Showing papers in "Opsearch in 2019"

The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem

Abstract: Keeping track of employees’ time and attendance is difficult and time-consuming task for the companies. Today many companies are performing the digital time and attendance systems that automatically track and process the data to improve their operations and save money. There are many alternatives for the time and attendance systems in the market and appropriate selection among them is not easy in the presence of multiple, usually conflicting, criteria. So this selection may be considered as a Multi Criteria Decision Making (MCDM) problem. In this paper, the new combined decision making approach based on Criteria Importance Through Inter criteria Correlation (CRITIC) and Weighted Aggregated Sum Product Assessment (WASPAS) methods is used for the time and attendance software selection problem of the private hospital. The weights of the criteria are determined by CRITIC method and the alternatives are ranked by WASPAS method for finding the most suitable alternative. The novelty of this paper to the literature is to combine CRITIC and WASPAS methods for the first time.

Fuzzy inventory model for deteriorating items in a green supply chain with carbon concerned demand

Abstract: With the environment deterioration becoming a serious concern, numerous industries have realized that it’s critical to focus on manufacturing with reduced waste and low carbon emission. Studies show that consumers are getting cognizant of environment preservation and prefer low-carbon developed products. It is seen that in some cases, customers are willing to pay even more for products developed using low carbon emission technologies. Furthermore, government initiatives towards going green has resulted in industries focusing on reducing their carbon footprints throughout the supply chain by employing green supply chain methodologies. In this study, we will develop an inventory model for deteriorating items in green supply chain considering recycling, reverse logistics and remanufacturing. Demand is assumed to be carbon dependent. Products are considered to be deteriorating in nature with time dependent deterioration rate. A crisp model is developed to minimize total average cost. In the crisp model, it is assumed that demand, deterioration and returned rate are precisely known. However, in reality these parameters are imprecise in nature. To model this impreciseness, a fuzzy model is developing considering these parameters as triangular fuzzy numbers. Total cost function is defuzzified using signed distance method and is shown to be convex and a unique solution exists. Numerical analysis is carried out for both crisp and fuzzy cases.

Evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem

Abstract: This paper presents a multi-objective dual-resource constrained flexible job-shop scheduling problem (MODRCFJSP) with the objectives of minimizing the makespan, critical machine workload and total workload of machines simultaneously. Two types of multi-objective evolutionary algorithms including fast elitist non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are proposed for solving MODRCFJSP. Some efficient mutation and crossover operators are adapted to the special chromosome structure of the problem for producing new solutions in the algorithm’s generations. Besides, we provide controlled elitism based version of NSGA-II and NRGA, namely controlled elitist NSGA-II (CENSGA-II) and controlled elitist NRGA (CENRGA), to optimize MODRCFJSP. To show the performance of the four proposed algorithms, numerical experiments with randomly generated test problems are used. Moreover, different convergence and diversity performance metrics are employed to illustrate the relative performance of the presented algorithms.

Strategic analysis of the Indian agri-food supply chain

Abstract: Indian agri-food supply chain (AFSC) is highly unorganized in terms of flow of products, funds, and information, resulting in low profitability of farmers. In this paper, we have identified various internal factors (strengths and weaknesses) and external factors (opportunities and threats) of AFSC to make a strategic viewpoint. Based on the identified factors, questionnaire are prepared and circulated among experts from academics and industries to prioritize the identified factors. Further, analytical hierarchy process (AHP) and Fuzzy-AHP methods are used to rank the identified strengths, weaknesses, opportunities and threats factors followed by comparative analysis. The results reveal that agriculture sectors significant contribute in national growth and it is a highest ranked strength. Small farm holding farmers is a major weakness of Indian agriculture system. These factors need to be addressed in designing of Indian AFSC, which has capability to stimulate the growth of overall agriculture sector as well as the nation.

