Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Yousefi, Hossein; Tavakkoli–Moghaddam, Reza; Oliaei, Mahyar Taheri Bavil; Mohammadi, Mohammad; Mozaffari, Ali

doi:10.5267/j.ijiec.2017.1.003

Cited by 12 publications

(6 citation statements)

References 45 publications

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“…Equation (9) indicates that the vehicle must meet the time window requirements of the visiting customers during the driving process. Equations (10)-(11) are value constraints.…”

Section: Mathematical Model Constructionmentioning

confidence: 99%

“…The research on vehicle routing problem (VRP) began in 1959. Based on the unremitting efforts of experts at home and abroad in this field, a series of intelligent algorithms have been introduced into the field of research, such as the tabu search algorithm, simulated annealing algorithm, ant colony algorithm, genetic algorithm, artificial neural network, and particle swarm optimization algorithm [1][2][3][4][5][6][7][8][9]. However, there are few articles on intelligent algorithms to explore the optimal decision-making problem of spare parts supply paths with time windows.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Spare Parts Supply Route Optimization with Hard Time Windows

Ming¹,

Wang²,

Cheng³

2019

IJPE

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In view of the spare parts supply route problem with hard time windows in transportation, an optimal model that aims to minimize the total delivery time is put forward. In this paper, the supply route optimization problem with the shortest transportation distance as the decision target is discussed in detail. Taking the shortest total transportation distance of all vehicles as the optimization goal, a mathematical analytical model that considers the hard time window requirements of each customer is established. Based on the improved ant colony algorithm, the algorithm flow of solving the vehicle routing problem with hard time windows is designed to solve the difficulty of the model. In addition, the feasibility of the improved algorithm is verified by a case considering the actual terrain. The results show that the improved algorithm can quickly find the solution of VRPTW and provide an effective delivery plan for decision makers.

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“…Equation (9) indicates that the vehicle must meet the time window requirements of the visiting customers during the driving process. Equations (10)-(11) are value constraints.…”

Section: Mathematical Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of Spare Parts Supply Route Optimization with Hard Time Windows

Ming¹,

Wang²,

Cheng³

2019

IJPE

View full text Add to dashboard Cite

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“…Many ways to deal with this uncertainty have been presented, all of which are based on stochastic techniques and are known as stochastic location-routing problems (SLRPs). The goal of such issues is to find a group of near-optimal solutions for each of the unknown parameters in a variety of worst-case situations (Yousefi et al , 2017).…”

Section: Introductionmentioning

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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“…Many approaches have been proposed to address this uncertainty, which are based on stochastic methods and are referred to as stochastic VRPs. The purpose of such problems is to achieve a set of near-optimal answers that optimize each of the uncertain parameters for a set of worst-case scenarios (Yousefi et al , 2017).…”

Section: Introductionmentioning

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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Solving a bi-objective vehicle routing problem under uncertainty by a revised multi-choice goal programming approach

Cited by 12 publications

References 45 publications

Modeling of Spare Parts Supply Route Optimization with Hard Time Windows

Modeling of Spare Parts Supply Route Optimization with Hard Time Windows

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

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

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