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The classical multi-period inventory routing problem (MIRP) combines inventory management and vehicle routing problems, attempting to maintain customer inventory levels via autonomous deliveries, with the assumption that stock-out never occurs at the supply side. While this is no more the truth when a common third-party logistics company operates a central warehouse for a group of small and medium sized retailing companies. Procurements have to be made to refill inventory at the central warehouse. This study thus extends the traditional MIRP by incorporating procurement decisions (MIRP-PD) and tries to minimize the total cost of procurement, inventory holding and transportation. To our knowledge, this paper is among the first to formally describe MIRP-PD and model it as a mixed integer linear program. A hybrid two-level heuristic is proposed to address large-scale instances. The upper level of the algorithm determines the visiting schedule while the lower level works out procurement and routing decisions accordingly. Components from tabu search and adaptive threshold acceptance are also embedded to help escape from local optima. Extensive numerical instances are generated from a real case in Chengdu (China), based on which the computational results reveal the effectiveness and efficiency of the proposed algorithm compared with the commercial solver CPLEX. The comparison between MIRP and MIRP-PD has demonstrated the cost advantage of incorporating procurement decisions into inventory and routing plans. Impacts on costs of key factors including delivery frequency and buffer size of stores have also been examined to provide managerial implications to practitioners. [ABSTRACT FROM AUTHOR]

We investigate a ferry network design problem with candidate service arcs (FNDP-SA), which is a new variant motivated by areal-world case from Zhuhai Islands. In this research, the design of a ferry schedule must conform to strict constraints including technical feasibility, safety issues, environmental impacts and moreover, avoid conflicting with ferries from neighboring cities (i.e. HongKong, Shenzhen and Macao). Therefore, a set of cautiously approved service arcs are firstly given, based on which the ferry service network is optimized. This study is among the first to formally describe the FNDP-SA and model it as an integer program. A hybrid variable neighborhood descent (VND)-based algorithm is developed. Two sets of instances are generated based on the case of Zhuhai Islands, where the first set is based on historical ticket sales data while the second set is derived by incorporating increased demands in the future according to a questionnaire survey. Numerical studies have shown that 59% cost reduction on the first set can be achieved by the proposed VND algorithm when compared with manual results. Research outcomes of this study have been adopted and implemented to facilitate the sustainable development of the ferry service of Zhuhai Islands. [ABSTRACT FROM AUTHOR]

This paper considers an auction-based parking reservation problem where a parking management platform is the auctioneer and the drivers are bidders. The platform is in charge of multiple homogeneous parking spaces. A winner may leave earlier or occupy the parking space longer than the time he has reserved. The phenomena are known as (ex post) demand disturbances, which can occur only after the last auction terminates. The platform may penalize or compensate a driver who causes demand disturbance. Besides, investigation is conducted into three types of driver behaviors, namely, gain/loss neutrality, loss aversion , and gain seeking , and the reference effects are examined. An effective multi-stage Vickrey–Clarke–Groves (MS-VCG) auction mechanism is raised. Expect for the disturbance makers, the MS-VCG auction is capable to achieve allocative efficiency, incentive compatibility, and individual rationality. As shown by computational results, in the absence of reference effect, both penalty and compensation rise with the number of bidders. If drivers are gain/loss neutral, the average utility of winners reaches the highest, while the penalty and compensation reach the lowest, as compared to loss-averse and gain-seeking drivers. The total VCG payment received by the auctioneer increases with the reference price. Finally, based on our proposed model, the platform has the ability to deal with the demand disturbances by holding a certain number of parking spaces that are not auctioned for reservation. [ABSTRACT FROM AUTHOR]

