37 research outputs found

    Consistent Time Window Assignments for Stochastic Multi-Depot Multi-Commodity Pickup and Delivery

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    In this paper, we present the problem of assigning consistent time windows for the collection of multiple fresh products from local farmers and delivering them to distribution centers for consolidation and further distribution in a short agri-food supply chain with stochastic demand. We formulate the problem as a two-stage stochastic program. In the first stage, the time windows are assigned from a set of discrete time windows to farmers and in the second stage, after the demand is realized, the collection routes are planned by solving yet a newly introduced multi-depot multi-commodity team orienteering problem with soft time windows. The objective is to minimize the overall travel time and the time window violations. To solve our problem, we design a (heuristic) progressive hedging algorithm to decompose the deterministic equivalent problem into subproblems for a sampled set of demand scenarios and guide the scenarios toward consensus time windows. Through numerical experiments, we show the value of considering demand uncertainty over solving the deterministic expected value problem and the superiority of our approach over benchmarks when it comes to reducing the routing cost as well as the inconvenience for farmers

    Robotized sorting systems: Large-scale scheduling under real-time conditions with limited lookahead

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    A major drawback of most automated warehousing solutions is that fixedly installed hardware makes them inflexible and hardly scalable. In the recent years, numerous robotized warehousing solutions have been innovated, which are more adaptable to varying capacity situations. In this paper, we consider robotized sorting systems where autonomous mobile robots load individual pieces of stock keeping units (SKUs) at a loading station, drive to the collection points temporarily associated with the orders demanding the pieces, and autonomously release them, e.g., by tilting a tray mounted on top of each robot. In these systems, a huge number of products approach the loading station with an interarrival time of very few seconds, so that we face a very challenging scheduling environment in which the following operational decisions must be taken in real time: First, since pieces of the same SKU are interchangeable among orders with a demand for this specific SKU, we have to assign pieces to suitable orders. Furthermore, each order has to be temporarily assigned to a collection point. Finally, we have to match robots and transport jobs, where pieces have to be delivered between loading station and selected collection points. These interdependent decisions become even more involved, since we (typically) do not posses complete knowledge on the arrival sequence but have merely a restricted lookahead of the next approaching products. In this paper, we show that even in such a fierce environment sophisticated optimization, based on a novel two-step multiple-scenario approach applied under real-time conditions, can be a serviceable tool to significantly improve the sortation throughput

    On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles

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    Ubiquitous computing technologies and information systems pave the way for real-time planning and management. In the process of dynamic vehicle dispatching, the adherent challenge is to develop decision support systems using real-time information in an appropriate quality and at the right moment in order to improve their value creation. As real-time information enables replanning at any point in time, the question arises when replanning should be triggered. Frequent replanning may lead to efficient routing decisions due to vehicles’ diversions from current routes while less frequent replanning may enable effective assignments due to gained information. In this paper, the authors analyze and quantify the impact of the three main triggers from the literature, exogenous customer requests, endogenous vehicle statuses, and replanning in fixed intervals, for a dynamic vehicle routing problem with stochastic service requests. To this end, the authors generalize the Markov-model of an established dynamic routing problem and embed the different replanning triggers in an existing anticipatory assignment and routing policy. They particularly analyze under which conditions each trigger is advantageous. The results indicate that fixed interval triggers are inferior and dispatchers should focus either on the exogenous customer process or the endoge- nous vehicle process. It is further shown that the exogenous trigger is advantageous for widely spread customers with long travel durations and few dynamic requests while the endogenous trigger performs best for many dynamic requests and when customers are accumulated in clusters

    Heatmap-based Decision Support for Repositioning in Ride-Sharing Systems

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    In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure a vehicle distribution that benefits both users and drivers, and consequently the provider. We propose an intuitive means to improve idle ride-sharing vehicles\u27 repositioning: repositioning opportunity heatmaps. These heatmaps highlight driver-specific earning opportunities approximated based on the expected future demand, fleet distribution, and location of the specific driver. Based on the heatmaps, drivers make decentralized yet better-informed repositioning decisions. As our heatmap policy changes the driver distribution, we propose an adaptive learning algorithm for designing our heatmaps in large-scale ride-sharing systems. We simulate the system and generate heatmaps based on previously learned repositioning opportunities in every iteration. We then update these based on the simulation\u27s outcome and use the updated values in the next iteration. We test our heatmap design in a comprehensive case study on New York ride-sharing data. We show that carefully designed heatmaps reduce service cancellations therefore revenue loss for platform and drivers significantly

