The capacitated general windy routing problem with turn penalties

In this paper we present the capacitated general windy routing problem with turn penalties. This new problem subsumes many important and well-known arc and node routing problems, and it takes into account turn penalties and forbidden turns, which are crucial in many real-life applications, particularly in downtown areas and for large vehicles. We provide a way to solve this problem both optimally and heuristically by transforming it into a generalized vehicle routing problem.     

Solving arc routing problems with turn penalties

In several arc routing problems, it is necessary to take turn penalties into account when designing a solution. Traditionally, this is done through a transformation of the arc routing problem into an equivalent vertex routing problem. In this paper it is shown that a more direct approach, not resorting to such a transformation, may be more efficient.     

Modeling and solving the mixed capacitated general routing problem

We tackle the mixed capacitated general routing problem (MCGRP) which generalizes many other routing problems. We propose an integer programming model for the MCGRP and extend some inequalities originally introduced for the capacitated arc routing problem (CARP). Identification procedures for these inequalities and for some relaxed constraints are also discussed. Finally, we describe a branch and cut algorithm including the identification procedures and present computational experiments over instances derived from the CARP.     

The mixed capacitated general routing problem under uncertainty

We study the General Routing Problem defined on a mixed graph and with stochastic demands. The problem under investigation is aimed at finding the minimum cost set of routes to satisfy a set of clients whose demand is not deterministically known. Since each vehicle has a limited capacity, the demand uncertainty occurring at some clients affects the satisfaction of the capacity constraints, that, hence, become stochastic. The contribution of this paper is twofold: firstly we present a chance-constrained integer programming formulation of the problem for which a deterministic equivalent is derived. The introduction of uncertainty into the problem poses severe computational challenges addressed by the design of a branch-and-cut algorithm, for the exact solution of limited size instances, and of a heuristic solution approach exploring promising parts of the search space. The effectiveness of the solution approaches is shown on a probabilistically constrained version of the benchmark instances proposed in the literature for the mixed capacitated general routing problem.     

A post-improvement procedure for the mixed load school bus routing problem

This paper aims to develop a mixed load algorithm for the school bus routing problem (SBRP) and measure its effects on the number of required vehicles. SBRP seeks to find optimal routes for a fleet of vehicles, where each vehicle transports students from their homes and to their schools while satisfying various constraints. When mixed load is allowed, students of different schools can get on the same bus at the same time. Although many of real world SBRP allow mixed load, only a few studies have considered these cases. In this paper, we present a new mixed load improvement algorithm and compare it with the only existing algorithm from the literature. Benchmark problems are proposed to compare the performances of algorithms and to stimulate other researchers’ further study. The proposed algorithm outperforms the existing algorithm on the benchmark problem instances. It has also been successfully applied to some of real-world SBRP and could reduce the required number of vehicles compared with the current practice.     

The mixed general routing polyhedron

 In Arc Routing Problems, ARPs, the aim is to find on a graph a minimum cost traversal satisfying some conditions related to the links of the graph. Due to restrictions to traverse some streets in a specified way, most applications of ARPs must be modeled with a mixed graph. Although several exact algorithms have been proposed, no polyhedral investigations have been done for ARPs on a mixed graph. In this paper we deal with the Mixed General Routing Problem which consists of finding a minimum cost traversal of a given link subset and a given vertex subset of a mixed graph. A formulation is given that uses only one variable for each link (edge or arc) of the graph. Some properties of the associated polyhedron and some large families of facet-inducing inequalities are described. A preliminary cutting-plane algorithm has produced very good lower bounds over a set of 100 randomly generated instances of the Mixed Rural Postman Problem. Finally, applications of this study to other known routing problems are described. Received: June 30, 1999 / Accepted: March 2002 Published online: March 21, 2003 Key Words. polyhedral combinatorics – facets – routing – arc routing – rural postman problem – general routing problem – mixed chinese postman problem     

Very large street routing problem with mixed transportation mode

Providers of logistic services in recent years are under a big pressure to lower their expenses. One way to accomplish this task is centralization of logistic activities. This creates a distribution centers with a large number of customers. The capacity or time of one delivery person is limited, but, at the same time, it usually serves many customers. This problem is often called a Street Routing Problem (SRP). This paper is a survey of aggregation heuristics that can be used for a solution of Very Large SRP (VLSRP). Performance of heuristics has been evaluated based on real data. This paper presents several approximations of length for a SRP with mixed transportation mode and compares them with published approximations used for Vehicle Routing Problem (VRP) or Traveling Salesman Problems (TSP). The method was tested in seven real world instances ranging from 11000 to 29000 customers. Several aggregation methods including two new are presented and compared for the creation of delivery districts. New measurements for the quality of aggregation are created and tested on real data with all discussed aggregation methods.     

