The research group Logistics (LOG) aims to achieve theoretical advances in modelling and solving real-life decision making problems in the field of logistics and to valorise results in cooperation with the industry. Planning problems are studied using techniques from operations research to increase the efficiency of transportation and logistics companies. Currently, five research streams exist within the group: vehicle routing, warehouse management, intermodal transportation, healthcare logistics and human factors in logistics.
Vehicle routing is a complex operational planning problem in which customers/locations have to be assigned to vehicles, and a route for each vehicle has to be created, so as to optimize a predefined objective (e.g., minimizing the distance to be travelled). In our research, we are focusing on a variety of vehicle routing problems in the context of both freight transportation (e.g., routing trucks to deliver goods to customers) and passenger transportation (e.g., routing vans to transport elderly and disabled people). Our aim is to develop new mathematical models for problems which account for real-life issues faced by companies (e.g., axle weight limitations), providing efficient solution methods for these models, and analysing the effect of problem characteristics and algorithm parameters on solutions.
A warehouse can be defined as a facility where activities of receiving, storage, order picking and shipping are performed, often by human operators. Trends such as shortened product life cycles, e-commerce, greater product variety and point-of-use delivery expose order picking activities to new challenges. In our research, we aim to optimize warehouse operations in an integrated way in order to avoid a suboptimal solution for the total warehouse. Furthermore, we try to incorporate human factors in order picking planning problems to ensure that order picking policies are conform the skills and overall requirements of the individual order picker. Another research topic within this research stream is the integrated order picking-vehicle routing problem, which optimizes internal warehouse operations and distribution operations simultaneously. This leads to cost savings or a higher service level by allowing customers to place their orders as late as possible.
Our research group has a strong experience in modelling intermodal transport operations. On the one hand, we develop models for supporting operational planning problems such as intermodal train loading planning. On the other hand, we analyze bundling opportunities for freight and investigate the complexity of sharing cost savings fairly among shippers who bundle freight flows in order to reach economies of scale in intermodal transport. Synchromodal transportation goes a step further with its ability to dynamically select the most appropriate transport mode, route and terminals depending on the network conditions.
In this research stream, we focus on three different topics: dial-a-ride problems, simulation and optimization of emergency departments, and inventory management in healthcare supply chains. A dial-a-ride problem tackles the routing arising in collective on-demand transportation systems. The aim is to develop efficient vehicle routes and time schedules, respecting service level requirements from customers. By simulating and optimizing emergency departments we aim to make these ED’s work more efficiently and evaluate the improvement options for solving the problem of ED crowding. Inventory management in healthcare supply chains minimizes the total multi-echelon inventory costs. Multiple supply chain configurations are compared in order to detect the optimal configuration.
Even in the era of automation and digitalization, the vast majority of companies still heavily rely on humans for their day-to-day operations. People still hold an important advantage over robots regarding physical and cognitive skills. However, human operators have become a scarce resource in logistics, partly due to the strenuous physical work. On top of that, the introduction of collaborative robots (or cobots) seriously impacts the psychosocial well-being of humans on the work floor. Our research aims for human-centric work systems in which well-being and productivity go hand in hand. With a mix of quantitative and qualitative research, we investigate the preconditions for sustainably viable work in logistics.
promotor: prof. dr. An Caris
Increasingly, cities aim at improving the sustainability and efficiency of their urban supply chains, by restricting the use of certain vehicles or improving collaboration among supply chain actors. Additionally, new technologies and trends are emerging, e.g., micro consolidation centers, sidewalk robots, autonomous vehicles, synchromodality, etc. These trends generate a myriad of difficult intertwined strategic and operational decision problems. Currently, however, the optimization and simulation tools required to support decision-making in such a complex environment are (1) disjointed and scattered across the scientific literature, or (2) not mature enough to adequately handle emerging urban logistic trends. To bridge this gap in the literature, and to aid cities and supply chain actors in modelling and optimizing the supply chains, we propose to develop a comprehensive and complementary suite of urban logistics optimization algorithms for “the” city. These problems of optimizing urban supply chains are typically large-scale, multi-level and multi-objective. In order to appropriately model and solve these problems, significant advances in operations research techniques are required. They will ultimately contribute to improving the understanding of the impact of new technologies, trends, and policies for all urban logistics stakeholders, and support their decisions towards a more sustainable city.
