Total Setup Research Articles

Selection of Different Trees/Shrubs Species for Rehabilitation of Degraded Lands in Wag-Lasta Area, Northeastern Ethiopia

Due to human and livestock population pressure, land degradation is the main problem in many part of Ethiopia. To avert the situation, tree and shrub species selection on degraded lands are valuable tool for ecological restoration. Therefore, the aim of this study was to select the best performing tree and shrub species in three agro-ecological zones of Waglasta area, Ethiopia. The experiment was conducted in randomized block design with three replications in highland, mid altitude, and lowland sites. For total experimental setup 21 (seven for lowland, eight for mid altitude and six for highland) tree and shrub species were used. Growth and survival rate data were collected every 3 months interval for one year and every 6 months up to the end of the experiment. The result indicated that L. pallid (2.52 ± 0.19 m) at lowland site, Acacia senegal (1.32±0.23 m) at mid altitude and Acacia decurrens (4.0 ± 0.46 m) at highland site had shown better performance in height. Similarly, Moringa stenopetala at lowland, Jatropha carcus at mid altitude and Acacia saligna at highland site had shown 8.63 ± 2.37 cm, 3.1 ± 0.2 cm and 7.06 ± 0.75 cm performance in root collar diameter, respectively. However, the survival rate was higher for A. senegal (43 ± 7%) and M. stenoptella (44 ± 17%) at lowland site, A. senegal (98 ± 2%) at mid altitude and A. saligna (63.9 ± 20.0%) at highland site. Therefore, to restore degraded lands and to support the on-going land rehabilitation programs of Wag-lasta area, we recommend A. senegal and M. stenoptella for lowland site, A. senegal for mid altitude areas and A. saligna for highland site of Lalibela, Abergele and Sekota and similar agro-climatic zones. However, additional studies regarding soil nutrient dynamics, fostering effect and tree nursing should be integrated to develop sound ecological restoration strategies in the growth corridor as well as in the region.

Multi-objective scheduling in hybrid flow shop: Evolutionary algorithms using multi-decoding framework

Hybrid flow shops are common manufacturing environments applied in many industrial fields. This paper tackles the scheduling problem in hybrid flow shop with unrelated machines, machine eligibility and sequence-dependent setup times (SDST) to minimize the bi-criteria of total tardiness and total setup time. Evolutionary algorithms (EAs) are adopted to solve the problem. Firstly, four efficient decoding algorithms using different machine selection rules are developed for constructing a schedule from a job permutation. These decoding algorithms are able to map the job permutation space to distinct regions in the objective space. Then, we propose a multi-decoding framework (MDF) for taking advantage of multiple decoding algorithms along one evolution path. The hybridization of MDF and EAs leads to a hyper-heuristic approach. The proposed MDF is coupled with a genetic algorithm to solve the problem in “a priori” approach, that is, to optimize a convex combination of the objectives given user preference information. The framework is also embedded to a multi-objective genetic algorithm, known as NSGA-II, to solve the problem in “a posteriori” approach, which aims at approximating the Pareto-optimal set for the user to make posterior decisions. The efficiency of the proposed methods is validated by numerical results. More specifically, when “a priori” approach is used, the proposed MDF helps EAs adjusting the adopted decoding scheme and generating solution aligned to the user preference; when “a posteriori” approach is applied, the MDF extends the search space and improves the solution quality.

Experimental investigation regarding emissivity of black nickel coated on aluminium surface

Coating on the surface of the material create impact on their property especially emissivity is desirable property for the radiation absorbing part in solar related projects and equipments. In this experimental investigation discussed about the emissivity of the aluminium which is coated with black Nickel. This coating has the thickness of 0.2 mm on the surface. Emissivity of the coated surface obtained through traditional relation of black body and polished body surface temperatures measurements. Temperature measurements completed by the thermocouples connected through corresponding surfaces total setup placed in closed chamber for accurate outcome of experimental conditions.

Sequencing mixed-model assembly lines with demand management: problem development and efficient multi-objective algorithms

As a kind of production line, the mixed-model assembly line (MMAL) has been progressively adopted by industries to satisfy the diversification of customer demands. This study applied an MMAL sequencing problem for a make-to-order (MTO) strategy. Using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach, this study sorted customer orders, satisfying these by a desired due date being the main feature in MTO systems. This MMAL problem was investigated to achieve three minimization objectives: total set-up cost, number of work overload situations, and total earliness and tardiness costs, according to the specified priority of orders. Accordingly, a multi-objective particle swarm optimization (MOPSO) algorithm was developed for the proposed NP-hard model and the algorithm parameters were tuned by applying the Taguchi technique to the experimental design. The computational results of a comparison between the proposed MOPSO model and four multi-objective algorithms confirmed its advantages.

