Eco-efficient supply chain networks: Development of a design framework and application to a real case study

© 2015 Taylor & Francis. This paper presents a supply chain network design framework that is based on multi-objective mathematical programming and that can identify 'eco-efficient' configuration alternatives that are both efficient and ecologically sound. This work is original in that it encompasses the environmental impact of both transportation and warehousing activities. We apply the proposed framework to a real-life case study (i.e. Lindt & Sprüngli) for the distribution of chocolate products. The results show that cost-driven network optimisation may lead to beneficial effects for the environment and that a minor increase in distribution costs can be offset by a major improvement in environmental performance. This paper contributes to the body of knowledge on eco-efficient supply chain design and closes the missing link between model-based methods and empirical applied research. It also generates insights into the growing debate on the trade-off between the economic and environmental performance of supply chains, supporting organisations in the eco-efficient configuration of their supply chains

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

Hybrid Simulation-based Planning Framework for Agri-Fresh Produce Supply Chain

The ever-increasing demand for fresh and healthy products raises the economic importance of managing Agri-Fresh Produce Supply Chain (AFPSC) effectively. However, the literature review has indicated that many challenges undermine efficient planning for AFPSCs. Stringent regulations on production and logistics activities, production seasonality and high yield variations (quantity and quality), and products vulnerability to multiple natural stresses, alongside with their critical shelf life, impact the planning process. This calls for developing smart planning and decision-support tools which provides higher efficiency for such challenges. Modelling and simulation (M&S) approaches for AFPSC planning problems have a proven record in offering safe and economical solutions. Increase in problem complexity has urged the use of hybrid solutions that integrate different approaches to provide better understanding of the system dynamism in an environment characterised by multi-firm and multi-dimensional relationships. The proposed hybrid simulation-based planning framework for AFPSCs has addressed internal decision-making mechanisms, rules and control procedures to support strategic, tactical and operational planning decisions. An exploratory study has been conducted using semi-structured interviews with twelve managers from different agri-fresh produce organisations. The aim of this study is to understand management practices regarding planning and to gain insights on current challenges. Discussions with managers on planning issues such as resources constraints, outsourcing, capacity, product sensitivity, quality, and lead times have formed the foundation of process mapping. As a result, conceptual modelling process is then used to model supply chain planning activities. These conceptual models are inclusive and reflective to system complexity and decision sensitivity. Verification of logic and accuracy of the conceptual models has been done by few directors in AFPSC before developing a hybrid simulation model. Hybridisation of Discrete Event Simulation (DES), System Dynamics (SD), and Agent-Based Modelling (ABM) has offered flexibility and precision in modelling this complex supply chain. DES provides operational models that include different entities of AFPSC, and SD minds investments decisions according to supply and demand implications, while ABM is concerned with modelling variations of human behaviour and experience. The proposed framework has been validated using Table Grapes Supply Chain (TGSC) case study. Decision makers have appreciated the level of details included in the solution at different planning levels (i.e., operational, tactical and strategic). Results show that around 58% of wasted products can be saved if correct hiring policy is adopted in the management of seasonal labourer recruitment. This would also factor in more than 25% improved profits at packing house entity. Moreover, an anticipation of different supply and demand scenarios demonstrated that inefficiency of internal business processes might undermine the whole business from gaining benefits of market growth opportunities

Last Mile Distribution of COVID-19 Vaccines: A Cold Chain Logistical Challenge

The COVID-19 pandemic is a global health and humanitarian crisis that has wreaked havoc on economies and industries around the world. This study aims to address the distribution of the COVID-19 vaccines at the last mile by evaluating the vaccine supply chain and how it can be effectively utilized to address the last mile distribution of the COVID-19 vaccines through simulation. The first part includes a systematic literature review and bibliometric study of vaccine supply chain and cold chain logistics studies conducted in the last decade. The second part examines the distribution of COVID-19 vaccines in Norway as a case study. The study develops a two-stage optimization simulation method to analyse and improve the logistical performance of the COVID-19 vaccine distribution in Inland County, Norway. The study analyses the impact of fleet size and the use of heterogeneous vehicles in the last mile distribution network on some key performance indicators. The findings from the study reveal that the service level, transportation costs and environmental performance of the vaccine logistics system are significantly influenced by routing decisions, fleet size, fleet composition and the types of heterogeneous vehicles used. Based on the findings from the study, some managerial insights are outlined to help logistics managers better understand the interactions between the key parameters of a cold chain vaccine distribution system

Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models

This is an Author's Accepted Manuscript of an article published in [include the complete citation information for the final version of the article as published in the International Journal of Production Research (2018) © Taylor & Francis, available online at: http://doi.org/10.1080/00207543.2018.1447706[EN] Agri-food sector performance strongly impacts global economy, which means that developing optimisation models to support the decision-making process in agri-food supply chains (AFSC) is necessary. These models should contemplate AFSC¿s inherent characteristics and sources of uncertainty to provide applicable and accurate solutions. To the best of our knowledge, there are no conceptual frameworks available to design AFSC through mathematical programming modelling while considering their inherent characteristics and sources of uncertainty, nor any there literature reviews that address such characteristics and uncertainty sources in existing AFSC design models. This paper aims to fill these gaps in the literature by proposing such a conceptual framework and state of the art. The framework can be used as a guide tool for both developing and analysing models based on mathematical programming to design AFSC. The implementation of the framework into the state of the art validates its. Finally, some literature gaps and future research lines were identified.This first author was partially supported by the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport [grant number FPU15/03595]; the partial support of Project 'Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector.' Ref. GV/2017/025, funded by the Generalitat Valenciana. The other authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research. 56(13):4418-4446. https://doi.org/10.1080/00207543.2018.1447706S44184446561

Development of a hybrid simulation framework for the production planning process in the atlantic salmon supply chain

The farmed salmon supply chain has a highly complex and integrated structure, where activities occur both in the sea and on land. Due to this complexity, the supply chain needs appropriate decision-support tools to aid the production planning process, which capture the material flows, information flows and behaviours of the decision makers in the chain. This paper proposes a hybrid simulation framework for production planning using the case of the Norwegian Atlantic salmon supply chain. This hybrid simulation comprises agent-based modelling (ABM) to capture the autonomous and interacting decision making behaviour of the supply chain actors, while discrete-event simulation (DES) is employed to model the various production processes within the chain. The simulation is implemented using AnyLogic™ version 8.0 simulation software, using a case study from the Norwegian farmed salmon sector. The proposed modelling framework provides a deeper understanding of the activities in the salmon supply chain, thereby enabling improved decision making.publishedVersio

Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty

The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and CO2e emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. The solutions for transportation networks with up to 250 services and 20 orders show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans

Reefer logistics and cool chain transport

Reefer logistics is an important part of the cool chain in which reefer containers are involved as the packaging for transporting perishable goods. Reefer logistics is challenging, as it deals with cost and time constraints as well as the product quality and sustainability requirements. In many situations, there is a trade-off between these factors (e.g., between transportation time and the quality of fresh products). Furthermore, considering the high value of reefers, the efficient logistics of is as important as the efficient cargo flows. This causes technical complications and the conflict of interests between actors, especially, between cargo owners (or shippers) and the asset owners (or transport/terminal operators). Improving the efficiency of reefer logistics calls for a thorough understanding of the trade-offs and complexities. This paper aims to help develop such an understanding using a systematic literature review and a socio-technical system analysis. The results can be used to provide managerial insights for actors involved in a cool chain to design tailored solutions for reefer