Enabling industrial symbiosis by a facilities management optimization approach

2012 ◽  
Vol 35 ◽  
pp. 263-273 ◽  
Author(s):  
Antonella Meneghetti ◽  
Gioacchino Nardin
Keyword(s):  
Facilities Management ◽  
Optimization Approach ◽  
Industrial Symbiosis ◽  
Management Optimization

Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management

2010 ◽  
pp. 142-160 ◽  
Author(s):  
Pandian M. Vasant
Keyword(s):  
Production Management ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Computational Techniques ◽  
Optimization Approach ◽  
Non Linear Programming ◽  
Decision Variables ◽  
Line Search Method ◽  
Non Linear ◽  
Management Optimization

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research chapter is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

scholarly journals Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming

2021 ◽  
Vol 184 (1) ◽  
pp. 3-10
Author(s):  
Di Zhu ◽  
Ewan Pritchard ◽  
Sumanth Dadam ◽  
Vivek Kumar ◽  
Yang Xu
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Measurement System ◽  
Hybrid Electric Vehicle ◽  
Management Strategies ◽  
Optimization Approach ◽  
Rule Based ◽  
Management Optimization ◽  
Vehicle Test ◽  
Vehicle Dynamic Model

Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.

scholarly journals ANALISIS BIOEKONOMI DAN OPTIMASI PENGELOLAAN SUMBER DAYA IKAN LAYANG DI PERAIRAN KABUPATEN MUNA SULAWESI TENGGARA (Bioeconomic Analysis and Resource Management Optimization of Mackerel Scad in Muna District, South East Sulawesi)

2016 ◽  
Vol 6 (1) ◽  
pp. 13
Author(s):  
Wa Ode Piliana ◽  
Tridoyo Kusumastanto ◽  
. Diniah
Keyword(s):  
Resource Management ◽  
Dynamic Optimization ◽  
Fishing Effort ◽  
Fish Stock ◽  
Optimization Approach ◽  
Economic Optimization ◽  
Estimation Model ◽  
Economic Management ◽  
Fish Resources ◽  
Management Optimization

<p>ABSTRACT<br />The resource utilization of mackerel scad (Decapterus spp.) has been increasing in Muna waters which will be able to reduce the fish stock and if it exceeds the carrying capacity, it will lead to "overfishing". This study aims to determine the level of production, fishing effort and the optimal economic chain, both biologically and economically, so that the management of fish resources in Muna waters can be carried out in a sustainable way. The study was conducted by the case study method. The analysis method used in this research is biotechnic; bioeconomy; static and dynamic optimization apprOAch of Walter-Hilbon (WH) estimation model. The results of the study provide an indicator that the mackerel scad in Muna waters has not experienced "overfishing" both biological and economic. MEY condition that is optimal for economic management will be achieved when the maximum effort as many as 213.734 trips, production amounted to 3.117,61 tons and maximum economic chain at IDR 33.434,41 billion. The amount of fishing effort in MSY condition within the model is 248.342 trips whereas the production amounted to 3.179,35 tons with the economic chain of IDR 32.557,81 billion. Results of biotechnic, bioeconomy, static and dynamic optimization showed that actual production is still below the sustainable production indicated by high production, effort and economic chain. Based on this analysis, the business effort of mackerel scad can be increased from the current fishing effort of 128.496 trips to 213.734 trips that will give the maximum economic chain and management of fish resources sustainably.</p><p><br />Keywords: bioeconomic, economic optimization, mackerel scad, resource management</p><p>-------</p><p><br />ABSTRAK</p><p>Pemanfaatan sumber daya ikan layang (Decapterus spp.) yang meningkat di perairan Kabupaten Muna dapat menurunkan stok ikan layang dan apabila melebihi daya dukung, maka akan menyebabkan terjadinya “overfishing”. Penelitian ini bertujuan untuk menentukan tingkat produksi, upaya tangkap dan rantai ekonomi yang optimal, baik secara biologi dan ekonomi, sehingga pengelolaan sumber daya ikan layang di perairan Kabupaten Muna dapat dilaksanakan secara berkelanjutan. Penelitian dilakukan dengan metode studi kasus. Metode analisis yang digunakan dalam penelitian ini adalah analisis bioteknis, bioekonomi, optimasi statik dan dinamik menggunakan pendekatan model estimasi Walter-Hilbon (W-H). Hasil penelitian memberikan indikator bahwa ikan layang di perairan Kabupaten Muna belum mengalami “overfishing” baik secara biologi (biological overfishing) dan ekonomi (economic overfishing). Dalam kondisi MEY yang merupakan pengelolaan ekonomi optimal, terestimasi effort sebanyak 213.734 trip, produksi sebesar 3.117,61 ton dan rantai ekonomi maksimum yakni Rp 33.434.410.000. Jumlah effort dalam kondisi MSY adalah 248.342 trip, produksi sebesar 3.179,35 ton dan rantai ekonomi sebesar Rp 32.557.810.000. Hasil analisis bioteknis, bioekonomi, optimasi statik dan dinamik menunjukkan produksi aktual masih berada di bawah nilai produksi lestari baik dari produksi, effort dan rantai ekonomi. Berdasarkan analisis tersebut, effort usaha ikan layang dapat ditingkatkan dari upaya aktual sebesar 128.496 trip menjadi 213.734 trip yang akan memberikan rantai ekonomi maksimum dan pengelolaan sumber daya ikan layang secara lestari.</p><p><br />Kata kunci: bioekonomi, optimasi ekonomi, pengelolaan sumber daya, ikan layang</p>