MULTI-OBJECTIVE ENERGY EFFICIENT MIXED MODEL ASSEMBLY LINE SEQUENCING FOR SUSTAINABLE MANUFACTURING

The sustainability of manufacturing systems is not thoroughly investigated in the existing literature despite that fact that it affects service-oriented organisations. This paper addresses this gap by incorporating the criterion of energy consumption in mixed model assembly line sequencing (MMALS) to explore the potential for energy saving. Energy consumption was integrated with makespan and sequence dependent setup time of mixed models to achieve the sustainability in sequencing. A mathematical model was developed for the optimisation of energy efficient MMAL (EEMMAL). The multi-objective intelligent genetic algorithm (MOIGA) was proposed for minimisation of three conflicting objectives. A case study was conducted using centrifugal pump assembly line to test the performance of proposed MOIGA and the results were compared with those obtained from multi-objective genetic algorithm (MOGA). Finally, a trade-off analysis was conducted between total setup time, makespan (a service level measure on shop floor) and energy consumption (a factor of environmental sustainability). This analysis indicated the effectiveness of MOIGA (compared to MOGA) for EEMMALS problems.

Integrated production scheduling and distribution allocation for multi-products considering sequence-dependent setups: a practical application

This paper proposes an integrated multi-product production planning and distribution allocation in the supply chain network, including the detailed production sequences in each production line for a dairy manufacturing. The models are designed for manufacturers that produce multi-products. One of the concerns of the multi-products manufacturers is the sequence-dependent changeover times and costs that occur when switching from one type of product to another. The models are developed into two-stages due to different levels of decisions. The first stage models the supply chain network that determines the optimum production planning for each product type on each production line, delivery schedule, and inventory planning for the entire supply chain simultaneously to maximize the total profit. Sequentially, based on the optimal production planning, detailed production schedules are then optimized in the second stage of the model with minimizing total setup costs as the objective. The result shows that the proposed models significantly reduce the total cost and consequently increase the annual gross profit.

An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints

Abstract The flexible job shop scheduling problem is a very important problem in factory scheduling. Most of existing researches only consider the processing time of each operation, however, jobs often require transporting to another machine for the next operation while machines often require setup to process the next job. In addition, the times associated with these steps increase the complexity of this problem. In this paper, the flexible job scheduling problem is solved that incorporates not only processing time but setup time and transportation time as well. After presenting the problem, an improved genetic algorithm is proposed to solve the problem, with the aim of minimizing the makespan time, minimizing total setup time, and minimizing total transportation time. In the improved genetic algorithm, initial solutions are generated through three different methods to improve the quality and diversity of the initial population. Then, a crossover method with artificial pairing is adopted to preserve good solutions and improve poor solutions effectively. In addition, an adaptive weight mechanism is applied to alter mutation probability and search ranges dynamically for individuals in the population. By a series of experiments with standard datasets, we demonstrate the validity of our approach and its strong performance.

Enhancing Benders decomposition algorithm to solve a combat logistics problem

This paper proposes a multi-time period, two-stage stochastic programming model for the design and management of a typical combat logistics problem. The design shall minimize the total path setup cost, commodity preposition and processing costs, and expected transportation, storage, and shortage costs across all possible path failure scenarios. Due to the complexity associated with solving the model, we propose an accelerated Benders decomposition algorithm to solve the model in a realistic-size network problem within a reasonable amount of time. The Benders decomposition algorithm incorporates several algorithmic improvements such as pareto-optimal cuts, multi-cuts, knapsack inequalities, integer cuts, input ordering, mean-value cuts, and the rolling horizon heuristic. Computational experiments are performed to assess the efficiency of different enhancement techniques within the Benders decomposition algorithm.

