scholarly journals A Model-Based Optimisation Approach for Process Synthesis of Olefins from Petroleum with Application to the Malaysian Petrochemical Industry

2019 ◽  
Vol 12 ◽  
pp. 1-15
Author(s):  
Khor Cheng Seong
Keyword(s):  
Practical Knowledge ◽  
Optimal Solution ◽  
Raw Material ◽  
Mixed Integer ◽  
Hydrocarbon Mixture ◽  
Continuous Variables ◽  
Mixed Integer Linear Program ◽  
Actual Case ◽  
Model Based ◽  
Recent Developments

The shale gas revolution has rekindled interest in olefins production due to the abundance of ethane as a raw material resource. However, the main technology still revolves around the cost-intensive distillation operation. Hence this work aims to investigate the economic optimisation of olefins synthesis from petroleum in the light of recent developments. A model-based approach is applied to determine the optimal sequencing of separation and reaction processes for a multi-component hydrocarbon mixture feed to produce mainly ethylene and propylene. a mixed-integer linear program (MILP) is formulated based on a superstructure that captures numerous plausible synthesis alternatives. The model comprises linear mass balance reactor representation and simple sharp distillation based on split fractions for product recovery. Integer binary variablesis used for selecting the task for equipment and continuous variables for representing the flowrate of each task. To expedite converging to an optimal solution of a least total annualised cost configuration, the formulation is appended with logical constraints on the design and structural specifications derived from heuristics based on practical knowledge and experience. The modelling approach on actual case studies based on two such petrochemical facilities operating in Malaysia is implemented. Additionally, the solution analysis is enriched with the investigation on a second- and third-best (suboptimal) configurations obtained through appropriate integer cuts as constraints to the model. The results show good agreement with existing plant configurations, thus substantiating the value and verification of the proposed model-based optimisation approach.

Simplified Evacuation Planning Model on Naval Ship

2010 ◽  
Vol 44-47 ◽  
pp. 1739-1744 ◽  
Author(s):  
Chong Liu ◽  
Chang Hua Qiu
Keyword(s):  
Emergency Evacuation ◽  
Linear Program ◽  
Integer Linear Program ◽  
Mixed Integer ◽  
Evacuation Planning ◽  
Planning Model ◽  
Mixed Integer Linear Program ◽  
Evacuation Time ◽  
Model Based ◽  
Naval Ship

In light of the growth in the numbers of maritime disasters during recent years, there is a growing interest in the evacuation of passengers and crew at sea, as documented in recent discussions at the Marine Safety Committee of the IMO. NATO has also developed analogous regulations as part of its Naval Ship Code. In this paper we propose a simplified evacuation model based on the shortest emergency evacuation time for emergency evacuation of a naval ship, which using the theory of mixed-integer linear program to solve navy ship evacuation problem. This evacuation planning model focused on the average evacuation time to travel through the ordered sequence of consecutive arcs by each group. In addition, the model revealed the amount of flow per time unit through the door. Secondly, we proposed a heuristic algorithm to produce sub-optimal evacuation plan. A numerical example is given at last. Results show that the model based on the theory of the mixed-integer linear program can provide more available evacuation solution for naval ship evacuation process.

scholarly journals ORNInA: A decentralized, auction-based multi-agent coordination in ODT systems

2021 ◽  
pp. 1-17
Author(s):  
Alaa Daoud ◽  
Flavien Balbo ◽  
Paolo Gianessi ◽  
Gauthier Picard
Keyword(s):  
Synthetic Data ◽  
Continuous Optimization ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Multi Agent ◽  
Run Time ◽  
Agent Coordination

On-Demand Transport (ODT) systems have attracted increasing attention in recent years. Traditional centralized dispatching can achieve optimal solutions, but NP-Hard complexity makes it unsuitable for online and dynamic problems. Centralized and decentralized heuristics can achieve fast, feasible solution at run-time with no guarantee on the quality. Starting from a feasible not optimal solution, we present in this paper a new solution model (ORNInA) consisting of two parallel coordination processes. The first one is a decentralized insertion-heuristic based algorithm to build vehicle schedules in order to solve a particular case of the dynamic Dial-A-Ride-Problem (DARP) as an ODT system, in which vehicles communicate via Vehicle-to-vehicle communication (V2V) and make decentralized decisions. The second coordination scheme is a continuous optimization process namely Pull-demand protocol, based on combinatorial auctions, in order to improve the quality of the global solution achieved by decentralized decision at run-time by exchanging resources between vehicles (k-opt). In its simplest implementation, k is set to 1 so that vehicles can exchange only one resource at a time. We evaluate and analyze the promising results of our contributed techniques on synthetic data for taxis operating in Saint-Étienne city, against a classical decentralized greedy approach and a centralized one that uses a classical mixed-integer linear program (MILP) solver.

