scholarly journals Search Algorithms on Logistic and Manufacturing Problems

2021 ◽  
Author(s):  
Gladys Bonilla-Enriquez ◽  
Santiago-Omar Caballero-Morales
Keyword(s):  
Supply Chain ◽  
Production Scheduling ◽  
Search Algorithm ◽  
Approximate Solutions ◽  
Distribution Networks ◽  
Search Algorithms ◽  
Scheduling Problems ◽  
Exact Methods ◽  
Product Selection ◽  
Manufacturing Problems

The supply chain comprehensively considers problems with different levels of complexity. Nowadays, design of distribution networks and production scheduling are some of the most complex problems in logistics. It is widely known that large problems cannot be solved through exact methods. Also, specific optimization software is frequently needed. To overcome this situation, the development and application of search algorithms have been proposed to obtain approximate solutions to large problems within reasonable time. In this context, the present chapter describes the development of Genetic Algorithms (an evolutionary search algorithm) for vehicle routing, product selection, and production scheduling problems within the supply chain. These algorithms were evaluated by using well-known test instances. The advances of this work provide the general discussions associated to designing these search algorithms for logistics problems.

A Hybrid Approach Based on Artificial Neural Network (ANN) and Differential Evolution (DE) for Job-Shop Scheduling Problem

2010 ◽  
Vol 26-28 ◽  
pp. 754-757 ◽  
Author(s):  
Fu Qing Zhao ◽  
Jian Hua Zou ◽  
Ya Hong Yang
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Scheduling Problems ◽  
Shop Scheduling

In this paper, we proposed a new hybrid approach, combining ANN and DE(Differential Evolution), for job-shop scheduling. Job-shop scheduling can be decomposed into a constraint satisfactory part and an optimization part for a specified scheduling objective. For this, an NN and DE-based hybrid scheduling approach is proposed in this paper. First, several specific types of neuron are designed to describe these processing constraints, detecting whether constraints are satisfied and resolving the conflicts by their feedback adjustments. Constructed with these neurons, the constraint neural network (CNN) can generate a feasible solution for the JSSP. CNN here corresponds to the constraint satisfactory part. A gradient search algorithm can be applied to guide CNN operations if an optimal solution needs to be found at a fixed sequence. For sequence optimization, a DE is employed. Through many simulation experiments and practical applica¬tions, it is shown that the approach can be used to model real production scheduling problems and to efficiently find an optimal solution. The hybrid approach is an ideal combination of the constraint analysis and the optimization scheduling method.

scholarly journals Socially Motivated Partial Cooperation in Multi-agent Local Search

2018 ◽  
Author(s):  
Tal Ze'evi ◽  
Roie Zivan ◽  
Omer Lev
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Scheduling Problems ◽  
Multi Agent Systems ◽  
Local Search Algorithms ◽  
Partial Cooperation ◽  
Multi Agent ◽  
Socially Motivated

Partial Cooperation is a paradigm and a corresponding model, proposed to represent multi-agent systems in which agents are willing to cooperate to achieve a global goal, as long as some minimal threshold on their personal utility is satisfied. Distributed local search algorithms were proposed in order to solve asymmetric distributed constraint optimization problems (ADCOPs) in which agents are partially cooperative. We contribute by: 1) extending the partial cooperative model to allow it to represent dynamic cooperation intentions, affected by changes in agents’ wealth, in accordance with social studies literature. 2) proposing a novel local search algorithm in which agents receive indications of others’ preferences on their actions and thus, can perform actions that are socially beneficial. Our empirical study reveals the advantage of the proposed algorithm in multiple benchmarks. Specifically, on realistic meeting scheduling problems it overcomes limitations of standard local search algorithms.

scholarly journals Optimal Integration of Photovoltaic Sources in Distribution Networks for Daily Energy Losses Minimization Using the Vortex Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4418
Author(s):  
Alejandra Paz-Rodríguez ◽  
Juan Felipe Castro-Ordoñez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Objective Function ◽  
Power Flow ◽  
Search Algorithm ◽  
Peak Load ◽  
Distribution Networks ◽  
Energy Losses ◽  
Continuous Version ◽  
Continuous Section ◽  
Daily Energy ◽  
Discrete Part