Bus manufacturing workshop scheduling method with routing buffer

Aiming at solving the problem that the moving route is complicated and the scheduling is difficult in the routing buffer of the bus in the manufacturing workshop, a routing buffer mathematical programming model for bus manufacturing workshop is proposed. We design a moving approach for minimizing the total setup cost for moving in routing buffer. The framework and the solution ofthe optimization problem of such a bus manufacturing workshop scheduling with routing buffer arepresented. The evaluation results show that, comparing with the irregularly guided moving method, the proposed method can better guide the bus movement in routing buffer by reducing the total setup time of all buses processed at the next stage, and obtaining a better scheduling optimization solution with minimize maximum total completion time.

Bus manufacturing workshop scheduling method with routing buffer

Aiming at solving the problem that the moving route is complicated and the scheduling is difficult in the routing buffer of the bus in the manufacturing workshop, a routing buffer mathematical programming model for bus manufacturing workshop is proposed. We design a moving approach for minimising the total setup cost for moving in routing buffer. The framework and the solution of the optimisation problem of such a bus manufacturing workshop scheduling with routing buffer are presented. The evaluation results show that, compared with the irregularly guided moving method, the proposed method can provide a better guide line for the bus movement in the routing buffer by reducing the total setup time of all buses processed at the next stage, and a better scheduling optimisation solution with minimise maximum total completion time.

SMED methodology applied to the deep drawing process in the automotive industry

The automotive industry has evolved, becoming increasingly demanding. Thus, it becomes necessary to increase the availability of equipment, reacting efficiently to be able to respond to customer requests. Nowadays, the strong development of this industry is based on three main pillars: competitiveness, product quality and response time to requests. The focus of this work is to increase the availability of a deep drawing machine with extremely high setup time, with the main objective of reducing the average setup time by 20%. This reduction is even more important when it comes to the internal setup time, the time the machine is stopped. Thus, the Single-Minute Exchange of Die technique was applied and work standards were improved to reduce the equipment setup time, increasing the machine availability, leading to a production increased output. As a result of this work, it was possible to standardize setup, reducing 38% of total machine setup time, 53% of internal setup time and increasing 7.7% of OEE (Overall Equipment Effectiveness) availability.

A shifting bottleneck procedure with multiple objectives in a complex manufacturing environment

Scheduling problems have been studied extensively over the years. Broad ranges of focus areas exist, from optimization to heuristics, production planning to production sequencing, customized algorithms to general-purpose algorithms, and from simple machines to complex environments. In this research, a heuristic approach has been proposed to overcome the scheduling problem on a complex job shop found at a manufacturer of commercial building products. The research is aimed at sequencing production orders in near-real-time, primarily to minimize total tardiness, but also to reduce total setup time. A layered Shifting Bottleneck Procedure is employed, with the top layer determining release dates and due dates for individual jobs, and the bottom layer applying algorithms to individual work centers. The outcome of this research is a better production schedule than current methods with minimal computation cost. The proposed framework performs well and could be applied to other production areas.

Solution Approach for Solving Multi-Objective Single-Machine Problem with Sequence-Dependent Setup Times and Setup Costs

A single machine scheduling problem with sequence-dependent setup times and setup costs has a critical role in manufacturing and service sectors to minimize orders delays and costs. This study provides multiple objectives model to solve the single machine total weighted tardiness and setup costs scheduling problem associated with sequence-dependent setup times and sequence-dependent setup costs (MOSTWTSCSD). The objectives are to minimize the total weighted tardiness and the total setup costs for all jobs without any certain relationship between the setup time and the setup cost, as happened in some machines. An alternative heuristic procedure based on discrete particle swarm optimization (DPSO) is suggested to find representative Pareto front (non-dominated) solutions. Many efficient methods have been used inside the DPSO to improve its performance. Results show that the ability of the suggested solution approach to provide a representative Pareto fronts in reasonable computational efforts compared with another heuristic.

Solving a flexible job shop lot sizing problem with shared operations using a self-adaptive COA

This paper deals with lot sizing decisions in a flexible job shop manufacturing system considering dependencies between lot sizing decisions. A novel mathematical model is developed to optimise lot sizing and scheduling simultaneously using a product-oriented approach as opposed to the job-oriented approach which has been prevalently considered in the literature. Moreover, the proposed model considers further underlying assumptions such as assembly operations, sequence-dependent setup times, initial inventory and safety stock levels, as well as lots with unequal and variable sizes. The objective is to minimise the total production, setup, and tardiness penalty costs of the system. To solve the formulated problem, a self-adaptive Cuckoo Optimisation Algorithm (COA) embedded with three new immigration mechanisms is developed. Numerical experiments are conducted to demonstrate the validity of the model and investigate the efficiency and effectiveness of the employed optimisation algorithm.

