scholarly journals Integrated optimization and simulation models for short-term open-pit mine planning

2020 ◽  
Vol 120 (11) ◽  
Author(s):  
V.F. Navarro Torres ◽  
G.R. Mateus ◽  
A.G. Martins ◽  
W. Carneiro ◽  
L.S. Chaves
Keyword(s):  
Linear Programming ◽  
Decision Support ◽  
Discrete Event Simulation ◽  
Support System ◽  
Discrete Event ◽  
Mine Planning ◽  
Integrated Models ◽  
Short Term ◽  
Mining Operations ◽  
Event Simulation

SYNOPSIS Operational mine planning is a fundamental activity in mine operations and should take into account various characteristics of the material, the available mining faces, the requirements of discharge points, and production hiatuses due to reduced equipment operational efficiency, in order to efficiently allocate shovels and trucks and deliver the required tonnage and quality to the proper destinations. This paper presents an approach for optimizing short-term day-to-day mining operations using simulation. A mathematical model based on integer linear programming is developed. The solution is obtained through two different software packages using discrete event simulation (Arena) and a mathematical optimization model (Lingo). The two integrated models search an efficient solution to optimize a set of criteria by applying goal programming to hierarchically optimize five objective functions in a logical priority order under the operator's standpoint and by simulating mining operations and unproductive events to evaluate how closely the optimized results are actually achieved. The integrated models are applied to a real large-scale iron ore mine in southeastern Brazil. A decision support system (DSS) prototype that meets the production requirements is also applied. The results show that an increase in the available loading equipment will not result necessarily in increased production, as expected. The models show satisfactory results and applicability to real and complex mining situations, and the formulation allows for easy adaptation to other mine situations. Keywords: discrete event simulation, optimization, decision support system, mine planning, linear programming.

Success Surrogates in Representational Decision Support Systems

2005 ◽  
pp. 2672-2677
Author(s):  
Roger McHaney
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Software Development ◽  
Discrete Event Simulation ◽  
Support System ◽  
Support Systems ◽  
Discrete Event ◽  
Event Simulation ◽  
Business Environments ◽  
New Software Development

Rapid and frequent organizational change has been a hallmark of business environments in the past two decades. Frequently, technology and new software development are embraced as aspects of complex strategies and tactical plans. Without sufficient analysis, the unforeseen consequences of change can result in unexpected disruptions and the loss of productivity. In order to better control these contingencies, modern managers often employ a variety of decision support aids. One such aid, classified as a representational decision support system, is discrete event simulation (DES).

Representational Decision Support Systems Success Surrogates

2011 ◽  
pp. 3268-3272
Author(s):  
Roger McHaney
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Software Development ◽  
Discrete Event Simulation ◽  
Support System ◽  
Support Systems ◽  
Discrete Event ◽  
Event Simulation ◽  
Business Environments ◽  
New Software Development

Rapid and frequent organizational change has been a hallmark of business environments in the past two decades. Frequently, technology and new software development are embraced as aspects of complex strategies and tactical plans. Without sufficient analysis, the unforeseen consequences of change can result in unexpected disruptions and the loss of productivity. In order to better control these contingencies, modern managers often employ a variety of decision support aids. One such aid, classified as a representational decision support system, is discrete event simulation (DES).

Integrating fuzzy case-based reasoning and discrete-event simulation to develop a decision support system for part-fixture assignment and fixture flow control

2019 ◽  
Vol 14 (2) ◽  
pp. 312-338 ◽  
Author(s):  
Fentahun Moges Kasie ◽  
Glen Bright
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Flow Control ◽  
Discrete Event Simulation ◽  
Support System ◽  
Discrete Event ◽  
Case Based Reasoning ◽  
Content Type ◽  
Event Simulation ◽  
Case Based

Purpose This study aims to propose a decision support system (DSS) that performs a decision-based part-fixture assignment and fixture flow control in planned production periods. Design/methodology/approach The principal approaches were fuzzy case-based reasoning (FCBR) and discrete-event simulation (DES). Besides, the fuzzy analytic hierarchy process (FAHP), an object-oriented (OO) method and a fuzzy weighted Euclidean distance were used to support the decision-making process. Findings It shows that integrating FCBR and DES systems is a promising approach to address part-fixture planning problems. The FCBR subsystem proposed various stable numbers of fixtures as scenarios. The DES model analyzed the future performances of these scenarios and identified the best alternative. Research limitations/implications The DSS was tested in laboratory environments using a numerical analysis; however, it was not validated in industrial situations. Originality/value The synergy of integrating FCBR and DES systems was not exploited in the past in part-fixture assignment and fixture flow control problems.

Network resilience modelling: a New Zealand forestry supply chain case

2020 ◽  
Vol 31 (2) ◽  
pp. 291-311
Author(s):  
Paul Childerhouse ◽  
Mohammed Al Aqqad ◽  
Quan Zhou ◽  
Carel Bezuidenhout
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Supply Chains ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Network Resilience ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Event Simulation ◽  
Export Supply

PurposeThe objective of this research is to model supply chain network resilience for low frequency high impact disruptions. The outputs are aimed at providing policy and practitioner guidance on ways to enhance supply chain resilience.Design/methodology/approachThe research models the resilience of New Zealand's log export logistical network. A two-tier approach is developed; linear programming is used to model the aggregate-level resilience of the nation's ports, then discrete event simulation is used to evaluate operational constraints and validate the capacity of operational flows from forests to ports.FindingsThe synthesis of linear programming and discrete event simulation provide a holistic approach to evaluate supply chain resilience and enhance operational efficiency. Strategically increasing redundancy can be complimented with operational flexibility to enhance network resilience in the long term.Research limitations/implicationsThe two-tier modelling approach has only been applied to New Zealand's log export supply chains, so further applications are needed to insure reliability. The requirement for large quantities of empirical data relating to operational flows limited the simulation component to a single regionPractical implicationsNew Zealand's log export supply chain has low resilience; in most cases the closure of a port significantly constrains export capacity. Strategic selection of location and transportation mode by foresters and log exporters can significantly enhance the resilience of their supply chains.Originality/valueThe use of a two-tiered analytical approach enhances validity as each level's limitations and assumptions are addressed when combined with one another. Prior predominantly theoretical research in the field is validated by the empirical investigation of supply chain resilience.

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