Assessing the Impact of Driver Overtime in the Distribution Network of a Flower Retail Chain

In this work, we consider the Vehicle Routing Problem with Simultaneous Delivery and Pickup, and constrained by time windows, to improve the performance and responsiveness of the supply chain by transporting goods from one location to another location in an efficient manner. In this class of problem, each customer demands a quantity to be delivered as a part of the forward supply service and another quantity to be picked up as a part of the reverse recycling service, and the complete service has to be done simultaneously in a single visit of a vehicle, and the objective is to minimize the total cost, which includes the traveling cost and dispatching cost for operating vehicles. We propose a Mixed Integer Linear Programming (MILP) model for solving this class of problem. In order to evaluate the performance of the proposed MILP model, a comparison study is made between the proposed MILP model and an existing MILP model available in the literature, with the consideration of heterogeneous vehicles. Our study indicates that the proposed MILP model gives tighter lower bound and also performs better in terms of the execution time to solve each of the randomly generated problem instances, in comparison with the existing MILP model. In addition, we also compare the proposed MILP model (assuming homogeneous vehicles) with the existing MILP model that also considers homogeneous vehicles. The results of the computational evaluation indicate that the proposed MILP model gives much tighter lower bound, and it is competitive to the existing MILP model in terms of the execution time to solve each of the randomly generated problem instances.

In this paper a model‐based heuristic approach for a typical distribution problem is presented. In order to be cost‐effective, the distribution process for many customers, each of them with orders of considerable volume, should be dealt with like a combination of two well‐known problems: the vehicle routing problem (VRP) and the container loading problem (CLP). This paper studies a particular integration of these two problems called the vehicle routing and loading problem (VRLP). The VRLP is an operational problem that must be solved daily by many production and distribution companies. Like the two original problems (VRP and 3D‐CLP), the VRLP is NP‐hard. In this work, regardless of the complexity of this problem, a mixed integer linear programming (MILP) model that characterizes the VRLP with time windows is presented and it is also used to solve the problem optimally. Then, a model‐based heuristic that improves the computational time, when bigger instances need to be solved, is also presented. In order to prove the viability of the approach and the developed MILP model, tests with the benchmark instances of the VRLP were made and the results compared with other published works. Despite the long computational time needed to solve bigger instances, the VRLP model could be used to compute optimal solutions or at least good lower bounds, in order to have a base of comparison when nonexact methods are applied to the VRLP.

Mixed Integer Linear Programming Model for Vehicle Routing Problem for Hazardous Materials Transportation

The periodic vehicle routing problem (PVRP) is an extension of the well-known vehicle routing problem. In this paper, the PVRP with time windows and time spread constraints (PVRP-TWTS) is addressed, which arises in the high-value shipment transportation area. In the PVRP-TWTS, period-specific demands of the customers must be delivered by a fleet of heterogeneous capacitated vehicles over the several planning periods. Additionally, the arrival times to a customer should be irregular within its time window over the planning periods, and the waiting time is not allowed for the vehicles due to the security concerns. This study, proposes novel mixed-integer linear programming (MILP) and constraint programming (CP) models for the PVRP-TWTS. Furthermore, we develop several valid inequalities to strengthen the proposed MILP and CP models as well as a lower bound. Even though CP has successful applications for various optimization problems, it is still not as well-known as MILP in the operations research field. This study aims to utilize the effectiveness of CP in solving the PVRP-TWTS. This study presents a CP model for PVRP-TWTS for the first time in the literature to the best of our knowledge. Having a comparison of the CP and MILP models can help in providing a baseline for the problem. We evaluate the performance of the proposed MILP and CP models by modifying the well-known benchmark set from the literature. The extensive computational results show that the CP model performs much better than the MILP model in terms of the solution quality.

A column generation algorithm for vehicle scheduling and routing problems

Money collection presents particular problems in terms of effective vehicle routing. Planning the collection or distribution of money for ATMs or parking meters gives rise to two problems: while the total collecting time should be minimized, tours on successive days should be different to prevent robberies. The combination of these two problems is named as the Dissimilar Routing Problem. When the safes to be collected are located along the streets, it corresponds to an arc routing problem, which we call D, and when the money is from ATMs, it corresponds to a vehicle routing problem, usually referred to as the peripatetic routing problem. The former problem arises in a Portuguese company in charge of street parking in Lisbon. The firm needs to define tours to collect safes from parking meters, minimizing the total collecting time. To avoid robberies these tours cannot be repeated or somehow anticipated. For this new problem, we present a mixed integer linear programming (MILP) model and develop a matheuristic. Preliminary experiments are provided with data that mimic the real confidential data. Results point to a good performance of the matheuristic, while the smaller instances can be solved to optimality with the MILP model and a commercial solver. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(3), 233–245 2017

Mixed Integer linear programming and constraint programming models for the online printing shop scheduling problem

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

The growing rate of population and technological advancement have led to an increase in natural resource consumption, which has caused irreparable damage to the environment. The implementation of green supply chain management is one of the most effective ways to deal with environmental degradation. Therefore, in this paper, a bi-objective mixed integer linear programming model is developed to design a green supply chain network. In the proposed model, the possibility of customer storage, being faced with shortage, locating of distribution centres, green vehicle routing problem, split delivery, multi-depot vehicle routing problem (VRP), capacitated VRP, and uncertainty in demands will be considered. The aim of the proposed model is to minimise the total cost and total shortages simultaneously and, therefore, a fuzzy solution approach is applied for this purpose. The results of implementing this model in a production chain of automotive parts in Iran indicate the exact and efficient performance of the proposed model.

Mathematical models for green vehicle routing problems with pickup and delivery: A case of semiconductor supply chain

Variable Neighborhood Search heuristic for the Inventory Routing Problem in fuel delivery

Vehicle flow formulation for two-echelon time-constrained vehicle routing problem

A robust optimization approach for the production-inventory-routing problem with simultaneous pickup and delivery

Location-Routing Problem (LRP) emerges as one of the hybrid optimization problems in distribution networks in which, total cost of the system would be reduced significantly by simultaneous optimization of locating a set of facilities among candidate locations and routing vehicles. In this paper, a mixed integer linear programming model is presented for a two-echelon location-routing problem with simultaneous pickup and delivery. In the investigated problem, one echelon of facilities, which is called the middle depot echelon, is positioned between central distribution centers and customers echelons. The number and capacity of middle depots and vehicles are considered to be limited. Besides, each network customer demands for both receiving a type of commodities and delivering another type to vehicles to be returned to the depot. In the literature of location routing problem, the majority of researches have been conducted in the deterministic conditions. However, we present a model in which data uncertainty is also taken into account and customers' demand is assumed to be a fuzzy parameter. We utilize a fuzzy programming approach to cope with uncertain demands. Moreover, a combined heuristic method based on simulated annealing (SA) algorithm and genetic algorithm (GA) is devised for solving the presented model. The results achieved from solving the problem in different sizes of numerical examples imply that the proposed hybrid algorithm outperforms other algorithms within reasonable length of time. The effectiveness of the proposed solution method is examined through a comprehensive numerical experiments. Finally, valuable insights are provided via conducting a number of sensitivity analyses.

The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems