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This paper deals with the problem of routing in-house transport vehicles that feed parts to workstations in assembly plants or workshops just in time. The capacitated vehicles, typically so-called tow trains, perform their assigned route cyclically without break and provide each station with the exact quantity of parts required until the next arrival of the vehicle. Hence, the demand of each station depends on the duration of the route serving the station: The longer the route duration, the less frequently the station is visited and the higher its demand. The goal is to minimize first the number of vehicles and second the total route duration, while respecting given minimum service frequencies at the stations. We provide a mathematical formulation of this novel problem and address it by means of a large neighborhood search. The algorithm is able to solve realistic instances in acceptable time and vastly outperforms a default solver. We discuss two variants of the problem, one in which split deliveries to stations are allowed and another assuming that all stations lie on a straight line. Finally, we investigate the extent to which assuming constant demand rates may lead to problems during the day-to-day operations of the part-feeding system, where demands are not necessarily constant.

In recent years, many OEMs, especially in the automotive industry, have installed so-called supermarkets on their shopfloors to feed parts to assembly lines in a flexible and just-in-time manner. Supermarkets are small logistics areas within the factory where parts are intermediately stored to be transferred, often in the form of presorted kits, to nearby workstations frequently and in small lots. While this greatly alleviates inventory concerns at the assembly line, care must be taken that the supermarket itself always be adequately stocked. In this paper, we tackle the problem of determining when which part types should be taken from central receiving storage to the supermarket in what quantities, such that, on the one hand, shopfloor traffic remains manageable, while, on the other hand, inventory costs are not excessive. We formalize the problem, investigate the computational complexity, and develop a bounding procedure as well as a heuristic decomposition approach. Computational tests show that our procedures work very well on instances of realistic size. Moreover, we study the tradeoff inherent in the problem between delivery frequency and in-process inventory.

This paper treats a berth allocation problem (BAP) in dedicated container terminals where feeder ships and container vessels are jointly served. When assigning quay space and a service time to each calling ship particular focus is put on the container exchange between feeder ships and mother vessels, so that the weighted number of containers delivered by feeder missing their intended mother vessel (and vice versa) does not exceed a given upper bound. The resulting BAP is formalized, complexity proofs are provided, and suited optimization procedures are presented and tested.

We consider the problem of scheduling a set of direct deliveries between a depot and multiple customers using a given heterogeneous truck fleet. The trips have time windows and weights, and they should be completed as soon after release as possible (minimization of maximum weighted flow time). Moreover, some trips can optionally be combined in predefined milk runs (i.e., round trip tours), which need not be linear combinations of the constituent direct trips, accounting, e.g., for consolidation effects because the loading dock needs to be approached only once. This problem has applications, e.g., in just-in-time, humanitarian, and military logistics. We adapt a mixed-integer programming model from the literature to this problem and show that deciding feasibility is NP-complete in the strong sense on three levels: assigning trips to trucks, selecting milk runs, and scheduling trips on each individual truck. We also show that, despite this complexity, a state-of-the-art constraint programming solver and a problem-specific approach based on logic-based Benders decomposition can solve even large instances with up to 175 trips in many cases, while the mixed-integer programming model is essentially unsolvable using commercial optimization software. We also investigate the robustness of the maximum flow time objective in the face of unforeseen delays as well as the influence of milk runs.

The problem of sequencing assembly lines consists of determining the order in which a given set of products is launched down the line. Since individual products may require different parts in different quantities, the production sequence has a big influence on line-side inventory. Classically, sequences are often optimized with the goal of attaining level schedules, i.e., the part demand should be smooth during the planning horizon. However, this approach does not necessarily work well if parts are delivered at discrete points in time in bulk quantities. In this paper, we consider a production system where bins of parts are delivered periodically by a tow train from a central depot at fixed times. Due to the limited space at the assembly line, the maximum number of bins in stock at any time at any station should be minimal. We propose an exact solution method based on combinatorial Benders decomposition as well as bounding procedures and heuristics for this problem. The algorithms are shown to perform well both on instances from the literature and on new data sets. We also investigate whether classic level scheduling methods are effective at reducing line-side stock in an assembly system supplied by tow train, and to what degree line-side stock can be traded off for more frequent deliveries.