scholarly journals Offline Joint Network and Computational Resource Allocation for Energy-Efficient 5G and beyond Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10547
Author(s):  
Marios Gatzianas ◽  
Agapi Mesodiakaki ◽  
George Kalfas ◽  
Nikos Pleros ◽  
Francesca Moscatelli ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Network Function Virtualization ◽  
User Association ◽  
Computational Resource ◽  
Mixed Integer Linear Program ◽  
Resource Allocation Algorithm ◽  
Network Function

In order to cope with the ever-increasing traffic demands and stringent latency constraints, next generation, i.e., sixth generation (6G) networks, are expected to leverage Network Function Virtualization (NFV) as an enabler for enhanced network flexibility. In such a setup, in addition to the traditional problems of user association and traffic routing, Virtual Network Function (VNF) placement needs to be jointly considered. To that end, in this paper, we focus on the joint network and computational resource allocation, targeting low network power consumption while satisfying the Service Function Chain (SFC), throughput, and delay requirements. Unlike the State-of-the-Art (SoA), we also take into account the Access Network (AN), while formulating the problem as a general Mixed Integer Linear Program (MILP). Due to the high complexity of the proposed optimal solution, we also propose a low-complexity energy-efficient resource allocation algorithm, which was shown to significantly outperform the SoA, by achieving up to 78% of the optimal energy efficiency with up to 742 times lower complexity. Finally, we describe an Orchestration Framework for the automated orchestration of vertical-driven services in Network Slices and describe how it encompasses the proposed algorithm towards optimized provisioning of heterogeneous computation and network resources across multiple network segments.

scholarly journals MIPaaL: Mixed Integer Program as a Layer

2020 ◽  
Vol 34 (02) ◽  
pp. 1504-1511 ◽  
Author(s):  
Aaron Ferber ◽  
Bryan Wilder ◽  
Bistra Dilkina ◽  
Milind Tambe
Keyword(s):  
Broad Class ◽  
Linear Constraints ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Linear Programs ◽  
Continuous Variables ◽  
Two Phase ◽  
Mixed Integer Linear Program ◽  
Submodular Optimization ◽  
Solution Approach

Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures average accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a mixed integer linear program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, an algorithm that iteratively tightens the continuous relaxation by adding constraints removing fractional solutions. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and optimization separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP. Lastly, we demonstrate generalization performance in several transfer learning tasks.

scholarly journals GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems

2018 ◽  
Vol 26 (1) ◽  
pp. 117-143 ◽  
Author(s):  
Krzysztof L. Sadowski ◽  
Dirk Thierens ◽  
Peter A.N. Bosman
Keyword(s):  
Evolutionary Algorithm ◽  
Model Building ◽  
State Of The Art ◽  
Continuous Model ◽  
Structural Knowledge ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Integer Optimization ◽  
Model Based ◽  
General Algebraic Modeling System

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

Branch-and-cut for combinatorial optimization problems without auxiliary binary variables

2001 ◽  
Vol 16 (1) ◽  
pp. 25-39 ◽  
Author(s):  
I. R. DE FARIAS ◽  
E. L. JOHNSON ◽  
G. L. NEMHAUSER
Keyword(s):  
Production Scheduling ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Polyhedral Combinatorics ◽  
Branch And Cut ◽  
Combinatorial Structure ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Binary Variables ◽  
Combinatorial Optimization Problems

Many optimisation problems involve combinatorial constraints on continuous variables. An example of a combinatorial constraint is that at most one variable in a group of nonnegative variables may be positive. Traditionally, in the mathematical programming community, such problems have been modeled as mixed-integer programs by introducing auxiliary binary variables and additional constraints. Because the number of variables and constraints becomes larger and the combinatorial structure is not used to advantage, these mixed-integer programming models may not be solved satisfactorily, except for small instances. Traditionally, constraint programming approaches to such problems keep and use the combinatorial structure, but do not use linear programming bounds in the search for an optimal solution. Here we present a branch-and-cut approach that considers the combinatorial constraints without the introduction of binary variables. We review the development of this approach and show how strong constraints can be derived using ideas from polyhedral combinatorics. To illustrate the ideas, we present a production scheduling model that arises in the manufacture of fibre optic cables.

scholarly journals A multiagent simulator for supporting logistic decisions of unloading petroleum ships in habors

