The wear of cutting tools remains a major obstacle. The effects of wear are not only antagonistic at the lifespan and productivity, but also harmful with the surface quality. The present work deals with some machinability studies on flank wear, surface roughness, and lifespan in finish turning of AISI 304 stainless steel using multilayer Ti(C,N)/Al2O3/TiN coated carbide inserts. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), and combined effects of two cutting parameters, namely cutting speed and feed rate on lifespan (T), are explored employing the analysis of variance (ANOVA). The relationship between the variables and the technological parameters is determined using a quadratic regression model and optimal cutting conditions for each performance level are established through desirability function approach (DFA) optimization. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. In addition, it is indicated that the cutting time is the dominant factor affecting workpiece surface roughness followed by feed rate, while lifespan is influenced by cutting speed. The optimum level of input parameters for composite desirability was found Vc1-f1-t1 for VB, Ra and Vc1-f1 for T, with a maximum percentage of error 6.38%.

Silicon carbide (SiC) particulate impregnated Al 7075 matrix composite was fabricated by stir casting method and then heat treated to T6 condition. It was then machined with multiple layer of TiN coated tungsten carbide (WC) inserts in dry environment and pollution free Spray Impingement Cooling (SIC) environment to compare the machining performance. SIC environment presented better machining performance with respect to cutting tool temperature (T), average roughness of the machined surface (Ra) and tool flank wear (VBc). Quadratic response surface models were developed by computing the experimental data. Weighted Principal Component Analysis (WPCA) based Taguchi technique was adopted to optimize the multiple responses simultaneously, which resulted 40 m/min of cutting speed (V), 0.05 mm/rev of feed (f) and 0.2 mm of cutting depth (d) was the optimal combination of process parameters.

In Australia, Bioenergy plays an important role in modern power systems, where many biomass resources provide greenhouse gas neutral and electricity at a variety of scales. By 2050, the Biomass energy is projected to have a 40-50 % share as an alternative source of energy. In addition to conversion of biomass, barriers and uncertainties in the production, supply may hinder biomass energy development. The sugarcane is an essential ingredient in the production of Bioenergy, across the whole spectrum ranging from the first generation to second generation, e.g., production of energy from the lignocellulosic component of the sugarcane initially regarded as waste (bagasse and cane residue). Sustainable recovery of the Lignocellulosic component of sugarcane from the field through a structured process is largely unknown and associated with high capital outlay that have stifled the growth of bioenergy sector. In this context, this paper develops a new scheduler to optimise the recovery of lignocellulosic component of sugarcane and cane, transport and harvest systems with reducing the associated costs and operational time. An Optimisation Algorithm called Limited Discrepancy Search has been adapted and integrated with the developed scheduling transport algorithms. The developed algorithms are formulated and coded by Optimization Programming Language (OPL) to obtain the optimised cane and cane residues transport schedules. Computational experiments demonstrate that high-quality solutions are obtainable for industry-scale instances. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and criteria.

The hub location problem (HLP) is one of the strategic planning problems encountered in different contexts such as supply chain management, passenger and cargo transportation industries, and telecommunications. In this paper, we consider a reliable uncapacitated multiple allocation hub location problem under hub disruptions. It is assumed that every open hub facility can fail during its use and in such a case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service in which case a penalty must be paid. The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.

Article history: Received February 4 2016 Received in Revised Format August 16 2016 Accepted September 8 2016 Available online September 8 2016 The objective of the present work is to use a suitable method that can optimize the process parameters like pulse on time (TON), pulse off time (TOFF), wire feed rate (WF), wire tension (WT) and servo voltage (SV) to attain the maximum value of MRR and minimum value of surface roughness during the production of a fine pitch spur gear made of copper. The spur gear has a pressure angle of 200 and pitch circle diameter of 70 mm. The wire has a diameter of 0.25 mm and is made of brass. Experiments were conducted according to Taguchi’s orthogonal array concept with five factors and two levels. Thus, Taguchi quality loss design technique is used to optimize the output responses carried out from the experiments. Another optimization technique i.e. desirability with grey Taguchi technique has been used to optimize the process parameters. Both the optimized results are compared to find out the best combination of MRR and surface roughness. A confirmation test was carried out to identify the significant improvement in the machining performance in case of Taguchi quality loss. Finally, it was concluded that desirability with grey Taguchi technique produced a better result than the Taguchi quality loss technique in case of MRR and Taguchi quality loss gives a better result in case of surface roughness. The quality of the wire after the cutting operation has been presented in the scanning electron microscopy (SEM) figure. © 2017 Growing Science Ltd. All rights reserved

