New mathematical models of the generalized vehicle routing problem and extensions

The generalized vehicle routing problem (GVRP) is an extension of the vehicle routing problem (VRP) and was introduced by Ghiani and Improta [1]. The GVRP is the problem of designing optimal delivery or collection routes from a given depot to a number of predefined, mutually exclusive and exhaustive node-sets (clusters) which includes exactly one node from each cluster, subject to capacity restrictions. The aim of this paper is to provide two new models of the GVRP based on integer programming. The first model, called the node formulation is similar to the Kara-Bekta? formulation [2], but produces a stronger lower bound. The second one, called the flow formulation, is completely new. We show as well that under specific circumstances the proposed models of the GVRP reduces to the well known routing problems. Finally, the GVRP is extended for the case in which the vertices of any cluster of each tour are contiguous. This case is defined as the clustered generalized vehicle routing problem and both of the proposed formulations of GVRP are adapted to clustered case.     

A matheuristic approach for solving a home health care problem

We deal with a Home Health Care Problem (HHCP) which objective consists in constructing the optimal routes and rosters for the health care staffs. The challenge lies in combining aspects of vehicle routing and staff rostering which are two well known hard combinatorial optimization problems. To solve this problem, we initially propose an integer linear programming formulation (ILP) and we tested this model on small instances. To deal with larger instances we develop a matheuristic based on the decomposition of the ILP formulation into two problems. The first one is a set partitioning like problem and it represents the rostering part. The second problem consists in the routing part. This latter is equivalent to a Multi-depot Traveling Salesman Problem with Time Windows (MTSPTW).     

A two-phase algorithm for the partial accessibility constrained vehicle routing problem

In the partial accessibility constrained vehicle routing problem, a route can be covered by two types of vehicles, i.e. truck or truck + trailer. Some customers are accessible by both vehicle types, whereas others solely by trucks. After introducing an integer programming formulation for the problem, we describe a two-phase heuristic method which extends a classical vehicle routing algorithm. Since it is necessary to solve a combinatorial problem that has some similarities with the generalized assignment problem, we propose an enumerative procedure in which bounds are obtained from a Lagrangian relaxation. The routine provides very encouraging results on a set of test problems.     

Design of a recovery network for WEEE collection: the case of Galicia,Spain

This research aims to optimize the design of the reverse logistic network for the collection of Waste of Electric and Electronic Equipment (WEEE), in the Spanish region of Galicia. As a basis for our study a three-phase hierarchical approach is proposed. In the first phase a facility location problem is formulated and solved by means of a mixed integer linear programming; in the second phase a new integer programming formulation for the corresponding heterogeneous fleet vehicle routing problem is presented, and a savings-based heuristic algorithm is developed to efficiently solve the related collection routing problems; in the third phase a simulation study is performed on the collection routes in order to assess the overall performance of the recovery system. The results show a good performance of the proposed procedure, and an improved configuration of the recovery network compared to the one currently in use (particularly transportation costs are reduced by 29.2%).     

Bi-criteria dynamic location-routing problem for patrol coverage

In this paper, we address the problem of dynamic patrol routing for state troopers for effective coverage of highways. Specifically, a number of state troopers start their routes at temporary stations (TS), patrol critical locations with high crash frequencies, and end their shifts at other (or the same) TS so the starting points for the next period are also optimized. We determine the number of state troopers, their assigned routes, and the locations of the TS where they start and end their routes. The TS are selected from a given set of potential locations. The problem, therefore, is a multi-period dynamic location-routing problem in the context of public service. Our objective is to maximize the critical location coverage benefit while minimizing the costs of TS selections, vehicle utilizations, and routing/travel. The multi-objective nature of the problem is handled using an ?-constraint approach. We formulate the problem as a mixed integer linear programming model and solve it using both off-the-shelf optimization software and a custom-built, efficient heuristic algorithm. The heuristic, utilizing the hierarchical structure of the problem, is built on the decomposition of location and routing problems. By allowing routing to start from multiple locations, our model improves the coverage by as much as 12% compared with the single-depot coverage model.     

