Search results

Our project work is performed on take-down tension and production/min of a Mayer and Cie machine. The main output of this paper is that with increase in production, the take-down tension is also increased. In this project work we have established a relationship between take-down tension and production/min. When we divide the production/min by 0.038, we obtain the take down tension of the Mayer and Cie machine against the different production parameters of a full feeder lycra single jersey fabric.

This chapter examines elements of the regulatory framework in effect in the Brazilian Border Region and neighboring countries as they interact with elements of the culture industry. Located in what is referred to as the Southern Arc, the first city we examine, Foz do Iguaçu-PR, lies on the border between Paraguay and Argentina. The second city is Tabatinga-AM, part of the conurbation region made up by a Colombian city and including the Peruvian border, coming to be known as the Northern Arc.

Our research was produced through the triangulation of primary data obtained in two trips into the field, carried out in 2013 and 2014, secondary data (official and semi-official) and academic bibliography.

Although projects relating to border integration, citizenship and economic development do exist, they do not question or challenge a nationalistic and politicized regime of representation portraying border areas primarily as routes for cocaine traffic or home to terrorist cells. The representation regime disseminated by mainstream media thus reduces the rich color and dynamics of the region to impoverished tones of gray recognizable in terms of “the name of the other.”

This chapter provides a relevant contribution to our understanding of communication processes carried out in two different regions of Brazil, both of them located far from the spotlights of mainstream Brazilian media. We employ a theoretical framework that combines geography of communication with perspectives on communication in borderland regions.

The purpose of this paper is to provide insights into the rationale of Dutch pig farmers concerning animal welfare and animal‐friendly production. It aims to show the interrelations between farmers' production logic, their ideas about good farming and animal welfare and the characteristics of Dutch pig production.

In total, 62 Dutch pig farmers, participating in quality assurance schemes with different focuses on animal welfare, were interviewed about animal welfare, legislation, quality assurance schemes, and possibilities for animal‐friendly production.

Farmers' attitudes towards animal welfare and the implementation of animal welfare measures follow their understanding of good farming practices, which in turn are strongly influenced by the rationale of the market in which they operate. Two groups can be distinguished. Farmers operating in markets that focus on price and production‐efficiency tend to define animal welfare in terms of animal health and optimal zoo‐technical performance. Farmers operating in markets with a broader sense of quality, which incorporates values such as naturalness, animal welfare and care for the environment, define animal welfare in terms of the room the animals have to express natural behaviour.

This article provides insights into the perception of Dutch pig farmers about animal welfare and their readiness to change towards more animal‐friendly production methods. It points to the interrelations between animal welfare attitudes and behaviour, farmers' perception of good farming and the production logic of the farm. These interrelated influences should be borne in mind by policy makers and researchers seeking to raise welfare levels in pig production.

In the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored. The goal of addressing the issue is to reduce delivery times and system costs for retailers so that routing and distributor location may be determined.

By adding certain unique criteria and limits, the issue becomes more realistic. Customers expect simultaneous deliveries and pickups, and retail service start times have soft and hard time windows. Transportation expenses, noncompliance with the soft time window, distributor construction, vehicle purchase or leasing, and manufacturing costs are all part of the system costs. The problem's conceptual model is developed and modeled first, and then General Algebraic Modeling System software (GAMS) and Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms are used to solve it in small dimensions.

According to the mathematical model's solution, the average error of the two suggested methods, in contrast to the exact answer, is less than 0.7%. In addition, the performance of algorithms in terms of deviation from the GAMS exact solution is pretty satisfactory, with a divergence of 0.4% for the biggest problem (N = 100). As a result, NSGAII is shown to be superior to MOSPSO.

Since this paper deals with two bi-objective models, the priorities of decision-makers in selecting the best solution were not taken into account, and each of the objective functions was given an equal weight based on the weighting procedures. The model has not been compared or studied in both robust and deterministic modes. This is because, with the exception of the variable that indicates traffic mode uncertainty, all variables are deterministic, and the uncertainty character of demand in each level of the supply chain is ignored.

The suggested model's conclusions are useful for any group of decision-makers concerned with optimizing production patterns at any level. The employment of a diverse fleet of delivery vehicles, as well as the use of stochastic optimization techniques to define the time windows, demonstrates how successful distribution networks are in lowering operational costs.

According to a multi-objective model in a three-echelon supply chain, this research fills in the gaps in the link between routing and location choices in a realistic manner, taking into account the actual restrictions of a distribution network. The model may reduce the uncertainty in vehicle performance while choosing a refueling strategy or dealing with diverse traffic scenarios, bringing it closer to certainty. In addition, two modified MOPSO and NSGA-II algorithms are presented for solving the model, with the results compared to the exact GAMS approach for medium- and small-sized problems.

This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located.

The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms.

According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO.

In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered.

The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs.

This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

Model the drivers of Australian housing affordability and forecast equilibrium affordability. The paper aims to discuss these issues.

Uses autoregressive distributed lag (ARDL) approach to model housing affordability measured by the Housing Industry Association's Housing Affordability Index (HAI) and the housing price-earnings multiplier (HPE). Six sets of explanatory variables, including housing finance, housing construction activity and costs, economic growth, population, alternative investments and taxation.

Primary long-run drivers are housing finance, dwelling approvals and financial assets. Economic and population growth only have a short-run influence, while housing taxation has limited impact in long run. Forecasts indicate long-run HAI equilibrium values of 109 (above the historical minimum of 107) and a HPE of seven (below the recent historical maximum of 8.2).

Reduced form model encompassing both demand and supply factors involves complicated interpretation given direct and indirect effects on affordability. Analysis at national level ignores regional impacts that may also affect housing affordability.

The impact of the low rate of new dwelling approvals (public and private sector in the long run and public sector in the short run) points to a persistent structural gap between the demand and supply of housing. Strong economic and population growth often blamed for the worsening of housing affordability, at least in the 2000s, has no impact at the aggregate national level.

Only known paper to provide quantitative estimates of macro drivers of Australian housing affordability over a long period using alternative measures of relative housing affordability.