On hidden Z-matrix and interior point algorithm

Abstract: We propose an interior point method to compute solution of linear complementarity problem LCP (q, A) given that A is a real square hidden Z-matrix (generalization of Z-matrix) and q is a real vector. The class of hidden Z-matrix is important in the context of mathematical programming and game theory. We study the solution aspects of linear complementarity problem with $$A \in$$ hidden Z-matrix. We observe that our proposed algorithm can process LCP (q, A) in polynomial time under some assumptions. Two numerical examples are illustrated to support our result.

Performance and economic analysis of a single server feedback queueing model with vacation and impatient customers

Abstract: In this investigation, a single server M/M/1/N feedback queueing system with vacation, balking, reneging and retention of reneged customers is analyzed. By considering the mathematical modeling, we derive the steady state probabilities of the number of customers in the system. We obtain important measures of effectiveness of the model by using the stationary distribution, and develop a cost model of the queueing system. Further, a numerical study and a cost profit analysis are carried out.

A comparative study on performance measurement of Indian public sector banks using AHP-TOPSIS and AHP-grey relational analysis

Abstract: Banks are the financial intermediaries and important means for the advancement of economies. In the cutthroat competitions, the increase in market shares is a matter of concern for all. Banks are expected to increase their efficiency to boost competitive capacity, which also helps the Decision-maker to know about grey areas for development. Therefore, performance measurements of efficiency calculation, by using different methods are the concern for research across the world. This paper tries to use the combination of AHP, TOPSIS, and Grey Relational Analysis for efficiency calculation of different public sector banks in India and finally, results were compared. AHP is used to determine the weight criteria and Grey Relational Analysis and TOPSIS are used to rank the bank performances. The proposed method of this study used various inputs and outputs criteria which were taken from various banks annual reports. Descriptive statistics and correlation matrix were used to test the validity of the criteria. The findings reveal that banks which are considered as efficient are close to relative closeness to the ideal solution, expose an alternative ranking of the banks, present research also provides better insight to focus on the area of improvement in comparison to others banks. The Comparative result shows both models have the almost same interpretation. Little deviation in their ranks is due to methodological differences. The proposed research will provide a framework for further applications and both approaches will help decision maker of Indian Public sector banks to find optimal solutions to the complex problems by assessing various alternatives.

An EPQ model for deteriorating items with imperfect production, inspection errors, rework and shortages : a type-2 fuzzy approach

Abstract: In this paper, a production inventory model is studied considering imperfect production and deterioration of item, simultaneously. Both the serviceable and reworkable items are assumed to deteriorate with time. A cost-minimizing model is developed incorporating both Type I and Type II inspection errors. Shortages are allowed that are completely backlogged. All the screened items are reworked at the end of the production process. To encounter a more practical situation, the deterioration rate is considered to be a type-2 fuzzy number. Such a situation arises when the vendor assigns, with similar priority, a number of experts to determine the rate of deterioration and the decision given by each expert is in linguistic term, which may be replaced by a fuzzy number. The aim of the proposed model is to calculate the maximum back-order quantity allowed and the optimal lot size that must be produced in order to minimize the overall inventory cost. The problem is solved for both the crisp and fuzzy models and a numerical example with practical application is also presented to exemplify the procedure. A novel method for solving a type-2 fuzzy optimization problem is developed which results in a set of Pareto optimal solutions for the proposed problem. It is followed by presenting a sensitivity analysis of various parameters involved on the decision variables and the cost function for a better illustration of the model.

ABC classification according to Pareto’s principle: a hybrid methodology

Abstract: So far, many methods have been proposed to classify items based on ABC analysis, but the results of these methods have had relatively low compliance with the principles of ABC. More precisely, collective value and sometimes the number of items belonging to each category in the methods provided do not meet the basic requirements of ABC called Pareto’s principle. In this study, a number of hybrid methodologies including Shannon’s entropy, TOPSIS (the technique for order preference by similarity to ideal solution) and goal programming are respectively used for determining the weight of criteria which are effective in the inventory items classification, calculations of each item value and its classification based on Pareto’s principle. To this end, the value of each item as well as classification of inventory items is calculated based on Pareto’s principle. The performance of the proposed method is evaluated through (1) statistical analysis, (2) checking the percentage of similarity with other methods and (3) comparison with another method in terms of the number and value allocated to each class. The results confirm the capability of the listed method.

