Production planning and scheduling optimisation for underground mining operations has continued to attract significant attention over the last decades. This has been necessitated by the growing need for operations to ...
详细信息
Production planning and scheduling optimisation for underground mining operations has continued to attract significant attention over the last decades. This has been necessitated by the growing need for operations to meet their shareholder's expectations sustainably under increasingly challenging operational dynamics. Several studies have been undertaken to utilise mathematical programming models such as mixed-integerprogramming, heuristics and simulation algorithms including combinations of these techniques for production scheduling optimisation with some notable achievements noted in extant literature. However, the limited reach of standalone mathematical optimisation models under increasing volumes of input data spurred by the booming information technology (IT) platforms has become more apparent and pertinent for increased scholarly attention. The growing emergence of big data, driven by the industrial digitisation and automation has seen an increased appetite for data-driven optimisation planning and scheduling largely in manufacturing and operations management. However, the scarcity of discussion in this novel and fast-evolving area in the underground mining space presents a glaring blind spot that appeals for thoughtful conversations to narrow that gap. This paper seeks to discuss opportunities for application of data analytics and machine learning to improve production planning and scheduling efficacy in underground mining. Specific focus will then be narrowed to opportunities for incorporating predictive analytics and machine learning to improve the accuracy of mathematical optimisation models. The overarching intent is to support the attainment of mineral production targets through enabling schedule dynamic response to variability in key determinant variables such as ore grade and tonnages.
Background: We present a way to compute the minimal semi-positive invariants of a Petri net representing a biological reaction system, as resolution of a Constraint Satisfaction Problem. The use of Petri nets to manip...
详细信息
Background: We present a way to compute the minimal semi-positive invariants of a Petri net representing a biological reaction system, as resolution of a Constraint Satisfaction Problem. The use of Petri nets to manipulate Systems Biology models and make available a variety of tools is quite old, and recently analyses based on invariant computation for biological models have become more and more frequent, for instance in the context of module decomposition. Results: In our case, this analysis brings both qualitative and quantitative information on the models, in the form of conservation laws, consistency checking, etc. thanks to finite domain constraint programming. It is noticeable that some of the most recent optimizations of standard invariant computation techniques in Petri nets correspond to well-known techniques in constraint solving, like symmetry-breaking. Moreover, we show that the simple and natural encoding proposed is not only efficient but also flexible enough to encompass sub/sur-invariants, siphons/traps, etc., i.e., other Petri net structural properties that lead to supplementary insight on the dynamics of the biochemical system under study. Conclusions: A simple implementation based on GNU-Prolog's finite domain solver, and including symmetry detection and breaking, was incorporated into the BIOCHAM modelling environment and in the independent tool Nicotine. Some illustrative examples and benchmarks are provided.
In order to successfully undertake the rapid growth of container transportation volume between China and ASEAN region brought by the ＂One Belt, One Road＂ policy, this paper analyzes the specific composition of shipp...
详细信息
In order to successfully undertake the rapid growth of container transportation volume between China and ASEAN region brought by the ＂One Belt, One Road＂ policy, this paper analyzes the specific composition of shipping cost based on the previous research on the optimization of container route network, and uses the mathematics of mixed integer programming. The method is to establish a container route network optimization model with the minimum total transportation cost as the target, and use MATLAB software to solve the problem. Finally, taking the route network between Xiamen Port and the five important ports of ASEAN countries as an example, the feasibility of the model and solution method is verified. The results show that the model constructed in this paper and the solution method used can solve the practical problems better, and can solve the more reasonable and efficient China-ASEAN container transport route network through software solution calculation.
This paper addresses the integrated flexible job shop and operators scheduling problem, introducing shift-based constraints on operators. We investigate how the advanced modeling and solution techniques, specifically,...
详细信息
This paper addresses the integrated flexible job shop and operators scheduling problem, introducing shift-based constraints on operators. We investigate how the advanced modeling and solution techniques, specifically, mixed-integerprogramming (MIP) and Constraint programming (CP) perform on this intricate scheduling problem. We test the effectiveness of both the MIP and CP models on an illustrative example as well as on a set of larger instances and draw conclusions, which indicate the need to integrate these two models into some approximation scheme to tackle large-scale instances for this complex problem.
The problem of data reconciliation and the detection and identification of gross errors, such as measurement bias, are closely related. This close relationship prompted the development of a technique that combines the...
详细信息
The problem of data reconciliation and the detection and identification of gross errors, such as measurement bias, are closely related. This close relationship prompted the development of a technique that combines these ideas within a mixedinteger optimization framework. This paper describes such an approach and demonstrates its performance with a challenging test problem.
