Ruibin Bai

An Investigation of Automated Planograms Using a Simulated Annealing Based Hyper-Heuristic

This paper formulates the shelf space allocation problem as a non-linear function of the product net profit and store-inventory. We show that this model is an extension of multi-knapsack problem, which is itself an NP-hard problem. A two-stage relaxation is carried out to get an upper bound of the model. A simulated annealing based hyper-heuristic algorithm is proposed to solve several problem instances with different problem sizes and space ratios. The results show that the simulated annealing hyper-heuristic significantly outperforms two conventional simulated annealing algorithms and other hyper-heuristics for all problem instances. The experimental results show that our approach is a robust and efficient approach for the shelf space allocation problem.

A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Nurse rostering is an important search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimization benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better at finding feasible solutions, but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridize it with a recently proposed simulated annealing hyper-heuristic (SAHH) within a local search and genetic algorithm framework. Computational results show that the hybrid algorithm performs better than both the genetic algorithm with stochastic ranking and the SAHH alone. The hybrid algorithm also outperforms the methods in the literature which have the previously best known results.

A Simulated Annealing Hyper-heuristic Methodology for Flexible Decision Support

Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are generally applicable to more problems. One of our motivating goals for investigating hyper-heuristic methodologies is to provide a more general search framework that can be easily and automatically employed on a broader range of problems than is currently possible. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark datasets drawn from two very different and difficult problems, namely; course timetabling and bin packing. The contribution of this paper is to present a method which can be readily (and automatically) applied to different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.

Heuristic, meta-heuristic and hyper-heuristic approaches for fresh produce inventory control and shelf space allocation

The allocation of fresh produce to shelf space represents a new decision support research area which is motivated by the desire of many retailers to improve their service due to the increasing demand for fresh food. However, automated decision making for fresh produce allocation is challenging because of the very short lifetime of fresh products. This paper considers a recently proposed practical model for the problem which is motivated by our collaboration with Tesco. Moreover, the paper investigates heuristic and meta-heuristic approaches as alternatives for the generalized reduced gradient algorithm, which becomes inefficient when the problem size becomes larger. A simpler single-item inventory problem is firstly studied and solved by a polynomial time bounded procedure. Several dynamic greedy heuristics are then developed for the multi-item problem based on the procedure for the single-item inventory problem. Experimental results show that these greedy heuristics are much more efficient and provide competitive results when compared to those of a multi-start generalized reduced gradient algorithm. In order to further improve the solution, we investigated simulated annealing, a greedy randomized adaptive search procedure and three types of hyper-heuristics. Their performance is tested and compared on a set of problem instances which are made publicly available for the research community.

Good Laboratory Practice for Optimization Research

Good Laboratory Practice has been a part of non-clinical research for over 40 years. Optimization Research, despite having many papers discussing standards being published over the same period of time, has yet to embrace standards that underpin its research. In this paper we argue the need to adopt standards in optimization research. Building on previous papers, many of which have suggested that the optimization research community should adopt certain standards, we suggest a concrete set of recommendations that the community should adopt. We also discuss how the proposals in this paper could be progressed.

Memory Length in Hyper-heuristics: An Empirical Study

Hyper-heuristics are an emergent optimisation methodology which aims to give a higher level of flexibility and domain-independence than is currently possible. Hyper-heuristics are able to adapt to the different problems or problem instances by dynamically choosing between heuristics during the search. This paper is concerned with the issues of memory length on the performance of hyper-heuristics. We focus on a recently proposed simulated annealing hyper-heuristic and choose a set of hard university course timetabling problems as the test bed for this empirical study. The experimental results show that the memory length can affect the performance of hyper-heuristics and a good choice of memory length is able to improve solution quality. Finally, two dynamic approaches are investigated and one of the approaches is shown to be able to produce promising results without introducing extra sensitive algorithmic parameters.

Population Diversity Maintenance In Brain Storm Optimization Algorithm

Abstract The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithms exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.

Swarm Intelligence in Big Data Analytics

This paper analyses the difficulty of big data analytics problems and the potential of swarm intelligence solving big data analytics problems. Nowadays, the big data analytics has attracted more and more attentions, which is required to manage immense amounts of data quickly. However, current researches mainly focus on the amount of data. In this paper, the other three properties of big data analytics, which include the high dimensionality of data, the dynamical change of data, and the multi-objective of problems, are discussed. Swarm intelligence, which works with a population of individuals, is a collection of nature-inspired searching techniques. It has effectively solved many large-scale, dynamical, and multi-objective problems. Based on the combination of swarm intelligence and data mining techniques, we can have better understanding of the big data analytics problems, and designing more effective algorithms to solve real-world big data analytics problems.

A new model and a hyper-heuristic approach for two-dimensional shelf space allocation

In this paper, we propose a two-dimensional shelf space allocation model. The second dimension stems from the height of the shelf. This results in an integer nonlinear programming model with a complex form of objective function. We propose a multiple neighborhood approach which is a hybridization of a simulated annealing algorithm with a hyper-heuristic learning mechanism. Experiments based on empirical data from both real-world and artificial instances show that the shelf space utilization and the resulting sales can be greatly improved when compared with a gradient method. Sensitivity analysis on the input parameters and the shelf space show the benefits of the proposed algorithm both in sales and in robustness.

A novel approach to independent taxi scheduling problem based on stable matching

This paper describes a taxi scheduling system, which aims to improve the overall efficiency of the system, both from the perspective of the drivers and the customers. This is of particular relevance to Chinese cities, where hailing a taxi on the street is by far the most common way in which taxis are requested, since the majority of taxi drivers operate independently, rather than working for a company. The mobile phone and Global Positioning System-based taxi scheduling system, which is described in this paper, aims to provide a decision support system for taxi drivers and facilitates direct information exchange between taxi drivers and passengers, while allowing drivers to remain independent. The taxi scheduling problem is considered to be a non-cooperative game between taxi drivers and a description of this problem is given. We adopt an efficient algorithm to discover a Nash equilibrium, such that each taxi driver and passenger cannot benefit from changing their assigned partner. Two computational examples are given to illustrate the effectiveness of the approach.