Optimizing a sustainable logistics problem in a renewable energy network using a genetic algorithm

Abstract: Renewable energy sources, including bio-energy technologies, have been introduced to overcome sustainability challenges, such as negative environmental impacts and energy insecurity due to reliance on fossil fuels. Logistics activities have a significant effect on the cost and environmental impacts of renewable energy supply chains. Understanding and reducing the carbon footprint of renewable energy supply chains can aid in mitigating environmental impacts. Thus, this research presents a mathematical model that can be used to optimize renewable energy supply chain logistics costs and carbon footprint. The proposed model considers a biomass-to-bio-oil supply chain, including harvesting and collection sites, bio-refineries, and distribution centers. It is assumed that mobile and fixed refineries will be used to produce bio-oil. The model considers the mass of biomass and bio-oil, number of mobile and fixed refineries, and number of truck trips to minimize total cost, where a carbon tax is used to represent carbon footprint in the mathematical cost model. A genetic algorithm is designed to obtain a near-optimal solution. Six scenarios for mobile and fixed refinery capacity are tested in performing sensitivity analysis of the model. The results indicate that the mathematical model of the bio-oil supply chain has reasonable relationships between input and output variables. The model is able to incorporate the impact of carbon emissions in a mixed-refinery bio-oil supply chain as a cost parameter. It was also found that increasing mobile refinery capacity has the greater effect on reducing total cost and carbon emissions than increasing fixed refinery capacity.

Total cost measures with probabilistic cost function under varying supply and demand in transportation problem

Abstract: In the present competitive world, it is often said that “Time is Money” in almost every aspect of life. Time is a factor which affects the various real-life problems directly or indirectly. So, in order to incorporate the “time” as a factor in transportation problems (TPs), we have considered the probabilistic cost/profit function termed as “survival cost/profit” which is again a time-dependent function. In this study, we have assumed that the supply and demand quantities are varying between some specified intervals. Due to the variation in the supply and demand quantities, the value of the objective function is also obtained between interval which is bounded by lower and upper values. Based on the above-stated assumptions, we have developed a couple of mathematical optimization models for the TPs. The solution procedure has also been discussed to solve the proposed mathematical models. At last, a numerical illustration has been presented to show the validity of the model and solution procedure which is helpful in the decision-making process.

Comparative performance of electric vehicles using evaluation of mixed data

Abstract: The electric vehicle (EV) technology has been getting momentum due to rapid depletion of fossil fuels and also in taking care of environment. Many manufacturers are investing a lot in electric vehicles for a particular outcome coming from it which can show a sign for replacement of conventional I.C engines. They are taking interest about the customer findings in a car. There are various factors which affect the performance of an electric vehicle such as battery capacity, charging time, price, driving range etc. As we know there are many electric vehicle models that are present in market with different combinations and this study is based on the performance evaluation of electric vehicles using multiple criteria decision making tool from customer point of view. This study highlights the best electric vehicle model in Asian market so that findings of an EV buyer can be fulfilled. Fuzzy analytic hierarchy process has been used to determine criteria weight whereas evaluation of mixed data has been used for performance evaluation and ranking. According to the study BYD E6 becomes the best electric vehicle model in Asian market.

Challenges for implementing green concept in sustainable manufacturing: a systematic review

Abstract: Global warming and the environmental problems arising from it are posing a serious threat to the peaceful co-existence of the human and the natural worlds. The drastic depletion of fossil fuels, in particular, has made it imperative that we find alternative ways of manufacturing that can support upcoming industries without causing further loss or damage to our natural resources. This critical situation has given rise to a pressing need for a cohesive and strategic research that can address the needs of industries while simultaneously ensuring the implementation of environmentally sustainable manufacturing methods. However, the adoption of environmentally sustainable concepts is a challenging task for industries and a thorough, comprehensive and insightful research is essential in order to support and facilitate this task. From the literature available, it can be assumed with certainty that industries are currently making substantial efforts to ensure that their manufacturing-related operations are as green and as sustainable as possible. The literature review contained in this paper endeavours to assess, from a global perspective, the various challenges that hinder these efforts while bearing in mind the positive trend in industrial operations. It may also be worth noting that there are not much research so far on green concept in sustainable manufacturing and this paper adds value to the existing research by focusing on this hitherto neglected area.

