Prompting transdisciplinary research: Promising futures for using the performance metaphor in research

Transdisciplinary research is increasingly recognised as important for investigating and addressing ‘wicked’ problems such as climate change, food insecurity and poverty, but is far from commonplace. There are structural impediments to transdisciplinarity such as university structures, publication requirements and funding preferences that perpetuate disciplinary differences and researchers often lack transdisciplinary experience and expertise. In this paper we present a heuristic that aims to encourage researchers to think about their current research as performance and then imagine different performances, with the view to encouraging reflection and creativity about the transdisciplinary potential and dilemmas. The heuristic is inspired by the metaphor of performance that Erving Goffman uses to understand everyday, face-to-face interactions. The heuristic includes scaffolding for imagining research as performance through a transdisciplinary lens, a suggested process for using the tool, and examples based on the every day research projects. The paper describes the application of the heuristic in a graduate masterclass, reflecting on whether it does indeed ‘prompt’ transdisciplinary research. Limitations and lessons learned for further refinement of the heuristic are also included. The authors conclude that the heuristic has a range of uses including for self-reflection, and as a practical learning tool that can also be used at the start of integrative research projects.     

A hybrid parallel genetic algorithm for yard crane scheduling

This paper aims at postulating a novel strategy in terms of yard crane scheduling. In this study, a dynamic scheduling model using objective programming for yard cranes is initially developed based on rolling-horizon approach. To resolve the NP-complete problem regarding the yard crane scheduling, a hybrid algorithm, which employs heuristic rules and parallel genetic algorithm (PGA), is then employed. Then a simulation model is developed for evaluating this approach. Finally, numerical experiments on a specific container terminal yard are used for system illustration. Computational results suggest that the proposed method is able to solve the problem efficiently.     

Two-stage flowshop earliness and tardiness machine scheduling involving a common due window

This paper studies a non-preemptive two-stage flowshop scheduling problem to minimize the earliness and tardiness under the environment of a common due window. The window size and the window location are considered to be given parameters. The just-in-time problem exists naturally and has many practical applications. The problem is shown to be NP-complete in the strong sense. We develop a branch and bound algorithm and a heuristic to solve the problem. We conduct the computational experiments to test the performances of the algorithms. A strong lower bound is derived for the branch and bound algorithm that can efficiently solve 15 jobs problem for about 5 minutes. The heuristic is shown to be efficient and effective, which can solve the problem of 150 jobs for about 20 seconds and provide near-optimal solution. We justify that the heuristic is an excellent solution approach for large problem instances. We also show that four special cases are either polynomial solvable or NP-complete in the ordinary sense.     

Scheduling jobs with values dependent on their completion times

The paper concerns two scheduling problems with job values and losses of job values (costs) dependent on job completion times. In the first problem, we consider scheduling jobs with stepwise values in parallel processor environment. In the stepwise value, there is given a number of moments at which the job value decreases and between them the job value is constant (thus, the value deteriorates over time). The maximized criterion is the total job value. We prove strong NP-hardness of a single processor case of the problem and construct a pseudo-polynomial time algorithm for a special case with fixed number of unrelated parallel processors and fixed number of common moments of job value changes. Additionally, for uniform and unrelated parallel processors we construct and experimentally test several heuristic algorithms based on the list strategy. The second problem is a single processor one with piecewise linear losses of job values (the loss increases over time). The minimized criterion is the total loss of job value. We prove strong NP-hardness of the problem and existence of a pseudo-polynomial time exact algorithm for its special case. We also construct some heuristic algorithms for this problem and verify experimentally their efficiency.     

基于时段偏好的高速铁路列车停站方案双层规划方法

旅客出行需求不断提高,需要同时考虑旅客出行时段选择与运输企业效益,来优化调整高速铁路列车停站方案。针对一条拥挤的高速铁路客运走廊,分时段确定列车停站计划和开行频率,阐述影响旅客出行时段偏好的2个重要因素——吸引度与可达度,据此构建旅客出行阻抗函数,构建双层规划模型,上层规划是以运营成本最小为目标的整数规划模型,用于确定列车停站方案;下层规划是一个用户平衡模型,用于计算客流在停站方案上的分配结果。根据模型特点设计启发式算法,并通过算例对模型和算法进行验证和分析。研究表明,考虑旅客出行时段偏好优化高速铁路列车停站方案的方法,能更好地匹配旅客需求分布,为旅客提供优质服务。     

阿德兰启发式算法在加油站选址中的应用

随着成品油销售市场的放开,成品油销售市场竞争日趋激烈。围绕加油站选址问题,分析了加油站选址的影响因素和选址方法方面的研究成果,认为影响加油站竞争能力的关键因素是客户加油成本。在此基础上,提出了用阿德兰启发式算法计算客户加油成本的选址方法。     

A GRASP algorithm for the container stowage slot planning problem

This work presents a generalization of the Slot Planning Problem which raises when the liner shipping industry needs to plan the placement of containers within a vessel (stowage planning). State-of-the-art stowage planning relies on a heuristic decomposition where containers are first distributed in clusters along the vessel. For each of those clusters a specific position for each container must be found. Compared to previous studies, we have introduced two new features: the explicit handling of rolled out containers and the inclusion of separations rules for dangerous cargo. We present a novel integer programming formulation and a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. The approach is able to find high-quality solution within 1 s. We also provide comparison with the state-of-the-art on an existing and a new set of benchmark instances.     

The joint replenishment and freight consolidation of a warehouse in a supply chain

We have developed joint replenishment and consolidated freight delivery policies for a third party warehouse that handles multiple items, which have deterministic demand rates in a supply chain. Two policies are proposed and mathematical models are developed to obtain the optimal parameters for the proposed policies. Four efficient algorithms are presented to solve the mathematical models for the two policies. The performances of the two policies with the parameters obtained from the proposed algorithms are then compared with the common cycle approach for 1600 randomly generated problems. The results show the robust performance of the proposed algorithm for both policies.     

Stochastic Optimization: a Review

We review three leading stochastic optimization methods—simulated annealing, genetic algorithms, and tabu search. In each case we analyze the method, give the exact algorithm, detail advantages and disadvantages, and summarize the literature on optimal values of the inputs. As a motivating example we describe the solution—using Bayesian decision theory, via maximization of expected utility—of a variable selection problem in generalized linear models, which arises in the cost-effective construction of a patient sickness-at-admission scale as part of an effort to measure quality of hospital care.     

基于启发式算法的物流配送中心选址研究

目前,物流已成为企业第三利润源泉,是现代化生产的重要组成部分,合理的物流网络规划对企业至关重要。其中,配送中心作为联结着需求点和资源点的桥梁,在整个物流网络中起着举足轻重的作用,配送中心的选址问题更是备受关注,精准的选址方案可以提高物流网络的整体效益,节约企业的运作成本。