A hybrid adaptive large neighbourhood search for multi-depot open vehicle routing problems

In this paper we address the multi-depot open vehicle routing problem (MDOVRP), a complex and difficult problem arising in several real-life applications. In the MDOVRP vehicles start from several depots and do not need to return to the depot at the end of their routes. We propose a hybrid adaptive large neighbourhood search algorithm to solve the MDOVRP coupled with improvement procedures yielding a hybrid metaheuristic. The performance of the proposed metaheuristic is assessed on various benchmark instances proposed for this problem and its special cases, containing up to 48 customers (single-depot version) and up to six depots and 288 customers. The computational results indicate that the proposed algorithm is very competitive compared with the state-of-the-art methods and improves 15 best-known solutions for multi-depot instances and one best-known solution for a single-depot instance. A detailed sensitivity analysis highlights which components of the metaheuristic contribute most to the solution quality.     

A capacitated vehicle routing problem with order available time in e-commerce industry

In this article, a variant of the well-known capacitated vehicle routing problem (CVRP) called the capacitated vehicle routing problem with order available time (CVRPOAT) is considered, which is observed in the operations of the current e-commerce industry. In this problem, the orders are not available for delivery at the beginning of the planning period. CVRPOAT takes all the assumptions of CVRP, except the order available time, which is determined by the precedent order picking and packing stage in the warehouse of the online grocer. The objective is to minimize the sum of vehicle completion times. An efficient tabu search algorithm is presented to tackle the problem. Moreover, a Lagrangian relaxation algorithm is developed to obtain the lower bounds of reasonably sized problems. Based on the test instances derived from benchmark data, the proposed tabu search algorithm is compared with a published related genetic algorithm, as well as the derived lower bounds. Also, the tabu search algorithm is compared with the current operation strategy of the online grocer. Computational results indicate that the gap between the lower bounds and the results of the tabu search algorithm is small and the tabu search algorithm is superior to the genetic algorithm. Moreover, the CVRPOAT formulation together with the tabu search algorithm performs much better than the current operation strategy of the online grocer.     

Variable neighbourhood simulated annealing algorithm for capacitated vehicle routing problems

This article presents the variable neighbourhood simulated annealing (VNSA) algorithm, a variant of the variable neighbourhood search (VNS) combined with simulated annealing (SA), for efficiently solving capacitated vehicle routing problems (CVRPs). In the new algorithm, the deterministic ‘Move or not’ criterion of the original VNS algorithm regarding the incumbent replacement is replaced by an SA probability, and the neighbourhood shifting of the original VNS (from near to far by k← k+1) is replaced by a neighbourhood shaking procedure following a specified rule. The geographical neighbourhood structure is introduced in constructing the neighbourhood structures for the CVRP of the string model. The proposed algorithm is tested against 39 well-known benchmark CVRP instances of different scales (small/middle, large, very large). The results show that the VNSA algorithm outperforms most existing algorithms in terms of computational effectiveness and efficiency, showing good performance in solving large and very large CVRPs.     

A coordinated algorithm for integrated production scheduling and vehicle routing problem

In this paper, the integrated production scheduling and vehicle routing problem is considered for a Make-to-Order manufacturer, who has a single machine for production and limited vehicles with capacity constraints for transportation. The objective is to determine production scheduling and vehicle routing, which are two interacted decisions, to minimise the maximum order delivery time. A property on optimal production sequence is proposed first, based on which backward and forward batching methods are developed and are embedded into a proposed genetic algorithm. The proposed genetic algorithm is capable of providing high-quality solutions by determining the two decisions simultaneously. For comparison purpose, a two-stage algorithm is developed, which decomposes the overall problem into two successively solved sub-problems. The experiments show that the proposed genetic algorithm can provide higher quality solutions than the proposed two-stage algorithm and two published algorithms studying related problems.     

