Designing difficult office space allocation problem instances with mathematical programming

Office space allocation (OSA) refers to the assignment of room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional constraints. In this paper, a mathematical programming approach is developed to model and generate test instances for this difficult and important combinatorial optimisation problem. Systematic experimentation is then carried out to study the difficulty of the generated test instances when the parameters for adjusting space misuse (overuse and underuse) and constraint violations are subject to variation. The results show that the difficulty of solving OSA problem instances can be greatly affected by the value of these parameters

An adaptive evolutionary multi-objective approach based on simulated annealing

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search towards the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well-established multi-objective metaheuristic algorithms on both the (constrained)multi-objective knapsack problemand the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated

A 0/1 integer programming model for the office space allocation problem

We propose a 0/1 integer programming model to tackle the office space allocation (OSA) problem which refers to assigning room space to a set of entities (people, machines, roles, etc.), with the goal of optimising the space utilisation while satisfying a set of additional requirements. In the proposed approach, these requirements can be modelled as constraints (hard constraints) or as objectives (soft constraints). Then, we conduct some experiments on benchmark instances and observe that setting certain constraints as hard (actual constraints) or soft (objectives) has a significant impact on the computational difficulty on this combinatorial optimisation problem

Diversity-based adaptive genetic algorithm for a workforce scheduling and routing problem

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total operational cost. One of the main obstacles in designing a genetic algorithm for this highly-constrained combinatorial optimisation problem is the amount of empirical tests required for parameter tuning. This paper presents a genetic algorithm that uses a diversity-based adaptive parameter control method. Experimental results show the effectiveness of this parameter control method to enhance the performance of the genetic algorithm. This study makes a contribution to research on adaptive evolutionary algorithms applied to real-world problems

A study of genetic operators for the Workforce Scheduling and Routing Problem

The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational scenarios. We investigate a set of genetic operators including problem-specific and well-known generic operators used in related problems. The aim is to conduct an in-depth analysis on their performance on this very constrained scheduling problem. In particular, we want to identify genetic operators that could help to minimise the violation of customer/workers preferences. We also develop two cost-based genetic operators tailored to the WSRP. A Steady State Genetic Algorithm (SSGA) is used in the study and experiments are conducted on a set of problem instances from a real-world Home Health Care scenario (HHC). The experimental analysis allows us to better understand how we can more effectively employ genetic operators to tackle WSRPs

Term frequency with average term occurrences for textual information retrieval

In the context of Information Retrieval (IR) from text documents, the term-weighting scheme (TWS) is a key component of the matching mechanism when using the vector space model (VSM). In this paper we propose a new TWS that is based on computing the average term occurrences of terms in documents and it also uses a discriminative approach based on the document centroid vector to remove less significant weights from the documents. We call our approach Term Frequency With Average Term Occurrence (TF-ATO). An analysis of commonly used document collections shows that test collections are not fully judged as achieving that is expensive and may be infeasible for large collections. A document collection being fully judged means that every document in the collection acts as a relevant document to a specific query or a group of queries. The discriminative approach used in our proposed approach is a heuristic method for improving the IR effectiveness and performance, and it has the advantage of not requiring previous knowledge about relevance judgements. We compare the performance of the proposed TF-ATO to the well-known TF-IDF approach and show that using TF-ATO results in better effectiveness in both static and dynamic document collections. In addition, this paper investigates the impact that stop-words removal and our discriminative approach have on TFIDF and TF-ATO. The results show that both, stopwords removal and the discriminative approach, have a positive effect on both term-weighting schemes. More importantly, it is shown that using the proposed discriminative approach is beneficial for improving IR effectiveness and performance with no information in the relevance judgement for the collection

A study of genetic operators for the Workforce Scheduling and Routing Problem

The Workforce Scheduling and Routing Problem (WSRP) is concerned with planning visits of qualified workers to different locations to perform a set of tasks, while satisfying each task time-window plus additional requirements such as customer/workers preferences. This type of mobile workforce scheduling problem arises in many real-world operational scenarios. We investigate a set of genetic operators including problem-specific and well-known generic operators used in related problems. The aim is to conduct an in-depth analysis on their performance on this very constrained scheduling problem. In particular, we want to identify genetic operators that could help to minimise the violation of customer/workers preferences. We also develop two cost-based genetic operators tailored to the WSRP. A Steady State Genetic Algorithm (SSGA) is used in the study and experiments are conducted on a set of problem instances from a real-world Home Health Care scenario (HHC). The experimental analysis allows us to better understand how we can more effectively employ genetic operators to tackle WSRPs

Hyper-volume evolutionary algorithm

We propose a multi-objective evolutionary algorithm (MOEA), named the Hyper-volume Evolutionary Algorithm (HVEA). The algorithm is characterised by three components. First, individual fitness evaluation depends on the current Pareto front, specifically on the ratio of its dominated hyper-volume to the current Pareto front hyper-volume, hence giving an indication of how close the individual is to the current Pareto front. Second, a ranking strategy classifies individuals based on their fitness instead of Pareto dominance, individuals within the same rank are non-guaranteed to be mutually non-dominated. Third, a crowding assignment mechanism that adapts according to the individual’s neighbouring area, controlled by the neighbouring area radius parameter, and the archive of non-dominated solutions. We perform extensive experiments on the multiple 0/1 knapsack problem using different greedy repair methods to compare the performance of HVEA to other MOEAs including NSGA2, SEAMO2, SPEA2, IBEA and MOEA/D. This paper shows that by tuning the neighbouring area radius parameter, the performance of the proposed HVEA can be pushed towards better convergence, diversity or coverage and this could be beneficial to different types of problems

Comparing hybrid constructive heuristics for university course timetabling

This extended abstract outlines four hybrid heuristics to generate initial solutions to the University course timetabling problem. These hybrid approaches combine graph colouring heuristics and local search in different ways. Results of experiments using two benchmark datasets from the literature are presented. All the four hybrid initialisation heuristics described here are capable of generating feasible initial timetables for all the test problems considered in these experiments

A development and integration framework for optimisation-based enterprise solutions

The operations research literature includes some papers describing collaborative work between researchers and industry. However, not much literature exists that outlines methodologies to guide the development of a decision support module and its integration into an existing information management system. Here we describe a framework to aid the collaborative development of an optimisation solution by researchers and information system developers. The proposed framework also helps in the effective integration of the information management system and the decision support module. The framework is divided into three main components: a data model, a data extractor and validator, and a solution visualisation and auxiliary platform. We also describe our experience and positive results from applying the proposed development and integration framework to a project involving the development on an optimisation-based solution for workforce scheduling and optimisation problems. We hope that this contribution would be particularly useful for less experienced researchers and practitioners who embark on a collaborative development of a decision support module based on optimisation techniques