With fluctuating market demands, the tugboat industry confronts the challenge of adapting its supply chain to meet customer customisation needs while managing low order predictability. This study examines the shift from a Make-to-Stock (MTS) to three alternative production configurations based on the Assemble-to-Order (ATO) and Make-to-Order (MTO) configuration, focusing on the tugboat industry’s need for flexibility in response to market changes and customer-specific requirements.

The core issue addressed is the trade-off between investment risk and customer satisfaction, as the proposed configurations increase delivery lead times. To quantify the costs associated with adapting delivery lead times, a Bill of Materials and Operations (BOMO) is utilised, combining the Bill of Materials (BOM) with the production sequence (Bill of Operations, BOO).

A mathematical algorithm is developed to calculate the financial effects of the configurations based on BOMO data. The model involves a three-step process: importing part data, merging BOM, BOO, supplier, and transport data into a BOMO dataset, and performing value analysis on the BOMO data to quantify risk exposure and financing costs over time.

The study’s findings indicate that while increasing delivery lead times, the MTO configuration significantly reduces the risk exposure and financing costs. The BOMO is a strategic tool for analysing material costs and delivery lead times, providing insights into the financial implications of different production strategies. The research concludes that the MTO configuration is viable for Damen’s tugboat production, balancing risk exposure, financing costs, and delivery lead times.

In their Distribution Centre (DC) in Maasdam, FrieslandCampina (FC) uses a four-crew shift schedule to prepare all necessary orders for their clients, 24 hours of each Monday to Saturday. Their large automated warehouse is home to 10 000 pallet places, containing fresh dairy products. From here, orders are either prepared as full pallets, machine-picked layers or hand-picked "colli". In the last department especially, personnel cost is high relative to the throughput. Definitive picking deadlines are often ambiguous, posing challenges in job and personnel scheduling. The study goal is twofold. Firstly, to find out whether full knowledge of picking deadlines can contribute to a more efficient job, and so, shift schedule. Secondly, to offer insight for a trade-off between shift types to absorb workload. To reach this study goal, a Shift Minimisation Personnel Task Scheduling Problem (Krishnamoorthy et. al., 2012) and a Bin Packing Problem (Paquay et. al., 2014) were combined and tailored to fit the scheduling problem at FC's DC. In three weekly scenarios, the MILP model scheduled picking jobs in the least expensive shifts through a cost minimisation function. Two model configurations were used, one to prefer the shift between 09:00 and 17:00 (flex), and one to prefer either one of the 06:00-14:00 (morning) or the 14:00-22:00 (afternoon) shifts. Both model configurations inherently avoided the most expensive 22:00-06:00 (night) shift. Main findings include the possibility to absorb workload using the morning and afternoon shift and to avoid the night shift. Additionally, it was confirmed that insight in picking deadlines can contribute to an efficient personnel schedule a great deal.

The Automated Guided Vehicle (AGV) is the most commonly used piece of horizontal movement that uses electric power from an internal battery pack. With much focus on the charging strategy, current charge operation models lack the ability to model large scale terminal operations and to incorporate new types of horizontal movement. The objective of this research is to show the impact of recharge operations on terminal performances. A discrete event simulation model is based on the characteristics of APMT MVII to model the current impact. The results of numerical experiments show charging operations have a limited impact on the productivity of Lift-AGV. An exception is the change in battery type. Using a Li-ion battery lowers the need for recharging and increases total productivity. The findings show the importance of integrating modelling to accurately project terminal operations.

