Periodic Location Routing Problem Research Articles

A Novel Exact and Heuristic Solution for the Periodic Location-Routing Problem Applied to Waste Collection

A Novel Exact and Heuristic Solution for the Periodic Location-Routing Problem Applied to Waste Collection

A waste collection problem with service type option

Efficient solid waste management is one of the most relevant issues for urban communities. With regard to the service type of household waste collection, there are two approaches in practice: when collecting household waste via a door-to-door system, the collection vehicles drive down all the streets to empty the garbage cans on the curb. Using a bring system, waste is accumulated at central collection sites with a larger capacity. While residents do have to travel an additional distance to dispose of their waste at these central collection sites, a bring system can reduce the distance to be covered by collection vehicles because they no longer have to drive down all the streets. To combine the advantages of both systems, in this article the service type decision is introduced. The corresponding waste collection problem with service type option (WCPSTO) is modeled as a new variant of a periodic location routing problem and an adaptive large neighborhood search-based solution approach is proposed. This solution approach not only solves the newly presented problem, but also outperforms a solution algorithm for a related waste collection problem seen in the literature, which can be seen as a special case of the WCPSTO.

Mobile healthcare services in rural areas: an application with periodic location routing problem.

In this study, we focus on the delivery of mobile healthcare services in rural areas, where doctors visit remote villages which do not have a healthcare facility nearby. The aim is to increase the accessibility of healthcare services for such population centers. We aim to determine the village assignments of the doctors, their monthly visit schedules and base hospitals where they start and end their tours. We model this as a periodic location routing problem and use the policies of Ministry of Health of Turkey as a basis for our mathematical formulation. These policies include the essential components of mobile healthcare services, namely, continuity of care and determining evenly distributed periodic visits. We determine the visit schedules, i.e. routes, of doctors endogenously while satisfying these policies. We also develop a heuristic algorithm based on a cluster first-route second approach and solve larger instances more effectively. The computational experiments support that this solution methodology can effectively find optimal or near-optimal solutions and improve the computational times significantly.

Local search algorithm for solving periodic location routing problem

This study examines the problem of facility location and vehicle routes determination in supply chain system periodically. The problem is determining the location and route to meet the supply of commodity to the power plant that taken from the case of an electricity company in Indonesia. In this study, the problem is called Periodic Location Routing Problem (PLRP). Periodic Location Routing Problem is a non-polynomial hard problem (NP-hard), if locations considered is increase, the computation time used is increasing exponentially. Therefore, to solve a large-scale of this problem, a local search algorithm is proposed. Local search is a very simple metaheuristic algorithm. To explore the solution space for the problem, this algorithm will use the relocation operator. The results of computational experiments from 10 instances indicate that the results of the solution have a small standard deviation with short computing time in less than 2 hours.

The bi-objective periodic closed loop network design problem

Reverse supply chains are becoming a crucial part of retail supply chains given the recent reforms in the consumers’ rights and the regulations by governments. This has motivated companies around the world to adopt zero-landfill goals and move towards circular economy to retain the product’s value during its whole life cycle. However, designing an efficient closed loop supply chain is a challenging undertaking as it presents a set of unique challenges, mainly owing to the need to handle pickups and deliveries at the same time and the necessity to meet the customer requirements within a certain time limit. In this paper, we model this problem as a bi-objective periodic location routing problem with simultaneous pickup and delivery as well as time windows and examine the performance of two procedures, namely NSGA-II and NRGA, to solve it. The goal is to find the best locations for a set of depots, allocation of customers to these depots, allocation of customers to service days and the optimal routes to be taken by a set of homogeneous vehicles to minimise the total cost and to minimise the overall violation from the customers’ defined time limits. Our results show that while there is not a significant difference between the two algorithms in terms of diversity and number of solutions generated, NSGA-II outperforms NRGA when it comes to spacing and runtime.

A Lagrangean decomposition approach for a novel two-echelon node-based location-routing problem in an offshore oil and gas supply chain

In this paper, a new node-based mixed-integer non-linear programming (MINLP) model in an offshore oil and gas industry is proposed. The model, which is a supply vessel planning (SVP) one, is developed to a two-echelon fleet composition mix periodic location-routing problem with time windows (2E-FCMPLRPTW). In the first echelon, the number and type of vehicles, routes, schedules and the best allocation of suppliers are determined. In the second echelon, the number and type of supply vessels, related voyages and schedules are calculated. A Lagrangean decomposition method, which is a particular case of Lagrangean relaxation, is presented to solve the problem.