• A less-than-truckload (LTL) transportation service procurement (TSP) in the waste collection industry is studied. • A one-sided VCG auction mechanism is proposed, with the central carrier being the auctioneer and shippers being the bidders. • A two-layered variable neighborhood tabu search algorithm is devised to solve the problem. • A number of managerial implications are obtained. We study a less-than-truckload (LTL) transportation service procurement (TSP) in the waste collection industry, where shippers make small-volume requests and a single carrier optimizes the collection routes to realize maximum profitability. In our proposed auction-based waste collection synchronization (A-WCS) mechanism, the carrier plays a role as the auctioneer to decide on who wins the bids and the corresponding payments while the shippers act as the bidders. This is attributed to the carrier-centric structure where a carrier has to consolidate multiple LTL shipping requests so as to provide cost-effective services. Furthermore, a single shipper (e.g., a construction site) may generate multiple types of waste and highly desires for a synchronous service, requiring multiple vehicles dedicated for different waste types to arrive almost simultaneously. The synchronization is beneficial to prevent repeated handling and reduce disruption caused to the minimum. The synchronization requirements are innovatively factored by incorporating time windows into each customer bid. The A-WCS mechanism can encourage truthful bidding from shippers and obtain fair payments for both sides, therefore can benefit the market in the long run. A-WCS problems are extremely difficult to solve due to the integration of two combinatorial problems. We address the A-WCS with a two-layered algorithm, where the variable neighborhood search (VNS) is purposed to search auction allocations in the upper layer and tabu search (TS) is conducted to generate efficient vehicle routes in the lower layer. The capability of our algorithm is demonstrated by an extensive computational study, comparing with a number of state-of-the-art exact and heuristic approaches. Finally, a number of managerial implications for the practitioners are obtained, with insights into the impacts caused by changes in market size, task complexity, customer distribution pattern, and auction frequency. [ABSTRACT FROM AUTHOR]

Highlights • An e-commerce city logistics problem with synchronization (sync) requirements is studied. • The sliding time window (STW) is adopted to model the sync requirements. • A divide and conquer algorithm is developed to solve the proposed VRP with partially unknown time constraints. Abstract The cost-effective and high-quality city logistics service is a key for the success of e-commerce enterprises. Synchronization (sync) is emerging as a typical yet challenging requirement, which asks for simultaneous deliveries of multiple products to the same customer. This paper is among the first to model the sync constraints with sliding time windows (STW). STW is a special type of time window of which only the window size is defined. Unlike traditional time windows, the start and end time of STW could be adjusted earlier or later, so long as their difference equals to the pre-defined window size. In this sense, the STW is a time constraint with partially unknown factors. Such flexibility of STW can greatly improve the efficiency of vehicle tours, because customers could be served in a more flexible sequence decided by the transporter. A novel divide and conquer based algorithm is developed to tackle the proposed problem with partially unknown time constraints. The values for STWs will dynamically be determined during the algorithm. Numerical studies show that by modelling sync requirements with STW, transportation cost could be saved as much as in half. We also carry out sensitivity analyses on the key factors such as promised sync level to customers and the complexity of online orders. [ABSTRACT FROM AUTHOR]

The aim of route optimization system (ROS) is to design a set of vehicle routes to fulfill transportation demands, in an attempt to minimize cost and/or other negative social and environmental impacts. ROS, established based on the fruitful studies of vehicle routing problem (VRP), has been applied in various industries and forms. During daily operations, dynamic traffic conditions, varying restriction policies, road constructions, drivers' progressing familiarity with the routes and destinations are all common factors affecting the performance of ROS. However, most current systems are designed in a one-way and open-loop manner, i.e. these systems do not track how the planned vehicle routes are performed, which hinders the continuous improvement of the system and would lead to the failure of the system. This study proposes a smart product-service system (SPSS) approach to design an IoT-based ROS, arguing that the product (i.e. the ROS) and services (updating base data and learning users' behaviors automatically to optimize the system) should be designed as a bundle. For this end, IoT devices are employed to acquire real-time information and feedbacks of vehicles and drivers, which are used to assess the execution of planned routes and dynamically modify the base data. Moreover, the driving records from IoT devices reveal drivers' improving familiarity with routes and destinations, which will be considered to optimize the assignment of routes to drivers. Finally, we use a case of retailing industry to show the advantages of the proposed SPSS approach. [ABSTRACT FROM AUTHOR]