Variable neighborhood search based approaches to a vehicle scheduling problem in agriculture (original) (raw)
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Metaheuristic approaches to a vehicle scheduling problem in sugar beet transportation
Operational Research, 2019
A variant of vehicle scheduling problem (VSP) arising from the sugar beet transportation in a sugar factory in Serbia is introduced. The objective of the considered VSP is to minimize the required transportation time under problem-specific constraints. The problem is formulated as a mixed integer linear program (MILP). Within the framework of commercial CPLEX solver the proposed MILP model was able to produce optimal solutions for small size problem instances. Therefore, two metaheuristic methods, variable neighborhood search (VNS) and greedy randomized adaptive search procedure (GRASP), are designed to solve problem instances of larger dimensions. The proposed GRASP and VNS are evaluated and compared against CPLEX and each other on the set of real-life and generated problem instances. Computational results show that VNS is superior method with respect to the solution quality, while GRASP is able to find high quality solutions within very short running times.
Variable neighborhood search for optimizing the transportation of agricultural raw materials
Electronic Notes in Discrete Mathematics, 2017
Low price of raw materials in sugar industry and characteristics of production method lead to the specific transport organization problem. A new variant of Vehicle Scheduling Problem (VSP) that arises from transportation of sugar beet is considered. The problem is formulated as a Mixed Integer Quadratically Constraint Programming (MIQCP) model, which reflects the objective and specific constrains from practice. Computational experiments are conducted on real-life instances obtained from a sugar company in Serbia and the set of generated instances of larger dimensions. The proposed MIQCP model is used within the framework of Extended Lingo 15 solver, providing optimal solutions on smallsize instances only. In order to find solutions on larger problem instances, a metaheuristic method based on Variable Neighborhood Search (VNS) is designed. Obtained computational results show that the proposed VNS quickly reaches all known optimal solutions on small-size instances. On larger problem instances, for which Lingo 15 solver could not find even a feasible solution, VNS provides best solutions in relatively short running times.
2009
Abstract The world's second largest producer of pork, Danish Crown, also provides a fresh meat supply logistics system within Denmark. This is used by the majority of supermarkets in Denmark. This article addresses an integrated vehicle routing and driver scheduling problem arising at Danish Crown in their fresh meat supply logistics system. The problem consists of a 1-week planning horizon, heterogeneous vehicles, and drivers with predefined work regulations.
Procedia Computer Science, 2014
In this paper we propose a hybrid metaheuristic based on General Variable Neighborhood search and Integer Linear Programming for solving the vehicle routing problem with time windows (VRPTW). The problem consists in determining the minimum cost routes for a homogeneous fleet of vehicles to meet the demand of a set of customers within a specified time windows. The proposed heuristic, called VNS-SCP is considered as a matheuristic where the hybridization of heuristic (VNS) and exact (Set Covering Problem (SCP)) method is used in this approach as an intertwined collaborative cooperation manner. In this approach an initial solution is first created using Solomon route-construction heuristic, the nearest neighbor algorithm. In the second phase the solutions are improved in terms of the total distance traveled using VNS-SCP. The algorithm is tested using Solomon benchmark. Our findings indicate that the proposed procedure outperforms other local searches and metaheuristics.
A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows
International Journal of Industrial Engineering Computations, 2017
This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an 'always feasible' search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable time.
An efficient variable neighborhood search heuristic for very large scale vehicle routing problems
Computers & Operations Research, 2007
In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times. ᭧
2020
In job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the propose...
Algorithms
The Vehicle Routing Problem with Time Windows (VRPTW) is an NP-Hard optimization problem which has been intensively studied by researchers due to its applications in real-life cases in the distribution and logistics sector. In this problem, customers define a time slot, within which they must be served by vehicles of a standard capacity. The aim is to define cost-effective routes, minimizing both the number of vehicles and the total traveled distance. When we seek to minimize both attributes at the same time, the problem is considered as multiobjective. Although numerous exact, heuristic and metaheuristic algorithms have been developed to solve the various vehicle routing problems, including the VRPTW, only a few of them face these problems as multiobjective. In the present paper, a Multiobjective Large Neighborhood Search (MOLNS) algorithm is developed to solve the VRPTW. The algorithm is implemented using the Python programming language, and it is evaluated in Solomon’s 56 benchma...
Metaheuristic Optimization for Vehicle Routing Problem with Time Windows (VRPTW)
2023
The problem of determining vehicle routes in logistics has a role Which on pIt isnting bagi company to reduce transportation costs and late fees. Time window constraints are very common in current distribution processes. This research focuses on optimizing fuel costs and penalties. In this study the authors used the Vehicle Routing Problem with Time Windows (VRPTW). VRPTW is one of the most tackled transportation problems in real world situations. Artificial Bee Colony Algorithm (ABC) and Camel Algorithm (CA) will be used in the research. Study This is Focused on optimizing fuel costs and penalties. Furthermore, the two algorithms will be compared which of the two algorithms is the most optimal for solving the route determination problem in VRPTW. Numerical testing will be carried out for the proposed algorithm, as well as conducting experiments on several parameters to determine the effect of parameters for fuel costs and penalties.