Chairunnisa Chairunnisa, Siti Maemunah, M. Kadarisman
This study aims to analyze the effect of vendor trucking management, delivery timeliness, and fleet availability on export delivery performance at PT XYZ Logistics Jakarta. A quantitative research approach was employed, utilizing hypothesis testing to examine the relationships among these variables. Data collection was conducted using a structured questionnaire with a five-point Likert scale, involving logistics personnel engaged in freight forwarding activities for IKEA. The collected data was analyzed using the Partial Least Squares Structural Equation Model (PLS-SEM). The results indicate that vendor trucking management has a positive effect on delivery timeliness. Fleet availability significantly influences export delivery performance. Additionally, delivery timeliness positively impacts export efficiency. The findings also reveal that vendor trucking management indirectly affects export delivery performance through its influence on delivery timeliness. Moreover, fleet availability moderates the relationship between vendor management and delivery success. The study suggests that logistics firms should prioritize vendor accountability, adopt AI-driven scheduling analytics, and invest in fleet modernization to enhance operational resilience and shipment accuracy.
Refentse L. Selepe, Olasumbo A. Makinde, Thomas Munyai
Background: In an era marked by rapid technological advancements, the manufacturing sector is increasingly adopting Fourth Industrial Revolution (4IR) technologies to streamline sourcing processes within supply chains. Sourcing and supplier management are crucial to achieving competitive advantages, cost reductions and sustainable practices.
Objectives: The study examines the application and the integration of various 4IR technologies in sourcing activities to achieve improved supplier selection, cost control, on-time delivery and supply chain resilience.
Method: A systematic literature review (SLR) was conducted, following a structured methodology across five stages: database selection, keyword generation, application of filters (inclusion and exclusion criteria), search area selection and final document review. The Scopus database was used for data collection, and VOSviewer was employed for keyword co-occurrence analysis.
Results: Out of 1530 documents, 16 relevant studies were identified, highlighting the usage of specific 4IR technologies in the sourcing process. Simulation, Internet of things (IoT), machine learning (ML), additive manufacturing and radio frequency identification (RFID) were found to be critical technologies.
Conclusion: Fourth Industrial Revolution technologies are pivotal in optimising sourcing Key Performance Indicators (KPIs), although gaps exist in the literature around ethical sourcing, augmented reality and cybersecurity. Moreover, the study identifies emerging trends such as crowdsourcing and IoT, which indicate a shift towards data-driven decision-making in sourcing.
Contribution: This study serves as an eye-opener to unveil appropriate 4IR technologies that could be deployed by supply chain managers to ensure effective manufacturing sourcing operations. The study also unveils further research on ethical sourcing and cybersecurity.
Shipment of goods. Delivery of goods, Transportation and communications
Robert Bredereck, Andrzej Kaczmarczyk, Junjie Luo
et al.
Envy-freeness is one of the most prominent fairness concepts in the allocation of indivisible goods. Even though trivial envy-free allocations always exist, rich literature shows this is not true when one additionally requires some efficiency concept (e.g., completeness, Pareto-efficiency, or social welfare maximization). In fact, in such case even deciding the existence of an efficient envy-free allocation is notoriously computationally hard. In this paper, we explore the limits of efficient computability by relaxing standard efficiency concepts and analyzing how this impacts the computational complexity of the respective problems. Specifically, we allow partial allocations (where not all goods are allocated) and impose only very mild efficiency constraints, such as ensuring each agent receives a bundle with positive utility. Surprisingly, even such seemingly weak efficiency requirements lead to a diverse computational complexity landscape. We identify several polynomial-time solvable or fixed-parameter tractable cases for binary utilities, yet we also find NP-hardness in very restricted scenarios involving ternary utilities.