This research addresses the critical role of the Yard Utilization Rate (YUR) in the performance of container terminals, focusing on Rubber-Tyred Gantry Crane (RTG) container terminals. Existing literature reveals a gap in studies specifically targeting high YURs in RTG container terminals. The primary research question is, ”How to reduce Quay Crane (QC) productivity loss in RTG container terminals with highly utilized storage yards?” A Root Cause Analysis (RCA) identifies key factors affecting the loading and unloading processes of QCs, with a focus on the root causes "Too few TTs deployed", "Congestion", and "YUR too high". Design alternatives are proposed to address these root causes, and three design alternatives are tested through simulation: varying the number of Terminal Trucks (TTs), sacrificing driving lanes for additional storage space, and implementing an alternative shuffle policy with a different bay distribution. Results show a significant influence of the number of TTs on QC productivity, with an optimum at 8 TTs per QC. Sacrificing driving lanes for extra storage space proves counterproductive due to increased congestion. However, the alternative shuffle policy Multi-bay with Sets Shuffle Policy with a Clustered Bay Distribution shows promise, offering a potential solution to mitigate QC productivity loss in highly utilized RTG container terminals, with an average increase of 0.4 boxes per hour. In conclusion, the study successfully addresses the identified knowledge gap, providing valuable insights and proposing a practical solution for reducing QC productivity loss in RTG terminals with highly utilized storage yards. The suggested approach of Clustered Bay Distribution with a Multi-bay with Sets Shuffle Policy proves effective in maintaining terminal performance during periods of an increased YUR.

This research paper focuses on improving the performance of cross-docking operations under uncertainty in the context of e-commerce logistics. The growth of e-commerce sales has increased product returns and complexity to supply chains. To address this issue, this study investigates how cross-docking operations can be improved under external and internal uncertainty factors. The research begins with a literature review to understand cross-docking facilities (CDFs) and measures to mitigate the effects of uncertainty. The current state of a CDF in a case study for a Fourth Party Logistics (4PL) provider is examined, and by reflecting on the literature overview, two potential means for decreasing the effects of uncertainty are identified: staging-level design and load carrier-type design.

A Discrete Event Simulation (DES) model is developed to test the effects of staging-level design and load carrier types on the performance of the CDF. The simulation model captures input factors such as truck arrivals, freight levels, and the purity level of cross-docking. The simulation model’s performance is tested for different scenarios, and the effects of different design alternatives are analyzed.

The results demonstrate that two-stage cross-docking with pallets can significantly reduce the total makespan and improve operational efficiency compared to single-stage cross-docking with pallets. The results also show that using roll containers significantly decreases the chance of intra-terminal congestion but also results in longer unloading and reloading times. The research contributes to the understanding of cross-docking operations under uncertainty, stresses the importance of staginglevel
and load carrier type design on CDF performance, and provides insights for logistics companies seeking to optimize their e-commerce supply chains.

The worlds awareness of our emissions and the effect it has on our surroundings is increasing. This influences efforts to put new agreements into place to reduce emissions and the effects on global warming. Along with the Paris agreements, the International Maritime Organisation also came with measures to achieve these needed reductions of emissions for the maritime industry. The set goal for 2050 is a reduction of 50% of greenhouse gas emissions. To achieve this new fuels need to be used and research need to be done on these fuels.

In this thesis the workings of a liquid bulk terminal are analysed, with the use of ammonia as fuel for ships in the maritime industry. The current liquid bulk terminals need to change with the future increase in demand and use of ammonia. This thesis focusses on how this can take place. The transport and bunkering methods of ammonia need to be researched and used to formulate a model to investigate the various options.

It is opted to develop a Discrete Event Simulation model of a liquid bulk terminal. The bunkering operations and supply of ammonia within a terminal are modelled. Various bunkering and supply methods are modelled for different sizes of terminal and at various levels of supply and demand.

The model is implemented using python and a Discrete Event Simulation package Salabim. With the models simulations it is investigated how bunker and supply methods at the terminal perform at different levels of scale. This is to gain insights for the future transition to the less polluting bunker fuel ammonia. The model is verified and validated with the use of data from literature on similar terminals using LNG.

The simulation results show that using pipeline bunkering and supply options are the most suitable. This option gives the shortest time that a ship has to spend on average at the terminal. The use of pipeline bunkering is therefore more efficient and this increases with the increasing scale of size, supply and demand of ammonia at a liquid bulk terminal.

The costs of all the facilities needed for the different sizes of terminal and the various scenarios of methods are analysed. The costs show that for smaller scales, bunker vessels and train supply, are more cost efficient. For large scale supply, demand and terminal size, using pipeline supply and bunkering is the most cost efficient.

