Comparing Industry and Academic Perspectives on Cross-docking Operations

This paper performs a comparative analysis on the industry and academic perspectives on cross-docking operations. Detailed descriptions are provided for three typical cross-dock settings by means of case illustrations. The purpose of these descriptions is to inspire break-through innovations in future cross-docking research by identifying constraints, decision problems, and performance indicators that are thoroughly anchored in current practice

Maritime Location Decisions for Lng Bunkering Facilities

Liquefied natural gas (LNG) is one of the upcoming fuels to be used for more sustainable shipping activities in the maritime sector. For a widespread adoption by end-users, a refuelling network requiring capital intensive investments, needs to be in place. From a macro perspective of suppliers of LNG, it makes sense to develop the infrastructure at strategic locations that capture as many vessels and ships as possible. The goal of this research is to develop a facility location model that can contribute to the location selection of LNG facilities in a new fuelling network. The new model will fit the maritime sector and specifically the LNG transition. Experiments, with data obtained from expert interviews, have been performed to study facility location decisions in the North Sea areas

Assessment approaches to logistics for offshore wind energy installation

AbstractOffshore wind farm installation planning is highly complex, due to the high dependency on weather and the oversized components that impose specific constraints in areas such as transportation and lifting. Currently, there is very little transparency vis-à-vis the logistics challenges in the industry. We extend the literature by creating an overall view of the coherency between logistical methods and project performance. We develop knowledge about how to use the various approaches by analyzing different logistical solutions. A holistic view of the coherency between the approaches in terms of logistics and project performance, taking into consideration the external influence of weather, is provided through analysis of actual projects in the North Sea region. Case study findings reveal the major factors to be pre-assembly, vessel load, and the distance to shore. We suggest a pre-assembly strategy comprised of a minimum number of components for installation onsite and a maximum number of turbines to be loaded on a vessel. These findings are especially important for the new wind farms being positioned further offshore. We show by means of a case study, with specific characteristics and weather conditions, that the appropriate strategies can be arrived at by using a simulation-based decision-support tool we developed

The pickup and delivery traveling salesman problem with handling costs

This paper introduces the pickup and delivery traveling salesman problem with handling costs (PDTSPH). In the PDTSPH, a single vehicle has to transport loads from origins to destinations. Loading and unloading of the vehicle is operated in a last-in-first-out (LIFO) fashion. However, if a load must be unloaded that was not loaded last, additional handling operations are allowed to unload and reload other loads that block access. Since the additional handling operations take time and effort, penalty costs are associated with them. The aim of the PDTSPH is to find a feasible route such that the total costs, consisting of travel costs and penalty costs, are minimized. We show that the PDTSPH is a generalization of the pickup and delivery traveling salesman problem (PDTSP) and the pickup and delivery traveling salesman problem with LIFO loading (PDTSPL). We propose a large neighborhood search (LNS) heuristic to solve the problem. We compare our LNS heuristic against best known solutions on 163 benchmark instances for the PDTSP and 42 benchmark instances for the PDTSPL. We provide new best known solutions on 52 instances for the PDTSP and on 15 instances for the PDTSPL, besides finding the optimal or best known solution on 102 instances for the PDTSP and on 23 instances for the PDTSPL. The LNS finds optimal or near-optimal solutions on instances for the PDTSPH. Results show that PDTSPH solutions provide large reductions in handling compared to PDTSP solutions, increasing the travel distance by only a small percentage

A dynamic thompson sampling hyper-heuristic framework for learning activity planning in personalized learning

Personalized learning is emerging in schools as an alternative to one-size-fits-all education. This study introduces and explores a weekly demand-driven flexible learning activity planning problem of own-pace own-method personalized learning. The introduced problem is a computationally intractable optimization problem involving many decision dimensions and also many soft constraints. We propose batch and decomposition methods to generate good-quality initial solutions and a dynamic Thompson sampling based hyper-heuristic framework, as a local search mechanism, which explores the large solution space of this problem in an integrative way. The characteristics of our test instances comply with average secondary schools in the Netherlands and are based on expert opinions and surveys. The experiments, which benchmark the proposed heuristics against Gurobi MIP solver on small instances, illustrate the computational challenge of this problem numerically. According to our experiments, the batch method seems quicker and also can provide better quality solutions for the instances in which resource levels are not scarce, while the decomposition method seems more suitable in resource scarcity situations. The dynamic Thompson sampling based online learning heuristic selection mechanism is shown to provide significant value to the performance of our hyper-heuristic local search. We also provide some practical insights; our experiments numerically demonstrate the alleviating effects of large school sizes on the challenge of satisfying high-spread learning demands