The goal of this project is to develop a set of models and algorithms to optimize urban logistics operations. Our algorithms, unlike other similar projects, will focus specifically on the urban context and take into account city governments' measures that impact logistics operations on their territory. Such measures may include car-free zones, specific parking spaces or time slots for truck deliveries, banning certain vehicles in certain areas, etc. The algorithms can be used to evaluate the impact of these measures. The algorithms will allow an evaluation of the impact of these measures to help reduce the negative effects of logistics operations on city livability and strengthen the competitiveness of supply chain players. Unlike existing optimization problems, the models developed in STRAUSS will be based on in-depth economic analysis. The algorithms will be able to support both strategic, tactical and operational decisions, and will be useful to (1) a single company, (2) a coalition of collaborating companies, and (3) the city government.
Routing problems are one of the most studied problems in operations research due to their inherent complexity and their prevalence in a variety of (logistics) business processes. This project focuses on pickup and delivery problems, a subset of routing problems in which routes are created for transporting items between individual pickup and delivery locations. Current work assumes that these respective locations are predefined and that sufficient items are available at the pickup locations. Yet, in practice the location of where to pick items up is often part of the decision process and pickup locations may be subject to limited inventory. This project will investigate how decisions on routing and (pickup) location selection can be integrated, while accounting for limited inventory levels, and the benefit of such integrated decision-making under various problem conditions.
The field of logistics faces tremendous challenges in the upcoming years due to, among others, the growing share of e-commerce transactions and a larger amount of product varieties, while customers expect fast and accurate delivery. Meeting these expectations in a cost-efficient way requires the optimal organisation and management of logistical processes, now more than ever. The harmonisation of warehouse characteristics on the one hand and employees’ qualities and traits on the other hand offers an interesting, yet challenging opportunity.
Order picking, i.e. retrieving products from storage locations and mainly performed manually, is by far the costliest activity within a warehouse, and is currently organised by means of several key planning problems. However, previous studies have highlighted that these planning problems barely take the affected human operators into account. Consequently, the main goal of this research is to develop models that are capable of capturing the true dynamics within a warehouse, without the utopic abstraction of human operators.
The PhD project provides innovative tools that can be used both by practitioners as academics in order to enhance their logistic models and make them even more realistic. Additionally, the research contributes to the operations management community in general by presenting innovative algorithms and new insights that contribute to the efficient problem-solving of similar challenges in related research domains.
promotor: prof. dr. An Caris
The logistics sector plays a key role in today's economy. Fast and efficient distribution of goods is demanded by industrial partners and consumers. An efficient logistics system is also essential in healthcare and emergency operations. However, the logistics sector is a highly competitive market with small profit margins and faces increasing pressure to operate in a sustainable manner. Operations research (OR) as a research discipline supports the problem-solving and decision-making capabilities of organizations. Numerous OR models have already been developed to support the logistics sector, however, few models have been incorporated into the day-to-day operations of logistics companies. This is because the logistics reality has become increasingly complex and many realistic features are still missing from existing OR models. This WOG brings together a large network of renowned experts in the OR community working on real logistics optimization problems. By sharing knowledge, we can help close that gap between research and practice and have a major impact on the efficiency and sustainability of the logistics industry.
Warehouses play, more than ever, an essential role in today’s economy. The revival of e-commerce has emphasized the importance of a company’s supply chain and, more specifically, its warehouses. The use of digitalization and technology, such as voice picking and robotization, is rising. Although warehouses are becoming more and more automated, many tasks still need to be performed by humans. The academic literature on warehouses mainly focuses on optimizing processes and increasing productivity and performance. Even in the Human Resources Management (HRM) research domain, the attention goes mainly to the organization and productivity, not to the employee. One of the main problems in logistics companies is finding and keeping qualified staff. And although the shortage of logistics personnel is not new, research on warehouse employee turnover is limited. Considering all the previous, my research will focus on the well-being of employees in warehouses, and on how HRM can contribute to this employee well-being.