With growing interest in sustainable logistics, electric vehicle (EV)-based deliveries offer a promising alternative for urban distribution. However, EVs face challenges due to their limited battery capacity, requiring careful planning for recharging. This depends on factors such as the charging point (CP) availability, cost, proximity, and vehicles' state of charge (SoC). We propose CARGO, a framework addressing the EV-based delivery route planning problem (EDRP), which jointly optimizes route planning and charging for deliveries within time windows. After proving the problem's NP-hardness, we propose a mixed integer linear programming (MILP)-based exact solution and a computationally efficient heuristic method. Using real-world datasets, we evaluate our methods by comparing the heuristic to the MILP solution, and benchmarking it against baseline strategies, Earliest Deadline First (EDF) and Nearest Delivery First (NDF). The results show up to 39% and 22% reductions in the charging cost over EDF and NDF, respectively, while completing comparable deliveries.
Matthias Bentert, Robert Bredereck, Eva Deltl
et al.
We consider the problem of resolving the envy of a given initial allocation by adding elements from a pool of goods. We give a characterization of the instances where envy can be resolved by adding an arbitrary number of copies of the items in the pool. From this characterization, we derive a polynomial-time algorithm returning a respective solution if it exists. If the number of copies or the total number of added items are bounded, the problem becomes computationally intractable even in various restricted cases. We perform a parameterized complexity analysis, focusing on the number of agents and the pool size as parameters. Notably, although not every instance admits an envy-free solution, our approach allows us to efficiently determine, in polynomial time, whether a solution exists-an aspect that is both theoretically interesting and far from trivial.
I. Vasylenko, A. Viniukov-Proshchenko, V. Voitsehovskiy
et al.
The intelligentisation of the global economy continues to grow, and this process is only going to deepen and expand in the future. Modelling of the delivery process is becoming one of the priority components of intelligentisation in transport. The methodological basis of the study is the provisions of the theories of transport processes and systems, logistics, management and the concept of sustainable development. Mathematical modelling methods were applied relevant models with discrete and continuous variables and nonlinear functions were designed. The study proposes a mathematical toolkit for modelling the delivery of special categories of cargo, which allows the logistics operator to optimise the delivery process using the principles of transport intelligence. The proposed toolkit is implemented as a result of forming a scheme for modelling the delivery of special cargo categories in the air-road connection and justifying its components, clarifying the subtask of delivering a consignment of special cargo by road, as well as creating mathematical equations for the number of samples of perishable goods by destination, their weight, volume, number of packages by types and destinations, and determining the urgency of shipments with the relevant characteristics. The mathematical toolkit meets the modern requirements and global economy trends, is scientifically substantiated and appropriately tested.
Bojan Jovanović, Željko Stević, Jelena Mitrović Simić
et al.
The importance of managing goods delivery in urban areas has reached its peak in recent years, driven by the constant and rapid growth of online commerce. Under such conditions, where smaller quantities of goods are ordered, yet the number of shipments continues to rise, the question of last-mile delivery (LMD) efficiency becomes increasingly relevant. This paper addresses the issue of last-mile delivery zone efficiency through the application of a new methodological approach. First, the concept of measuring last-mile delivery productivity is defined using a specific example from an urban environment. Next, Key Performance Indicators (KPIs) are established to enable a proper assessment of urban zone efficiency in line with the LMD concept. The main contribution of this study is the development of the IRN OWCM (Interval Rough Number Opinion Weight Criteria Method), which is used to calculate the weights of the criteria. To assess suitable delivery zones in terms of efficiency based on the defined KPIs, the previously developed IRN OWCM method is integrated with IRN AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization). The results identify delivery zones that are suitable in terms of meeting standardized user needs. The developed model demonstrated stability through additional verification tests and can be adequately applied in cases when it is needed to minimize subjectivity and uncertainties.
