Strategies for an Integrated Distribution Problem

E-Marketing ◽

10.4018/978-1-4666-1598-4.ch029 ◽

2012 ◽

pp. 482-505

Author(s):

Helena R. Lourenço ◽

Rita Ribeiro

Keyword(s):

Customer Loyalty ◽

Computational Experiment ◽

Iterated Local Search ◽

Routing Problem ◽

Marketing Department ◽

Planning Strategy ◽

Objective Model ◽

The Cross ◽

Functional Areas ◽

Commercial Distribution

Problems arising in the logistics of commercial distribution are complex and involve several players and decision levels. One of the most important decisions is the design of the routes to distribute the products in an efficient and inexpensive way but also satisfying marketing objectives such as customer loyalty. This chapter explores three different distribution routing strategies. The first strategy corresponds to the classical vehicle routing problem where total distance or cost is minimized. This one is usually an objective of the Logistics department. The second strategy is a master route strategy with daily adaptations where customer loyalty is maximized, which is one of the objectives of the Marketing department. The authors propose a third strategy which takes into account the cross-functional planning between the Logistics and the Marketing department through a multi-objective model. All strategies are analyzed in a multi-period scenario. A metaheuristic algorithm based on the Iterated Local Search is proposed and applied to optimize each strategy. An analysis and comparison of the three strategies is presented through a computational experiment. The cross-functional planning strategy leads to solutions that put in practice the coordination between the two functional areas of Marketing and Logistics and better meet business objectives in general.

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Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

Algorithms ◽

10.3390/a14020045 ◽

2021 ◽

Vol 14 (2) ◽

pp. 45

Author(s):

Rafael D. Tordecilla ◽

Pedro J. Copado-Méndez ◽

Javier Panadero ◽

Carlos L. Quintero-Araujo ◽

Jairo R. Montoya-Torres ◽

...

Keyword(s):

Fuzzy Logic ◽

Local Search ◽

Vehicle Routing Problem ◽

Large Scale ◽

Iterated Local Search ◽

Location Routing Problem ◽

Routing Problem ◽

Location Routing ◽

Fuzzy Pattern ◽

Reasonable Computing

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.

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A Fast and Scalable Heuristic for the Solution of Large-Scale Capacitated Vehicle Routing Problems

Transportation Science ◽

10.1287/trsc.2021.1059 ◽

2021 ◽

Author(s):

Luca Accorsi ◽

Daniele Vigo

Keyword(s):

Vehicle Routing ◽

Large Scale ◽

Computing Time ◽

Search Space ◽

Iterated Local Search ◽

Solution Quality ◽

Routing Problem ◽

Acceptance Criterion ◽

Acceleration Techniques ◽

Capacitated Vehicle

In this paper, we propose a fast and scalable, yet effective, metaheuristic called FILO to solve large-scale instances of the Capacitated Vehicle Routing Problem. Our approach consists of a main iterative part, based on the Iterated Local Search paradigm, which employs a carefully designed combination of existing acceleration techniques, as well as novel strategies to keep the optimization localized, controlled, and tailored to the current instance and solution. A Simulated Annealing-based neighbor acceptance criterion is used to obtain a continuous diversification, to ensure the exploration of different regions of the search space. Results on extensively studied benchmark instances from the literature, supported by a thorough analysis of the algorithm’s main components, show the effectiveness of the proposed design choices, making FILO highly competitive with existing state-of-the-art algorithms, both in terms of computing time and solution quality. Finally, guidelines for possible efficient implementations, algorithm source code, and a library of reusable components are open-sourced to allow reproduction of our results and promote further investigations.

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A biobjective capacitated vehicle routing problem using metaheuristic ILS and decomposition

International Journal of Industrial Engineering Computations ◽

10.5267/j.ijiec.2021.2.002 ◽

2021 ◽

Vol 12 (3) ◽

pp. 293-304 ◽

Cited By ~ 1

Author(s):

Luis Fernando Galindres-Guancha ◽

Eliana Toro-Ocampo ◽

Ramón Gallego-Rendón

Keyword(s):