A new mixed integer linear model for a rich vehicle routing problem with docking constraints

In this paper we address a rich vehicle routing problem that arises in real-life applications. Among other aspects we consider time windows, simultaneous delivery and pick-up at customer locations and multiple use of vehicles. To guarantee a coordinated material flow at the depot, we include the timed allocation of vehicles to loading bays at which the loading and unloading activities can occur. The resulting vehicle routing problem is formulated as a two-index vehicle-flow model which integrates the routing under real-life conditions and the assignment of vehicles to loading bays at the depot. We use CPLEX 11.0 to solve medium-sized instances that are derived from the extended Solomon test set. The selective implementation of preprocessing techniques and cutting planes improves the solver performance significantly.     

A multi-objective capacitated rural school bus routing problem with heterogeneous fleet and mixed loads

Four multi-objective meta-heuristic algorithms are presented to solve a multi-objective capacitated rural school bus routing problem with a heterogeneous fleet and mixed loads. Three objectives are considered: the total weighted traveling time of the students, the balance of routes among drivers, and the routing costs. The proposed methods were compared with one from the literature, and their performance assessed observing three multi-objective metrics: cardinality, coverage, and hyper-volume. All four devised methods outperformed the one from the literature. The algorithm with a path relinking procedure embedded during the crowding distance selection scheme had the best overall performance.     

Combinatorial approximation algorithms for the robust facility location problem with penalties

In this paper, we consider the robust facility location problem with penalties, aiming to serve only a specified fraction of the clients. We formulate this problem as an integer linear program to identify which clients must be served. Based on the corresponding LP relaxation and dual program, we propose a primal–dual (combinatorial) 3-approximation algorithm. Combining the greedy augmentation procedure, we further improve the above approximation ratio to 2.     

带次模惩罚的优先设施选址问题的近似算法

研究带次模惩罚的优先设施选址问题, 每个顾客都有一定的服务水平要求, 开设的设施只有满足了顾客的服务水平要求, 才能为顾客提供服务, 没被服务的顾客对应一定的次模惩罚费用. 目标是使得开设费用、连接费用与次模惩罚费用之和最小. 给出该问题的整数规划、 线性规划松弛及其对偶规划. 基于原始对偶和贪婪增广技巧, 给出该问题的两个近似算法, 得到的近似比分别为3和2.375.     

A memetic algorithm for the inventory routing problem

Journal of Heuristics - In this article, we study an Inventory Routing Problem with deterministic customer demand in a two-tier supply chain. The supply chain network consists of a supplier using a...     

A memetic algorithm for the multiperiod vehicle routing problem with profit

In this paper, we extend upon current research in the vehicle routing problem whereby labour regulations affect planning horizons, and therefore, profitability. We call this extension the multiperiod vehicle routing problem with profit (mVRPP). The goal is to determine routes for a set of vehicles that maximizes profitability from visited locations, based on the conditions that vehicles can only travel during stipulated working hours within each period in a given planning horizon and that the vehicles are only required to return to the depot at the end of the last period. We propose an effective memetic algorithm with a giant-tour representation to solve the mVRPP. To efficiently evaluate a chromosome, we develop a greedy procedure to partition a given giant-tour into individual routes, and prove that the resultant partition is optimal. We evaluate the effectiveness of our memetic algorithm with extensive experiments based on a set of modified benchmark instances. The results indicate that our approach generates high-quality solutions that are reasonably close to the best known solutions or proven optima, and significantly better than the solutions obtained using heuristics employed by professional schedulers.     

A new modified one-step smoothing Newton method for solving the general mixed complementarity problem

In last decades, there has been much effort on the solution and the analysis of the mixed complementarity problem (MCP) by reformulating MCP as an unconstrained minimization involving an MCP function. In this paper, we propose a new modified one-step smoothing Newton method for solving general (not necessarily P₀) mixed complementarity problems based on well-known Chen-Harker-Kanzow-Smale smooth function. Under suitable assumptions, global convergence and locally superlinear convergence of the algorithm are established.     

A multi-stop routing problem

The problem of determining the sequence of stops and the amount of load to carry in each segment route, named the Multi-Stop Routing Problem (MSRP) is addressed. A 0/1 mixed integer linear program and formulation refinements which facilitate the solution process are presented. Since the constraint set of the MSRP includes 0/1 mixed rows, valid inequalities for this type of regions are presented. Then these results are applied to the constraint set of the routing problem, presenting additional valid inequalities. In addition, polynomial separation algorithms associated with the valid inequalities are given, computational results are also included.     

Maximum principle for the general optimal control problem with phase and regular mixed constraints

Translated from Optimal'nost Upravlyaemykh Dinamicheskikh Sistem, Sbornik Trudov VNIISI, No. 14, pp. 26–42, 1990.