Synchromodality in the physical internet: Dual sourcing and real-time switching between transport modes

Synchromodality, also referred to as "synchronized intermodality", employs multiple transport modes in a flexible, dynamic way in order to induce a modal shift towards more environmentally friendly transport modes like rail or inland waterways, without compromising on responsiveness and quality of service. It is characterized by the synchronized parallel usage of different transport modes and/or the ability to switch freely between transport modes at particular times while a consignment is in transit. We present a decision rule that can integrate both the parallel usage, as well as real-time switching of transport modes, either in combination or separately. It takes into account real-time stock levels and service requirements of the shipper. The policy first determines at the source which volumes will be shipped using each modes at an intermediate terminal. Using a simulation study we demonstrate how our synchromodal transport policy can induce a modal shift towards low carbon transport modes

Optimal robust inventory management with volume flexibility: matching capacity and demand with the lookahead peak-shaving policy

We study inventory control with volume flexibility: A firm can replenish using period-dependent base capacity at regular sourcing costs and access additional supply at a premium. The optimal replenishment policy is characterized by two period-dependent base-stock levels but determining their values is not trivial, especially for nonstationary and correlated demand. We propose the Lookahead Peak-Shaving policy that anticipates and peak shaves orders from future peak-demand periods to the current period, thereby matching capacity and demand. Peak shaving anticipates future order peaks and partially shifts them forward. This contrasts with conventional smoothing, which recovers the inventory deficit resulting from demand peaks by increasing later orders. Our contribution is threefold. First, we use a novel iterative approach to prove the robust optimality of the Lookahead Peak-Shaving policy. Second, we provide explicit expressions of the period-dependent base-stock levels and analyze the amount of peak shaving. Finally, we demonstrate how our policy outperforms other heuristics in stochastic systems. Most cost savings occur when demand is nonstationary and negatively correlated, and base capacities fluctuate around the mean demand. Our insights apply to several practical settings, including production systems with overtime, sourcing from multiple capacitated suppliers, or transportation planning with a spot market. Applying our model to data from a manufacturer reduces inventory and sourcing costs by 6.7%, compared to the manufacturer's policy without peak shaving.info:eu-repo/semantics/publishedVersio

Synchromodality in the Physical Internet: Dual Sourcing and Real-time Switching between Transport Modes

© 2019, Lemmens et al. Synchromodality, also referred to as “synchronized intermodality”, employs multiple transport modes in a flexible, dynamic way in order to induce a modal shift towards more environmentally friendly transport modes like rail or inland waterways, without compromising on responsiveness and quality of service. It is characterized by the synchronized parallel usage of different transport modes and/or the ability to switch freely between transport modes at particular times while a consignment is in transit. We present a decision rule that can integrate both the parallel usage, as well as real-time switching of transport modes, either in combination or separately. It takes into account real-time stock levels and service requirements of the shipper. The policy first determines at the source which volumes will be shipped using each mode of transport, and subsequently depicts whether it should switch modes at an intermediate terminal. Using a simulation study we demonstrate how our synchromodal transport policy can induce a modal shift towards low carbon transport modes.status: publishe

Use of proximal policy optimization for the joint replenishment problem

Deep reinforcement learning has been coined as a promising research avenue to solve sequential decision making problems, especially if few is known about the optimal policy structure. We apply the proximal policy optimization algorithm to the intractable joint replenishment problem. We demonstrate how the algorithm approaches the optimal policy structure and outperforms two other heuristics. Its deployment in supply chain control towers can orchestrate and facilitate collaborative shipping in the Physical Internet

Use of proximal policy optimization for the joint replenishment problem

status: Published onlin

Synchromodal Transportation Planning using Travel Time Information

status: Published onlin

Synchromodal transportation planning using travel time information

Synchromodal transportation planning is de ned by the possibility to re-route shipments to alternative transportation modes at intermediate terminals based on real-time information about the shipment in transit. We present a synchromodal decision support model to determine the optimal modal choice for a single shipment in a multimodal network that is characterized by stochastic travel times. The model is formulated as a Markov decision process and allows adaptations to the modal choice based on real-time information on the travel time. Our formulation trades of transportation and late delivery penalty costs, and captures the value of synchromodal planning. We demonstrate the use of our model in a numerical case study, where we evaluate synchromodal against static intermodal transportation planning. The latter does not allow real-time adjustments to the modal choice. Compared to intermodality, synchromodal planning has most value when the penalty for late delivery is high and transportation services are more frequent

Innovative technology at the interface of finance and operations

The financial services industry covers banks, insurance companies and investment managers, as well as transaction or message processing companies. To streamline billions of daily transactions, lean principles and operational excellence programs have found theirway to financial service firms. Today, the resulting cost and risk reductions are further enhanced by embracing newdigital technologies—such as those falling under the umbrella of Industry 4.0. These digital technologies stimulate a new wave of operational efficiency improvements by making processes more automated, autonomous, and smart. Inspired by Boute and Van Mieghem (2021), we provide a framework to evaluate the transition towards digital, autonomous and smart operations in financial services. We report our findings from the digital operations journey of Euroclear, a service provider of settlements for securities transactions, on their quest for increased automation and autonomy. We also shed light on the potential of artificial intelligence in financial services. Data-driven solutions may support financial service firms from purely descriptive models and methods with strong predictive power, towards prescriptive decision-making algorithms

Optimal robust inventory management with volume flexibility: Matching capacity and demand with the lookahead peak-shaving policy

We study inventory control with volume flexibility: A firm can replenish using period-dependent base capacity at regular sourcing costs and access additional supply at a premium. The optimal replenishment policy is characterized by two period-dependent base-stock levels but determining their values is not trivial, especially for non-stationary and correlated demand. We propose the Lookahead Peak-Shaving policy that anticipates or peak shaves orders from future peak-demand periods to the current period, thereby matching capacity and demand. Peak shaving anticipates future order peaks and partially shifts them forward. This contrasts with conventional smoothing, which recovers the inventory deficit resulting from demand peaks by increasing later orders. Our contribution is three-fold. Firstly, we use a novel iterative approach to prove the robust optimality of the Lookahead Peak-Shaving policy. Secondly, we provide explicit expressions of the period-dependent base-stock levels and analyze the amount of peak shaving. Finally, we demonstrate how our policy outperforms other heuristics in stochastic systems. Most cost savings occur when demand is non-stationary and negatively correlated, and base capacities fluctuate around the mean demand. Our insights apply to several practical settings, including production systems with overtime, sourcing from multiple capacitated suppliers, or transportation planning with a spot market. Applying our model to data from a manufacturer reduces inventory and sourcing costs by 6.7%, compared to the manufacturer's policy without peak shaving

Reward shaping to improve the performance of deep reinforcement learning in perishable inventory management

Deep reinforcement learning (DRL) has proven to be an effective, general-purpose technology to develop ‘good’ replenishment policies in inventory management. We show how transfer learning from existing, well-performing heuristics may stabilize the training process and improve the performance of DRL in inventory control. While the idea is general, we specifically implement potential-based reward shaping to a deep Q-network algorithm to manage inventory of perishable goods that, cursed by dimensionality, has proven to be notoriously complex. The application of our approach may not only improve inventory cost performance and reduce computational effort, the increased training stability may also help to gain trust in the policies obtained by black box DRL algorithms