A fuzzy robust stochastic mathematical programming approach for multi-objective scheduling of the surgical cases

Abstract: In this paper, the problem of elective surgery scheduling is studied, and resources like surgeons, nurses and operating rooms (ORs) are considered. The problem is to assign surgeries to operating rooms in order to meet three goals: (1) maximizing the number of surgeries that can be done using given fixed resources, (2) minimizing the total fixed costs and overtime costs of the ORs, and (3) minimizing the maximum of completion time of operating rooms. We take into account the uncertainty with the stochastic parameter for the regular operating time of OR in model and fuzzy constraint for resources and overtime. A multi-objective model is proposed to choose the operations to be scheduled on the selected day, and to assign the elective surgeries to OR sessions. In the first phase, we formulated a fuzzy robust optimization model and in the second phase, the sensitivity of the model to different values for penalties in the objective function, is analyzed. The efficiency of the proposed solution is validated by numerical results of applying the model to the case of a public hospital in Iran.

Solution approach to multi-objective linear fractional programming problem using parametric functions

Abstract: In this paper, an iterative technique based on the use of parametric functions is proposed to obtain the best preferred optimal solution of a multi-objective linear fractional programming problem. The decision maker ascertains own desired tolerance values for the objectives as termination constants and imposes them on each iteratively computed objective functions in terms of termination conditions. Each fractional objective is transformed into non-fractional parametric function using certain initial values of parameters. The parametric values are iteratively computed and $$\epsilon $$ -constraint method is used to obtain the pareto (weakly) optimal solutions in each step. The computations get terminated when all the termination conditions are satisfied at a pareto optimal solution of an iterative step. A numerical example is discussed at the end to illustrate the proposed method and fuzzy max–min operator method is applied to validate the obtained results.

An EOQ model for fuzzy defective rate with allowable proportionate discount

Abstract: In this paper, we developed both crisp and fuzzy EOQ models with proportionate discount (discount increases when number of defects decrease in each lot) for items with imperfect quality. First, we construct an optimal order quantity of crisp case. Next, proposed three different fuzzy inventory models where in the first case the defective rate is fuzzified, in the next case, both defective rate and annual demand rate are fuzzified and finally in the case of the third model all costs, defective rate and annual demand are taken to be fuzzy. Lastly, we developed the model for items with imperfect quality with inspection errors, as the inspector may commit errors while screening the lot. The probability of misclassification errors is assumed to be known. The inspection process may consist of three costs: (a) cost of inspection (b) cost of Type I errors and (c) cost of Type II errors. The defective items, classified by the inspector and the buyer, would be salvaged as a single batch that is sold at a discounted price. The objective is to find the optimal lot size for models to maximize the total profit (both for crisp and fuzzy models) and used fuzzy numbers for defective items, demand rate and/or all types of costs (exclusively for fuzzy models). We considered the triangular fuzzy numbers to represent the fuzziness of all types of costs, defective items and annual demand. Finally, the optimum order quantity is obtained using the signed distance method. A numerical example is provided to illustrate the results of the proposed models and the sensitivity analysis is conducted to know the effect of changes made for the values of different parameters on the actual lot size and the profit respectively.