An adaptive large neighbourhood search metaheuristic for hourly learning activity planning in personalised learning

Personalised learning offers an alternative method to one-size-fits-all education in schools, and has seen increasing adoption over the past several years. Personalised learning’s focus on learner-driven education requires novel scheduling methods. In this paper we introduce the hourly, learner-driven activity planning problem of personalised learning, and formulate scheduling methods to solve it. We present an integer linear programming model of the problem, but this model does not generate schedules sufficiently quickly for use in practice. To overcome this, we propose an adaptive large neighbourhood search metaheuristic to solve the problem instead. The metaheuristic’s performance is compared against optimal solutions in a large numerical study of 14,400 instances. These instances are representative of secondary education in the Netherlands, and were developed from expert opinions. Solutions on average deviate only 1.6% from optimal results. Further, our experiments numerically demonstrate the mitigating effects changes to the structure and staffing of secondary education have on the challenges of satisfying learner instruction demands in personalised learning

Control Policies for a Dynamic Storage System With Multiple Lifts and Shuttles

New types of Automated Storage and Retrieval Systems, able to achieve high throughput levels, are continuously being developed and require new control polices to take full advantage of the developed system. In this paper we study a dynamic storage system as developed by Vanderlande Industries consisting of a conveyor, two lifts, multiple transfer shuttles, and a storage rack. One of the decision problems for this system is the scheduling problem of the two lifts. In other words, which lift is going to handle which request and in which order. In this paper, we derive an integrated look-ahead heuristic based on enumeration to simultaneously assign a set of pre-defined requests to the lifts and to schedule the lifts. As main performance measure we use the total time required to serve all requests

A Comparison of Priority Rules for Non-passing Automated Stacking Cranes

A recent trend in container ports is to operate dual non-passing Automated Storage Cranes (ASCs) that collaborate to serve storage and retrieval requests from opposite ends of a storage block. Since the ASCs are unable to pass each other, there is an exchange zone that serves as a temporary storage location so that one crane can start a request and leave it to the other crane to complete it. In this study, twelve priority rules are introduced and evaluated to determine which rule minimizes the total makespan for serving all requests, given the sequence in which each ASC will serve the requests. Preliminary results from 12 randomly generated experiments indicate that the priority rules favoring the crane furthest away from the origin of the next request (LonOri) and the longest individual completion times (LonTot) outperformed all other rules in terms of the average percent difference with the best found solution and in terms of the percent of times the priority rule yield the best found solution. Also, combining priority rules AdvFun and LonRem yields the best makespan in 11 of the 12 (91.67%) problem instances tested. Results of this study transcend container ports as it is applicable to any material handling system composed of non-passing MHE and that has pickup/deposit points at the ends of the system

A solution approach for deriving alternative fuel station infrastructure requirements

When an alternative fuel is introduced, the infrastructure through which that fuel is made available to the market is often underdeveloped. Transportation service providers relying on such infrastructures are unlikely to adopt alternative fuel vehicles as it may impose long detours for refueling. In this paper, we design and apply a new solution approach to derive minimum infrastructure requirements, in terms of the number of alternative fuel stations. The effectiveness of our approach is demonstrated by applying it to the case of introducing liquefied natural gas (LNG) as a transportation fuel in The Netherlands. From this case, we learn that, depending on the driving range of the LNG trucks and the size of area on which those trucks operate, a minimum of 5-12 LNG fuel stations is necessary to render LNG trucks economically and environmentally beneficial

An inventory control policy for liquefied natural gas as a transportation fuel

In this paper, we study a novel stochastic inventory management problem that arises in storage and refueling facilities for Liquefied Natural Gas (LNG) as a transportation fuel. In this inventory problem, the physio-chemical properties of LNG play a key role in the design of inventory policies. These properties are: (1) LNG suffers from both quantity decay and quality deterioration and (2) the quality of on-hand LNG can be upgraded by mixing it with higher-quality LNG. Given that LNG quality can be upgraded, an inventory control policy for this problem needs to consider the removal of LNG as a decision variable. We model and solve the problem by means of a Markov Decision Process (MDP) and study the structural characteristics of the optimal policy. The insights obtained in the analysis of the optimal policy are translated into a simple, though effective, inventory control policy in which actions (i.e., replenishment and/or removal) are driven by both the quality and the quantity of the inventories. We assess the performance of our policy by means of a numerical study and show that it performs close to optimal in many numerical instances. The main conclusion of our study is that it is important to take quality into consideration when design inventory control policies for LNG, and that the most effective way to cope with quality issues in an LNG inventory system involves both the removal and the replenishment of inventories