Optimum Value Of Objective Function Research Articles

Improving supply chain resilience through investment in flexibility and innovation

This study examines how a firm can improve its resilience to respond effectively to supply chain disruptions through investment in flexibility and innovation. There are several approaches and conceptualizations of flexibility and innovation in the operations and supply chain management literature. Nevertheless, how flexibility and innovation can enhance an organisational response to disruptions has not received much attention. We extend the research in supply chain resilience by investigating the relationships between flexibility, innovation, and resilience under disruption risk. The findings show that innovation and flexibility have a positive impact on resilience. A numerical example based on these relationships shows significant differences between optimum values of objective functions when we have high disruption risk versus low disruption risk imposed on the system. By developing a novel multi-objective mixed-integer linear programming, the findings reveal that disruption risks affect resilience and firms’ ROI, so investing in improvements in flexibility and innovation decreases the impact of disruptions.

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization.

BackgroundData exchange and management have been observed to be improving with the rapid growth of 5G technology, edge computing, and the Internet of Things (IoT). Moreover, edge computing is expected to quickly serve extensive and massive data requests despite its limited storage capacity. Such a situation needs data caching and offloading capabilities for proper distribution to users. These capabilities also need to be optimized due to the experience constraints, such as data priority determination, limited storage, and execution time.MethodsWe proposed a novel framework called Genetic and Ant Colony Optimization (GenACO) to improve the performance of the cached data optimization implemented in previous research by providing a more optimum objective function value. GenACO improves the solution selection probability mechanism to ensure a more reliable balancing of the exploration and exploitation process involved in finding solutions. Moreover, the GenACO has two modes: cyclic and non-cyclic, confirmed to have the ability to increase the optimal cached data solution, improve average solution quality, and reduce the total time consumption from the previous research results.ResultThe experimental results demonstrated that the proposed GenACO outperformed the previous work by minimizing the objective function of cached data optimization from 0.4374 to 0.4350 and reducing the time consumption by up to 47%.

Optimization of water productivity in Bhagwanpur distributary command of India employing TLBO and cuckoo search algorithms

Abstract Efficient and judicious use of land and water is the need of the hour. In other words, evolving a cropping pattern, which optimizes productivity or net return considering prevailing constraints is quite useful to farmers, because such a cropping pattern is expected to be better than the existing cropping pattern in terms of yielding optimum productivity or net return. A single objective problem consisting of an objective function of optimization of water productivity with prevailing constraints was formulated and three optimization algorithms, namely (i) LINPROG, (ii) teaching learning based optimization (TLBO) and (iii) cuckoo search (CS) were employed to compute optimum water productivity corresponding to various affinity levels. It was observed that all three approaches yielded exactly the same values of water productivity at different affinity levels. TLBO showed better convergence capability as it reached the optimum value of objective function at a lower number of iterations than CS technique. Optimum water productivity at 20% affinity level seems quite practical and reasonable to be recommended in this distributary command for adoption because water productivity value is 2.57 times higher with 98.25 ha less cropped area as compared to the value of water productivity for the existing cropping pattern.

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.

Comparison of demand limiting and load leveling operating modes of ice cold energy storage (ICES) in an air-conditioning system

Application of ice cold energy storage (ICES) is for reducing power consumption in air-conditioning systems. ICES systems have two full and partial operating modes. Partial operating mode of ICES system is modeled here at two demand limiting (POM-DL) and load leveling (POM-LL) conditions. After energy, exergy, and economic and environmental modeling/analysis of POM-DL and POM-LL, multi-objective (exergy and total cost) genetic algorithm (GA) optimization technique is applied to find the optimum values of objective functions and system design parameters. Then, the performance of POM-DL and POM-LL systems is compared. For our case study, the acquired results show 11.20% and 10.94% reduction in electricity consumption for POM-DL and POM-LL systems, respectively, in comparison with that for a traditional VCR (an electrical chiller). Furthermore, these systems reduce 13.58% and 12.36% electricity cost by shifting the electricity consumption of cooling system from on peak (ONP) hours to off peak (OFP) hours for POM-DL and POM-LL, respectively.