The maximin HAZMAT routing problem

The hazardous material routing problem from an origin to a destination in an urban area is addressed. We maximise the distance between the route and its closest vulnerable centre, weighted by the centre’s population. A vulnerable centre is a school, hospital, senior citizens’ residence or the like, concentrating a high population or one that is particularly vulnerable or difficult to evacuate in a short time. The potential consequences on the most exposed centre are thus minimized. Though previously studied in a continuous space, the problem is formulated here over a transport (road) network. We present an exact model for the problem, in which we manage to significantly reduce the required variables, as well as an optimal polynomial time heuristic. The integer programming formulation and the heuristic are tested in a real-world case study set in the transport network in the city of Santiago, Chile.     

A heuristic method for the capacitated arc routing problem with refill points and multiple loads

We describe a solution procedure for a capacitated arc routing problem with refill points and multiple loads. This problem stems from the road network marking in Quebec, Canada. Two different types of vehicles are used: the first type (called servicing vehicle—SV) with a finite capacity to service the arcs and the other (called refilling vehicle—RV) to refill the SV vehicle.The RV can deliver multiple loads, which means that it meets the SV several times before returning to the depot. The problem consists of simultaneously determining the vehicle routes that minimize the total cost of the two vehicles.We present an integer formulation and a route first-cluster second heuristic procedure. Computational results are provided.     

The berth allocation problem in terminals with irregular layouts

As international trade thrives, terminals attempt to obtain higher revenue while coping with an increased complexity with regard to terminal management operations. One of the most prevalent problems such terminals face is the Berth Allocation Problem (BAP), which concerns allocating vessels to a set of berths and time slots while simultaneously minimizing objectives such as total stay time or total assignment cost. Complex layouts of real terminals introduce spatial constraints which limit the mooring and departure of vessels. Although significant research has been conducted regarding the BAP, these real-world restrictions have not been taken into account in a general way. The present work proposes both a mixed integer linear programming formulation and a heuristic, which are capable of obtaining optimal or near-optimal solutions to this novel variant of the BAP. In order to assess the quality of the heuristic, which is being employed in a real tank terminal in Belgium, it is compared against the exact approach by way of randomly-generated instances and real-world benchmark sets derived from the tank terminal.     

The vehicle routing problem with coupled time windows

In this article we introduce the vehicle routing problem with coupled time windows (VRPCTW), which is an extension of the vehicle routing problem with time windows (VRPTW), where additional coupling constraints on the time windows are imposed. VRPCTW is applied to model a real-world planning problem concerning the integrated optimization of school starting times and public bus services. A mixed-integer programming formulation for the VRPCTW within this context is given. It is solved using a new meta-heuristic that combines classical construction aspects with mixed-integer preprocessing techniques, and improving hit-and-run, a randomized search strategy from global optimization. Solutions for several randomly generated and real-world instances are presented.     

The multi-depot vehicle routing problem with inter-depot routes

This article addresses an extension of the multi-depot vehicle routing problem in which vehicles may be replenished at intermediate depots along their route. It proposes a heuristic combining the adaptative memory principle, a tabu search method for the solution of subproblems, and integer programming. Tests are conducted on randomly generated instances.     

The capacitated arc routing problem with refill points

This article introduces the capacitated arc routing problem with refill points (CARP-RP). The vehicle servicing arcs must be refilled on the spot by using a second vehicle. The problem consists on simultaneously determining the vehicles routes that minimize the total cost. An integer linear programming model is proposed and tested.     

A branch-and-cut algorithm for the capacitated open vehicle routing problem

In open vehicle routing problems, the vehicles are not required to return to the depot after completing service. In this paper, we present the first exact optimization algorithm for the open version of the well-known capacitated vehicle routing problem (CVRP). The algorithm is based on branch-and-cut. We show that, even though the open CVRP initially looks like a minor variation of the standard CVRP, the integer programming formulation and cutting planes need to be modified in subtle ways. Computational results are given for several standard test instances, which enables us for the first time to assess the quality of existing heuristic methods, and to compare the relative difficulty of open and closed versions of the same problem.     

Integer linear programming models for a cement delivery problem

We consider a cement delivery problem with an heterogeneous fleet of vehicles and several depots. The demands of the customers are typically larger than the capacity of the vehicles which means that most customers are visited several times. This is a split delivery vehicle routing problem with additional constraints. We first propose a two phase solution method that assigns deliveries to the vehicles, and then builds vehicle routes. Both subproblems are formulated as integer linear programming problems. We then show how to combine the two phases in a single integer linear program. Experiments on real life instances are performed to compare the performance of the two solution methods.     

