scholarly journals Metaheuristic Approaches for Solving Truck and Trailer Routing Problems with Stochastic Demands: A Case Study in Dairy Industry

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Seyedmehdi Mirmohammadsadeghi ◽  
Shamsuddin Ahmed
Keyword(s):  
Sensitivity Analysis ◽  
Service Providers ◽  
Memetic Algorithm ◽  
Stochastic Demand ◽  
Routing Problem ◽  
Benchmark Instances ◽  
Using Data ◽  
Number Of Customers ◽  
Sizeable Number

Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered the deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant, first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the solutions have been tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results obtained by three algorithms and sensitivity analysis. Since the differences between the results are insignificant, the algorithms are found to be appropriate and relevant for solving real-world TTRPSD problem.

scholarly journals On the Selective Vehicle Routing Problem

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 771 ◽  
Author(s):  
Cosmin Sabo ◽  
Petrică C. Pop ◽  
Andrei Horvat-Marc
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
The Novel ◽  
Routing Problem ◽  
Proposed Model ◽  
Benchmark Instances ◽  
Number Of Customers ◽  
Optimal Set

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.

Data analytics dilemma at Alpen Hotel

2019 ◽  
Vol 9 (2) ◽  
pp. 58-63
Author(s):  
Tammy Wee ◽  
Arif Perdana ◽  
Detlev Remy
Keyword(s):  
Customer Relationship Management ◽  
Data Analytics ◽  
Data Privacy ◽  
Hospitality Industry ◽  
Service Providers ◽  
Relationship Management ◽  
Customer Relationship ◽  
Using Data ◽  
Privacy And Confidentiality

Data analytics is currently the buzzword for the hospitality industry to stay ahead of their competitors. Service providers use data analytics to ensure their brand remains relevant for customers. Using data analytics in customer relationship management is a relatively novel initiative for the hospitality industry to enhance the efforts of customer relationship management. Obtaining customers’ data (i.e. customers’ hotel stay and preferences) provides both opportunity and challenges for the hospitality industry. Data analytics helps the hospitality industry to quickly, effectively, and efficiently pursue data-driven decision-making. At the same time, acquiring relevant customers’ data is a challenge, for example, data privacy and confidentiality. This case study is based on Alpen Hotel (pseudonym), a luxury hotel in Singapore with a good standing in the hospitality industry. This case is focused on the issues they experienced in implementing data analytics as part of the hotel’s customer relationship management efforts. This case study aims to highlight data analytics dilemma at the hotel and may create an opportunity for hospitality educators to work interdisciplinary with faculties from an information systems or technology discipline. Finally, the case study may enhance knowledge and minimise the practice gap between industry and academia.

scholarly journals Development Of A New Genetic Algorithm For Solving Capacitated Vehicle Routing Problem In Short Time

2019 ◽  
Vol 8 (11) ◽  
pp. 903-907
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Benchmark Instances ◽  
Full Capacity ◽  
Short Time ◽  
Number Of Customers ◽  
Capacitated Vehicle

In this paper a new genetic algorithm is developed for solving capacitated vehicle routing problem (CVRP) in situations where demand is unknown till the beginning of the trip. In these situations it is not possible normal metaheuristics due to time constraints. The new method proposed uses a new genetic algorithm based on modified sweep algorithm that produces a solution with the least number of vehicles, in a relatively short amount of time. The objective of having least number of vehicles is achieved by loading the vehicles nearly to their full capacity, by skipping some of the customers. The reduction in processing time is achieved by restricting the number of chromosomes to just one. This method is tested on 3 sets of standard benchmark instances (A, M, and G) found in the literature. The results are compared with the results from normal metaheuristic method which produces reasonably accurate results. The results indicate that whenever the number of customers and number of vehicles are large the new genetic algorithm provides a much better solution in terms of the CPU time without much increase in total distance traveled. If time permits the output from this method can be further improved by using normal established metaheuristics to get better solution

A Hybrid Meta-Heuristic Approach Considering Workload Balancing for Vehicle Assignment and Routing Problem