Raw materials, parts, and product inventories are stored in almost all supply chains. A warehouse can fulfill this storing function. The main activities in such warehouses include receiving, storing, picking (i.e., collecting from storage), and shipping of items. In my research, I will focus on warehouse operations which are mainly researched under deterministic conditions, while this environment is sensitive to uncertainties in practice. Furthermore, researchers state that warehousing literature does not resemble real-life practice and is thus often not used by warehouse managers. Additionally, warehouses are under constant pressure to perform more efficiently due to the increasing expectations from e-commerce customers (e.g., small orders, an extensive product variety, and fast deliveries). Therefore warehouse managers could greatly benefit from stochastic real-life warehouse research.
In parallel, recent technological advancements have greatly facilitated the collection and analysis of data, creating major opportunities to enhance existing operations. More specifically, historical data can be used to learn patterns, predict future trends, and lead to data-driven decision-making. Data-driven decision-making has received considerable attention in a logistics context, mainly in the domains of manufacturing and transportation. However, data-driven warehouse optimization is still in an early stage. Therefore, during my PhD, I will focus on data-driven optimization in a warehousing context.
Home health care (HHC) may be defined as care workers visiting patients following predefined schedules in order to provide medical services at their home. Maintaining a sustainable and effective home health care system is a major challenge as a result of two trends: limited resources and a rise in demand. In response to these trends and increasing competitive pressures, HHC providers must discover ways to decrease costs and enhance productivity by optimizing the use of resources. For this reason, applying operations research
techniques in HHC is a promising research field.
A key opportunity for improvement is the integration of decisions at different decision-making levels to make better medium-term decisions. The goal of this project is to develop models that enable making better staff dimensioning decisions by taking the implications on rostering, clustering, scheduling and routing into account.
First, this project contributes to the academic state-of-the-art by proposing innovative models, algorithms and solution techniques (academic contribution). In this way, the benefits of an integrated approach will be quantified and managerial insights will be provided that tackle the challenges the HHC industry is facing. This will enable HHC providers to organize operations more efficiently (economic contribution), which not only benefits HHC service providers, but care workers and patients as well (social contribution).
Transport of goods is one of the crucial elements for supporting economic development in an urban area. There are several issues related to the transportation of goods, especially in the city area, e.g. cost, environmental and energy issues. With the problems of transportation of products in the urban area, the concept of city logistics is introduced. In general, city logistics has the aim to optimize the overall urban transportation system. Innovative solutions for city logistics have been introduced over the years. However, while there is a clear trend towards integrated decision making in the supply chain, such integrated decisions have received less attention in a city logistics context.
This project will address an integrated logistics solution by optimizing the vehicle routing and inventory management decision simultaneously in a city logistics context. This problem refers to the Inventory Routing Problem (IRP). We focus on the IRP in a city logistics setting and develop solution methods to heuristically solve the problems. The literature review shows that while the IRP has been studied extensively, only a few research on IRP pays attention to important characteristics that occur in urban transportation, such as time windows, multiple vehicle trips, the use of heterogeneous vehicles, and so on. Hence, one promising research direction is the study of IRP that includes more realistic features that appear in urban transportation. In addition, very few studies examine the integration of inventory and routing decisions along with collaboration aspects, while collaboration is considered a crucial element in city logistics. Therefore, we investigate the impact of integrated inventory routing decisions along with collaborative mechanisms in a city logistics context.
In business-to-consumer (B2C) e-commerce sales predefined deadlines are typically used to separate order picking activities in the warehouse (i.e., retrieving goods from their storage locations) from delivery operations to customer locations, allowing both activities to be scheduled independently. Integrating these operational decisions can be seen as one of the key opportunities for improvement. However, it is unclear how both problems can be integrated, and what the benefits are, in complex and realistic settings. Therefore, the main objective of this project is to develop adequate models and algorithms to support integrated decision making in a realistic setting, and to analyse the benefit of integrated decision making under different problem characteristics.