An optimal inventory model for perishable items under successive price discounts with permissible delay in payments

Abstract: This article derives an optimal ordering policy for non-instantaneously deteriorating items under successive price discounts with delay in payments. Here successive price discounts is a strategy to sell almost all the items before decomposition. The cause of implementing this concept in the model is the fact that about 25% of vegetables and fruits of India gets decayed before selling, due to lack of facilities and awareness of business strategies, although poverty is its vital factor. Thus we propose to offer successive price discounts of 20% and 40% after selling the stock up to 50% and 90% respectively to raise the customer’s inflow and so also rotate the cycle early to avoid more deterioration. This type of business not only reduces the factory of decomposability but also saves the holding cost partially. In addition to, the delay in payment is another business strategy of the wholesaler by which he keeps the retailers in track and so also reduces the holding cost. In this case the wholesaler offers an interest-free credit period to the retailers till the settlement of the accounts. The retailers earn interest on selling the revenue during this opportunity. Supplier charges interest on the outstanding balance after exceeding the period. Besides the above, again the inventory cost does not remaining constant due to various factors like inflation, salary, house rent etc. so a time varying holding cost has been well thought out instead of constant holding cost. Once more customers’ flow not only depends upon the price instigating but also on timings, hence we assume an exponential demand function which depends on these variables. The main idea of the task is to determine optimal selling price, optimal refill schedule and optimal ordering quantity such that the total profit is maximized under exponential constraints. The literal idea of the work is (a) to avoid the deterioration through successive price discounts, (b) to attract the customers through successive price discounts, (c) to sell all the items through successive price discounts, (d) to rotate the cycle early through successive price discounts, (e) to keep the retailers in track through delay in payment, (f) to keep variable holding cost instead of constant holding cost due to market inflation, (g) to get the optimum results under exponential constraints.

Performance evaluation of lean production based on balanced score card method using ANP and SIR: a case from Iranian home appliance industry

Abstract: Lean production is a productive philosophy with systematic perspective which takes steps toward eliminating waste materials by applying continual improvement in the sophisticated business processes. Appropriate implementation of this philosophy results in significant changes within a business. Despite the ample efforts devoted to lean production’s evaluation and implementation, this system’s efficient evaluation and implementation are still experiencing countless issues, which seem to be due to absence of a comprehensive model for examining and evaluating lean production within manufacturing companies. Having knowledge of the companies’ performance status, provides us with the possibilities of discovering weakness and strengths, allowing lead strategic managers to have higher performance comparing to their competitors by allocating more volume of market share to themselves. Balanced score card is an important management system which it will be explained using the following four dimensions: management system, exclusive reliance on financial criteria is incomplete and defective. This paper aims at performance evaluation of lean production using balanced score card (BSC), analytic network process (ANP) and inferiority and superiority based ranking (SIR) approaches where, four dimensions have been considered including financial performance, customer, internal business processes and innovation and learning. The expert questionnaire was used to evaluate lean production’s performance based on BSC, DEMATEL survey—for recognizing element’s internal relationships—and TOPSIS survey—for evaluating leanness of production line. To aid us in ranking the production line, data analysis was completed based on Super Decision and Visual PROMETHEE where, the fourth production line with total score of 0.77 stood in the first order, meaning the internal operations with the least level of cost which proves its leanness. The first and sixth line were placed in next ranks with total score of 0.72 and 0.36 which demonstrates leanness level respectively.

A hybrid regression model for water quality prediction

Abstract: In this work, we propose a hybrid regression model to solve a specific problem faced by a modern paper manufacturing company Boiler inlet water quality is a major concern for the paper machine If water treatment plant can not produce water of desired quality, then it results in poor health of the boiler water tube and consequently affects the quality of the paper This variation is due to several crucial process parameters We build a hybrid regression model based on regression tree and support vector regression for boiler water quality prediction and show its excellent performance as compared to other state-of-the-art