Recommendations for future research includes the use of hybrid terminals using multiple bunkering methods at the same time. Investigating the safety risks for each method of bunkering and supply. And to use the proposed model for a case study at a location to investigate the suitability of the bunkering and supply methods for such locations.

This study aims to investigate the effectiveness of a Truck Appointment System (TAS) in improving dock utilization and overall operational efficiency at Royal Floraholland Aalsmeer. The study focuses on the impact of TAS on dock turnaround times, truck wait times, and maximum throughput time, as well as its implications on cost. The research explores how a TAS could work, including the scheduling of appointments and truck assignments, and the effects of more distributed arrivals. The study analyzes the performance indicators; truck waiting time, maximum time between flower check-in time and storage, parking wait times, cost, and examines the impact of assignment systems on these indicators. To be able to evaluate these performances a Discrete Event Simulation model is made, and different scenarios are analyzed. The study shows that requiring reservations has a significant impact on spreading out the pattern of arrivals, with gains seen at about 70% reservation percentages. The biggest improvements are observed at the throughput times of trolleys to cooled storage, with maximum throughput times decreasing up to 18%. The results suggest that a TAS can improve overall operational efficiency and decrease variability in maximum and average throughput time, while not lowering dock times significantly.

Air transport has gone from a once in a lifetime experience for the extremely rich to a day to day method of transportation for everybody. With the increase in passenger movements the amount of baggage that goes through airports has increased as well. This could cause problems for airports if they have a limited capacity in one of the parts of the baggage journey. An important part is transfer baggage, if something goes wrong the transfer bag misses their connecting flight which is an expensive problems for the airlines and airports.

To cope with the increase in demand of the capacity there are several solutions. The solution that will be explored in this paper is to make a more efficient use of the capacity that is available by making a prioritization in transfer baggage based on transfer time. The process in called cold buffering and the aim is flatten the infeed peaks. In order to explore this potential solution the baggage journey was explored and part of journey was modelled in a simulation. From the model it followed that it is possible to obtain 28.4% of peak shaving by using the cold buffering.

This thesis contributes to the research of a complementary Truck Arrival Management system in order to increase the efficiency of the Liquid Bulk Terminal as a system in a global intermodal supply chain. Nowadays, terminals often suffer from congestion, despite the use of a Truck Arrival Management system. A Truck Arrival Management system controls truck arrivals at terminals. Currently, the Truck Appointment System is the most used and researched Truck Arrival Management system at terminals. The Truck Appointment System has advantages and disadvantages. Therefore, Drop and Swap has been researched as second Truck Arrival Management system. A complementary Truck Arrival Management system
is developed and researched in which Truck Appointment System and Drop and Swap cooperate with the objective to overcome disadvantages and enhance benefits. The system should be capable to serve all kind of trucks within the global intermodal supply chain.

For high efficiency and flexibility, a fleet of Automated Guided Vehicles (AGVs) both homogeneous and heterogeneous are widely used automation products for material handling in warehouses and automated production lines. Given the layout capacity, the AGVs interact with each other, which provokes challenges in Driverless Transport Vehicle System (DTVS) traffic management in dynamic environments. One of the main challenges is how to avoid collision and deadlock between heterogeneous fleets of AGVs in a bidirectional layout. This research study proposes a deadlock detection and avoidance algorithm that follows the deadlock prediction and uses a dynamic rerouting strategy with Dijkstra to avoid deadlocks. The mission paths are checked in space and time for overlapping edges by four check conditions and cumulative weights and return a boolean value to avoid cyclic deadlocks. For the heterogeneous fleet of AGVs, a standard communication protocol VDA5050 is used to maintain a standard communication interface between vehicles and the traffic management module with cloud-based microservices for increased processing time and interoperability within the warehouse. This communication interface is used to communicate the novel deadlock control algorithm to a heterogeneous fleet of AGVs. The proposed algorithm not only improves the throughput by increasing vehicle operational time but also successfully avoids congestion and deadlocks with high traffic management efficiency in the logistic transportation system.

