An application of Lagrangian relaxation approach in reverse logistics problem

This research examines the reverse logistics problem in which manufacturers need to determine the collection methods for used product at the end of its life. Three collection methods are studied namely pick-up, drop-off and mail return. The research investigates the problem of assigning appropriate collection methods that can maximize manufacturer’s profit. Initially, a mixed integer non-linear programming model integrating the three collection methods is proposed to tackle the problem. In the later part, a Lagrangian heuristic approach is then proposed due to the complexity of the problem and the inability of the previous solution method to solve larger problem instances effectively.The proposed solution is tested using some problem instances and the results are promising.The issues, potential and benefits of the proposed solution are highlighted

A mixed integer non-linear programming model for optimizing the collection methods of returned products

This paper studies the problem manufacturers facing to determine the collection methods for used product at the end of its life. Three collection methods that are practically applicable are studied namely pick-up, drop-off and mail return. This paper investigates the problem of assigning collection methods to collect returned products from customer zones that can maximize manufacturer's profit. A mixed integer non-linear programming model integrating the three collection methods is proposed to tackle the problem. The model is then tested using some problem instances and the results are promising. The potential and benefits of the proposed model are also highlighted

Selection of return channels and recovery options for used products

Due to legal, economic and socio-environmental factors, reverse logistics practices and extended producer responsibility have developed into a necessity in many countries. The end results and expectations may differ, but the motivation remains the same. Two significant components in a reverse logistics system -product recovery options and return channels - are the focus of this thesis. The two main issues examined are allocation of the returned products to recovery options, and selection of the collection methods for product returns. The initial segment of this thesis involves the formulation of a linear programming model to determine the optimal allocation of returned products differing in quality to specific recovery options. This model paves the way for a study on the effects of flexibility on product recovery allocation. A computational example utilising experimental data was presented to demonstrate the viability of the proposed model. The results revealed that in comparison to a fixed match between product qualities and recovery options, the product recovery operation appeared to be more profitable with a flexible allocation. The second segment of this thesis addresses the methods employed for the initial collection of returned products. A mixed integer nonlinear programming model was developed to facilitate the selection of optimal collection methods for these products. This integrated model takes three different initial collection methods into consideration. The model is used to solve an illustrative example optimally. However, as the complexity of the issue renders this process ineffective in the face of larger problems, the Lagrangian relaxation method was proposed to generate feasible solutions within reasonable computational times. This method was put to the test and the results were found to be encouraging

A theoretical investigation on End-of-Life (EOL) product collection methods in a closed-loop logistics network

The importance of reverse logistics is evident in various industries.The benefits of reverse logistics and product recovery have also been highlighted in previous theoretical investigations.The motivations of its implementation are generally divided into legal, economic and socio-environmental factors. One of the crucial components of a reverse logistics system is product return channels. The success of other components especially the recovery operations depends on the performance of the return channels. Although numerous investigations on product return channels have been carried out, research on some critical aspects remains wanting. This study presents a review that highlights this deficiency; depicts latest research development on product return channels, relevant decisions making issues and direction for future research. The review emphasizes on the collection methods of drop-off, pick-up and mail return as the main return avenues that have largely been neglected in the previous research. At the end of the study, we propose a new closed-loop logistics network and future research propositions

A mathematical model for optimization of an integrated network logistic design

In this study, the integrated forward/reverse logistics network is investigated, and a capacitated multi-stage, multi-product logistics network design is proposed by formulating a generalized logistics network problem into a mixed-integer nonlinear programming model (MINLP) for minimizing the total cost of the closed-loop supply chain network. Moreover, the proposed model is solved by using optimization solver, which provides the decisions related to the facility location problem, optimum quantity of shipped product, and facility capacity. Numerical results show the power of the proposed MINLP model to avoid th sub-optimality caused by separate design of forward and reverse logistics networks and to handle various transportation modes and periodic demand

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods

Collection Center Location with Equity Considerations in Reverse Logistics Networks

This is an Accepted Manuscript of an article published by Taylor & Francis in INFOR: Information Systems and Operational Research on 2014-11-01, available online: http://dx.doi.org/10.3138/infor.52.4.157In this paper, we study a collection center location problem with equity considerations within reverse logistics network design. The aim of the problem is to determine the locations and the capacities of the collection centers through the planning horizon. For each time period, the decisions to be made include the location and the capacities of the collection centers, the amounts of products to send from each generation point to each collection center, and the amounts of products to send from each collection center to each firm. The problem has three objectives. The first one is to minimize total cost, the second one is to ensure equity among different firms, and the third is to provide steady flow of products to each firm along the planning horizon. The problem is modeled as a multi-objective mixed integer programming formulation. An implementation of the problem in Turkey within the context of waste electrical and electronic equipment collection is presented. Sensitivity analyses are conducted to observe the effect of changes in the problem parameters on the solutions. The analyses include changes in the fixed costs and container capacities, changes in the amount of supply and changes in the growth rate. In addition, the solution potential of the model and value of using a multi-period model as opposed to using a static one are investigated

Moving forward in reverse : a review into strategic decision making in reverse logistics

Reverse Logistics, the process of managing the backward flows of materials from a point of use to a point of recovery or proper disposal, has gained increased industry acceptance as a strategy for both competitive advantage and sustainable development. This has stimulated a growing number of researchers to investigate Strategic management issues relating to the set up and control of effective and efficient Reverse Logistics systems. This paper systematically reviews the most important works in this field, with a focus on papers that concentrate on the strategic decision making process involved in the design and operation of a Reverse Logistics process with remanufacturing. The review found that: the majority of work is primarily focused on OEM specific issues; the sectors receiving the most attention are the ones under the greatest pressure from environmental legislation; and previous research findings from Rubio et al. (2009) and Fleischmann et al. (2000) are reaffirmed that the Reverse Logistics field is growing, but characterised by mainly quantitative, mathematical models. Future research efforts should be focused on the empirical investigation of the Reverse Logistics design process for all types of remanufacturers