Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem

Sori, Ali Abbaszadeh; Ebrahimnejad, Ali; Motameni, Homayun

doi:10.1111/coin.12258

Cited by 39 publications

(11 citation statements)

References 39 publications

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“…The robot's fuzzy constrained shortest route problem model was introduced by (Abbaszadeh Sori et al ., 2020) to determine the shortest route in robots. The information on energy usage and distance traveled along the journey is fuzzy.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing a locating-routing three-echelon supply chain network under uncertainty

Hashemi

Mahmoodi

Jasemi

et al. 2022

IJICC

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PurposeIn the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored. The goal of addressing the issue is to reduce delivery times and system costs for retailers so that routing and distributor location may be determined.Design/methodology/approachBy adding certain unique criteria and limits, the issue becomes more realistic. Customers expect simultaneous deliveries and pickups, and retail service start times have soft and hard time windows. Transportation expenses, noncompliance with the soft time window, distributor construction, vehicle purchase or leasing, and manufacturing costs are all part of the system costs. The problem's conceptual model is developed and modeled first, and then General Algebraic Modeling System software (GAMS) and Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms are used to solve it in small dimensions.FindingsAccording to the mathematical model's solution, the average error of the two suggested methods, in contrast to the exact answer, is less than 0.7%. In addition, the performance of algorithms in terms of deviation from the GAMS exact solution is pretty satisfactory, with a divergence of 0.4% for the biggest problem (N = 100). As a result, NSGAII is shown to be superior to MOSPSO.Research limitations/implicationsSince this paper deals with two bi-objective models, the priorities of decision-makers in selecting the best solution were not taken into account, and each of the objective functions was given an equal weight based on the weighting procedures. The model has not been compared or studied in both robust and deterministic modes. This is because, with the exception of the variable that indicates traffic mode uncertainty, all variables are deterministic, and the uncertainty character of demand in each level of the supply chain is ignored.Practical implicationsThe suggested model's conclusions are useful for any group of decision-makers concerned with optimizing production patterns at any level. The employment of a diverse fleet of delivery vehicles, as well as the use of stochastic optimization techniques to define the time windows, demonstrates how successful distribution networks are in lowering operational costs.Originality/valueAccording to a multi-objective model in a three-echelon supply chain, this research fills in the gaps in the link between routing and location choices in a realistic manner, taking into account the actual restrictions of a distribution network. The model may reduce the uncertainty in vehicle performance while choosing a refueling strategy or dealing with diverse traffic scenarios, bringing it closer to certainty. In addition, two modified MOPSO and NSGA-II algorithms are presented for solving the model, with the results compared to the exact GAMS approach for medium- and small-sized problems.

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Section: Literature Reviewmentioning

confidence: 99%

Designing a locating-routing three-echelon supply chain network under uncertainty

Hashemi

Mahmoodi

Jasemi

et al. 2022

IJICC

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“…[ 28 ] Studied on probabilistic multi-objective linear fractional programming problems under fuzziness. [ 29 ] proposed elite artificial bees’ colony algorithm to solve robot’s fuzzy constrained routing problem [ 30 ] studied application of multi-objective probabilistic fractional programming problem in production planning. [ 31 ] studied application of fuzzy random-based multi-objective linear fractional programming to inventory management problem.…”

Section: Literature Surveymentioning

confidence: 99%

Genetic algorithmic based approach for multiple-objective probabilistic fractional programming problem involving discrete random variables

Belay

Gessesse

2022

PLoS ONE

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This manuscript presents a technique for solving a multiple-objective probabilistic fractional programming problem with discrete random variables. A multiple-objective probabilistic mathematical model is constructed with fractional objectives. In the model, some parameters of coefficients and right hand side parameters of restrictions are assumed as random variables having Pascal and Hyper geometric distributions. The feasibility of probabilistic constraints is checked by means of stochastic simulation. Genetic algorithm approach method is used to obtain the Pareto optimal solution of the proposed model without finding the deterministic model. Genetic algorithm parameters are fixed in all generation. The proposed method is coded by C++ programming language. To illustrate the method, a numerical example and practical example on the case of supply chain management are presented. The result shows that the values of the objective functions are conflicting each other.

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“…Abbaszadeh et al (2020) have presented Robot’s Fuzzy constrained shortest route problem (FCSRP) model to find the SP in robots. Data on energy consumption along the path and the distance traveled are considered fuzzy.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms

Hashemi

Mahmoodi

Jasemi

et al. 2021

SRT

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Purpose This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located. Design/methodology/approach The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms. Findings According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO. Research limitations/implications In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered. Practical implications The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs. Originality/value This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

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Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem

Cited by 39 publications

References 39 publications

Designing a locating-routing three-echelon supply chain network under uncertainty

Designing a locating-routing three-echelon supply chain network under uncertainty

Genetic algorithmic based approach for multiple-objective probabilistic fractional programming problem involving discrete random variables

Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms

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