Article history: Received March 4 2016 Received in Revised Format April 27 2016 Accepted May 14 2016 Available online May 16 2016 We introduce a new robust simulation optimization method in which the probability of occurrence of uncertain parameters is considered. It is assumed that the probability distributions are unknown but historical data are on hand and using φ-divergence functionality the uncertainty region for the uncertain probability vector is defined. We propose two approaches to formulate the robust counterpart problem for the objective function estimated by Kriging. The first method is a minimax problem and the second method is based on the chance constraint definition. To illustrate the methods and assess their performance, numerical experiments are conducted. Results show that the second method obtains better robust solutions with less simulation runs. © 2016 Growing Science Ltd. All rights reserved

This study aims to investigate the multi-item inventory model in a production/rework system with multiple production setups. Rework can be depicted as the transformation of production rejects, failed, or non-conforming items into re-usable products of the same or lower quality during or after inspection. Rework is very valuable and profitable, especially if materials are limited in availability and also pricey. Moreover, rework can be a good contribution to a ‘green image environment’. In this paper, we establish a multi-item inventory model to determine the optimal inventory replenishment policy for the economic production quantity (EPQ) model for imperfect, deteriorating items with multiple productions and rework under inflation and learning environment. In inventory modelling, Inflation plays a very important role. In one cycle, production system produces items in n production setups and one rework setup, i.e. system follows (n, 1) policy. To reduce the deterioration of products preservation technology investment is also considered in this model. Holding cost is taken as time dependent. We develop expressions for the average profit per time unit, including procurement of input materials, costs for production, rework, deterioration cost and storage of serviceable and reworkable lots. Using those expressions, the proposed model is demonstrated numerically and the sensitivity analysis is also performed to study the behaviour of the model.

bENST-ex CT siege DG. SNVI, Route Nationale N° 5 Z. I.16012, Rouiba, Algerie c Departement de Construction Mecanique et Productique, FGM&GP, USTHB, BP 32 El-Alia, Bab-Ezzouar, 16111, Alger, Algerie C H R O N I C L E A B S T R A C T

When a supplier announces a price increase at a certain time in the future, for each retailer it is important to choose whether to purchase supplementary stock to take benefit of the current lower price or procure at a new price. This article focuses on the possible effects of price increase on a retailer's replenishment strategy for constant deterioration of items. Here, quadratic demand is debated; which is appropriate for the products for which demand increases initially and subsequently it starts to decrease with the new version of the substitute. We discuss two scenarios in this study: (I) when the special order time coincides with the retailer's replenishment time and (II) when the special order time falls during the retailer's sales period. We determine an optimal ordering policy for each case by maximizing total cost savings between special and regular orders during the depletion time of the special order quantity. Scenarios are established and illustrated with numerical examples. Through, sensitivity analysis important inventory parameters are classified. Graphical results, in two and three dimensions, are exhibited with supervisory decision.

Article history: Received August 31 2015 Received in Revised Format Septmber 26 2015 Accepted December 8 2015 Available online December 14 2015 In the competitive business world, applying a reliable and powerful mechanism to support decision makers in manufacturing companies and helping them save time by considering varieties of effective factors is an inevitable issue. Advanced Available-to-Promise is a perfect tool to design and perform such a mechanism. In this study, this mechanism which is compatible with the Make-to-Forecast production systems is presented. The ability to distinguish between batch mode and real-time mode advanced available-to-promise is one of the unique superiorities of the proposed model. We also try to strengthen this mechanism by integrating the inventory allocation and job shop scheduling by considering due dates and weighted earliness/tardiness cost that leads to more precise decisions. A mixed integer programming (MIP) model and a heuristic algorithm according to its disability to solve large size problems are presented. The designed experiments and the obtained results have proved the efficiency of the proposed heuristic method. © 2016 Growing Science Ltd. All rights reserved