Modelling and analysis of healthcare inventory management systems

Abstract: The core competency of the healthcare system is to provide treatment and care to the patient. The prime focus has always been towards appointing specialized physicians, well-trained nurses and medical staffs, well-established infrastructure with advanced medical equipment, and good quality pharmacy items. But, of late, the focus is driven towards management side of healthcare systems which include proper capacity planning, optimal resource allocation, and utilization, effective and efficient inventory management, accurate demand forecasting, proper scheduling, etc. and may be dealt with a number of operations research tools and techniques. In this paper, a Markov decision process inventory model is developed for a hospital pharmacy considering the information of bed occupancy in the hospital. One of the major findings of this research is the significant reduction in the inventory level and total inventory cost of pharmacy items when the demand for the items is considered to be correlated with the number of beds of each type occupied by the patients in the healthcare system. It is observed that around 53.8% of inventory cost is reduced when the bed occupancy state is acute care, 63.9% when it is rehabilitative care, and 55.4% when long-term care. This may help and support the healthcare managers in better functioning of the overall healthcare system.

A new reliable performance evaluation model: IFB-IER–DEA

Abstract: Data envelopment analysis (DEA) is a well-known performance evaluation model for measuring the relative efficiency of multiple homogeneous decision making units (DMUs). Input/output of the DMUs can be uncertain especially when they are obtained from subjective human judgments. In this paper, Evidential Reasoning approach with interval assessment score and linguistic interval fuzzy belief degree (IFB-IER approach) is proposed to deal with the uncertainties. This approach does not restrict experts to give a precise point and provides more reliable and realistic performance evaluation. Eventually, the IFB-IER data are converted to standard interval data. Then, the efficiency of the DMUs is calculated using interval DEA model and Anderson Peterson model is applied to rank them. Finally, a numerical example is given to illustrate the validity and applicability of the proposed model.

Performance analysis and control F-policy for fault-tolerant system with working vacation

Abstract: This investigation presents a Markov model for the performance analysis of the fault tolerant machining system with failure-prone server and supported by warm standbys. To utilize the server’s idle time, provision of server’s working vacation has been done which make the system cost effective. The online and warm standby machines may fail and can be repaired by a single skilled repairman. Due to capacity constraint, when the system reaches its full capacity, no more jobs for repairing of failed machines are allowed until the workload of repair jobs reduces to a threshold level ‘F’. Before initiating the repair of the failed machines in case of coming back from the vacation state, the server requires the setup time. To make system fault tolerable, apart from standby provisioning and repairing of failed machines, the concepts of reboot and recovery are included for the formulation of Markov model. The various performance measures including the reliability indices are derived by using the transient probabilities which are computed using Runge–Kutta method. By taking a suitable numerical illustration, various system indices are examined with respect to different parameters. The computational tractability and sensitivity analysis carried out for the established metrics will provides valuable insights for the maintainability and up-gradation of the existing machining systems.

An intelligent computing technique based on a dynamic-size subpopulations for unit commitment problem

Abstract: A new intelligent computing based approach for solving multi-objective unit commitment problem (MOUCP) and its fuzzy model is presented in this paper. The proposed intelligent approach combines binary-real-coded genetic algorithm (BRCGA) and K-means clustering technique to find the optimal schedule of the generation units in MOUCP. BRCGA is used in order to tackle both the unit scheduling and load dispatch problems. While, K-means clustering technique is used to divide the population into a specific number of subpopulation with-dynamic-sizes. In this way, different genetic algorithm (GA) operators can apply to each sub-population, instead of using the same GA operators for all population. The proposed intelligent algorithm has been tested on standard systems of MOUCPs. The results showed the efficiency of the proposed approach to solve (MOUCP) and its fuzzy model.

Airplane boarding optimization considering reserved seats and passengers’ carry-on bags

Abstract: In this paper, we develop a model to assign airplane seats to ordinary passengers to minimize their boarding time while some seats have been reserved earlier by high priority passengers. Our proposed mixed integer programming model assigns ordinary passengers to seats based on the amount of their carry-on bags and results in the minimum time to complete boarding of the airplane. The proposed model can result in 5% to 20% reductions in the average boarding time compared to the situation when passengers’ luggage is not considered.

