The considerable increase in parcel deliveries has negatively impacted the accessibility and livability of cities. One solution strategy is to decouple short-distance from long-distance shipping so that last-mile transport can be performed with low-footprint vehicles. Such solutions are referred to in the literature as multi-echelon distribution systems. This study introduced a new variant of the two-echelon vehicle routing problem that considers multiple alternative transport modes as well as multiple commodities over a multi-period time horizon, where customers can obtain their commodities from any store. We referred to this problem as the two-echelon multi-commodity multimodal vehicle routing problem (MCM-2E-VRP). The objective of service providers is to minimize total generalized costs while satisfying customer requirements. We formulated this as a mathematical model based on a space–time network and introduced a random utility discrete choice model to capture variations in performance and preferences. We developed an adaptive large-neighborhood search (ALNS) algorithm to provide solutions for newly generated MCM-2E-VRP instances based on the Beijing Yizhuang transportation network. Extensive numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. A sensitivity analysis revealed some policy-relevant findings regarding the effects of store distribution and vehicle capacity.

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The rapid increase in e-commerce and the emergence of combined passenger/freight systems in urban areas have raised the question of how best to integrate public transport services into door-to-door deliveries. This paper develops a variant of the pickup and delivery problem, called the two-echelon pickup and delivery problem using public transport (2E-PDP-PT). In the 2E-PDP-PT, the transportation network is split into two echelons. Different vehicles are utilized across the first and second echelons to ensure distribution efficiency. Parcels are delivered by public transport with free capacity or via trucks between satellites in the first echelon, and logistics vehicles are operated in the second echelon. The satellites are located at the echelon borders to transfer commodities between echelons. The 2E-PDP-PT aims to minimize total delivery costs and improve public transport capacity utilization. We formulate a new mathematical model based on a space-time network and adopt an adaptive large neighborhood search (ALNS) algorithm for the 2E-PDP-PT. The effectiveness of the ALNS algorithm is validated using newly generated small-scale instances. Furthermore, we investigate large-scale instances based on the Beijing Yizhuang transportation network. The computations show that an average total delivery cost savings of 4.5% is feasible. In addition, we analyze the impact of demand distributions and compare the ALNS algorithm and the LNS algorithm. Finally, we conclude that dynamically integrating public transport into freight transport services can benefit both logistics companies and public transport operators.

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Transshipment can be a detour for carriers to bypass congested locks. Therefore, the local government provides subsidies to carriers reluctant to adopt transshipment due to high costs. Using the Three Gorges Dam (TGD) as the subject, we address the interaction between the government and carriers and the rational routine choice for carriers when facing severe congestion. Specifically, we investigate pricing competition among carriers under different scenarios. A two-stage game model based on Evolutionary game theory and Bertrand game is used for the study. The results confirm that: (1) Subsidies for the road alternative can alleviate congestion in waterways transport before TGD; (2) Road transport is an efficient way to alleviate lock congestion, especially under emergency states; (3) Public subsidies for road transport support this change of modes at a reasonable price to shippers. Additionally, carriers with transshipment mode can provide more competitive freight prices and more convenient services to customers.

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Crowdshipping has recently emerged as a sustainable option for urban parcel delivery, where transport tasks are carried out by regular citizens who are engaged in a passenger trip, instead of the usual professional carriers. A key challenge for such as service offering is to maintain a sustainable network of customers (demand) and couriers (supply). This paper quantitatively explores how demand and supply functions for bicycle crowdshipping meet in a parcel delivery market. Earlier literature has identified factors that determine the success of this business model. We build on this to estimate the potential of the new services, by means of a market equilibrium model. A case study is presented from the Netherlands which uses tailor-made surveys, bicycle trip data and online shopping statistics. The study quantifies the importance of influencing factors. The insights can be used to design a bike crowdshipping platform which brings together the demand and the supply sides effectively. Future research could go into platform design, more accurate behavioral models as well as into advanced pricing approaches.@en

We introduce an approach to formulate and solve the multi-class user equilibrium traffic assignment as a mixed-integer linear programming (MILP) problem. Compared to simulation approaches, the analytical MILP formulation makes the solution of network assignment problems more tractable. When applied in a multi-class context, it obviates the need to assume a symmetrical influence between classes and thereby allows richer traffic behavior to be taken into account. Also, it integrates naturally in optimization problems such as maintenance planning and traffic management. We develop the model and apply it for the Sioux Falls network, showing that it outperforms the traditional Beckmann-based and MSA approaches in smaller-scale problems. Further research opportunities lie in developing extensions of MILP-based assignment, with different variants of user equilibrium or dynamic assignment, and in improving the model and solution algorithms to allow large-scale application.