Errors during truck loading at dock doors lead to unwanted wrong deliveries in logistics. Due to the falling price of RFID tags, RFID dock door discrimination is now being used for product registration. The problems that arise with the current system for RFID Dock Door Discrimination are cross-reads and miss-reads. The purpose of this research is to increase the accuracy of product registration by the proposed Dock Door Discrimination method with Naive Bayes Classifier (NBC). A hardware design, including 4 RFID antennas at three adjacent dock doors and 1 added antenna at the staging area, and software design, including the implementation of the NBC, are proposed to improve the RFID Dock Door Discrimination. The Experimental Setup and Plan were used to gather data to compare the current RFID transition system with the new proposed NBC system in six scenarios, three with and without noise at other gates. For each individual scenario, the accuracy improved most with NBC with one input feature. The accuracy for all scenarios combined for the collected data improved from 82.1$ (current) to 93.6(NBC).These results mean that there is a solid improvement in implementing the Naive Bayes Classifier over the current RFID transition system for Dock Door Discrimination.

This study evaluates the impact of operational philosophy on the efficiency of omni-channel warehouses. Suppliers want to integrate an online order channel in existing infrastructure, while efficient integration of omni-channel operations is challenging due to the difference in nature of e-commerce orders and store orders. Integration of operations is necessary for cost efficient omni-channel implementation, however little scholarly works exist exploring operational implications in the omni-channel context. The impact of operational philosophy is analysed through a discrete events simulation of the alternative operation designs. Through a sensitivity analysis on order arrival pattern and order type skewness, the performance of the design alternatives are compared. The considered operation designs have been found in literature. For realistic input values a case is considered of a distribution warehouse from a Fast Moving Consumer Goods (FMCG) supplier. From the results it is apparent that in an omni-channel warehouse, a dedicated zone is required to handle expected web order throughput efficiently, although such an operations design requires lots of space, which is often constrained. Space is constraint due to supplier seeking to integrate the online channel into existing operations, which turns out not to be durable. Therefore, it is concluded that for future throughput non of the considered operational philosophies is adequate.

Sometimes seaport capacity is a bottleneck during disaster relief operations. This research investigates a flexible, floating and modular seaport. This seaport is stored all around the world and when necessary it ships seaport handling capacity from those storage facilities to the disaster area in order to help with the disaster relief effort. After usage of a mathematical optimization model and the determination of several possible future disaster scenarios we propose that an applicable logistical design of this seaport stores 1,950 TEU/h of handling capacity of this flexible seaport at Kuala Lumpur and 812 TEU/h at Algiers. Next to the logistics contribution the way in which the possible usage of such a seaport influences the communication process of actors involved in a disaster relief effort is studied. We propose that the collaborative sensemaking in disasters is the object of interest. With the help of an emergency simulation role playing game experiment it is concluded that the usage of the seaport leads to, among other things; more equally distributed participation, quicker start of sharing of information, increased awareness of the role of others and more agreement on the common goal. The hypothesis is that this is because of an increased complexity. Integrating this complexity with the logistical applicable design leads to storing a smaller amount of seaport handling capacity at a lot of facilities. This results in a design of opening facilities in Kuala Lumpur (with 1350 TEU/h stored), Algiers (with 356 TEU/h stored), Dakar (with 57 TEU/h stored), Maputo (with 279 TEU/h stored), Berbara (with 35 TEU/h stored) and Corinto (with 332 TEU/h stored).

This research investigates ways to maximize the outbound handling and freight to customer operations efficiency based on the real data of The Kraft Heinz Company. A cost to serve model has been developed to evaluate the performance of various design alternatives deriving from the field of "quantity adjustment" as the dependence of cost efficiency upon labour intensity at the warehouses and the truck volume utilization indicated. The research concludes upon the selection of the optimal design alternative to be implemented per customer as well as upon which design alternative is more suitable for clusters of customers that share specific characteristics.