Propofol use in children: updates and controversies.

Advantages of propofol use in children may include less airway complications, less emergence agitation, and less postoperative behavioral changes. However, needle phobia and the complexity of total intravenous anesthesia set-up, as well as the pharmacokinetic and pharmacodynamic restrictions may limit the wide use of propofol-based anesthesia in the form of total intravenous anesthesia. Furthermore, pediatric infusion models and monitoring techniques are not fully validated yet. The choice of anesthesia type in children seems to be the result of a complex interplay between many factors related to the patient and the provider as well as logistic and operational factors that contribute to the decision-making process. Propofol has earned its place as a valuable choice in pediatric anesthesia. In addition, propofol and inhalation anesthesia should not be looked at as mutually exclusive; a combination of both may sometimes be the best approach to complex clinical dilemmas.

The continuous single-source capacitated multi-facility Weber problem with setup costs: formulation and solution methods

The continuous single-source capacitated multi-facility Weber problem (SSCMFWP) where setup cost of opening facilities is taken into account is investigated. The aim is to locate a set of facilities on the plane, to define their respective capacities which can be linked to the configuration of the processing machines used, and to allocate customers to exactly one facility with the objective being the minimisation of the total transportation and setup costs. A new nonlinear mathematical model based on the SSCMFWP is introduced where Rectilinear and Euclidean distances are used. Efficient metaheuristic approaches based on Variable Neighbourhood Search and Simulated Annealing are also developed. The proposed metaheuristics incorporate an exact method and the commonly used Cooper’s alternate location-allocation method. We also constructed a new data set to reflect the characteristic of this particular location problem as no data set is available in the literature. Computational experiments show that the proposed metaheuristics generate interesting results for this class of continuous location problems.

Development of X-ray fluorescence methods by use of a Mo X-ray tube and their application in the case of biological samples

In the present work two different experimental arrangements, aiming in the X-ray fluorescence analysis of solid and liquid biological samples respectively, were developed and studied both theoretically and experimentally in combination with a Mo anode X-ray tube: The secondary target XRF (STXRF) and the Total Reflection XRF set-up (TXRF). The secondary target XRF irradiation chamber was designed and constructed in our laboratory. Careful choice of the materials combined with the tight geometry used, minimize the presence of interference peaks in the spectrum and offer high intensity of the exciting beam at the sample position. Optimum performance was ensured by means of an extended theoretical and experimental study of various parameters affecting the analytical capabilities of secondary target XRF systems, such as: The choice of secondary target material and the operational conditions of the X-ray tube. The secondary target XRF set-up was applied in the determination of the concentrations of trace elements in a pool blood serum sample. A simple preparation technique, like the freeze-drying process was employed, resulting in detection limits of 15 ng/ml for most metal trace elements and for a 6000 sec measuring time. The accuracy of the whole experimental procedure, examined by internal standardization and by analyzing the reference standard material A-13, was found to be about 4% over the whole atomic number range. For the determination of the mass absorption coefficients, necessary in the quantification step, a modified version of the emission transmission method was employed. Total Reflection X-Ray Fluorescence (TXRF) is a well-established method, mainly applied in the analysis of liquid samples, offering very low detection limits in most of the cases. Direct application of the TXRF method is not so efficient in blood serum analysis, since the high content of the organic matrix increases significantly the background due to Compton scattering. Chemical treatment of the blood serum samples and related preconcentration techniques have been suggested in literature, but they are time consuming and increase the possibility of adding contaminants in the sample. In this work, the applicability of direct TXRF analysis in blood serum samples was examined. The insertion of a Mo filter after the cut-off reflector has been found to improve significantly the peak to background ratio, especially for the elements of interest such as Cu, Zn, Se and Br. The influence of self-absorption phenomena in the quantification procedure was also investigated with respect to the internal standard used and the sample mass analyzed. Precision and accuracy in the analysis was found to be about 4% over the whole atomic number range. Detection limits were in the order of 25ng/ml for most metal trace elements and a measuring time of 3600 sec.