Incorporating supplier selection and order allocation into the vehicle routing and multi-cross-dock scheduling problem

In the vehicle routing problem with cross-docking (VRPCD), it is assumed that the selected suppliers and the quantity of the products purchased from each supplier are known. This paper presents an MILP model which incorporates supplier selection and order allocation into the VRPCD in a multi-cross-dock system minimising the total costs, including purchasing, transportation, cross-docking, inventory and early/tardy delivery penalty costs. The sensitivity of the model on the key parameters of the objective function is analysed and the supply decisions are evaluated when the coefficients of the distribution cost are changed. A two-stage solution algorithm (TSSA) is proposed and the results of the TSSA for small-sized instances are compared with the exact solutions. Finally, a large-sized real case of an urban freight transport is solved using the TSSA.     

Study on the vehicle routing problem considering congestion and emission factors

To meet the requirement of greening transportation in poor traffic condition, vehicle routing problem (VRP) with consideration of fuel consumption and congestion is studied. We formulated a time-dependent green vehicle routing problem (TD-GVRP) model with minimised total cost as the objective function which includes fuel consumption cost, and the measurement of fuel consumption is based on the Comprehensive Modal Emissions Model (CMEM). In the model, the situation of waiting at customer nodes to avoid bad traffic is defined. To solve this model, a Response Surface Method (RSM)-based hybrid algorithm (HA) that combines genetic algorithm (GA) and particle swarm optimisation (PSO) is constructed. Finally, using instances from PRPLIB database, the following experiments are carried out and the corresponding conclusions are drawn. (i) Comparison of the proposed objective and traditional VRP objectives shows that fuel consumption can be greatly reduced by introducing fuel consumption factor into the objective function. (ii) Sensitivity analysis of congestion duration provides the influence of congestion duration on fuel consumption and travel time. (iii) Experiments based on different waiting time reveal that the optimisation of departure time can reduce fuel consumption and total cost to some extent.     

Modelling and solution of a large-scale vehicle routing problem at GE appliances & lighting

We consider a special case of the vehicle routing problem where not only each customer has specified delivery time window, but each route has limited time duration. We propose a solution algorithm using network reduction techniques and simulated annealing meta-heuristic. The objective is twofold: minimising the travel time and minimising the total number of vehicles required. The time-window constraint ensures delivery without delay, thus, a potentially higher level of customer satisfaction. The algorithm has helped the transportation planning team at General Electric Appliances & Lighting to significantly reduce the number of required trucks in two real cases, while its performance on randomly generated cases is also efficient when compared to properly selected benchmarking algorithms.     

A memetic algorithm with iterated local search for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a difficult vehicle routing problem, where given an undirected graph, the objective is to minimize the total cost of all vehicle tours that serve all required edges under vehicle capacity constraints. In this paper, a memetic algorithm with iterated local search (MAILS) is proposed to solve this problem. The proposed MAILS incorporates a new crossover operator, i.e., the longest common substring crossover (LCSX), an iterated local search (ILS) and a perturbation mechanism into the framework of the memetic algorithm (MA). The proposed MAILS is evaluated on the CARP benchmark instances and computational results show that the MAILS is very competitive.     

Field services design and management of natural gas distribution networks: a class of vehicle routing problem with time windows approach

Service operations management of metropolitan gas networks at operational level implies the optimisation of decisions related to logistic activities, taking into account multi-objectives and operational constraints. This paper proposes a metaheuristic approach for the operational planning of the daily logistic activities based on vehicle routing with time window model. Experimental results for a real planning case in a gas distribution network demonstrate the approach effectiveness.     

Solving a time constrained two-crane routing problem for material handling with an ant colony optimisation approach: an application in the roof-tile industry

This paper presents an ant colony optimisation (ACO)-based solution approach for a real-world two-crane routing problem, where a number of different load carriers must be moved within a given cycle time by two gantry cranes in a continuous production process for roof tiles. The cranes have to transport the roof-tile batches and to return the load carriers and intermediate pads for subsequent batches. A feasible solution has to observe workflow-, space-, collision-, and machine-cycle constraints. The objective is to find a feasible schedule that minimises the working time for both cranes. The authors compare different solution approaches in terms of learning – and visibility strategies based on ACO in extensive numerical studies. A visibility concept is used to both partition and balance workload between the cranes.