The DISpATch project will focus on organizational and technical enablers for seamless synchromodal transport services in Flanders. Given the real-time dynamics and flexible nature of synchromodal transport, different actors and transport modalities need to work together and adapt according to unexpected events as well as contextual information that affect transport processes. These events and contextual information can be positive or negative perturbations that shape freight movement and transport mode selection, such as newly incoming orders, transport delays, cancellations, collaborative bundling opportunities, accidents, water levels, strikes and many more. The project will develop a platform represented by a Digital Twin component in order to provide a testbed for synchromodal opportunities within a risk-free environment. Such a risk free environment allows for analysis and evaluation of triggering events (e.g., new orders, disruptions, delays) which induce physical movements. It will measure the real-time synchromodal complexity and evaluate various decisions and offer alternatives by making use of mathematical, simulation and machine learning models. The project is carried out in cooperation with partners from KU Leuven and VUB.
promotor: prof. dr. Kris Braekers
With this project Thomas More Mechelen-Antwerp and Hasselt University aim at transferring academic expertise to the Flemisch healthcare sector with respect to the integration of AI (machine learning (ML), demand forecasting and predictive analytics) for optimizing logistics flows in a healthcare context.
promotor: prof. dr. Kris Braekers
Hospitals are confronted with tight budgets and an increased demand for their services. As a result, they are continuously looking for ways to reduce their costs and improve the efficiency of their processes, while ensuring a high quality of care. Logistics costs are the second largest cost for hospitals, so effectively managing the hospital supply chain (SC) provides significant opportunities to reduce costs. In this project, based on the international trend towards developing hospital networks for providing better care, the benefits of a cooperative hospital SC are investigated. This proposal focuses on fundamental methodological research efforts to develop quantitative decision-support tools which enable the shaping and operational implementation of a cooperative hospital SC. Because of the complexity of hospital SCs, additional real-life characteristics should be included in existing mathematical models, resulting in challenging and innovative operations research problems. Firstly, the optimal structure of a cooperative hospital SC is determined by combining location and inventory decisions. Secondly, operations within a cooperative SC are optimised by integrating inventory and distribution decisions. Thirdly, cost allocation methods are determined to fairly allocate the benefits of a cooperative hospital SC between all hospitals involved. All three decision-support tools are more broadly applicable to real-life problems with characteristics similar to a hospital SC.
promotor: prof. dr. An Caris
In the transport industry, long term contracts between shippers and logistics service providers are common. These contracts typically contain commitments related to volume, lead time and price. As these contracts run over a longer period (e.g., a year), these commitments are made long before the execution of the transport service itself. Synchromodal transportation, however, is founded on the idea of having the flexibility to change decisions with respect to, e.g., route and transport mode in real time, which leads to challenges to make the typical commitments long in advance. In this project, we study pricing strategies from a network flow perspective. More specifically, we focus on the impact of this mix of long-term commitments (contracts) and ad-hoc shipping on network planning and pricing.
The ORDinL project seeks to develop innovative methodologies for data-driven optimisation in logistics. Such an approach would enable the use of available data to learn and find patterns, thereby continuously and automatically adapting and improving logistics optimisation processes. The project is carried out in cooperation with partners from KU Leuven and VUB.
promotor: prof. dr. An Caris
Hospitals and health facilities have limited insight in logistic costs. Logistics is no core business for health institutions, but that doesn’t make it less important. The pressure on the budgets, strict regulations and changing medical needs of the patients make it necessary to reorganise health logistics. In the project ‘Zorghubs’ (Healthcare hubs) VIL is researching the possibilities of healthcare hubs, central spaces where logistic activities of multiple health care institutions are consolidated and optimised.
Emergency departments constitute an important chain in a health care system. Due to a remarkable growth in demand and the ever tightening budgets, the need for services often exceeds the available resources. In this project, the aim is to analyse, optimise and manage emergency departments in order to reduce emergency department crowding and to make emergency departments work more efficiently by use of simulation and simulation-optimisation techniques.
The use of numerical models to simulate and analyse complex real world systems is now commonplace in many scientific and engineering domains. Depending on the system under study, and the assumptions of the modeller, the models can be deterministic (e.g., in the case of analytical functions) or stochastic (e.g., when Monte Carlo simulation or discrete-event simulation is used). Often, the goal of the modeller is to find the values of controllable parameters (i.e., decision variables) that optimize the performance measure(s) of interest. As the evaluation of the primary numerical model can be computationally expensive, different approaches have been developed to provide less expensive metamodels, also referred to as surrogate models. The goal of this research is to develop effective and efficient algorithms for multi-objective simulation optimization, using such metamodels, and to compare the performance of different algorithms using appropriate metrics. The challenge lies in the inherent randomness of the observed outputs, which complicates the search for the Pareto front, as well as the efficient identification of this front. Additionally, the simulation budget is typically limited, so a major question is how to allocate this budget optimally between the exploration and exploitation stages of the algorithms.
