, feed is the most influencing parameter for surface roughness followed by cutting speed whereas depth of cut has least significant from the experiments. In regression model, the value of R2 being 0.98 indicates that 98 % of the total variations are explained by the model. It indicates that the developed model can be effectively used to predict the surface roughness on the machining of D2 steel with 95% confidence intervals.

In this paper, we present a model for time dependent demand with multiple productions and rework setups. Production is demand dependent and greater than the demand rate. Production facility produces items in m production setups and one rework setup (m, 1) policy. The major reason of reverse logistic and green supply chain is rework, so it reduces the cost of production and other ecological problems. Most of the researchers developed a rework model without deteriorating items. A numerical example and sensitivity analysis is shown to describe the model.

Flexible manufacturing systems (FMSs) offer opportunities for the manufacturers to improve their technology, competitiveness and profitability through a highly efficient and focused approach to manufacturing effectiveness. Justification, evaluation and selection of FMSs have now been receiving significant attention in the manufacturing environment. Evaluating alternative FMSs in the presence of multiple conflicting criteria and performance measures is often a difficult task for the decision maker. Preference ranking tools are special types of multi-criteria decision-making methods in which the decision maker’s preferences on criteria are aggregated together to arrive at the final evaluation and selection of the alternatives. This paper deals with the application of six most potential preference ranking methods for selecting the best FMS for a given manufacturing organization. It is observed that although the performances of these six methods are almost similar, ORESTE (Organization, Rangement Et Synthese De Donnes Relationnelles) method slightly outperforms the others. These methods use some preference function or utility value or Besson ranking of criteria and alternatives, to indicate how much an alternative is preferred to the others. Most of these methods need quantification of criteria weights or different preference parameters, but ORESTE method, being an ordinal outranking approach, only requires ordinal data and attribute rankings according to their importance. Therefore, it is particularly applicable to those situations where the decision maker is unable to provide crisp evaluation data and attribute weights.

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 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.