An evolutionary approach to solving a new integrated quay crane assignment and quay crane scheduling mathematical model

This paper puts forward an integrated optimisation model that combines two distinct problems arising in container terminals, namely the Quay Crane Assignment Problem, and the Quay Crane Scheduling Problem. The model is of the mixed-integer programming type with the objective being to minimise the tardiness of vessels. Although exact solutions can be found to the problem using Branch-and-Cut, for instance, they are costly in time when instances are of realistic sizes. To overcome the computational burden of large scale instances, an adapted Genetic Algorithm, is used. Small to medium size instances of the combined model have been solved with both the Genetic Algorithm and the CPLEX implementation of Branch-and-Cut. Larger size instances, however, could only be solved approximately in acceptable times with the Genetic Algorithm. Computational results are included and discussed

A column generation based decomposition and aggregation approach for combining orders in inland transportation of containers

A significant portion of the total cost of the intermodal transportation is generated from the inland transportation of containers. In this paper, we design a Mixed Integer Linear Programming (MILP) model for combining orders in the inland, haulage transportation of containers. The pickup and delivery process of both 20 and 40 foot containers from the terminals to the customer locations and vice versa are optimized using heterogeneous fleet consisting of both 20ft and 40ft trucks/chasses. Important operational constraints such as the time window at order receivers, the payload weight of containers and the regulation of the working hours are considered. Based on an assignment problem structure, this MILP solves efficiently to optimality for problems with up to 120 orders. To deal with larger instances, a decomposition and aggregation heuristic is designed. The basic idea of this approach is to decompose order locations geographically into fanshaped sub-areas based on the angle of the order location to the port baseline, and solve the sub problems using the proposed MILP model. To balance the fleet size amongst all subgroups, column generation is used to iteratively adjust the number of allocated trucks according to the shadow-price of each truck type. Based on decomposed solutions, orders that are "fully" combined with others are removed and an aggregation phase follows to enable wider combination choices across subgroups. The decomposition and aggregation solution process is tested to be both efficient and cost-saving

A rolling-horizon dynamic programming approach for collaborative caching

In this paper, we study the online collaborative content caching problem from network economics point of view. The network consists of small cell base stations (SCBSs) with limited cache capacity and a macrocell base station (MCBS). SCBSs are connected with their neighboring SCBSs through high-speed links and collaboratively decide what data to cache. Contents are placed at the SCBSs "free of charge" at off-peak hours and updated during the day according to the content demands by considering the network usage cost. We first model the caching optimization as a finite horizon Markov Decision Process that incorporates an auto-regressive model to forecast the evolution of the content demands. The problem is NP-hard and the optimal solution can be found only for a small number of base stations and contents. To allow derivation of close to optimal solutions for larger networks, we propose the rolling horizon method, which approximates future network usage cost by considering a small decision horizon. The results show that the rolling horizon approach outperforms comparison schemes significantly. Finally, we examine two simplifications of the problem to accelerate the speed of the solution: (a) we restrict the number of content replicas in the network and (b) we limit the allowed content replacements. The results show that the rolling horizon scheme can reduce the communication cost by over 84% compared to that of running Least Recently Used (LRU) updates on offline schemes. The results also shed light on the tradeoff between the efficiency of the caching policy and the time needed to run the online algorithm

Approximate dynamic programming with B�zier Curves/Surfaces for Top-percentile Traffic Routing

Multi-homing is used by Internet Service Providers (ISPs) to connect to the Internet via different network providers. This study develops a routing strategy under multi-homing in the case where network providers charge ISPs according to top-percentile pricing (i.e. based on the ?th highest volume of traffic shipped). We call this problem the Top-percentile Traffic Routing Problem (TpTRP). Solution approaches based on Stochastic Dynamic Programming require discretization in state space, which introduces a large number of state variables. This is known as the curse of dimensionality in state space. To overcome this, in previous work we have suggested to use approximate dynamic programming (ADP) to construct value function approximations, which allow us to work in continuous state space. The resulting ADP model provides well performing routing policies for medium sized instances of the TpTRP. In this work we extend the ADP model, by using B�zier Curves/Surfaces to obtain continuous-time approximations of the time-dependent ADP parameters. This modification reduces the number of regression parameters to estimate, and thus accelerates the efficiency of parameter training in the solution of the ADP model, which makes realistically sized TpTRP instances tractable. We argue that our routing strategy is near optimal by giving bounds