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Crowdshipping systems are receiving increasing attention in both industry and academia. Different aspects of crowdshipping (summarized as platform, supply, and demand) are investigated in research. To date, the mutual influence of crowdshipping platform design and its supply side (with participating crowdshippers) has not yet been thoroughly investigated. This paper addresses this mutual influence by investigating the relations between shipping performance and intrusiveness to daily trips of commuters who voluntarily act as cycle couriers. In an experiment in The Hague, cyclists were asked to transport small parcels during a simulated daily commuting routine. The grid of commuting trips acted as a relay network to move parcels to their individual destinations. All the movements of the parcels were recorded by GPS trackers. The analysis indicates that a higher degree of complexity of rules in crowdshipping systems can lead to better system performance. Meanwhile, it also imposes higher intrusiveness, as participants need to deviate more from their routines of daily, uninterrupted trips. The case also suggests that a well-designed crowdshipping system can increase system performance without having to ask too much from crowdshippers. This study provides reference to better design such systems, and opens up directions for further research that can be used to provide thorough guidelines for the implementation of crowdshipping platforms.

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Controlling food quality and reducing waste is one of the most challenging tasks in the food industry, as it is facing high rates of wastage, leading to negative environmental impact. This research focuses on improving the scheduling and control of the supply chain of Irish lamb meat using real-time quality and temperature information. Temperature controlled reefers and sensor technologies can be used to monitor and set the temperature during transport and storage. In order to minimize waste, while at the same time optimizing cooling and transport costs, a mathematical model is proposed. The model consists of two aspects. The quality aspect considers the shelf life of Irish lamb which is related to temperature. The logistic aspect considers supply chain scheduling. With this model, a strategy is proposed to determine the movements of meat, as well as the temperature setting of cooling equipment. Results of simulation experiments indicate a sustainable approach can reduce or even eliminate waste and decrease operational costs when real-time monitoring and control is used.

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This thesis provides tools for decision support systems for perishable goods logistics. It also provides an approach for enterprises to estimate the benefit of adopting the proposed logistic systems. Perishable products are produced, transported, and consumed all over the world. Thanks to perishable goods supply chains, we can enjoy safe, fresh, and affordable products. Nevertheless, it is estimated that one third of the agricultural products produced globally for human consumption end up wasted, which amounts to 1.3 billion tonnes per year. This also means that one third of the resources and greenhouse gas emissions for producing and transporting these products are in vein. The wastage of these fresh products happens throughout the supply chains, which are often caused by the perishing nature and inefficiencies of supply chain planning. For instance, congestions at a certain location or over supply at a retailer may result in spoilage. Disruptions such as malfunctioning of cooling equipment can also contribute to wastage in supply chains. Recent technological developments provide new insights into supply chain management, allowing further waste reduction. With sensors and communication technologies, information of products such as location and freshness can be made known to supply chain planners in real-time. Thus the research question of this thesis is given real-time information of perishable goods logistics, in what ways can perishable goods supply chain players better control and coordinate logistic processes to reduce loss of perishable products? Traditional mathematical methods cannot capture enough details when describing perishable goods in their supply chains. Therefore, this thesis proposes a general framework in a system and control fashion to describe and control logistic operations. This general framework consists of a quality-aware modeling method and a model predictive control strategy. The quality-aware modeling method considers the perishable goods in the supply chain as a system, with quality and logistic features. The model predictive control strategy observes the system and steers the system in a manner that the wastage can be minimized. In this thesis, the proposed method is used in case studies of supply chains with three different commodities, namely bananas, starch potatoes, and cut roses. In each case study, because each commodity is unique in its physiological nature, it perishes in a unique way. Therefore, the supply chain takes care of the commodity in a way that may not suit other commodities. As a result, the logistic features of the supply chains are also different from each other. This requires the systems to be described differently, and control architectures can also vary. Results from the case studies show that the general framework can improve the effectiveness of supply chain logistic planning, and to reduce wastage. The improvements are quantified to illustrate the benefit of making full use of the information on perishable goods in supply chains. @en

Nowadays, optimization of ship energy efficiency attracts increasing attention in order to meet the requirement for energy conservation and emission reduction. Ship operation energy efficiency is significantly influenced by environmental factors such as wind speed and direction, water speed and depth. Owing to inherent time-variety and uncertainty associated with these various factors, it is very difficult to determine optimal sailing speeds accurately for different legs of the whole route using traditional static optimization methods, especially when the weather conditions change frequently over the length of a ship route. Therefore, in this paper, a novel dynamic optimization method adopting the model predictive control (MPC) strategy is proposed to optimize ship energy efficiency accounting for these time-varying environmental factors. Firstly, the dynamic optimization model of ship energy efficiency considering time-varying environmental factors and the nonlinear system model of ship energy efficiency are established. On this basis, the control algorithm and controller for the dynamic optimization of ship energy efficiency (DOSEE) are designed. Finally, a case study is carried out to demonstrate the validity of this optimization method. The results indicate that the optimal sailing speeds at different time steps could be determined through the dynamic optimization method. This method can improve ship energy efficiency and reduce CO₂ emissions effectively.

