Scheduling cross-docking operations under uncertainty: A stochastic genetic algorithm based on scenarios tree

A cross-docking terminal enables consolidating and sorting fast-moving products along supply chain networks and reduces warehousing costs and transportation efforts. The target efficiency of such logistic systems results from synchronizing the physical and information flows while scheduling receiving, shipping and handling operations. Within the tight time-windows imposed by fast-moving products (e.g., perishables), a deterministic schedule hardly adheres to real-world environments because of the uncertainty in trucks arrivals. In this paper, a stochastic MILP model formulates the minimization of penalty costs from exceeding the time-windows under uncertain truck arrivals. Penalty costs are affected by products' perishability or the expected customer’ service level. A validating numerical example shows how to solve (1) dock-assignment, (2) while prioritizing the unloading tasks, and (3) loaded trucks departures with a small instance. A tailored stochastic genetic algorithm able to explore the uncertain scenarios tree and optimize cross-docking operations is then introduced to solve scaled up instaces. The proposed genetic algorithm is tested on a real-world problem provided by a national delivery service network managing the truck-to-door assignment, the loading, unloading, and door-to-door handling operations of a fleet of 271 trucks within two working shifts. The obtained solution improves the deterministic schedule reducing the penalty costs of 60%. Such results underline the impact of unpredicted trucks’ delay and enable assessing the savings from increasing the number of doors at the cross-dock

Design and Analysis of Efficient Freight Transportation Networks in a Collaborative Logistics Environment

The increase in total freight volumes, reducing volume per freight unit, and delivery deadlines have increased the burden on freight transportation systems of today. With the evolution of freight demand trends, there also needs to be an evolution in the freight distribution processes. Today\u27s freight transportation processes have a lot of inefficiencies that could be streamlined, thus preventing concerns like increased operational costs, road congestion, and environmental degradation. Collaborative logistics is one of the approaches where supply chain partners collaborate horizontally or/and vertically to create a centralized network that is more efficient and serves towards a common goal or objective. In this dissertation, we study intermodal transportation, and cross-docking, two major pillars of efficient, cheap, and faster freight transportation in a collaborative environment. We design an intermodal network from a centralized network perspective where all the participants intermodal operators, shippers, carriers, and customers strive towards a synchronized and cost-efficient freight network. Also, a cross-dock scheduling problem is presented for competitive shippers using a centralized cross-dock facility. The problem develops a fast heuristic and meta-heuristic approach to solve large-scale real-world problems and draws key insights from a cross-dock operator and inbound carrier\u27s perspectives

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

Fuzzy Bi-level Decision-Making Techniques: A Survey

© 2016 the authors. Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques

Automated System for Freight Transportation Optimization on the Transport Network

An automated system for freight traffic optimization on a transport network has been developed, which is realized in the form of a complex computer program with application of the visual design environment of Embarcadero RAD Studio. The program complex consists of the main form from which two subprograms are loaded. The search of optimum routes performed by the routing optimization subprogram is used at transportations of freight on the set transport network based on different schemes (from one vertex (the supplier) to all other vertices (consumers), serially – from each vertex to all other vertices, and from two (or several) set of vertices to all other vertices). Freight delivery between the supply and consumption points was optimized by means of the freight transportation optimization subprogram, taking into account the restrictions on the volume of freight at the points of departure and destination