2020 ◽  
Author(s):  
Takuma Kawashima ◽  
Tatsuhiko Sakaguchi ◽  
Naoki Uchiyama
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Service Providers ◽  
Routing Algorithm ◽  
Manufacturing Companies ◽  
Routing Problem ◽  
Logistics Service Providers ◽  
Japanese City ◽  
Number Of Customers ◽  
Initial Route

Abstract In recent years, due to the globalization of the market and the expansion of e-commerce, logistics optimization attracts keen interest from manufacturing companies and service providers. The service area expands wider and the number of customers increases rapidly, thus logistics service providers need to determine the customer assignments and the routes for their trucks considering not only the efficiency of logistics but also the balance of workload for each truck. Therefore, in this study, we propose a customer assignment and vehicle routing algorithm based on the saving method and the simulated annealing. The algorithm first determines the customer assignment and initial route for each truck based on the saving method to balance the workload consisting of the number of customers, the demand of the customers, and distance. Then the initial route is improved by applying the simulated annealing. To evaluate the effectiveness of the proposed method, we conducted computational experiments. In experiments, we solved the waste collection vehicle routing problem in a Japanese city where the wastes generated from over 1000 customers are collected by 10 trucks starting from 1 depot. We evaluated the total cost consisting of the number of waste collecting points, the amount of waste, and the distance for this case study.

scholarly journals Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics

2020 ◽  
Vol 10 (16) ◽  
pp. 5585
Author(s):  
Jutamat Jintana ◽  
Apichat Sopadang ◽  
Sakgasem Ramingwong
Keyword(s):  
Monte Carlo Simulation ◽  
Association Rule ◽  
Data Analytics ◽  
Recommendation System ◽  
Decision Makers ◽  
Courier Service ◽  
Effective Cost ◽  
Using Data ◽  
Number Of Customers

The purpose of this research was to create a Matching Consignees/Shippers Recommendation System (MCSRS). We used the association rule to identify product associations, the clustering technique to group shippers and consignees according to behaviors when receiving goods from similar shipper groups, and the decision tree to identify possible matches between shippers and consignees. Finally, Monte Carlo simulation was used to estimate potential revenue. The case study is a courier company in Thailand. The results showed that garment products and clothes were the products with the highest association. Shippers and consignees of these products were segmented according to recency, frequency, monetary factors, number of customers, number of product items, weight, and day. Three rules are proposed that enabled the assignment of 8 consignees to 56 shippers with an estimated increase in revenue by 36%. This approach helps decision-makers to develop an effective cost-saving new marketing, inclusive strategy quickly.

scholarly journals A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study

2016 ◽  
Vol 250 (2) ◽  
pp. 279-308 ◽  
Author(s):  
Nasrin Asgari ◽  
Mohsen Rajabi ◽  
Masoumeh Jamshidi ◽  
Maryam Khatami ◽  
Reza Zanjirani Farahani
Keyword(s):  
Memetic Algorithm ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Multi Objective ◽  
Location Routing

A Fuzzy AHP Model for 3PL Selection in Lead Logistics Provider Scenarios

2010 ◽  
pp. 261-277 ◽  
Author(s):  
Rajbir Singh Bhatti ◽  
Pradeep Kumar ◽  
Dinesh Kumar
Keyword(s):  
Service Providers ◽  
Fuzzy Ahp ◽  
Analytic Hierarchy ◽  
Ahp Model ◽  
Decision Factors ◽  
Proposed Model ◽  
Using Data ◽  
Hierarchy Process ◽  
Selection Of

Selection of service providers in the global supply chains of today has been recognized as having a very important effect on the competitiveness of the entire supply chain. It results in achieving high quality end results (products and/or services), at reasonable cost coupled with high customer satisfaction. This article discusses the use of Fuzzy Analytic Hierarchy Process (FAHP) to effectively manage the qualitative and quantitative decision factors which are involved in the selection of providers of 3PL services under Lead Logistics Provider (LLP) environments of today. Lead logistics providers (LLP) are increasingly being banked upon to integrate the best of 3PL service providers and allow for synchronized and optimized operations. In the asset free environments of today, many a times, the LLP uses the services of the 3PL and hence the issue of reliably choosing them assumes increasingly greater significance. The fuzzy-AHP has been adequately demonstrated in literature to be an effective tool which can be used to factor-in the fuzziness of data. Triangular Fuzzy Numbers (TFN) have been deployed to make over the linguistic comparisons of criteria, sub-criteria and the alternatives. The FAHP based model formulated in this chapter is applied to a case study in the Indian context using data from three leading LSPs with significant operating leverages in the province of Uttrakhand (India). The proposed model can provide the guidelines and directions for the decision makers to effectively select their global service providers in the present day competitive logistics markets.