Approximate Method ◽

Vehicle Routing ◽

Real Life ◽

Minimum Cost ◽

Iterated Local Search ◽

Test Cases ◽

Nsga Ii ◽

Routing Problem ◽

Decomposition Approach ◽

Multiobjective Approach

Vehicle routing problems (VRPs) have usually been studied with a single objective function defined by the distances associated with the routing of vehicles. The central problem is to design a set of routes to meet the demands of customers at minimum cost. However, in real life, it is necessary to take into account other objective functions, such as social functions, which consider, for example, the drivers' workload balance. This has led to growth in both the formulation of multiobjective models and exact and approximate solution techniques. In this article, to verify the quality of the results, first, a mathematical model is proposed that takes into account both economic and work balance objectives simultaneously and is solved using an exact method based on the decomposition approach. This method is used to compare the accuracy of the proposed approximate method in test cases of medium mathematical complexity. Second, an approximate method based on the Iterated Local Search (ILS) metaheuristic and Decomposition (ILS/D) is proposed to solve the biobjective Capacitated VRP (bi-CVRP) using test cases of medium and high mathematical complexity. Finally, the nondominated sorting genetic algorithm (NSGA-II) approximate method is implemented to compare both medium- and high-complexity test cases with a benchmark. The obtained results show that ILS/D is a promising technique for solving VRPs with a multiobjective approach.

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Solving Practical Vehicle Routing Problem with Time Windows Using Metaheuristic Algorithms

PROMET - Traffic&Transportation ◽

10.7307/ptt.v24i4.443 ◽

1970 ◽

Vol 24 (4) ◽

pp. 343-351 ◽

Cited By ~ 4

Author(s):

Filip Taner ◽

Ante Galić ◽

Tonči Carić

Keyword(s):

Vehicle Routing ◽

Real World ◽

Vehicle Routing Problem ◽

Time Windows ◽

Iterated Local Search ◽

Metaheuristic Algorithms ◽

Benchmark Problems ◽

Routing Problem ◽

Constructive Heuristics ◽

Initial Feasible Solution

This paper addresses the Vehicle Routing Problem with Time Windows (VRPTW) and shows that implementing algorithms for solving various instances of VRPs can significantly reduce transportation costs that occur during the delivery process. Two metaheuristic algorithms were developed for solving VRPTW: Simulated Annealing and Iterated Local Search. Both algorithms generate initial feasible solution using constructive heuristics and use operators and various strategies for an iterative improvement. The algorithms were tested on Solomon’s benchmark problems and real world vehicle routing problems with time windows. In total, 44 real world problems were optimized in the case study using described algorithms. Obtained results showed that the same distribution task can be accomplished with savings up to 40% in the total travelled distance and that manually constructed routes are very ineffective.

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Iterated local search for the vehicle routing problem with a private fleet and a common carrier

Engineering Optimization ◽

10.1080/0305215x.2019.1681985 ◽

2019 ◽

Vol 52 (10) ◽

pp. 1796-1813

Author(s):

John F. Castaneda L. ◽

Eliana M. Toro ◽

Ramon A. Gallego R.

Keyword(s):

Local Search ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Iterated Local Search ◽

Routing Problem ◽

Common Carrier

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A Multi-Objective Model for Sustainable Perishable Food Distribution Considering the Impact of Temperature on Vehicle Emissions and Product Shelf Life

Sustainability ◽

10.3390/su12166668 ◽

2020 ◽

Vol 12 (16) ◽

pp. 6668

Author(s):

Amin Gharehyakheh ◽

Caroline C. Krejci ◽

Jaime Cantu ◽

K. Jamie Rogers

Keyword(s):

Shelf Life ◽

Prediction Models ◽

Food Distribution ◽

Routing Problem ◽

Multi Objective ◽

Distribution Process ◽

Tremendous Amount ◽

Objective Model ◽

Energy Consumption Prediction ◽

The Impact

The food distribution process is responsible for significant quality loss in perishable products. However, preserving quality is costly and consumes a tremendous amount of energy. To tackle the challenge of minimizing transportation costs and CO2 emissions while also maximizing product freshness, a novel multi-objective model is proposed. The model integrates a vehicle routing problem with temperature, shelf life, and energy consumption prediction models, thereby enhancing its accuracy. Non-dominated sorting genetic algorithm II is adapted to solve the proposed model for the set of Solomon test data. The conflicting nature of these objectives and the sensitivity of the model to shelf life and shipping container temperature settings are analyzed. The results show that optimizing freshness objective degrade the cost and the emission objectives, and the distribution of perishable foods are sensible to the shelf life of the perishable foods and temperature settings inside the container.

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