This research presents the group of green vehicle routing problems with environmental costs translated into money versus production of noise, pollution and fuel consumption. This research is focused on multi-objective...
详细信息
This research presents the group of green vehicle routing problems with environmental costs translated into money versus production of noise, pollution and fuel consumption. This research is focused on multi-objective green logistics optimization. Optimality criteria are environmental costs: minimization of amount of money paid as externality cost for noise, pollution and costs of fuel versus minimization of noise, pollution and fuel consumption themselves. Some mixed integer programming formulations of multi-criteria vehicle routing problems have been considered. Mathematical models were formulated under assumption of existence of asymmetric distance-based costs and use of homogeneous fleet. The exact solution methods are applied for finding optimal solutions. The software used to solve these models is the CPLEX solver with AMPL programming language. The researchers were able to use real data from a Spanish company of groceries. Problems deal with green logistics for routes crossing the Spanish regions of Navarre, Basque Country and La Rioja. Analyses of obtained results could help logistics managers to lead the initiative in area of green logistics by saving money paid for environmental costs as well as direct cost of fuel and minimization of pollution and noise.
Hybrid Systems consist of continuous time and/or discrete time processes interfaced with some logical or decision making process. In this paper, a class of hybrid systems - switched linear systems is considered. It is...
详细信息
Hybrid Systems consist of continuous time and/or discrete time processes interfaced with some logical or decision making process. In this paper, a class of hybrid systems - switched linear systems is considered. It is shown that for this class of hybrid systems, it is possible to combine subspace methods with mixed integer programming. While most approaches are based on an input-output framework, we a state space identification approach is advocated. The states of the system are extracted from input-output data using sub-space methods. Once these states are known, the switched system is re-written as a mixed logical dynamical (MLD) system and the model parameters are solved for via mixed integer programming. An example is reported at the end of this paper.
This paper proposes a multi-agent approach to decentralized power system restoration for distribution system networks. The proposed method consists of several Distributing Substation Agents (DSAGs) and Load Agents (LA...
详细信息
This paper proposes a multi-agent approach to decentralized power system restoration for distribution system networks. The proposed method consists of several Distributing Substation Agents (DSAGs) and Load Agents (LAGs). LAG corresponds to the customer load, while a DSAG corresponds to the distribution substation. LAG restores self-load, while a DSAG supplies the electricity to LAGs. From the simulation results, it can be seen the proposed multi-agent system could reach the right solution by making use of only the local information. In addition, the proposed method is able to get the restorative plan which is better than the solution of the mixed integer programming. Therefore, the interaction of several simple agents leads to a dynamic restoration system, allowing approximation solution efficiently. This means that the proposed multi-agent restoration system is a promising approach to more large-scale distribution networks.
This article compares algorithms for solving portfolio optimization problems involving value-at-risk (VaR). These problems can be formulated as mixedinteger programs (MIPs) or as chance-constrained mathematical progr...
详细信息
This article compares algorithms for solving portfolio optimization problems involving value-at-risk (VaR). These problems can be formulated as mixedinteger programs (MIPs) or as chance-constrained mathematical programs (CCMPs). We propose improvements to their state-of-the-art MIP formulations. We also specialize an algorithm for solving general CCMPs, featuring practical interpretations. We present numerical experiments on practical-scale VaR problems using various algorithms and provide practical advice for solving these problems.
In this paper, the exam booklet distribution plan for the Higher Education Institutions Exam (HEIE) is studied. The accurate distribution plan is important to decrease the transportation cost and use the capacity effi...
详细信息
In this paper, the exam booklet distribution plan for the Higher Education Institutions Exam (HEIE) is studied. The accurate distribution plan is important to decrease the transportation cost and use the capacity efficiently. The exam booklets distribution should be considered as capacitated vehicle routing problem (VRP). In this context, the aim of this paper is minimizing the cost/distance of distribution from the depot where exam booklets are kept to the schools with capacitated vehicles. The case of Gaziantep city with 135 nodes (one depot and 134 schools) is considered. To model and solve the problem, a mixed integer programming (MIP) model is developed and applied. Due to large size of the problem, the VRP tool of Esri ArcGIS (well-known geographic information system (GIS) software) and OR-tool of Google are also applied to get an acceptable solution in a reasonable time. Finally, the proposed three distribution plans are compared each other and the results are discussed. Our numerical results show that the tools of Esri ArcGIS and OR-tool of Google decreases the total route distance by 8.21% and 3.02% compared to the MIP model, respectively. One of the main contributions of the paper is to show the applicability of network analyst tool of Esri ArcGIS and OR-tool of Google on a real-case CVRP.
暂无评论