Distribution Route Optimization of Gallon Water Using Genetic Algorithm and Tabu Search

AbstractDistributions of drinking water in gallons often do not pay attention to the problem of finding the most optimal route, thus causing inefficiency in the cost of shipping. To minimize incurred costs, it is necessary to minimize vehicle fleet and amount of travel distance, with the restriction that the vehicle must have sufficient capacity to transport the goods to be shipped and return it back to the depots. This problem could be framed as a Vehicle Routing Problem with pick-up and delivery (VRPPD).In this paper, we propose a method to optimize delivery route in a drinking water depot by combining genetic algorithm (GA) and Tabu search. GA has advantages by providing possible solutions while Tabu covers up its shortfall in identifying local solutions so that searching will able to avoid loop in the area of the same solution. Experimental results show that the proposed method is more efficient than a manually predetermined route

Hybrid genetic-tabu search algorithm to optimize the route for capacitated vehicle routing problem with time window

Optimization of transportation and distribution costs is one of the important issues in the supply chain management area. It is caused by their large contribution to the logistics costs that can reach up to 40%. Thus, choosing the right route is one of the efforts that can be done to resolve the issue. This study aims to optimize the capacitated vehicle routing problem with time windows (CVRPTW) for mineral water company distributor with pick-up and delivery problem. To achieve the aim, this study used hybrid algorithm, Genetic Algorithm (GA) and Tabu Search Algorithm (TS). The selection of this hybrid algorithm is due to its capability in minimizing travel distance. The result of this study shows that not only the algorithm has successfully reduced the existing route but also predicted the optimum number of homogenous fleet. By running the algorithm, this study concludes that the number of the optimum routes for this study can be reduced for up to 15.99% than the existing route

An Application of Genetic Algorithm in Determining Salesmen’s Routes: A Case Study

This paper presents a case study of determining vehicles’ routes. The case is taken from a pharmaceutical products distribution problem faced by a distribution company located in the city of Padang, Indonesia. The objective of this paper is to reduce the total distribution time required by the salesmen of the company. Since the company uses more than one salesman, then the problem is modeled as a multi travelling salesman problem (m-TSP). The problem is solved by employing genetic algorithm (GA) and a Matlab® based computer program is developed to run the algorithm. It is found that, by employing two salesmen only, the routes produced by GA results in a 30% savings in total distribution time compared to the current routes used by the company (currently the company employs three salesmen). This paper determines distances based on the latitude and longitude of the locations visited by the salesmen. Therefore, the distances calculated in this paper are approximations. It is suggested that actual distances are used for future research

Optimizing Book Delivery Routes Using Genetic Algorithms: Case Study of Erlangga Publisher Yogyakarta Branch

Optimizing Book Delivery Routes Using Genetic Algorithms: Case Study of Erlangga Publisher Yogyakarta Branch. This research aims to find the shortest route for book delivery using the Traveling Salesperson Problem (TSP) approach that is solved by a Genetic Algorithm (GA). The distance between the pair of locations will be known by using the longitude and latitude as the coordinates of the location (the place where books must be dropped and the trip continues). This network of the coordinates of locations is then viewed as TSP, which needs GA to solve the shortest path. Running the program for up to 100 iterations, this study resulted in the shortest route, 356 km in a whole route. Among the previous research, this research has its uniqueness, especially when the problem is viewed as a TSP, and when it comes to the crossover mechanism, it is quite rare. Moreover, the case of the Erlangga publisher is the first case that has used the GA

Optimizing Book Delivery Routes Using Genetic Algorithms: Case Study of Erlangga Publisher Yogyakarta Branch

Optimizing Book Delivery Routes Using Genetic Algorithms: Case Study of Erlangga Publisher Yogyakarta Branch. This research aims to find the shortest route for book delivery using the Traveling Salesperson Problem (TSP) approach that is solved by a Genetic Algorithm (GA). The distance between the pair of locations will be known by using the longitude and latitude as the coordinates of the location (the place where books must be dropped and the trip continues). This network of the coordinates of locations is then viewed as TSP, which needs GA to solve the shortest path. Running the program for up to 100 iterations, this study resulted in the shortest route, 356 km in a whole route. Among the previous research, this research has its uniqueness, especially when the problem is viewed as a TSP, and when it comes to the crossover mechanism, it is quite rare. Moreover, the case of the Erlangga publisher is the first case that has used the GA

Green logistic network design : intermodal transportation planning and vehicle routing problems.