Kristina Čižiūnienė, Augustė Šiugždinytė, J. Matijošius
Background: The research presented here looks into ongoing problems with the package delivery services of a State parcel company, especially concerning damaged, wrongly delivered, late, and missing packages. These problems greatly affect customer satisfaction, so it is important to understand how they are connected. Methods: Three hundred and seventy-five customer complaints made between 2021 and 2023 were analyzed. Paniotto’s method was used to ensure that the study data accurately represented the situation. Pearson’s correlation coefficients helped find statistical links between different delivery problems. Results: The analysis revealed significant linkages among the core delivery issues. A strong positive correlation was found between damaged shipments and misdelivered shipments (r = 0.93835) and between prolonged delivery delays and lost goods (r = 0.9188). These findings suggest that addressing one issue may reduce the prevalence of others, given their tendency to coexist. Conclusions: The study emphasizes the necessity for the State parcel firm to execute a comprehensive strategy to improve the overall quality of parcel delivery services. Addressing concerns such as poor delivery and delays is critical since they are closely related to package damage and loss, which affects customer satisfaction.
Splitting loads refers to the process of dividing large shipments into smaller units to facilitate transportation. This strategy offers significant advantages in reducing costs and improving efficiency, but it also increases the complexity of solving the vehicle routing problem. This paper proposes a solution framework based on a column generation algorithm for the pickup and delivery problem, which considers multiple time windows and split loads. The framework aims to efficiently address the optimization problem, which involves complex factors such as a heterogeneous fleet, multiple warehouses, split loads, and multiple time windows, while optimizing vehicle routing, goods loading, unloading, and splitting plans, as well as time schedules. This paper introduces an improved label-setting algorithm to solve the pricing subproblem, which combines the resource-constrained elementary shortest path problem with the linear programming-based bilevel knapsack problem. This enhanced algorithm incorporates specific label dominance criterion and accelerating strategies. The A* algorithm concept was incorporated by designing a cost-to-go function to prune labels. The experiments confirm that the Heuristic Column Generator proposed in this study significantly outperforms traditional label setting algorithm in solution efficiency and quality on complex test instances within a finite time. Additionally, the accelerating strategies markedly enhance solver efficiency.
Many advancements are being made within the domain of autonomous shipping, motivating discussions of corresponding amendments to international safety regulations within the International Maritime Organization. Near-coastal passenger ferries are a form of sea traffic that has been the target of automation trials due to their short voyages and relatively protected waters of operation. This study investigated emergency evacuation from a range of such ships, covering both the current situation (focused on crew tasks, external rescue actors and interactions) and safety aspects that should be considered when automation brings about new work patterns, such as remote supervision and control. The study employed qualitative methods – interviews, field visits and a stakeholder workshop. Results give insight into ferry evacuation processes and challenges in their current form. In addition, results from the application of different automated evacuation scenarios suggest that more detailed studies are needed within the areas of remote operation situation awareness, remote operator and onboard personnel competencies, passenger safety information and communication, simple and robust evacuation equipment, technical means allowing assistance between autonomous and regular ships, and lastly, both procedures and interfaces for collaboration in a changing rescue network.
This study finds exact closed-form solutions for compensating variation (CV) and equivalent variation (EV) for both marginal and non-marginal changes in public goods given homothetic, but non-separable, utility where a single sufficient statistic summarizes consumer preferences. The closed-form CV and EV expressions identify three economic mechanisms that determine magnitudes. One of these mechanisms, the relative preference effect, helps explain the disparity between willingness to pay (WTP) and willingness to accept (WTA) for public goods. We also show how our closed-form solutions can be employed to calculate WTP and WTA across income groups using estimates from existing empirical studies.
Addiction is a major societal issue leading to billions in healthcare losses per year. Policy makers often introduce ad hoc quantity limits-limits on the consumption or possession of a substance-something which current economic models of addiction have failed to address. This paper enriches Bernheim and Rangel (2004)'s model of addiction driven by cue-triggered decisions by incorporating endogenous choice of how much of the addictive good to consume, instead of just whether or not consumption happens. Stricter quality limits improve welfare as long as they do not preclude the myopically optimal level of consumption.