An approximate dynamic programming approach for collaborative caching

In this article, online collaborative content caching in wireless networks is studied from a network economics point of view. The cache optimization problem is first modelled as a finite horizon Markov decision process that incorporates an auto-regressive model to forecast the evolution of the content demands. The complexity of the problem grows exponentially with the system parameters, and even though a good approximation to the cost-to-go can be found, the single-stage decision problem is still NP-hard. To deal with cache optimization in industrial-size networks, a novel methodology called rolling horizon is proposed that solves the dimensionality of the problem by freezing the cache decisions for a short number of periods to construct a value function approximation. Then, to address the NP-hardness of the single-stage decision problem, two simplifications/reformulations are examined: (a) to limit the number of content replicas in the network and (b) to limit the allowed content replacements. The results show that the proposed approach can reduce the communication cost by over 84% compared to that of running least recently used updates on offline schemes in collaborative caching. The results also shed light on the trade-off between the efficiency of the caching policy and the time needed to run the online cache optimization algorithm

Photonic realization of topologically protected bound states in domain-wall waveguide arrays

We present an analytical theory of topologically protected photonic states for the two-dimensional Maxwell equations for a class of continuous periodic dielectric structures, modulated by a domain wall. We further numerically confirm the applicability of this theory for three-dimensional structures.Comment: 6 pages, 5 figures. To appear in the Phys. Rev.

Bridging Cancer Biology with the Clinic: Relative Expression of a <i>GRHL2</i>-Mediated Gene-Set Pair Predicts Breast Cancer Metastasis

Identification and characterization of crucial gene target(s) that will allow focused therapeutics development remains a challenge. We have interrogated the putative therapeutic targets associated with the transcription factor Grainy head-like 2 (GRHL2), a critical epithelial regulatory factor. We demonstrate the possibility to define the molecular functions of critical genes in terms of their personalized expression profiles, allowing appropriate functional conclusions to be derived. A novel methodology, relative expression analysis with gene-set pairs (RXA-GSP), is designed to explore the potential clinical utility of cancer-biology discovery. Observing that Grhl2-overexpression leads to increased metastatic potential in vitro, we established a model assuming Grhl2-induced or -inhibited genes confer poor or favorable prognosis respectively for cancer metastasis. Training on public gene expression profiles of 995 breast cancer patients, this method prioritized one gene-set pair (GRHL2, CDH2, FN1, CITED2, MKI67 versus CTNNB1 and CTNNA3) from all 2717 possible gene-set pairs (GSPs). The identified GSP significantly dichotomized 295 independent patients for metastasis-free survival (log-rank tested p = 0.002; severe empirical p = 0.035). It also showed evidence of clinical prognostication in another independent 388 patients collected from three studies (log-rank tested p = 3.3e–6). This GSP is independent of most traditional prognostic indicators, and is only significantly associated with the histological grade of breast cancer (p = 0.0017), a GRHL2-associated clinical character (p = 6.8e–6, Spearman correlation), suggesting that this GSP is reflective of GRHL2-mediated events. Furthermore, a literature review indicates the therapeutic potential of the identified genes. This research demonstrates a novel strategy to integrate both biological experiments and clinical gene expression profiles for extracting and elucidating the genomic impact of a novel factor, GRHL2, and its associated gene-sets on the breast cancer prognosis. Importantly, the RXA-GSP method helps to individualize breast cancer treatment. It also has the potential to contribute considerably to basic biological investigation, clinical tools, and potential therapeutic targets.</p

An efficient mixed integer programming model for pairing containers in inland transportation based on the assignment of orders

The inland transportation takes a significant portion of the total cost that arises from intermodal transportation. In addition, there are many parties (shipping lines, haulage companies, customers) who share this operation as well as many restrictions that increase the complexity of this problem and make it NP-hard. Therefore, it is important to create an efficient strategy to manage this process in a way to ensure all parties are satisfied. This paper investigates the pairing of containers/orders in drayage transportation from the perspective of delivering paired containers on 40-ft truck and/or individual containers on 20-ft truck, between a single port and a list of customer locations. An assignment mixed integer linear programming model is formulated, which solves the problem of how to combine orders in delivery to save the total transportation cost when orders with both single and multiple destinations exist. In opposition to the traditional models relying on the vehicle routing problem with simultaneous pickups and deliveries and time windows formulation, this model falls into the assignment problem category which is more efficient to solve on large size instances. Another merit for the proposed model is that it can be implemented on different variants of the container drayage problem: import only, import–inland and import–inland–export. Results show that in all cases the pairing of containers yields less cost compared to the individual delivery and decreases empty tours. The proposed model can be solved to optimality efficiently (within half hour) for over 300 orders