Natural-Gas-Derived Hydrogen in the Presence of Carbon Fuel Taxes and Concentrated Solar Power

This work focuses on the incorporation of renewable energy resources into the natural-gas-based production of hydrogen, via steam methane reforming (SMR). The novel concept of “energetically enhanced steam methane reforming” (EESMR) is introduced, which changes the endothermicity level of the SMR process through incorporation of carbon monoxide and steam into the SMR feed. Novel EESMR flow sheets are presented in which the aforementioned resources are internally generated and recycled, so as to create a natural-gas-based hydrogen production system with methane as raw material and a hybrid (methane/solar) energy supply. The current worldwide carbon tax legislative environment is briefly reviewed, and its potential impact on SMR-versus-EESMR economic viability is quantified. A novel method for solution of linear parametric programming problems is proposed based on the concept of dimensionality reduction—this allows the analytic quantification of the optimum objective function value and associated optimum va...

Work space optimization of a r-r planar manipulator using particle swarm optimization technique

A two link revolute planar robotic manipulator is optimized for maximization of work space covered by its end effector. A mathematical model for optimization is built considering singularities which control the range of design variables. Condition number which is the measure of change in output value (End effector position) for a small change in input value (joint angles) is modeled as the constraint. The non linear optimization model is initially linearised using Sequential Linear Programming technique and is solved graphically for optimum value of objective function. Particle swarm optimization technique is implemented on the non-linear optimization model for optimum value of objective function. The maximum value of objective function obtained from Particle swarm technique is found to be significantly higher than the value obtained using graphical approach.Keywords: Singularities, Condition number, Work-space

A mathematical multi-objective model for supply chain material requirements planning using Lexicograph Solution (the Topo beverage company)

The materials requirement planning is considered one of the major systems in material planning in production management section of the companies. In supply chain, the material requirement planning system (MRP) is used in product management process in order to supply material. The aim of this article is to propose a multi-objective mathematical model in material requirement planning of the Topo Aloe vera beverage company. A linear programming model, having two objectives is proposed in this article and the required data to write and to test the model is gathered from Topo Company. In this paper, the proposed model is solved using the lexicographyy model and after modeling the problem, the model is run using the MATLAB software, and the obtained results are analyzed and the optimum values of objective functions and the decision variables of the model are determined.

Truck capacity analysis in a cross‐dock transportation network considering direct shipment

SUMMARYIn a transportation network, decision making parameters may change and may cause the optimum value of objective function to vary in a specific range. Therefore, managers try to identify the effects of these changes by sensitive analysis to find appropriate solutions. In this paper, first, a model for cross‐dock transportation network considering direct shipment is presented, and then an algorithm based on branch and bound algorithm and dual price concept for sensitive analysis is developed. When managers encounter problems such as budget limit, they may decide to change the capacity of trucks as a procedure to reduce the transportation costs of the network. The algorithm provides a useful lower bound on the solutions of the problems and makes it easy for the managers to eliminate inappropriate options of truck capacities, which cannot lead to cost reduction. To verify the algorithm, an example will be given at the end of the paper. Copyright © 2013 John Wiley & Sons, Ltd.

Computing core allocations in cooperative games with an application to cooperative procurement

Cooperative game theory defines several concepts for distributing outcome shares in a cooperative game with transferable utilities. One of the most famous solution concepts is the core which defines a set of outcome allocations that are stable such that no coalition has an incentive to leave the grand coalition. In this paper we propose a general procedure to compute a core element (or to detect that no core allocation exists) which is based on mathematical programming techniques. The procedure proposed in this paper can be applied to a wide class of cooperative games where the characteristic function is given by the optimum objective function value of a complex optimization problem. For cooperative procurement, which is an example from the field of supply chain management where some literature on the core concept already exists, we prove the applicability and provide computational results to demonstrate that games with 150 players can be handled.

線形計画問題に対する乗法的罰金関数法の拡張

We shall extend Iri's multiplicative penalty function method for linear programming [4] so that it can handle the problem of unknown optimum value of the objective function, without solving both primal and dual problems simultaneously, and generate convergent dual solutions. By making use of these dual variables, lower bounds of the optimum objective function value are updated efficiently, which makes the total number of iterations required in the extended algorithm small. In doing so, a new duality on the multiplicative penalty function is discussed. A sufficient condition for a constraint to be inactive at all optimum solutions is given, which can be checked in the extended algorithm. Several computational techniques for enhancing the efficiency of the algorithm are also discussed. Some connection of the proposed algorithm with Sonnevend's and Renegar's methods [ 10, 11] is touched upon. Furthermore a method of estimating the optimum objective function value is given. Preliminary computational results on the random linear programming problem are finally shown.

A Note on a Nonlinear Minimax Location Problem in Tree Networks.

We present properties some new derivations of properties of a nonlinear version of a minimax tree network location problem. The provide necessary and sufficient conditions for optimality, a means of computing the optimum objective function value, and a means of constructing the unique optimum location.