Optimal fleet composition and mix periodic location-routing problem with time windows in an offshore oil and gas industry: A case study of National Iranian Oil Company

This paper presents a new Mixed-Integer Non-Linear Programming (MINLP) model for a Supply Vessel Planning (SVP) problem. The traditional SVP, which is a maritime transportation problem, is developed to a Maritime Fleet Sizing Mix Periodic Location-Routing Problem with Time Windows (MFSMPLRPTW) by considering suppliers, location of onshore-base(s) and some real life aspects. The objective of this model is to decide the composition of fleets, optimal voyages, schedules and also the optimal location(s) for onshore-base(s) in such a way that the total cost is minimized and the needs of operation regions are fulfilled. The MFSMPLPRTW model is solved by an exact two-phase solution approach for both small and medium cases. Also, two meta-heuristic algorithms are used to solve the large-sized instances. In order to justify and show how the model and solution can lead to significant economic improvements for real life instances, a case study by the IOOC is considered, which is the only offshore oil and gas producer in Iran that has lots of installations and operation regions in the Persian Gulf and the Sea of Oman.

A periodic location routing problem for collaborative recycling

ABSTRACTMotivated by collaborative recycling efforts for non-profit agencies, we study a variant of the periodic location routing problem, in which one decides the set of open depots from the customer set, the capacity of open depots, and the visit frequency to nodes in an effort to design networks for collaborative pickup activities. We formulate this problem, highlighting the challenges introduced by these decisions. We examine the relative difficulty introduced with each decision through exact solutions and a heuristic approach that can incorporate extensions of model constraints and solve larger instances. The work is motivated by a project with a network of hunger relief agencies (e.g., food pantries, soup kitchens and shelters) focusing on collaborative approaches to address their cardboard recycling challenges collectively. We present a case study based on data from the network. In this novel setting, we evaluate collaboration in terms of participation levels and cost impact. These insights can be generalized to other networks of organizations that may consider pooling resources.

A unified-adaptive large neighborhood search metaheuristic for periodic location-routing problems

This paper introduces three variants of the Periodic Location-Routing Problem (PLRP): the Heterogeneous PLRP with Time Windows (HPTW), the Heterogeneous PLRP (HP) and the homogeneous PLRP with Time Windows (PTW). These problems extend the well-known location-routing problem by considering a homogeneous or heterogeneous fleet, multiple periods and time windows. The paper develops a powerful Unified-Adaptive Large Neighborhood Search (U-ALNS) metaheuristic for these problems. The U-ALNS successfully uses existing algorithmic procedures and also offers a number of new advanced efficient procedures capable of handling a multi-period horizon, fleet composition and location decisions. Computational experiments on benchmark instances show that the U-ALNS is highly effective on PLRPs. The U-ALNS outperforms previous methods on a set of standard benchmark instances for the PLRP. We also present new benchmark results for the PLRP, HPTW, HP and PTW.

Sequential and parallel large neighborhood search algorithms for the periodic location routing problem

• We develop different sequential and parallel metaheuristics for the periodic location routing problem. • Computational results show that our algorithms outperform previous methods. • We also suggest a simple and effective parallelization strategy. We propose a large neighborhood search (LNS) algorithm to solve the periodic location routing problem (PLRP). The PLRP combines location and routing decisions over a planning horizon in which customers require visits according to a given frequency and the specific visit days can be chosen. We use parallelization strategies that can exploit the availability of multiple processors. The computational results show that the algorithms obtain better results than previous solution methods on a set of standard benchmark instances from the literature.

A hybrid evolutionary algorithm for the periodic location-routing problem

The well-known vehicle routing problem (VRP) has been studied in depth over the last decades. Nowadays, generalizations of VRP have been developed for tactical or strategic decision levels of companies but not both. The tactical extension or periodic VRP (PVRP) plans a set of trips over a multiperiod horizon, subject to frequency constraints. The strategic extension is motivated by interdependent depot location and routing decisions in most distribution systems. Low-quality solutions are obtained if depots are located first, regardless of the future routes. In the location-routing problem (LRP), location and routing decisions are tackled simultaneously. Here for the first time, except for some conference papers, the goal is to combine the PVRP and LRP into an even more realistic problem covering all decision levels: the periodic LRP or PLRP. A hybrid evolutionary algorithm is proposed to solve large size instances of the PLRP. First, an individual representing an assignment of customers to combinations of visit days is randomly generated. The evolution operates through an Evolutionary Local Search (ELS) on visit day assignments. The algorithm is hybridized with a heuristic based on the Randomized Extended Clarke and Wright Algorithm (RECWA) to create feasible solutions and stops when a given number of iterations is reached. The method is evaluated over three sets of instances, and solutions are compared to the literature on particular cases such as one-day horizon (LRP) or one depot (PVRP). This metaheuristic outperforms the previous methods for the PLRP.