Optimization of fuzzy bi-objective fractional assignment problem

Abstract: Theory and applications of fractional programming have been significantly developed in the few last decades and assignment problem is one of the fundamental combinatorial optimization problems in the branch of optimization. Generally, in real world problems, the possible values of coefficients of a linear fractional programming problem are often only imprecisely or ambiguously known to the decision maker, therefore, it would be certainly more appropriate to interpret the coefficients as fuzzy numerical data. In this article, a fuzzy bi-objective fractional assignment problem has been formulated. Here the parameters are represented by triangular fuzzy numbers and the fuzzy problem is transformed into standard crisp problem through $$\alpha $$ -cut and then the compromise solution is derived by fuzzy programming.

A stochastic inventory system with replacement of perishable items

Abstract: This article presents a continuous review perishable inventory system in which the perished items will be replaced by the supplier at a later time. Demands occur according to a Markov arrival process. The items in the inventory have exponential life times and these perished items are stored in a place, called pool for replacement. The (s, S) ordering policy is adopted. At the time of placing an order, the ordering quantity is adjusted with number of items in the pool. The lead time is assumed to have phase type distribution. The joint probability distribution of the inventory level and the number of pooled items is obtained in the steady state case using the matrix-geometric methods. Various system performance measures in the steady state are derived and the total expected cost rate is calculated under a prefixed cost structure. The results derived in this work are numerically illustrated.

Modeling and optimization of buffers and servers in finite queueing networks.

Abstract: The joint buffer and server optimization problem (BCAP) is a non-linear optimization problem with integer decision variables that optimizes the numbers of buffers and servers such that the resulting throughput is greater than a pre-defined threshold throughput. This work presents a detailed review of the current literature that addresses allocation problems, particularly the BCAP, and a quite effective methodology for solving this problem, which consists of a combination of approximate methods and the Powell algorithm, a derivative-free optimization algorithm. The methodology was applied to networks of queues in the basic topologies series, split, and merge, producing very encouraging results that pointed at robust and homogeneous solutions.

Bayesian optimum stopping rule for software release

Abstract: This Paper proposes a Bayesian approach to find out the optimum stopping rule of software testing. We consider a discrete periodic debugging framework so that software can be released for market once the criteria are fulfilled. Simplification of stopping rules were obtained by using some specific prior distributions of the number of remaining bugs. We also develop necessary and sufficient conditions for stopping the software testing. Some illustrative examples are presented.

Multi-objective multi-model assembly line balancing problem: a quantitative study in engine manufacturing industry

Abstract: This paper deals with multi-model assembly line balancing problem (MuMALBP). In multi-model assembly lines several products are produced in separate batches on a single assembly line. Despite their popular applications, these kinds of lines have been rarely studied in the literature. In this paper, a multi-objective mixed-integer linear programing model is proposed for balancing multi-model assembly lines. Three objectives are simultaneously considered in the proposed model. These are: (1) minimizing cycle time for each model (2) maximizing number of common tasks assigned to the same workstations, and (3) maximizing level of workload distribution smoothness between workstations. Performance of the proposed model is empirically investigated in a real world engine assembly line. After applying the proposed model, possible minimum cycle time is attained for each model. All common tasks are assigned to the same workstations and a highest possible level of workload distribution smoothness is achieved. It is shown that the best compromise solution has led to the best value of the first and second objective functions with a slight distance from the best value of third one.

Portfolio optimization using Laplacian biogeography based optimization

Abstract: Portfolio optimization is defined as the most appropriate allocation of assets so as to maximize returns subject to minimum risk. This constrained nonlinear optimization problem is highly complex due to the presence of a number of local optimas. The objective of this paper is to illustrate the effectiveness of a well-tested and effective Laplacian biogeography based optimization and another variant called blended biogeography based optimization. As an illustration the model and solution methodology is implemented on data taken from Indian National Stock Exchange, Mumbai from 1st April, 2015 to 31st March, 2016. From the analysis of results, it is concluded that as compared to blended BBO, the recently proposed LX-BBO algorithm is an effective tool to solve this complex problem of portfolio optimization with better accuracy and reliability.