Multi-objective TMA management optimization using the point merge system

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramazan Kursat Cecen
Keyword(s):  
Mathematical Model ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
First Come First Serve ◽  
Management Optimization ◽  
The Mathematical Model ◽  
Single Objective

Purpose The purpose of this study is to provide conflict-free operations in terminal manoeuvre areas (TMA) using the point merge system (PMS), airspeed reduction (ASR) and ground holding (GH) techniques. The objective is to minimize both total aircraft delay (TD) and the total number of the conflict resolution manoeuvres (CRM). Design/methodology/approach The mixed integer linear programming (MILP) is used for both single and multi-objective optimization approaches to solve aircraft sequencing and scheduling problem (ASSP). Compromise programming and ε-constraint methods were included in the methodology. The results of the single objective optimization approach results were compared with baseline results, which were obtained using the first come first serve approach, in terms of the total number of the CRM, TD, the number of aircraft using PMS manoeuvres, ASR manoeuvres, GH manoeuvres, departure time updates and on-time performance. Findings The proposed single-objective optimization approach reduced both the CRM and TD considerably. For the traffic flow rates of 15, 20 and 25 aircraft, the improvement of CRM was 53.08%, 41.12% and 32.6%, the enhancement of TD was 54.2%, 48.8% and 31.06% and the average number of Pareto-optimal solutions were 1.26, 2.22 and 3.87, respectively. The multi-objective optimization approach also exposed the relationship between the TD and the total number of CRM. Practical implications The proposed mathematical model can be implemented considering the objectives of air traffic controllers (ATCOs) and airlines operators. Also, the mathematical model is able to create conflict-free TMA operations and, therefore, it brings an opportunity for ATCOs to reduce frequency occupancy time. Originality/value The mathematical model presents the total number of CRM as an objective function in the ASSP using the MILP approach. The mathematical model integrates ATCOs’ and airline operators’ perspective together with new objective functions.

scholarly journals The Life Safety and Risk Management Optimization Problem

2013 ◽  
Vol 4 (2) ◽  
pp. 89-92
Author(s):  
Andrii Skrypnyk
Keyword(s):  
Risk Management ◽  
Coal Mining ◽  
Optimization Problem ◽  
Random Variable ◽  
Life Safety ◽  
Optimization Approach ◽  
Practical Application ◽  
Significance Level ◽  
Stochastic Nature ◽  
Management Optimization

Abstract It was considered the possibility of optimization approach of the risk management problems based on the stochastic nature of the losses. Since the losses functional magnitude is a random variable, then it was considered the value of losses on a significance level (losses probability).It was shown decision existence which minimized expected losses. The problem has practical application in coal mining, occasional transportation and other business projects associated with the possibility of life losses.

Innovative Hybrid Genetic Algorithms and Line Search Method for Industrial Production Management

2012 ◽  
pp. 1591-1608
Author(s):  
Pandian Vasant
Keyword(s):  
Production Management ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Computational Techniques ◽  
Optimization Approach ◽  
Non Linear Programming ◽  
Decision Variables ◽  
Line Search Method ◽  
Non Linear ◽  
Management Optimization

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research chapter is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

Interactive multiobjective DEA target setting using lexicographic DDF

2020 ◽  
Vol 54 (6) ◽  
pp. 1703-1722 ◽  
Author(s):  
Narges Soltani ◽  
Sebastián Lozano
Keyword(s):  
Efficient Frontier ◽  
Directional Distance Function ◽  
Fixed Number ◽  
Optimization Approach ◽  
Target Setting ◽  
Interactive Process ◽  
Decision Making Unit ◽  
Setting Approach ◽  
Promising Area ◽  
Current Region

In this paper, a new interactive multiobjective target setting approach based on lexicographic directional distance function (DDF) method is proposed. Lexicographic DDF computes efficient targets along a specified directional vector. The interactive multiobjective optimization approach consists in several iteration cycles in each of which the Decision Making Unit (DMU) is presented a fixed number of efficient targets computed corresponding to different directional vectors. If the DMU finds one of them promising, the directional vectors tried in the next iteration are generated close to the promising one, thus focusing the exploration of the efficient frontier on the promising area. In any iteration the DMU may choose to finish the exploration of the current region and restart the process to probe a new region. The interactive process ends when the DMU finds its most preferred solution (MPS).

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