With routing algorithms approaching optimization limits, other approaches are required to further improve the last-mile delivery of goods. In this paper, an analysis is made of the effectiveness of nudging customer delivery windows at order creation dependent on a dynamic spatial grouping to improve last-mile routing performance. During this research, the focus is placed on the development of a grouping model to spatially group customers at order creation. Next to the development of a grouping model a simulation is created to analyse different customer behaviours with the use of historic order data. The grouped order data is then planned in an existing planning tool and compared to an ungrouped benchmark. The results of the tests show that nudging customer delivery windows dependent on spatial grouping improve last-mile routing performance. A grouping model which uses travel distance grouping with a grouping distance of 1000 meters currently generates the best results, but further research into customer behaviour and improving the proximity metric used could improve this performance gain even further.

Healthcare systems worldwide rely heavily on the efficient allocation of caregivers in home- based care. Despite this, to this day, the majority of home healthcare (HHC) organisations plan their operations manually, an extremely time-consuming and labour-intensive task yielding sub-optimal solutions. Research efforts in the operations research (OR) field have extensively studied said problem under the HHC routing and scheduling problem (HHCRSP). However, the separate consideration of the planning of caregivers and re- sources required at visits are considered a relevant gap in the literature, as real-world organisations must plan both simultaneously.
As a solution toward bridging this gap, this paper proposes the introduction of home de- livery vehicles (HDVs) to support HHC operations, in which the vehicles deliver resources required at patient visits in time for consultation. A mathematical formulation consider- ing the routing, scheduling, and synchronisation of caregivers and vehicles is presented, in which partial battery charging alongside hard visit and working time windows are key characteristics. A genetic algorithm (GA) is further developed to solve the proposed for- mulation, which is proven to perform efficiently in benchmark instances for the vehicle routing problem (VRP). Simulations were run on data generated synthetically by a sta- tistical model recreating the HHC needs of the London Borough of Islington. The results highlight the potential benefits of adopting such technology, resulting in an average re- duction of the required medical resources of 49.94% and a 19.23% reduction in costs with respect to standard operations using the same number of resources.
Overall, the most economical operating strategy for a HHC organisation was identified to be the purchase of a number of resources equal to the number of visits requiring said re- sources. Such an approach was found to decrease the costs per visit by 1.26% with respect to HDVs. The marginal amount, however, is expected to be diminished in the coming years due to factors not captured in the analysis such as the higher scalability of HDVs and their resulting potential to meet the anticipated growth in demand for HHC, the ex- pected decrease in the price of electric vehicle technology, the economic incentives offered to adopt said technologies, and the potential collaboration between caregivers and HDVs. These, alongside extensions to the proposed model such as non-linear battery charging, are proposed as future research avenues.

Aircraft LRU (Line Replaceable Unit) MROs (Maintenance, Repair Organization) struggle to meet service levels. To stay competitive, literature describes two strategies; increasing contracted fleet size and not reaching projected repair turn around times. Besides these two strategies, literature does not describe the use of triple echelon order fulfillment for global contracts but it is recommended Sprong (2019) Munsters (2019). This research aims to find relations between the service level and the two strategies from the perspective of expansion rules and operations. A discrete time-step simulation is proposed because the systems component availability changes over time and because of the stochastic behavior of repair time and component failure. The found relations are firstly that send from base operational procedures and secondly that mbk stock influence the theoretic repair time performance. The second relation is that when adding more fleet units (apply a bigger pool), the current performance will deteriorate.

