Solving economic dispatch and unit commitment problem in smart grid system using eagle strategy based crow search algorithm

The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. The Unit commitment problem (UCP) is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. This research article integrates the crow search algorithm as a local optimizer of Eagle strategy to solve economic dispatch and unit commitment problem in smart grid system

Economic and emission dispatch using cuckoo search algorithm

The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. This paper proposes the use of Cuckoo Search Algorithm (CSA) for solving the economic and Emission dispatch. The effectiveness of the proposed approach has been tested on 3 generator system. CSA is a new meta-heuristic optimization method inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species.The results shows that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literatur

Fuzzy Goal Programming Approach for Integrating Production and Distribution Problem in Milk Supply Chain

In this paper, a bi-objective mixed integer programming model is proposed to deal with the production-distribution problem found in a dairy company in Morocco. The supply chain containing three echelons: multi-sites, multi-distribution centers and multi-customers. The model seeks to integrate two conflicting simultaneous objectives: maximizing benefit by considering the shelf life of products and the total cost (quantitative objective), including production, storage, and distribution, as well as maximizing the service level (qualitative objective), which relates to providing satisfactory services to customers. This is subject to several technological constraints that typically arise in the dairy industry, such as sequence-dependent changeover time, machine speed and storage capacity. Due to imprecise aspiration levels of goals, an interactive approach is proposed based on fuzzy goal additive variants to find an efficient compromise solution. Numerical results are reported to demonstrate the efficiency and applicability of the proposed model

Integrated Production and Distribution in Milk Supply Chain under Uncertainty with Hurwicz Criterion

In this paper, we propose a credibility-based fuzzy mathematical programming model for integrating the production and distribution in milk supply chain under uncertainty. The proposed model is a mixed integer linear programming, which takes into account technological constraints and aims to maximize the total profit including the total costs such as production, storage, and distribution. To bring the model closer to real-world planning problems, the objective function coefficients (e.g. production cost, inventory holding and transport costs) and other parameters (e.g., demand, production capacity, and safety stock level), are all considered fuzzy numbers. In the uncertain environment, the most known criteria widely employed are optimistic and pessimistic value criterions. Both criteria present some deficiency. For the optimistic criterion, it suggests an audacious who is attracted by high payoffs (low cost), while for the pessimistic criterion, it suggests a conservative decision-maker who tries to make sure that in the case of an unfavorable outcome (loss), there is at least (in most) a known minimum payoff (loss maximum). To overcome these problems, the Hurwicz criterion is used for the concerned problem. By varying the value of θ, it can balance the optimistic and pessimistic levels of the decision makers. Moreover, the different property of the credibility measure is used to build the crisp equivalent model, which is a MILP model that can solve, by using a commercial solver such as GAMS. Finally, numerical results are reported for a real case study to demonstrate the efficiency and applicability of the proposed model

A hybrid metaheuristic method to optimize the order of the sequences in continuous-casting

In this paper, we propose a hybrid metaheuristic algorithm to maximize the production and to minimize the processing time in the steel-making and continuous casting (SCC) by optimizing the order of the sequences where a sequence is a group of jobs with the same chemical characteristics. Based on the work Bellabdaoui and Teghem (2006) [Bellabdaoui, A., & Teghem, J. (2006). A mixed-integer linear programming model for the continuous casting planning. International Journal of Production Economics, 104(2), 260-270.], a mixed integer linear programming for scheduling steelmaking continuous casting production is presented to minimize the makespan. The order of the sequences in continuous casting is assumed to be fixed. The main contribution is to analyze an additional way to determine the optimal order of sequences. A hybrid method based on simulated annealing and genetic algorithm restricted by a tabu list (SA-GA-TL) is addressed to obtain the optimal order. After parameter tuning of the proposed algorithm, it is tested on different instances using a.NET application and the commercial software solver Cplex v12.5. These results are compared with those obtained by SA-TL (simulated annealing restricted by tabu list)

Supply chain integration within mass customization: tactical procurement, production and distribution modeling

Purpose: The actual market characteristic oriented toward customers’ requirements compels decision-makers to foresee customization abilities. Mass customization represents a valuable approach to combinecustomizable offers with mass production processes. From a supply chain standpoint, this paper attemptsto develop an integrated procurement, production and distribution modeling to describe the generatedframework structure formulation within tactical decision planning level.Design/methodology/approach: The paper provides a mixed integer linear programming model of athree echelon supply chain illustrated from the automotive industry with (a) customers: OriginalEquipment Manufacturers (OEMs) identified as leaders and (b) first-tier supplier: wiring harnessesmanufacturer (c) second-tier suppliers: raw material suppliers, identified as followers. The modelformulation is depicted through dyadic relationships between stakeholders considering the specificoperation enablers of the environment such as make to order, modular approach in addition to thecorresponding inventory management policy.Findings: The integrated model is solved by an exact method which illustrates the feasibility of theformulation in addition to the observance of the applied constraints. A sensitivity analysis is performed tohighlight the interdependency across some key parameters to provide managerial insights within thestudied framework while keeping the optimal solvability of the model.Research limitations/implications: The limitation of this study is the computational experiment study.An extensive experiment with a real-word case will outline the optimal solvability status of the exactmethod and the necessity for a performance benchmark through the approximate solving approaches.Originality/value: The present research aims to contribute as first studies toward mathematical modelingfor supply chain decision planning endeavor operating within mass customization business modelPeer Reviewe

Eagle Strategy based Crow Search Algorithm for solving Unit Commitment Problem

Eagle strategy is a two-stage optimization strategy, which is inspired by the observation of the hunting behavior of eagles in nature. In this two-stage strategy, the first stage explores the search space globally by using a Levy flight; if it finds a promising solution, then an intensive local search is employed using a more efficient local optimizer, such as hillclimbing and the downhill simplex method. Then, the two-stage process starts again with new global exploration, followed by a local search in a new region. One of the remarkable advantages of such a combina-tion is to use a balanced tradeoff between global search (which is generally slow) and a rapid local search. The crow search algorithm (CSA) is a recently developed metaheuristic search algorithm inspired by the intelligent behavior of crows.This research article integrates the crow search algorithm as a local optimizer of Eagle strategy to solve unit commitment (UC) problem. The Unit commitment problem (UCP) is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. There are many constraints in unit commitment problem such as spinning reserve, minimum up/down, crew, must run and fuel constraints. The proposed strategy ES-CSA is tested on 10 to 100 unit systems with a 24-h scheduling horizon. The effectiveness of the proposed strategy is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by reported numerical results, it has been found that proposed strategy yields global results for the solution of the unit commitment problem.