Due to earth\u27s climate change and global warming, environmental consideration in the design of logistic systems is accelerating in recent years. In this research we aim to design an efficient and environmentally friendly logistical system to satisfy both government and carriers. In particular, we considered three problems in this dissertation: intermodal network design, deterministic green vehicle routing problem and stochastic green vehicle routing problem. The first problem aims to design an economic and efficient intermodal network including three transportation modes: railway, highway and inland waterway. The intent of this problem is to increase the utilization percentage of waterway system in the intermodal transportation network without increasing the cost to the consumer. In particular, we develop a real world coal transportation intermodal network across 15 states in the United States including highway, railway and inland waterway. The demand data were obtained from the Bureau of Transportation Statistics (BTS) under the US Department of Transportation (DOT). Four boundary models are built to evaluate the potential improvement of the network. The first boundary model is a typical minimum cost problem, where the total transportation cost is minimized while the flow balance and capacity restrictions are satisfied. An additional constraint that help obtain an upper bound on carbon emission is added in the second boundary model. Boundary model 3 minimizes the total emission with flow balance and capacity restrictions the same as boundary model 1. Boundary model 4 minimizes the total emission with an additional current cost restriction to achieve a less-aggressive lower bound for carbon emission. With a motivation to minimize the transportation and environmental costs simultaneously, we propose multi-objective optimization models to analyze intermodal transportation with economic, time performance and environmental considerations. Using data from fifteen selected states, the model determines the tonnage of coal to be transported on roadways, railways and waterways across these states. A time penalty parameter is introduced so that a penalty is incurred for not using the fastest transportation mode. Our analysis provides authorities with a potential carbon emission tax policy while minimizing the total transportation cost. In addition, sensitivity analysis allows authorities to vary waterway, railway and highway capacities, respectively, and study their impact on the total transportation cost. Furthermore, the sensitivity analysis demonstrates that an intermodal transportation policy that uses all the three modes can reduce the total transportation cost when compared to one that uses just two modes. In contrast with traditional vehicle routing problems, the second problem intends to find the most energy efficient vehicle route with minimum pollution by optimization of travel speed. A mixed integer nonlinear programming model is introduced and a heuristic algorithm based on a savings heuristic and Tabu Search is developed to solve the large case for this problem. Numerical experiments are conducted through comparison with a solution obtained by BONMIN in GAMS on randomly generated small problem instances to evaluate the performance of the proposed heuristic algorithm. To illustrate the impact of a time window constraint, travel speed and travel speed limit on total carbon emission, sensitivity analysis is conducted based on several scenarios. In the end, real world instances are examined to further investigate the impact of these parameters. Based on the analysis from the second problem, travel speed is an important decision factor in green vehicle routing problems to minimize the fuel cost. However, the actual speed limit on a road may have variance due to congestion. To further investigate the impact of congestion on carbon emission in the real world, we proposed a stochastic green vehicle routing problem as our third problem. We consider a green vehicle problem with stochastic speed limits, which aims to find the robust route with the minimum expected fuel cost. A two-stage heuristic with sample average approximation is developed to obtain the solution of the stochastic model. Computational study compares the solutions of robust and traditional mean-value green vehicle routing problems with various settings

Optimizing fare structure and service frequency for an intercity transit system

This study presents an approach to jointly optimize service headway and differentiated fare for an intercity transit system with an objective of total profit maximization and with consideration given to the economic and social sustainability of the system. Service capacity and fleet size constraints are considered. The optimization problem is structured into four scenarios which are comprised of the combinations of whether the Ranges of Travel Distance (RTD) is fixed or variable and if the time period is for a single period or for multiple periods. A successive substitution method (specifically, a modified Gauss Southwell method) is applied to solve for the optimal solutions when the RTD is considered fixed, while a heuristic solution algorithm (specifically, a Genetic Algorithm) is developed to find the optimal solutions when the RTD is considered to be optimized. The methodology discussed in this dissertation contributes to the field of transportation network modeling because it establishes how to solve the fare and headway design problem for an intercity transit system. Intercity transit agencies are faced with the challenge of determining fares for a very complicated setting in which demand elasticity, realistic geographic conditions, and facility locations of the transit system all must be taken into account. A real world case study - Taiwan High Speed Rail is used to demonstrate the applicability of the developed methodology. Numerical results of optimal solutions and sensitivity analyses are presented for each scenario. The sensitivity analyses enable transit planners to quantify the impact of fare policies and address social equity issues, which can be a major hurdle of implementing optimal fare policy to achieve maximum profit operation. According to the sensitivity analysis, the total profit surfaces for various headways, fares, and RTD are relatively flat near the optimum. This indicates that the transit operator has flexibility in shifting the solution marginally away from the optimum without significantly reducing the maximum profit. By varying the elasticity parameters of fare and demand one can observe how these variables affect the optimized RTD. The results indicate that as the elasticity parameters of fare increase or demand decreases, the optimal number of RTD increase while the boundaries of RTD are concentrated in the range of shorter travel distances

DYNAMIC DECISION MAKING FOR LESS-THAN-TRUCKLOAD TRUCKING OPERATIONS

On a typical day, more than 53 million tons of goods valued at about $36 million are moved on the US multimodal transportation network. An efficient freight transportation industry is the key in facilitating the required movement of raw materials and finished products. Among different modes of transportation, trucking remains the shipping choice for many businesses and is increasing its market share. Less-than-truckload (LTL) trucking companies provide a transportation service in which several customers are served simultaneously by using the same truck and shipments need to be consolidated at some terminals to build economical loads. Intelligent transportation system (ITS) technologies increase the flow of available data, and offer opportunities to control the transportation operations in real-time. Some research efforts have considered real-time acceptance/rejection of shipping requests, but they are mostly focused on truckload trucking operations. This study tries to use real-time information in decision making for LTL carriers in a dynamically changing environment. The dissertation begins with an introduction of LTL trucking operations and different levels of planning for this type of motor carriers, followed by the review of literature that are related to tactical and operational planning. Following a brief discussion on multi commodity network flow problems and their solution algorithm, a mathematical model is proposed to deal with the combined shipment and routing problem. Furthermore, a decision making procedure as well as a decision support application are developed and are presented in this dissertation. The main step in the decision making procedure is to solve the proposed mathematical problem. Three heuristic solution algorithms are proposed and the quality of the solutions is evaluated using a set of benchmark solutions. Three levels of numerical experiments are conducted considering an auto carrier that operates on a hub-and-spoke network. The accuracy of the mathematical model and the behavior of the system under different demand/supply situations are examined. Also, the performance of the solutions provided by the proposed heuristic algorithms is compared and the best solution method is selected. The study suggests that significant reductions in operational costs are expected as the result of using the proposed decision making procedure