This paper addresses the operational planning problem of assigning orders and pods (i.e., mobile shelves) to picking stations in a multi-level robotic mobile fulfillment system (RMFS), which deals with two issues: deciding on which picking station handles which order, and from which pods to pick the ordered items, considering the limited storage capacity of the pods. Due to the relatively poor space utilization of single-level RMFS warehouses, such systems are often spread over multiple floors in practice. Therefore, we explicitly consider multi-level warehouse layouts with isolated levels (or zones) where a pod can only be brought to a station if both of them are on the same level. We optimize the problem with regard to a multi-criteria objective function that consists of three workload-oriented objectives: we aim to balance the total workload among all pickers, minimize the total order-consolidation effort for the packers, and the pod movement effort for the mobile robots. After formalizing the planning problem as a multi-objective optimization problem, we provide two mixed-integer linear programming models. Additionally, we propose a matheuristic that reduces the model size to the desired granularity so that realistically sized problem instances can be solved within less than four minutes of computation time. Moreover, we derive some managerial insights, such as the impact of the number of warehouse levels and picking waves on the objective values. We find evidence that running the RMFS warehouse in a multi-level facility can substantially compromise the consolidation effort at packing stations since it leads to a higher number of split orders. Furthermore, splitting the planning horizon into multiple short waves can lead to a higher number of pod-to-station assignments and, thus, to a raised pod-movement workload for mobile robots.

The surge of e-commerce has revolutionized distribution channels, escalating from simple single-channel frameworks to complex multi-channel and omni-channel networks. In particular developments in information technology and rising customer expectations have popularized the transition from multi- to omni-channel distribution, where the classic brick-and-mortar stores can also be part of the omni-channel distribution strategy. This evolution poses intricate challenges for manufacturers, especially in the integration and optimization of these channels. Thus, there is a strong need for an in-depth analysis of how manufacturers navigate the transition across diverse distribution channels to meet the varying needs of different customer segments. To this end, we investigate single-, multi-, and omni-channel distribution strategies for the case of a manufacturer selling both standard and customized products to different customer segments with varying preferences. A central contribution of this research is the creation of an integrated optimization model that resolves a location-routing problem, designing a complex and realistic supply chain configuration suitable for an omni-channel distribution system. This model strategically serves to fragmented customer demands through multiple shopping and delivery options. The outcomes of our study indicate that an omni-channel distribution system is a viable approach, capable of serving more customer segments while simultaneously minimizing logistics costs. In addition, we offer a detailed analysis of the cost implications of in-store pickup versus home-delivery options, providing a comprehensive evaluation of their respective impacts on total logistics costs and customer responsiveness.

High hopes are put in electric vehicles to lower global green house gas emissions. From an operational perspective, however, their limited range and the long recharging times add considerable complexity to the decision tasks planning their efficient application. In this context, we treat a problem setting where a single electric vehicle executes transport requests along a straight line, which, for instance, occurs when cranes, automated guided vehicles, or shuttles handle boxes in container terminals. From time to time the vehicle needs to be recharged and, thus, has to visit some charging station also located on the line. We investigate the scheduling of a single electric vehicle, so that the makespan for executing all transport requests is minimized and the vehicle is timely recharged. The solution algorithm developed is then applied to also explore the location planning of charging stations.

This paper addresses scheduling a set of jobs with release dates and deadlines on a set of unrelated parallel machines to minimize some minmax objective. This family of problems has a number of applications, e.g., in discrete berth allocation and truck scheduling at cross docks. We present a novel exact algorithm based on logic-based Benders decomposition as well as a heuristic based on a set partitioning reformulation of the problem. We show how our approaches can be used to deal with additional constraints and various minmax objectives common to the above-mentioned applications, solving a broad class of parallel machine scheduling problems. In a series of computational tests both on instances from the literature and on newly generated ones, our exact method is shown to solve most problems within a few minutes to optimality, while our heuristic can solve particularly challenging instances with tight time windows well in acceptable time.