In a traditional mobility policy, public transport is supplemented with (private) dial-a-ride services, providing demand-dependent door-to-door transport to people with reduced mobility. For efficiency reasons, many governments are currently implementing an innovative demand-driven mobility policy in which private dial-a-ride services also replace unprofitable public transport in rural areas. This project focuses on developing a dynamic matheuristic routing algorithm which provides integrated solutions, combining public transport and dial-a-ride services by ensuring synchronization between routes and modes.
Upcoming IT-driven market developments in the Flemish transport sector (e.g., e-CMR, real-time delivery information) force transport companies to revise their processes . The impact of these market developments is substantial, but facing these innovations is difficult, especially in small companies. This TETRA project, coordinated by the PXL and Hasselt University, aims to design a methodology in order to support small- and medium-sized transport companies to analyse and revise their processes independently. The methodology will enable these companies to rethink their business processes to respond to future innovations.
Tomas Ambra is currently an FWO-supported research associate at MOBI – Mobility, Logistics and Automotive technology (Vrije Universiteit Brussel) and the research group Logistics at Hasselt University. His joint PhD research, supervised by Prof. dr. Cathy Macharis and Prof. dr. An Caris, is situated in the field of sustainable logistics, with the main focus on synchromodal transport. He is developing the SYnchronization Model for Belgian Inland Transport (SYMBIT) which is a computational model that combines features of geographic information systems, agent-based modelling and discrete event simulations.
Laureate of the Belgian Francqui Chair 2018, professor of Logistics and Operations Management at the Rotterdam School of Management, Erasmus University. Prof. dr. De Koster is an international expert in the domain of warehouse optimization and incorporating human factors in operations management. During the lectures, Prof. dr. De Koster discusses recent developments in this research domain and formulate relevant research opportunities.
The delivery products to e-commerce customers forces SKF Logistics Services to enlarge the existing warehouse and rigorously revise their operations. This applied research project focuses on how to plan order picking operations in narrow-aisle order picking systems and how to integrate manual and automated order picking systems.
In the context of SALK (Strategisch Actieplan voor Limburg in het Kwadraat), business case logistics and mobility, LPL (Logistiek Platform Limburg), PXL (Logistics Intelligence Center) and Hasselt University (Transportation Research Institute and Research group Logistics) joined forces to stimulate innovation in the logistical sector in Limburg. Existing knowledge and expertise in the institutions are shared and scientific technological support is offered to logistical and transportation companies in Limburg to exchange know how between research institutions and companies, in this way stimulating the regional innovation.
The IUAP project 'COMEX' (Combinatorial Optimization: Metaheuristics and EXact methods) brings together the Belgian expertise on combinatorial optimization (ULB, UCL, HEC Liège, KULeuven, UA and UHasselt). The COMEX project aims to increase collaboration between the teams, generate research contributions and breakthroughs and create the largest excellence pole in combinatorial optimization in Belgium.
Due to demographic evolutions and developments in the healthcare sector, transport tailored to the needs of people with reduced mobility has become indispensable in today’s society. Dial-a-ride services provide demand-dependent door-to-door transport to this target group, where multiple users may simultaneously be combined into the same vehicle. Hence, providers face a complicated routing problem in their operational activities. This project focuses on developing metaheuristic routing algorithms and corresponding scheduling procedures that allow providers to balance their operational efficiency against the service quality offered to users (i.e., respecting their time preferences and limiting their ride times).
Following the research gap on the operational decisions and consequences of horizontal cooperation and its growing relevance in practice, the purpose of this PhD fellowship is to study horizontal logistics cooperation in depth on a strategic and operational level. Special attention is paid to the impact of partner selection and cost allocation decisions on collaborative performance and stability.
Instances (part I, part II, & part III) of Van Gils, T., Caris, A., Ramaekers, K. & Braekers, K. Formulating and Solving the Integrated Batching, Routing, and Picker Scheduling Problem in a Real-life Spare Parts Warehouse (2019). European Journal of Operational Research, 277 (3), 814-830. 10.1016/j.ejor.2019.03.012
Up to 2018
Publicly available datasets and experimental results corresponding to publications of up to 2018 are accessible via this link. For other datasets/results or more information, please contact the respective researcher(s) directly.