A two-stage stochastic model for location planning of temporary medical centers for disaster response

Devastating effects of disasters and global crises on people increase the importance of humanitarian logistics studies for pre and post-disaster stages. Location planning of Temporary Medical Centers/field hospitals is one of the most important problems for disaster response. We aimed to determine the location and number of temporary medical centers in case of disasters by considering the locations of the existing hospitals, casualty classification (triage), capacities of medical centers and possibilities of damage to the roads and hospitals. Besides, we aimed to assign different casualty classes to these medical centers for emergency medical response by considering the distances between disaster areas and medical centers. For this purpose, a two-stage stochastic programming model was developed. The proposed model finds an optimal TMC location solution while minimizing the total setup cost of the TMCs and the expected total transportation cost by considering casualty types, demand, possibilities of damage to the roads and hospitals, and distance between the disaster areas and the medical centers. In the model, α-reliability constraints for the expected number of unassigned casualties were also used. Besides, the model was reformulated without triage, in order to understand the impact of casualty classification on the solution of the problem. We performed a real case study for the district of Kartal expected to be widely damaged in the possible Istanbul earthquake, and a sensitivity analysis was made. The analysis of the results offer some managerial insights associated with the number of temporary medical centers’ needed, their locations, and additional hospital capacity requirements.

High-Throughput Satellite Characterization: Spot beam payload characterization of high-throughput satellites in a compact antenna test facility

An innovative measurement methodology is developed and successfully implemented for a spot beam antenna and payload characterization of high-throughput satellites (HTSs) in a compact antenna test facility (CATF). With the developed measurement methodology, total measurement time and measurement setup duration are reduced by more than 70% with respect to conventional measurement techniques. The proposed methodology is successfully implemented in multiple spacecraft (SC) of the Indian Space Research Organisation (ISRO).

Use of Partial Cone Beam CT (pCBCT) Reduces Total Setup Images and Estimated Radiation Exposure for Breast Radiation Verification Simulation

Electronic portal imaging (EPI) is standardly employed for patient setup verification in breast radiotherapy (RT). However, such imaging can be associated with substantial radiation exposure, particularly when repeated imaging is required to optimize patient positioning. This study aims to evaluate the impact of an institutional protocol using partial cone beam computer tomography (pCBCT) in addition to standard EPI on overall imaging-related radiation exposure during verification simulation (vsim) in breast RT. In 3/2016, our institution implemented a protocol for vsim in breast RT that newly utilized kilovoltage pCBCT for alignment at isocenter prior to obtaining standard megavoltage EPI. Patient demographic and treatment data including number of treatment fields, technique, laterality, supine vs. prone positioning, and use of breath hold procedure was collected for 2 temporal cohorts: pre-implementation of the protocol from 12/2013-2/2016, and post-implementation from 3/2016-4/2018. Number of EPI for each field taken and corresponding monitor unit (MU) settings were recorded for each patient’s vsim. Chi square tests compared the percentage of patients who received ≥2 double exposure EPI (one open and one shaped field) per treatment field during vsim between cohorts. Overall radiation exposure from all imaging modalities used during vsim was estimated. Data from 768 vsims for 3D conformal breast cancer treatments were collected from 12/2013 to 04/2018. 330 underwent vsim with EPI guidance only, and 440 were underwent vsim following implementation of the protocol with the use of pCBCT in addition to EPI. The addition of pCBCT was associated with significantly fewer patients receiving ≥2 EPI per field during vsim as compared to the pre-pCBCT cohort (35% vs. 64%, respectively, p<0.001). Most patients (94%) required only 1 pCBCT. The mean number of EPI per field was 2.9 in pre-pCBCT cohort and 2.4 with use of pCBCT protocol. Number of treatment fields, breast laterality, supine vs. prone positioning, and use of breath hold procedure were not significantly associated with ≥2 EPI per field; all subgroups required fewer images per field with the addition of pCBCT. Given that a single pCBCT confers an estimated scan dose of 1.8cGy and a double exposure EPI with 2 MU per field confers 2 cGy, we estimated that use of the CBCT protocol reduced imaging-related radiation exposure by at least 2.2cGy for 2-field, 4.2cGy for 3- field, and >6.2 cGy for >3 field treatments across patients during vsim. Our study demonstrates that the addition of pCBCT to vsim procedures may reduce the number of EPI required for patient setup in breast RT. These data support implementation of such CBCT protocols as a means to streamline setup and reduce imaging-related radiation exposure in this patient population.