In this paper, we address the optimization of an integrated line balancing process with workstation inventory management. While doing so, we have studied the interconnection between line balancing and its conversion process. Almost each and every moderate to large manufacturing industry depends on a long and integrated supply chain, consisting of inbound logistic, conversion process and outbound logistic. In this sense an approach addresses a very general problem of integrated line balancing. Research works reported in the literature so far mainly deals with minimization of cost for inbound and outbound logistic subsystems. In most of the cases conversion process has been ignored. We suggest a generic approach for linking the balancing of the line of production in the conversion area with the customers’ rate of demand in the market and for configuring the related stock chambers. Thus, the main aim of this paper is to translate the underlying problem in the form of mixed nonlinear programming problem and design the optimum supply chain so that the total inventory cost and the cost of balancing loss of the conversion process is jointly minimized and ideal cycle time of the production process is determined along with ideal sizes of the stock chambers. A numerical example has been added to demonstrate the suitability of our approach.

One of the primary issues on marketing planning is to know the customer & apos; s behavioral trends. A
customer & apos; s purchasing interest may fluctuate for different reasons and it is important to find the
declining or increasing trends whenever they happen. It is important to study these fluctuations
to improve customer relationships. There are different methods to increase the customer & apos; s
willingness such as planning good promotions, an increase on advertisement, etc. This paper
proposes a new methodology to measure customer & apos; s behavioral trends called customer
electrocardiogram. The proposed model of this paper uses K-means clustering method with
RFM analysis to study customer & apos; s fluctuations over different time frames. We also apply the
proposed electrocardiogram methodology for a real-world case study of food industry and the
results are discussed in details

This paper addresses a bi-criteria scheduling problem with deteriorating jobs on a single machine. We develop a model for a single machine bi-criteria scheduling problem (SMBSP) with the aim of minimizing total tardiness and work in process (WIP) costs. WIP cost increases as a job passes through a series of stages in the production process. Due to the uncertainty involved in real-world scheduling problems, it is sometimes unrealistic or even impossible to acquire exact input data. Hence, we consider the SMBSP under the hypothesis of fuzzy L-R processing time's knowledge and fuzzy L-R due date. The effectiveness of the proposed model and the denoted methodology is demonstrated through a test problem.

Supply chain management (SCM) is becoming critical as firms recognize that competition is
shifting from company versus company to supply chain versus supply chain. In the present
competitive scenario, the fierce competition has driven most companies to seek for means of
enhancing performance beyond their four wall boundaries. The firm’s ability in collaborating
with its upstream and downstream partners determines its success in attaining better
performance with supply chain collaboration; a firm is able to serve fragmented markets in
which end customers require more product varieties and availability with shorter product life
cycle and, at the same time, lower supply chain costs. This paper introduces the framework of
continuous supply chain collaboration (CSCC), which extends the traditional frame of reference
in strategic sourcing from a supplier centric to a supply-chain-scope as continuous
improvement efforts to enhance the customer satisfaction. CSCC practices are rather
exceptional, yet CSCC is believed to be the single most comprehensive framework for attaining
operational excellence.

This paper proposes a particle swarm optimization (PSO) algorithm to solve various types of economic dispatch (ED) problems in power systems such as, environmental/economic dispatch (EED) and multi-area environmental/economic dispatch. The proposed model considers the environmental impact to achieve the minimization of fuel costs and pollutant emissions, simultaneously. The EED problem is further extended to dispatch the power among different areas to aid emission allowance trading. The performance of the proposed PSO is compared with conventional method and genetic algorithm. The results clearly show that the proposed algorithms give global optimum solution compared to the other methods. The results obtained also show that the proposed PSO algorithms can provide comparable dispatch solutions with reduced computation time for all types of ED problems.

In today’s turbulent and competitive world, strategic planning plays an important role for the
success of firms. Despite the fact that there are literally numerous models proposed for different
companies in various states and structures, the possibility of using strategic planning for
startups has never been seriously considered. In this paper, we present a survey by asking
experts to find out whether strategic planning is suitable for startups. We also propose a model
for strategic planning in startups based on the strategic planning models for small businesses
and entrepreneurship concepts. The model is similar to other models and what differentiates
this models form other approaches is the methodology used for internal and external analysis
and the parameters taken into consideration. The proposed model is examined on an Iranian
food packaging industry for validation. The preliminary results indicate that the success of
startups depends on two sets of parameters: “entrepreneurial opportunities” and “competitive
advantages and entrepreneurial characteristics”.

In this paper, we present machining tests by turning En-31 steel alloy with tungsten carbide
inserts using soluble oil-water mixture lubricant under different machining conditions. Firstorder
and second-order cutting force prediction models were developed by using the
experimental data by applying response surface methodology combined with factorial design of
experiments. Analysis of variance (ANOVA) is also employed to check the adequacy of the
developed models. The established equations show that feed rate and depth of cut are the main
influencing factors on the cutting force followed by tool nose radius and cutting velocity. It
increases with increase in the feed rate, depth of cut and tool nose radius but decreases with an
increase in the cutting velocity. The predicted cutting force values of the samples have been
found to lie close to that of the experimentally observed values with 95% confident levels.
Moreover, the surface response counters have been generated from the model equations.
Desirability function is used for single response optimization.

Analytic Hierarchy Process (AHP) is one of the most popular approaches in the area of multiple attribute decision making (MADM). However, it is not practical any more if input information are fuzzy. In this paper, we propose a new method for fuzzy AHP which is especially useful to make decisions for multiple attribute problems. The method is developed by applying preference ratio concept which makes it practical since it assigns crisp weights and crisp scores to different alternatives. Two algorithms are proposed in this paper: The first one defines crisp and normalized weight by pairwise comparison with fuzzy data while the second one calculates fuzzy consistency ratio. The proposed method is applied to prioritize different short courses in a management school.

Personality is a stable set of characteristics and tendencies, which specifies similarities, and
differences of people & apos; psychological behavior. Personality is normally a permanent behavior
through time and may not be comprehended easily or recognized as immediate or direct
situations due to some environmental and social stresses. In this paper, we study the
relationship between employees & apos; personality and ability types. The paper identifies that there
are some relationships between employees & apos; personality type and their ability. There is also a
relationship between introvert, extrovert, sensitive, emotive, thoughtful, considerate and
judging personality type and employees & apos; ability.

This paper conducts a simulation study of the effects of violating the ANOVA normality assumption in the presence of Weibull data. Twelve specific Weibull distributions, characterizing the life data of a variety of real-world products and systems, are investigated. Confidence intervals on test significance and power are generated and compared against intervals from normally distributed data. The ANOVA procedure is found to be robust in the majority of cases. Furthermore, a designed experiment is conducted to isolate the effects of the Weibull shape and scale parameters within the preceding study. The shape parameter is found to have a significant effect on significance and power, whereas the scale parameter does not have a significant effect at the target α = 0.05 test significance level.