2010 ◽  
Vol 30 (3) ◽  
pp. 729-750 ◽  
Author(s):  
Robison Cris Brito ◽  
Cesar Augusto Tacla ◽  
Lúcia Valéria Ramos de Arruda
Keyword(s):  
Multiagent System ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Computational Time ◽  
Storage Tanks ◽  
Problem Size ◽  
Model Based ◽  
Milp Model ◽  
Test Scenarios ◽  
Very High

This work presents and evaluates the performance of a simulation model based on multiagent system technology in order to support logistic decisions in a harbor from oil supply chain. The main decisions are concerned to pier allocation, oil discharge, storage tanks management and refinery supply by a pipeline. The real elements as ships, piers, pipelines, and refineries are modeled as agents, and they negotiate by auctions to move oil in this system. The simulation results are compared with results obtained with an optimization mathematical model based on mixed integer linear programming (MILP). Both models are able to find optimal solutions or close to the optimal solution depending on the problem size. In problems with several elements, the multiagent model can find solutions in seconds, while the MILP model presents very high computational time to find the optimal solution. In some situations, the MILP model results in out of memory error. Test scenarios demonstrate the usefulness of the multiagent based simulator in supporting decision taken concerning the logistic in harbors.

scholarly journals An Optimal Transportation Schedule of Mobile Equipment

2012 ◽  
Vol 10 (5) ◽  
Author(s):  
S. Guillén-Burguete ◽  
H. Sánchez-Larios ◽  
J.G Vázquez-Vázquez
Keyword(s):  
Optimal Solution ◽  
Road Construction ◽  
Transportation Cost ◽  
Convex Polyhedra ◽  
Mixed Integer ◽  
Time Intervals ◽  
Mixed Integer Linear Program ◽  
Arrival Times ◽  
Indefinite Number ◽  
Total Transportation Cost

Motivated by a problem faced by road construction companies, we develop a new model to obtain an optimaltransportation schedule of mobile machines which have to travel to execute tasks. In this problem, each task ischaracterized by the location where it is to be executed, a work-content in terms of machine-time units, and one ormore time intervals within which it can be performed. The machines can be transported from one location to anotherat any time, thus the problem has an indefinite number of variables. However, this indefinite number of variables canbe reduced to a definite one because, as we prove, the problem has an optimal solution in which the arrivals ofmachines occur only at certain time instants. The objective is to minimize the total transportation cost such that all thetasks are executed within their time intervals. The constraints ensuring that the tasks are processed within theirprescribed time intervals are nonlinear; nevertheless, due to the sets of the possible arrival times of the machinesforming bounded convex polyhedra, our problem can be transformed into a mixed integer linear program by the samedevice used in the decomposition principle of Dantzig-Wolfe.

scholarly journals Optimization flow model for handling car flows at the marshalling yard

2018 ◽  
Vol 216 ◽  
pp. 02024
Author(s):  
Alexander Alexandrov ◽  
Alexander Galkin ◽  
Elena Timukhina ◽  
Nikolai Tushin
Keyword(s):  
Process Model ◽  
Heuristic Algorithms ◽  
Complex Structure ◽  
Optimal Solution ◽  
Operational Planning ◽  
Mixed Integer ◽  
Railway Transport ◽  
Mixed Integer Linear Program ◽  
Marshalling Yard

Mathematical modeling is widely used in studies of operational planning in railway transport. Mainly heuristic algorithms and simulation modeling is used in short-term planning at marshalling yards. There is an opportunity to improve the quality of planning using strict optimization models. The objective of the study was to find a way to optimize the multi-terminal transport flows of the complex structure by the example of operational planning for handling car flows at the marshalling yard. The process model was built on the basis of Mixed Integer Linear Program (MILP) method. The article provides the mathematical setting and describes results of calculations of the reference example. The LpSolve application package was used. The technological effect is estimated through checking the optimal solution on the simulation model.

Optimal Operational Planning of Utility Systems in Petrochemical Plants Using an Improved Particle Swarm Optimization Algorithm

2010 ◽  
Vol 143-144 ◽  
pp. 1364-1369
Author(s):  
Wen Zhi Dai ◽  
Zhao Yi Huo ◽  
Hong Chao Yin ◽  
Hai Feng Liang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Mixed Integer Linear Program ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Utility Systems ◽  
New Algorithms

In this paper, the operation optimization problem for utility systems is formulated and a mixed integer linear program (MILP) model is presented. The objective function of the model is to minimize the operational cost of utility systems during the whole operational period. In order to obtain the optimal solution of the foregoing model, an improved particle swarm optimization is proposed. Finally, a case with quantitive results presented is considered for illustrating the advantage of proposed optimization approach. Results show that the new algorithms are much more efficient than some existing particle swarm optimization algorithms.

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