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This paper proposes a new methodology for modeling and controlling quality degradation of perishable foods when zero-order kinetics are considered. This methodology approximates the nonlinear model of the zero-order quality kinetics using the piecewise affine (PWA) modeling representation. For obtaining a proper PWA model, two state-of-the-art methods are discussed, and eventually, a hybrid identification-based PWA model is considered after the comparison. This PWA model is then transformed into a computational mixed logical dynamical model, based on which an optimal control strategy is proposed that balances food quality and associated energy consumption. Furthermore, a model predictive control is proposed for improving energy efficiency when a dynamical weather environment is considered. Simulation experiments illustrate the potentials of the proposed optimal controller and the model predictive controller in a case study involving the bighead carp.

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Fresh cut flower supply chains are aware of the need for reducing spoilage and increasing customer satisfaction. This paper focuses on a part of the cut rose supply chain, from auction house to several end customers. A new business mode is considered that would allow end customers to subscribe to florists and have a continuous supply of bouquets of roses. To make this business mode feasible, we propose to benefit from real-time information on roses’ remaining vase life. First, a quality-aware modeling technique is applied to describe supply chain events and quality change of cut roses among several supply chain players. Then, a distributed model predictive control strategy is used to make up-to-date decisions for supply chain players according to the latest logistics and quality information. This approach provides a tool for multiple stakeholders to collaboratively plan the logistics activities in a typical cut rose supply chain based on roses’ estimated vase life in real time. The proposed approach is compared with a currently used business mode in simulation experiments. Results illustrate that the new business mode and the planning approach could reduce unmet demand and spoilage in a cut rose supply chain.

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We propose a strategy for dynamic temperature management inside reefer containers in banana supply chains, with the objective of reducing energy consumption and deliver bananas with good quality. In this paper a model is developed to determine optimal temperature profiles for the future days during shipment. To assure the right quality in a dynamic environment, continuous monitoring inside reefers can be used to check the status of bananas. The optimization takes into consideration of disturbances of banana quality and estimated arrival time. Therefore, decisions are updated each day to cope with the possible disturbances. Simulation experiments compare the proposed approach and currently used approach. Results show that with the proposed approach, bananas can be delivered with right quality and reduced energy consumption.@en

Modern technologies have enabled approaches to estimate freshness of perishable products during production and distribution. This allows supply chains to apply more advanced decision support systems in order to further reduce the loss of perishable products. In this paper we focus on the postharvest scheduling of starch potatoes. In particular we propose a quality-aware scheduling method that can be used in a decision support system for starch potato postharvest operations. Considering the quality of stored potatoes in real-time, the method determines when and how many potatoes should be harvested, sent for starch production, or stored. A centralized and a distributed control strategy are developed, with the aim of minimizing total starch loss in dynamic environments. Simulation experiments illustrate how the proposed approaches deal with disturbances, and that the total starch loss can be reduced when real-time quality information of potatoes is taken into account.@en

Modern technologies have enabled approaches to estimate freshness of perishable products during production and distribution. Nevertheless, the loss of perishable goods is still high due to the deteriorating nature and inefficiencies in supply chains. This research focuses on improving the scheduling of banana logistics using real-time quality information. Bananas are typically shipped from tropical production sites to other places in the world. With temperature controlled reefer containers and sensor technologies, bananas can be monitored during transport and situations like early ripening can be predicted. In order to minimize spoilage, we propose a mathematical model for scheduling logistics activities with the consideration of both the biological process and the logistics procedure of bananas. Results of simulation experiments indicate that the method could reduce spoilage using real-time monitoring and scheduling.@en

Innovative technological developments are leading to smart ways for producing food more efficiently and of higher quality. Nevertheless, lots of perishable goods are wasted because of inefficiencies during the subsequent transport process. The amount of wastage could be reduced via better planning and control of transport activities. Perishable goods logistics can be better supported by real-time information of goods and the emerging concept of synchromodality, as sensing and communication technology develops. In this paper a decision making system is proposed for perishable goods logistics service providers to reduce loss of freshness using synchromodal transport. The approach starts from the perspective of individual containers and the different types of equipment/vehicles used to transport these containers. With both the perishing feature and the transport situations of perishable goods considered, the controller decides when and where containers with such goods should be moved to. Simulation experiments illustrate how the approach could improve the quality and reduce the operation time during transport processes.

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This paper proposes a new mathematical model for predictive scheduling of perishable material transports with the aim of reducing losses of perishable goods. The model is particularly designed for allocation of potatoes from several farms to a nearby starch mill, which produces starch from a limited amount of potatoes each day. Scheduling should determine how much amount of potatoes be sent from which farm to the mill on each day. It is known that the quality of potatoes decreases over time and as a result less starch is produced. A model predictive control approach is proposed to maximize the production of starch. Simulation experiments indicate that predictive scheduling can yield higher starch production compared to non-predictive approaches.@en