Optimal location of spelling yards for the northern Australian beef supply chain

García-Flores, Rodolfo; Higgins, Andrew; Prestwidge, Di; McFallan, Stephen

doi:10.1016/j.compag.2014.01.015

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…The model used for identifying the best import quota policy of Fruits and Vegetables. García-Flores et al (2014) presented a mathematical optimization model aims at allocating the optimal location of cattle rest sites and the optimal flows from breeding farms to ports, abattoirs and sale-yards. Bortolini et al (2016) developed a three-objective distribution planner to tackle the tactical optimization issue of a fresh food distribution network.…”

Section: Multi-objective Optimization In Food Supply Chainsmentioning

confidence: 99%

The fuzzy multi-objective distribution planner for a green meat supply chain

Mohammed

Wang

2017

International Journal of Production Economics

123

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It is often a complex task in developing a product distribution plan for a supply chain (SC) network and a supportive decision tool can be useful for easing the role of decision makers. Moreover, it has been increasingly becoming a demand to design a supply chain network considering the environmental impact as a new dimension as required by authorities in many countries. This paper describes a development of a product distribution planner for a threeechelon green meat supply chain (MSC) design in terms of issues which include numbers and locations of facilities that should be opened in association with the product quantity flows. The problem was formulated into a fuzzy multi-objective programming model (FMOPM) with an aim to minimize the total transportation and implementation cost, impact on environment in particularly CO2 emissions and the distribution time of products from farms to abattoirs and from abattoirs to retailers and maximize the average delivery rate in satisfying product quantity as requested by abattoirs and retailers. The model was also formulated for handling the uncertainties of input data of the considered MSC. To optimize the four objectives simultaneously, three solution methods were investigated and used; these include methods of LP-metrics, ε-constraint and goal programming. The best solution by comparing the obtained Pareto solutions was determined using the Max-Min method. The implementation of the developed model based on a case study has also proved its applicability in making an optimal product distribution plan in trade-offs among the four objectives.

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Section: Multi-objective Optimization In Food Supply Chainsmentioning

confidence: 99%

The fuzzy multi-objective distribution planner for a green meat supply chain

Mohammed

Wang

2017

International Journal of Production Economics

123

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“…Thekdi and Lambert made use of a wealth of data not previously available, thanks to increased digitisation of transport operations, advances in satellite imagery, increased data storage capacities, and increased access to public data resources. Similarly, Higgins et al (2013) and García-Flores et al (2014) used recently collected data sets related to cattle movements to assess the robustness of road and cattle-producing infrastructure for the northern states of Australia, which is prone to disruption due to environmental change. The three models presented are strategic and operational, and cover simulation and optimisation aspects in order to provide recommendations on infrastructure repairs and new investments.…”

Section: Simulationmentioning

confidence: 99%

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

Flores¹,

Banerjee²,

Mathews³

2019

Preprint

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When stakeholders commit to building infrastructure as part of strategic, long-term planning, the final facilities are not normally amenable to modification after completion. A consequence of this is that users are forced to operatewithin the original specifications for, at least, as long as it takes to carry out major refurbishments or retrofitting, and even then, the constraints imposed by the original layout may be inescapable.On one hand, the original infrastructure plans enhance (or limit) the users' ability to operate efficiently for years to come. As time passes and the payback period approaches, changing operating conditions and unforeseen bottlenecks in the original blueprint can, at best, affect the economic returns and, at worst, defeat the purpose of the whole project (see, for example, Castellon airport in Spain, which was built but is grossly underutilised), producing unanticipated economical, social and political repercussions. On the other hand, managers and operators (that is, those living with the consequences of the strategic planning) have some leeway to compensate for miscalculations by means of their tacticaland operational planning.In this chapter, we explore the use of quantitative techniques to, first, amend bottlenecks and uncertain market and operating conditions that affect the performance of infrastructure investments (the tactic and operational levels), and second, validate the effectiveness of the original infrastructure design (the strategic level) under these changing conditions.More specifically, we present a rail scheduling case study where we combine demand forecasting using Machine Learning techniques and formal Operations Research methods to assess and maximise the value of already-existing infrastructure.Rail scheduling is a typical optimisation problem popular in the literature, but its potential value is bounded not only by its technical properties and specifications (how good the algorithm is) but also by the accuracy of data feeding the algorithm. Such data is critical in specifying the demand thata facility will experience in the future, and the costs that will be incurred to operate it. The use of intensive data analytics and appropriate Machine Learning techniques can resolve this and provide a substantial competitive edge forinvestors and operators of rail inter-modal terminals.We anticipate that Machine Learning algorithms that predict future demand, coupled with optimisation techniques that streamline operations of facilities, can be integrated to create tools that help policy makers and terminal operators maximise the value of their current infrastructure, while meeting ever-changing demand.

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“…For the external components, the Excel interface captures input data regarding existing and proposed infrastructure, demand, intake levels and capacities, and retrieves the results in spreadsheet format. It also has the capacity to display input and solutions as maps in KML format, by invoking Google Earth 6 . CPLEX is a commercial solver for mixed integer linear programming problems.…”

Section: Fig 2 Umlmentioning

confidence: 99%

“…http://jscience.org/, accessed on the 30 of October 2014 6. https://www.google.com/earth/, accessed on the 30 of October 2014.…”

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confidence: 99%

A Framework for Optimal Assessment of Planning Investments in Urban Water Systems

García-Flores

Moglia

Marlow³

2015

IFIP Advances in Information and Communication Technology

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Part 7: Analytics and VisualizationInternational audienceThe functionality expected by governments and citizens from urban water management systems (UWMS) has evolved in time from delivering basic services to enabling complex issues such as healthy ecosystems, environmental sustainability, and economic growth. Alongside these changing expectations, the pressure on policy makers to fulfill disparate performance metrics with diminishing resources is increasing. In this paper, we introduce the Water Assets and Infrastructure Network Decision Support (WAND) framework for modelling and planning urban water systems with the aim of assisting medium to long-term investment decisions. The framework combines economic performance measures with liveabililty and sustainability, and uses optimisation as a crucial component. To demonstrate its feasibility, we present a prototype environmental software system built upon the principles of legibility, adherence to engineering conventions, and extensive unit testing. We use the prototype system to analyse a model of a hypothetical urban water system formed by two coupled water networks, one for freshwater and one for stormwater collection, and which handles six different commodities. Our results suggest to planners the optimal combination of planning investments while considering capacities, service levels and network operating conditions

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Optimal location of spelling yards for the northern Australian beef supply chain

Cited by 11 publications

References 11 publications

The fuzzy multi-objective distribution planner for a green meat supply chain

The fuzzy multi-objective distribution planner for a green meat supply chain

Using Optimisation and Machine Learning to Validate the Value of Infrastructure Investments

A Framework for Optimal Assessment of Planning Investments in Urban Water Systems

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