Warehousing and Inventory Management in Dual Channel and Global Supply Chains

More firms are adopting the dual-channel supply chain business model where firms offer their products to customers using dual-channel sales (to offer the item to customers online and offline). The development periods of innovative products have been shortened, especially for high-tech companies, which leads to products with short life cycles. This means that companies need to put their new products on the market as soon as possible. The dual-channel supply chain is a perfect tool to increase the customer’s awareness of new products and to keep customers’ loyalty; firms can offer new products online to the customer faster compared to the traditional retail sales channel. The emergence of dual-channel firms was mainly driven by the expansion in internet use and the advances in information and manufacturing technologies. No existing research has examined inventory strategies, warehouse structure, operations, and capacity in a dual-channel context. Additionally, firms are in need to integrate their global suppliers base; where the lower parts costs compensate for the much higher procurement and cross-border costs; in their supply chain operations. The most common method used to integrate the global supplier base is the use of cross-dock, also known as Third Party Logistic (3PL). This study is motivated by real-world problem, no existing research has considered the optimization of cross-dock operations in terms of dock assignment, storage locations, inventory strategies, and lead time uncertainty in the context of a cross-docking system. In this dissertation, we first study the dual-channel warehouse in the dual-channel supply chain. One of the challenges in running the dual-channel warehouse is how to organize the warehouse and manage inventory to fulfill both online and offline (retailer) orders, where the orders from different channels have different features. A model for a dual-channel warehouse in a dual-channel supply chain is proposed, and a solution approach is developed in the case of deterministic and stochastic lead times. Ending up with numerical examples to highlight the model’s validity and its usefulness as a decision support tool. Second, we extend the first problem to include the global supplier and the cross-border time. The impact of global suppliers and the effect of the cross-border time on the dual-channel warehouse are studied. A cross-border dual-channel warehouse model in a dual-channel supply chain context is proposed. In addition to demand and lead time uncertainty, the cross-border time is included as stochastic parameter. Numerical results and managerial insights are also presented for this problem. Third, motivated by a real-world cross-dock problem, we perform a study at one of the big 3 automotive companies in the USA. The company faces the challenges of optimizing their operations and managing the items in the 3PL when introducing new products. Thus, we investigate a dock assignment problem that considers the dock capacity and storage space and a cross-dock layout. We propose an integrated model to combine the cross-dock assignment problem with cross-dock layout problem so that cross-dock operations can be coordinated effectively. In addition to lead time uncertainty, the cross-border time is included as stochastic parameter. Real case study and numerical results and managerial insights are also presented for this problem highlighting the cross-border effect. Solution methodologies, managerial insights, numerical analysis as well as conclusions and potential future study topics are also provided in this dissertation

Optimization Models for Locating Cross-docks under Capacity Uncertainty

The objective of this thesis is to develop mathematical models for locating cross-docks in a supply chain. Cross-docking is a strategy which can help consolidate the goods in the supply chain and save costs by reducing the number of truck trips. In this thesis four optimization models were developed. First two optimization models termed Model A and Model B were deterministic models. The goal of model A was to choose exactly P locations to locate cross-docks so that the transportation and handling costs are minimized. The goal of model B is to locate as many cross-docks as needed so that total routing, handling, and facility location costs are minimized. Then we developed a chance constraint model and a recourse model which accounted for capacity uncertainties at cross-dock location. The chance constraint model accounts for day to day operational uncertainties whereas the recourse model accounts to drastic reductions in capacities due to disruptions. Extensive computational analysis was conducted on two networks with parameters consistent with real world freight operations. The results reveal that cross-docking provides significant savings when the demand sizes are small and there is more potential for consolidation. For larger demands where the potential for consolidation is less, cross-dock savings diminish. The results were found to be consistent across a variety of capacity uncertainty scenarios

Simulation and optimization of a multi-agent system on physical internet enabled interconnected urban logistics.

An urban logistics system is composed of multiple agents, e.g., shippers, carriers, and distribution centers, etc., and multi-modal networks. The structure of Physical Internet (PI) transportation network is different from current logistics practices, and simulation can effectively model a series of PI-approach scenarios. In addition to the baseline model, three more scenarios are enacted based on different characteristics: shared trucks, shared hubs, and shared flows with other less-than-truckload shipments passing through the urban area. Five performance measures, i.e., truck distance per container, mean truck time per container, lead time, CO2 emissions, and transport mean fill rate, are included in the proposed procedures using real data in an urban logistics case. The results show that PI enables a significant improvement of urban transportation efficiency and sustainability. Specifically, truck time per container reduces 26 percent from that of the Private Direct scenario. A 42 percent reduction of CO2 emissions is made from the current logistics practice. The fill rate of truckload is increased by almost 33 percent, whereas the relevant longer distance per container and the lead time has been increased by an acceptable range. Next, the dissertation applies an auction mechanism in the PI network. Within the auction-based transportation planning approach, a model is developed to match the requests and the transport services in transport marketplaces and maximize the carriers’ revenue. In such transportation planning under the protocol of PI, it is a critical system design problem for decision makers to understand how various parameters through interactions affect this multi-agent system. This study provides a comprehensive three-layer structure model, i.e. agent-based simulation, auction mechanism, and optimization via simulation. In term of simulation, a multi-agent model simulates a complex PI transportation network in the context of sharing economy. Then, an auction mechanism structure is developed to demonstrate a transport selection scheme. With regard of an optimization via simulation approach and sensitivity analysis, it has been provided with insights on effects of combination of decision variables (i.e. truck number and truck capacity) and parameters settings, where results can be drawn by using a case study in an urban freight transportation network. In the end, conclusions and discussions of the studies have been summarized. Additionally, some relevant areas are required for further elaborate research, e.g., operational research on airport gate assignment problems and the simulation modelling of air cargo transportation networks. Due to the complexity of integration with models, I relegate those for future independent research