    Consistent Routing for Local Same-Day Delivery via Micro-Hubs

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    An increasing number of local shops offer local same-day delivery to compete with the online giants. However, the distribution of parcels from individual shops to customers reduces the rare consolidation opportunities in the last mile even further. Thus, shops start collaborating on urban same-day delivery by using shared vehicles for consolidated transportation of parcels. The shared vehicles conduct consistent daily routes between micro-hubs in the city, serving as transshipment and consolidation centres. This allows stores to bring orders to the next micro-hub, where the parcel is picked up by a vehicle and delivered to the microhub closest to its destination – if it is feasible with respect to the vehicle’s consistent daily schedule. Creating effective schedules is therefore very important. The difficulty of finding an effective consistent route is amplified by the daily uncertainty in order placements. We model the problem as a two-stage stochastic program. The first stage determines the vehicle schedules. The second stage optimises the flow of realized orders. The goal is to satisfy as many orders per day as possible with the shared vehicles. We propose a multiple scenario approach and suggest problem-specific consensus functions for this framework. We assess the method’s performance against an upper bound, a practically-inspired heuristic, and the original consensus function. Our approach clearly outperforms the practically-inspired heuristic and the original consensus function. We observe that collaborative delivery via micro-hubs is worthwhile for delivery time promises of two hours or more. Noticeably, for these service promises, the cost of consistency are surprisingly low

    The multi-vehicle stochastic-dynamic inventory routing problem for bike sharing systems

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    We address the operational management of station-based bike sharing systems (BSSs). In BSSs, users can spontaneously rent and return bikes at any stations in the system. Demand is driven by commuter, shopping, and leisure activities. This demand constitutes a regular pattern of bike usage over the course of the day but also shows a significant short-term uncertainty. Due to the heterogeneity and the uncertainty in demand, stations may run out of bikes or congest during the day. At empty stations, no rental demand can be served. At full stations, no return demand can be served. To avoid unsatisfied demand, providers dynamically relocate bikes between stations in reaction of current shortages or congestion, but also in anticipation of potential future demand. For this real-time decision problem, we present a method that anticipates potential future demands based on historical observations and that coordinates the fleet of vehicles accordingly. We apply our method for two case studies based on real-world data of the BSSs in Minneapolis and San Francisco. We show that our policy outperforms benchmark policies from the literature. Moreover, we analyze how the interplay between anticipation and coordination is essential for the successful operational management of BSSs. Finally, we reveal that the value of coordination and anticipation based on the demand-structure of the BSS under consideration

    Adaptive State Space Partitioning for Dynamic Decision Processes

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    With the rise of newbusiness processes that require real-time decision making, anticipatory decision making becomes necessary to use the available resources wisely. Dynamic real-time problems occur in many business fields, for example in vehicle routing applications with stochastic customer service requests expecting a fast response. For anticipatory decision making, offline simulation-based optimization methods like value function approximation are promising solution approaches. However, these methods require a suitable approximation architecture to store the value information for the problem states. In this paper, an approach is proposed that finds and adapts this architecture iteratively during the approximation process. A computational proof of concept is presented for a dynamic vehicle routing problem. In comparison to conventional architectures, the proposed method is able to improve the solution quality and reduces the required architecture size significantly

    Optimal Service Time Windows

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    Because customers must usually arrange their schedules to be present for home services, they desire an accurate estimate of when the service will take place. However, even when firms quote large service time windows, they are often missed, leading to customer dissatisfaction. Wide time windows and frequent failures occur because time windows must be communicated to customers in the face of several uncertainties: future customer requests are unknown, final service plans are not yet determined, and when fulfillment is outsourced to a third party, the firm has limited control over routing procedures. Even when routing is performed in-house, time windows typically do not receive explicit consideration. In this paper, we show how companies can communicate reliable and narrow time windows to customers in the face of arrival time uncertainty. Under mild assumptions, our main result characterizes the optimal policy, identifying structure that reduces a high-dimensional stochastic non-linear optimization problem to a root-finding problem in one dimension. The result inspires a practice-ready heuristic for the more general case. Relative to the industry standard of communicating uniform time windows to all customers, and to other policies applied in practice, our method of quoting customer-specific time windows yields a substantial increase in customer convenience without sacrificing reliability of service, providing results that nearly achieve the lower bound on the optimal solution. Our results show that (i) time windows should be tailored to individual customers, (ii) time window sizes should be proportional to the service level, (iii) larger time windows should be assigned to earlier requests and smaller time windows to later requests, (iv) larger time windows should be assigned to customers further from the depot of operation and smaller time windows to closer customers, and (v) two time windows for one customer are helpful in some cases

    Dynamic Priority Rules for Combining On-Demand Passenger Transportation and Transportation of Goods

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    Urban on-demand transportation services are booming, in both passenger transportation and the transportation of goods. The types of service differ in timeliness and compensation and, until now, providers operate larger fleets separately for each type of service. While this may ensure sufficient resources for lucrative passenger transportation, the separation also leaves consolidation potentials untapped. In this paper, we propose combining both services in an anticipatory way that ensures high passenger service rates while simultaneously transporting a large number of goods. To this end, we introduce a dynamic priority policy that uses a time-dependent percentage of vehicles mainly to serve passengers. To find effective time-dependent parametrizations given a limited number of runtime-expensive simulations, we apply Bayesian Optimization. We show that our anticipatory policy increases revenue and service rates significantly while a myopic combination of service may actually lead to inferior performance compared to using two separate fleets
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