A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application

Abstract:In this study, we combine the fuzzy customer information problem with the multicommodity multimodal routing with schedule-based services which was explored in our previous study [1]. The fuzzy characteristics of the customer information are embodied in the demanded volumes of the multiple commodities and the time windows of their due dates. When the schedule-based services are considered in the routing, schedule constraints emerge because the operations of block container trains should follow their predetermined schedules. This will restrict the routes selection from space-time feasibility. To solve this combinatorial optimization problem, we first build a fuzzy chance-constrained nonlinear programming model based on fuzzy possibility theory. We then use a crisp equivalent method and a linearization method to transform the proposed model into the classical linear programming model that can be effectively solved by the standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. The sensitivity of the best solution with respect to the values of the confidence levels is also examined.

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“…SD Proposition 2: Nonlinear Constraint Set (13) is equivalent to the following linear Constraint Sets (27) and (28).…”

Section: Linearization Of the Nonlinear Constraint Setsmentioning

confidence: 99%

“…Hence, we can use trapezoidal fuzzy numbers to represent the due date, and further measure the customer satisfaction quantitatively using the fuzzy membership function [27,28]. For commodity k, its due date is defined as:…”

Section: Fuzzy Soft Time Windowmentioning

confidence: 99%

On Solving the Fuzzy Customer Information Problem in Multicommodity Multimodal Routing with Schedule-Based Services

Sun

Lang

2016

Information

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“… Time or distance constraints  Time windows  Precedence relations between pairs of locations The objective of VRP is generally to design a set of minimum cost routes that serve a number of places. Since its first formulation in 1959, in the literature, there have been many studies (Ghannadpour, Noori, T.-Moghaddam and Ghoseiri, 2014). Lenstra and Rinnooy Kan (in 1981) have analyzed the complexity of the vehicle routing problem and they have concluded that practically all the vehicle routing problems are NP-hard because they are not solved in polynomial time.…”

Section: Introductionmentioning

confidence: 99%

Order Picking Problem in a Warehouse with Bi-Objective Genetic Algorithm Approach: Case Study

Kiris¹,

Deliktaş²,

Üstün³

2018

dogus

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ABSTRACT:In this paper, an order picking problem with the capacitated forklift in a warehouse is studied by considering the total distance and the penalized earliness/tardiness. These objectives are important to reduce transportation costs and to satisfy customer expectations. Since this problem has been known as NP-hard, a genetic algorithm (GA) is proposed to solve the bi-objective order picking problem. The proposed approach is applied to auto components industry that produces wire harnesses responsible for all electrical functions in the vehicle. Experimental design is used for tuning the influential parameters of the proposed GA. The GA approach was solved by weighted sum scalarization.

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“…In dynamic optimization problems, the optimum during dynamic changes is to be tracked [6,19,24,26]. Meta-Heuristic methods such as Variable Neighborhood Search, Genetic Algorithm, Artificial Bee Colony and Particle Swarm Optimization were intensively experimented in the past years to solve dynamic optimization problems [6,7,8,12,27,29,33].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Vehicle Routing Problem: Solution by Ant Colony Optimization with Hybrid Immigrant Schemes

K.M.¹,

Kanmani²

2017

IJISA

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Abstract-During past decades, several Meta-Heuristics were considered by researchers to solve Dynamic Vehicle Routing Problem.In this paper, Ant Colony Optimization integrated with Hybrid Immigrant Schemes methods are proposed for solving Dynamic Vehicle Routing Problem. Ant Colony Optimization with hybrid immigrant schemes methods namely HIACO-I, HIACO-II and HIACO-III focused on establishing the proper balance between intensification and diversification. The performance evaluation of the algorithms in which Random Immigrants and Elitism based Immigrants were hybridized in different proportions and added to Ant Colony Optimization algorithm showed that they had produced better results in many dynamic test cases generated from three Vehicle Routing Problem instances.

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A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application

Cited by 133 publications

References 41 publications

On Solving the Fuzzy Customer Information Problem in Multicommodity Multimodal Routing with Schedule-Based Services

On Solving the Fuzzy Customer Information Problem in Multicommodity Multimodal Routing with Schedule-Based Services

Order Picking Problem in a Warehouse with Bi-Objective Genetic Algorithm Approach: Case Study

Dynamic Vehicle Routing Problem: Solution by Ant Colony Optimization with Hybrid Immigrant Schemes

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