The shipping industry needs to reduce greenhouse gas emissions and other pollutants. In order to achieve this reduction, the shipping industry will be faced with stricter regulations that are currently unclear. The lifetime of a ship is about 25 years. As a result, it can be interesting to already take a possible refit to an alternative fuel into account, when designing a new ship. This study investigates which aspects of the ship’s design can be prepared for a refit to an alternative fuel, regarding a general cargo ship that starts sailing on LSFO and MGO. Due to the uncertainty in the future regulations, two possible scenarios are described. These scenarios are based on current regulations, known future regulations, the goals of the International Maritime Organization (IMO) greenhouse gas (GHG) strategy and a well-to-tank (WTT) analysis. The first scenario allows carbon-neutral fuels and the second scenario only allows carbon-free fuels. Hereafter, different fuels are identified to be applicable in these scenario’s. Then the most relevant aspects of the fuels that have an impact on the design of a ship are identified. These are the way the fuel should be stored, the volumetric and gravimetric energy density of the fuel, including the system and finally the applicability of the International Code of Safety for Ships using Gases or other Low-Flashpoint Fuels (IGF code). Based on these aspects, the fuels are compared with each other and a qualitative matrix is formed which shows how difficult it is to refit a ship from one fuel to another. The three most promising fuel refits are selected for a further conceptual design. The most manageable fuel switch in the carbon-neutral scenario is a switch towards hydrotreated vegetable oil (HVO), and the easiest in the carbon-free scenario is a switch to ammonia. Methanol is also selected, because, in the carbon-neutral scenario, it is relatively easy to refit to, and in terms of emissions, it does better than HVO. To evaluate the possible design options for an easier refit, a reference ship is used as a starting point. The properties of this ship are also used to define the starting requirements for sailing on LSFO and MGO. Subsequently, a design spiral is used to adjust the design of the reference ship, to prepare it as far as possible for the selected refits. It has been concluded that it is relatively easy to prepare the ship sailing on LSFO and MGO for HVO. Due to the lower energy density of HVO, it is recommended to decrease the sailing range after the refit, so that no further adjustments have to be made to the design of the ship. The ship’s range will be reduced by 8.6%. Because the ship has overcapacity, this has no impact on the sailing profile. For methanol, cofferdams could already be placed in relevant tanks. Double pipes can be laid to these tanks or a double duct can be installed. Unlike MGO and LSFO, methanol can be stored in double bottom ballast water tanks, so for example, cofferdams can also be placed in ballast water tanks. By preparing the ship, space will be lost due to the preparations, in order to meet the initial requirements, the ship must be 2.3 [m] longer. If there is refit in the future, the ship will not yet meet the requirements for methanol. In order to meet the refit requirements, the ship will have to surrender 40% of its range. Furthermore, 1.4% of the cargo capacity will have to be surrendered. It was concluded that, starting from LSFO and MGO, there was no point in preparing the ship for ammonia, because there are too many differences between the fuels. It could be interesting to build a ship for LNG in the coming years and then see if it can be prepared in the design for a refit to ammonia or hydrogen.

In this report, supply chains of factory outlet stores are analyzed. Here, the concept of factory outlet stores have been investigated in literature to be able to understand which goal these stores serves. Based on a pull based supply chain perspective, replenishment has been investigated. Here, demand forecast, inventory, order ful_llment and transport have been considered. Here, transport models from literature are introduced to see which factors are considered and for which purpose these models can be used. Also, the performance of supply chain has been discussed, which consists of _nancial as non _nancial aspects. A case study will be done at the factory outlet store of L'Oreal. Here, the supply chain has been analyzed, as the catalogue and type of products they o_er. The replenishment policy has been analyzed, in which the transport process from the CPD warehouse to the store will be investigated. Here, a model will be designed to be able to schedule the deliveries to the store in a more cost e_cient way. An analytical modelling method is selected to develop a model which captures the mentioned transport process. Here, aspects of models in literature are evaluated to be used in the transport model for scheduling deliveries. The model is developed as an integer mixed linear programming model and will schedule the amount of pallets per delivery to a storage location, in which two storage location are presented. This model is described as the delivery scheduling transport model (DSTM) and is checked on implementation in MATLAB in a veri_cation step. The DSTM has been con_gured for the case study at L'Oreal, which is checked by a validation step to see if the model meets the system requirements. Then, experiments with di_erent inputs are executed to see if savings can be realized by scheduling the transport via this model. This is realized in all the inputs scenarios. To see if more savings can be realized, sensitivity analysis have been performed to investigate how parameters can be tuned to realize lower cost. A conclusion will _nalize this research to conclude that replenishment orders can be scheduled in a more cost e_cient way by the DSTM, which is the result of a case study of L'Oreal.