Lower and upper bounds for location-arc routing problems with vehicle capacity constraints

This paper addresses multi-depot location arc routing problems with vehicle capacity constraints. Two mixed integer programming models are presented for single and multi-depot problems. Relaxing these formulations leads to other integer programming models whose solutions provide good lower bounds for the total cost. A powerful insertion heuristic has been developed for solving the underlying capacitated arc routing problem. This heuristic is used together with a novel location–allocation heuristic to solve the problem within a simulated annealing framework. Extensive computational results demonstrate that the proposed algorithm can find high quality solutions. We also show that the potential cost saving resulting from adding location decisions to the capacitated arc routing problem is significant.     

A self-tuning heuristic for a multi-objective vehicle routing problem

In this study, a heuristic free from parameter tuning is introduced to solve the vehicle routing problem (VRP) with two conflicting objectives. The problem which has been presented is the designing of optimal routes: minimizing both the number of vehicles and the maximum route length. This problem, even in the case of its single objective form, is NP-hard. The proposed self-tuning heuristic (STH) is based on local search and has two parameters which are updated dynamically throughout the search process. The most important advantage of the algorithm is the application convenience for the end-users. STH is tested on the instances of a multi-objective problem in school bus routing and classical vehicle routing. Computational experiments, when compared with the prior approaches proposed for the multi-objective routing of school buses problem, confirm the effectiveness of STH. STH also finds high-quality solutions for multi-objective VRPs.     

An exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles

The vehicle routing problem with multiple use of vehicles is a variant of the classical vehicle routing problem. It arises when each vehicle performs several routes during the workday due to strict time limits on route duration (e.g., when perishable goods are transported). The routes are defined over customers with a revenue, a demand and a time window. Given a fixed-size fleet of vehicles, it might not be possible to serve all customers. Thus, the customers must be chosen based on their associated revenue minus the traveling cost to reach them. We introduce a branch-and-price approach to address this problem where lower bounds are computed by solving the linear programming relaxation of a set packing formulation, using column generation. The pricing subproblems are elementary shortest path problems with resource constraints. Computational results are reported on euclidean problems derived from well-known benchmark instances for the vehicle routing problem with time windows.     

The min-max multi-depot vehicle routing problem: heuristics and computational results

In the multi-depot vehicle routing problem (MDVRP), there are several depots where vehicles can start and end their routes. The objective is to minimize the total distance travelled by all vehicles across all depots. The min-max multi-depot vehicle routing problem (Min-Max MDVRP) is a variant of the standard MDVRP. The primary objective is to minimize the length of the longest route. We develop a heuristic (denoted by MD) for the Min-Max MDVRP that has three stages: (1) simplify the multi-depot problem into a single depot problem and solve the simplified problem; (2) improve the maximal route; (3) improve all routes by exchanging customers between routes. MD is compared with two alternative heuristics that we also develop and an existing method from the literature on a set of 20 test instances. MD produces 15 best solutions and is the top performer. Additional computational experiments on instances with uniform and non-uniform distributions of customers and varying customer-to-vehicle ratios and with real-world data further demonstrate MD’s effectiveness in producing high-quality results.     

2-Facility manifold location routing problem

The location routing problem (LRP), known to be the combination of the facility location and vehicle routing problems, is solved in the literature by either assuming planar or spherical surfaces. In this work, the manifold location routing problem (MLRP), that is an LRP on Riemannian manifold surfaces, is explained for the 2-facility (2-MLRP) case with the corresponding heuristic algorithm solution. The 2-MLRP problem is a mixed integer non-linear programming problem that is determined to be NP-hard. Special cases of MLRP include LRP on planar surfaces, when the manifold’s curvature is 0, and LRP on spherical surfaces when the curvature of the manifold is 1.     

A Lagrangian relaxation approach to the mixed-product assembly line sequencing problem: A case study of a door-lock company in Taiwan

In mixed-product assembly line sequencing, the production resources required for the assembly lines should be scheduled to minimize the overall cost and meet customer demand. In this paper, we study an assembly line sequencing problem for the door-lock industry in Taiwan and develop an integer programming formulation with realistic constraints. The complex solution space makes the resulting program difficult to solve using commercial optimization packages. Therefore, a heuristic based on the Lagrangian relaxation principle is developed to solve this problem efficiently. We evaluate the efficiency of the developed Lagrangian relaxation heuristic by comparing its solutions with those obtained using a commercial optimization package: the computational results show that the developed heuristic solves the real-world problem faster than the optimization package by almost 15 times in CPU time at a comparable solution quality.