scholarly journals A Simulated Annealing Heuristic for the Capacitated Green Vehicle Routing Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Nur Mayke Eka Normasari ◽  
Vincent F. Yu ◽  
Candra Bachtiyar ◽  
Sukoyo
Keyword(s):  
Sensitivity Analysis ◽  
Simulated Annealing ◽  
Numerical Experiment ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Routing Problem ◽  
Total Distance ◽  
Number Of Customers

This research studies the capacitated green vehicle routing problem (CGVRP), which is an extension of the green vehicle routing problem (GVRP), characterized by the purpose of harmonizing environmental and economic costs by implementing effective routes to meet any environmental concerns while fulfilling customer demand. We formulate the mathematical model of the CGVRP and propose a simulated annealing (SA) heuristic for its solution in which the CGVRP is set up as a mixed integer linear program (MILP). The objective of the CGVRP is to minimize the total distance traveled by an alternative fuel vehicle (AFV). This research conducts a numerical experiment and sensitivity analysis. The results of the numerical experiment show that the SA algorithm is capable of obtaining good CGVRP solutions within a reasonable amount of time, and the sensitivity analysis demonstrates that the total distance is dependent on the number of customers and the vehicle driving range.

scholarly journals A more efficient cutting planes approach for the green vehicle routing problem with capacitated alternative fuel stations

2021 ◽  
Author(s):  
Maurizio Bruglieri ◽  
Simona Mancini ◽  
Ornella Pisacane
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Solution Method ◽  
Ad Hoc ◽  
Cutting Plane ◽  
Alternative Fuel ◽  
Routing Problem ◽  
Benchmark Instances ◽  
Number Of Customers ◽  
Fleet Manager

AbstractThe Green Vehicle Routing Problem with Capacitated Alternative Fuel Stations assumes that, at each station, the number of vehicles simultaneously refueling cannot exceed the number of available pumps. The state-of-the-art solution method, based on the generation of all feasible non-dominated paths, performs well only with up to 2 pumps. In fact, it needs cloning the paths between every pair of pumps. To overcome this issue, in this paper, we propose new path-based MILP models without cloning paths, for both the scenario with private stations (i.e., owned by the fleet manager) and that with public stations. Then, a more efficient cutting plane approach is designed for addressing both the scenarios. Numerical results, obtained considering a set of benchmark instances ad hoc generated for this work, show both the efficiency and the effectiveness of this new cutting plane approach proposed. Finally, a sensitivity analysis, carried out by varying the number of customers to be served and their distribution, shows very good performances of the proposed approach.

A Memetic Algorithm for the Multi-Depot Vehicle Routing Problem with Limited Stocks

2015 ◽  
pp. 411-445 ◽  
Author(s):  
Shi Li ◽  
Yahong Zheng
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Central Place ◽  
Combinatorial Problems ◽  
Logistics Management ◽  
Routing Problem ◽  
Benchmark Instances

The Vehicle Routing Problem (VRP) is one of important combinatorial problems, which holds a central place in logistics management. One of the most widely studied problems in the VRP family is the Multi-Depot Vehicle Routing Problem (MDVRP), where more than one depot is considered. In this chapter, the authors focus on a new extension of the MDVRP in which goods loaded by the vehicle are restricted due to limited stocks available at warehouses. More specifically, this extension consists in determining a least cost routing plan that can satisfy all the customs demands by delivering available stocks. Indeed, this problem is often encountered when goods are shortage in some warehouses. To deal with the problem efficiently, a memetic algorithm is proposed in this chapter. The authors study this approach on a set of modified benchmark instances and compare its performance to a pure genetic algorithm.

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