Addressing the question of how to achieve optimal decision-making under risk and uncertainty is crucial for enhancing the capabilities of artificial agents that collaborate with or support humans. In this work, we address this question in the context of Public Goods Games. We study learning in a novel multi-objective version of the Public Goods Game where agents have different risk preferences, by means of multi-objective reinforcement learning. We introduce a parametric non-linear utility function to model risk preferences at the level of individual agents, over the collective and individual reward components of the game. We study the interplay between such preference modelling and environmental uncertainty on the incentive alignment level in the game. We demonstrate how different combinations of individual preferences and environmental uncertainties sustain the emergence of cooperative patterns in non-cooperative environments (i.e., where competitive strategies are dominant), while others sustain competitive patterns in cooperative environments (i.e., where cooperative strategies are dominant).
Shipping cargo via airlines is one of the services provided by airlines to send goods or cargo over long distances in a short time. The complexity of the delivery process arises because of the many parties involved, which may be spread across several different countries. This condition demands coordination and transparency at every stage of the shipping process. The low transparency of transactions and the lack of a system that can trace the whereabouts of the goods being sent are crucial problems faced by shipping cargo via airlines. On the other hand, blockchain technology can provide high levels of transparency with the concept of peer-to-peer validation. Thus, the purpose of this research is to help the aviation industry improve transparency and traceability for cargo shipments. The research method uses a qualitative approach through a literature review to find problems in shipping cargo via airlines. A literature review is also used to study the mechanism of cargo delivery via airlines using a conventional system. The result of this research is a blockchain technology model and data simulation with blockchain framework to increase transparency and traceability of cargo shipments overseas via airlines.
Purpose: substantiation of the author's methodology for assessing the economic effect of saving time in supply chains, analysis based on the proposed methodology for saving time of commodity producers when sending goods from St. Petersburg (North-Western Federal District of Russia) to India, the cargo is sent along the eastern branch of the international transport corridor “North-South” to compared with the option of delivery via the “deep sea” technology via the Mediterranean Sea and the Suez Canal. Methods: economic modeling, statistical parameterization, analysis and optimization of supply chains. As a result of the conducted research: the parameters of cargo shipments from the North-Western Economic District, the region of the St. Petersburg agglomeration to India for five different delivery options are systematized; the author's methodology for assessing the economic effect of saving time is disclosed, on the basis of which the effects of saving time for goods sent to India from St. Petersburg are determined, when switching from the “deep sea” supply option to the “North-South” multimodal eastern route option. Conclusion: as a result of the study, high-yield cargoes were identified, in particular, pulp, paper, metal structures, the dispatch of which from Russia to India via the international transport corridor is economically attractive, since the effect of saving time overrides the higher cost of delivery.
M. S. Novelan, Syahril Efendi, Poltak Sihombing
et al.
Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of ‘nodes’ on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods.
Muhammad Isa Ansori, Ririen Kusumawati, M. A. Hariyadi
This study aimed to predict the service level agreement travel time for goods and document shipments at PT Pos Indonesia (Persero) from the island of Java to the islands of Kalimantan, Sulawesi, Maluku and Papua. This is very important because of the high competition between the logistics industry which is getting faster and faster. The random forest method was chosen because this method is easy to use and flexible for various kinds of data. The prediction results with Random Forest in this study have a good level of accuracy, namely 83.86% of the average 4 trials. This shows that the Random Forest method is the right choice for managing the existing data model at PT Pos Indonesia.
John Nsikan, Rawlings Micheal, Ogbari Mercy
et al.
Managing risks is crucial for efficient and resilient maritime supply chain. For Nigeria’s seaports, the sources of supply chain risks and the best practices for managing them is yet to be sufficiently understood. This paper leveraged the experiences of 67 seaport operators, cargo owners, and shipping professionals in Nigeria to delineate the robust practices in managing supply chain risks. Data collection was via the structured questionnaire and analysed descriptively. Results identified nine main sources of seaport supply chain risks with congestion within port terminals ranked the highest. In addition, six practices were considered as robust for managing seaport supply chain risks: Developing risk feedback mechanism; frequent risk assessment; risk mitigation documentation; keeping records of disruption incidents; regular cargo status update; and better stakeholder relationship management. We recommend that increased level of investment in port digital technologies and regular capacity building in supply chain risk management could be provide the relevant intervention for resilient seaport operations in Nigeria.