This paper deals with the optimal siting and sizing problem of photovoltaic (PV) generators in electrical distribution networks considering daily load and generation profiles. It proposes the discrete-continuous version of the vortex search algorithm (DCVSA) to locate and size the PV sources where the discrete part of the codification defines the nodes. Renewable generators are installed in these nodes, and the continuous section determines their optimal sizes. In addition, through the successive approximation power flow method, the objective function of the optimization model is obtained. This objective function is related to the minimization of the daily energy losses. This method allows determining the power losses in each period for each renewable generation input provided by the DCVSA (i.e., location and sizing of the PV sources). Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that: (i) the proposed DCVSA finds the optimal global solution for both test feeders when the location and size of the PV generators are explored, considering the peak load scenario. (ii) In the case of the daily operative scenario, the total reduction of energy losses for both test feeders are 23.3643% and 24.3863%, respectively; and (iii) the DCVSA presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software, which demonstrates the effectiveness and robustness of the proposed master-slave optimization algorithm.

scholarly journals Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm

Computation ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 80
Author(s):  
John Fernando Martínez-Gil ◽  
Nicolas Alejandro Moyano-García ◽  
Oscar Danilo Montoya ◽  
Jorge Alexander Alarcon-Villamil
Keyword(s):  
Objective Function ◽  
Power Flow ◽  
Phase Distribution ◽  
Search Algorithm ◽  
Distribution Networks ◽  
Discrete Version ◽  
Optimal Selection ◽  
Flow Method ◽  
Three Phase ◽  
Selection Of

In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) structure is used. As an objective function, minimization of the investment costs in conductors together with the technical losses of the network for a study period of one year is considered. Additionally, the model will be implemented in balanced and unbalanced test systems and with variations in the connection of their loads, i.e., Δ− and Y−connections. To evaluate the costs of the energy losses, a classical backward/forward three-phase power-flow method is implemented. Two test systems used in the specialized literature were employed, which comprise 8 and 27 nodes with radial structures in medium voltage levels. All computational implementations were developed in the MATLAB programming environment, and all results were evaluated in DigSILENT software to verify the effectiveness and the proposed three-phase unbalanced power-flow method. Comparative analyses with classical and Chu & Beasley genetic algorithms, tabu search algorithm, and exact MINLP approaches demonstrate the efficiency of the proposed optimization approach regarding the final value of the objective function.

scholarly journals Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

2021 ◽  
Vol 11 (5) ◽  
pp. 2175
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Jesus C. Hernández
Keyword(s):  
Objective Function ◽  
Reactive Power ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Solution Vector ◽  
The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

SUPPLY CHAIN FORMATION IN OPEN, MARKET-BASED MULTI-AGENT SYSTEMS

2002 ◽  
Vol 02 (03) ◽  
pp. 349-363 ◽  
Author(s):  
ANEURIN M. EASWARAN ◽  
JEREMY PITT
Keyword(s):  
Supply Chain ◽  
Service Providers ◽  
Search Algorithm ◽  
Multiple Criteria ◽  
Simultaneous Optimization ◽  
Open Market ◽  
Multi Agent Systems ◽  
Complex Tasks ◽  
Hierarchical Task Network ◽  
Service Allocation

Efficient allocation of services to form a supply chain to solve complex tasks is a crucial problem. Optimal service allocation based on a single criterion is NP-Complete. Furthermore, complex tasks in general have multiple criteria that may be conflicting and non-commensurable. This paper presents a two-stage brokering algorithm for optimal anytime service allocation based on multiple criteria. In the first stage, a hierarchical task network planner is used to identify the services required to solve a task. In the second stage, a genetic algorithm (GA) determines service providers based on multiple criteria to provide the services identified by the planner. We present our algorithm and results from various experiments conducted to analyze the effect of various parameters that influence the complexity of the problem. In general, the results show the GA finds optimal solutions much quicker than a standard search algorithm. The empirical results also indicate the performance of the algorithm is sub-linear or polynomial time for various parameters. The algorithm has the ability to deal with any number of criteria. By addressing this problem, we expand the range of problems being addressed to any that require simultaneous optimization of multiple criteria and/or planning.

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

Sign in / Sign up

Export Citation Format

Share Document