S. Afshin Mansouri

Search-based software engineering: Trends, techniques and applications

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

A search based approach to fairness analysis in requirement assignments to aid negotiation, mediation and decision making

This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space. The paper also reports on experiments to determine the most suitable algorithm for this problem, comparing the results of the NSGA-II algorithms, a widely used multi objective evolutionary algorithm, and the Two-Archive evolutionary algorithm, a recently proposed alternative.

Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption

Sustainability considerations in manufacturing scheduling, which is traditionally influenced by service oriented performance metrics, have rarely been adopted in the literature. This paper aims to address this gap by incorporating energy consumption as an explicit criterion in shop floor scheduling. Leveraging the variable speed of machining operations leading to different energy consumption levels, we explore the potential for energy saving in manufacturing. We analyze the trade-off between minimizing makespan, a measure of service level and total energy consumption, an indicator for environmental sustainability of a two-machine sequence dependent permutation flowshop. We develop a mixed integer linear multi-objective optimization model to find the Pareto frontier comprised of makespan and total energy consumption. To cope with combinatorial complexity, we also develop a constructive heuristic for fast trade-off analysis between makespan and energy consumption. We define lower bounds for the two objectives under some non-restrictive conditions and compare the performance of the constructive heuristic with CPLEX through design of experiments. The lower bounds that we develop are valid under realistic assumptions since they are conditional on speed factors. The Pareto frontier includes solutions ranging from expedited, energy intensive schedules to prolonged, energy efficient schedules. It can serve as a visual aid for production and sales planners to consider energy consumption explicitly in making quick decisions while negotiating with customers on due dates. We provide managerial insights by analyzing the areas along the Pareto frontier where energy saving can be justified at the expense of reduced service level and vice versa.

The lean-performance relationship in services: a theoretical model

Purpose – The extant literature on lean service reveals a noticeable lack of theoretical models establishing the core constructs of lean service, their interrelation and impact on organizational performance. The purpose of this paper is to address this gap by proposing a theoretical model in which lean constructs are identified and operationalized to establish their interrelation and impact on organizational performance. Design/methodology/approach – This paper synthesizes information drawing on a systematic review of the literature on lean service, other relevant academic literature to develop a theoretical model and a set of propositions. Drawing on the universal theory, socio-technical systems theory and contingency theory (CT), the paper highlights and clarifies the potential impact of lean service on operational and financial performance. Findings – This study identifies a comprehensive set of lean technical practices, lean supportive practices, inhibitors and expected outcome of lean service. Expect...

Heuristics for minimizing total completion time and maximum lateness on identical parallel machines with setup times

This paper aims at minimizing the total completion time together with the maximum lateness. Jobs are processed by parallel machines in batches. A setup is required before processing a batch, which is common for all jobs in the batch. Jobs are continuously processed after the setup time. The processing length of a batch is the sum of the setup time and processing times of the jobs it contains. Due to the availability constraint, the completion time of a job is the time when a batch is totally processed. Considering due dates, the jobs need to be processed in a way that the total completion time and the maximum lateness are minimized. This problem is a kind of NP-hard so first we present a constructive heuristic to solve the problem. Then we propose a genetic algorithm whose initial population is formed by using the heuristic approach. Computational experiments are carried out to evaluate the performance of the proposed algorithms.

From disaster to development: A systematic review of community-driven humanitarian logistics

A plethora of untapped resources exist within disaster-affected communities that can be used to address relief and development concerns. A systematic review of the literature relating to community participation in humanitarian logistics activities revealed that communities are able to form ad hoc networks that have the ability to meet a wide range of disaster management needs. These structures, characterised as Collaborative Aid Networks (CANs), have demonstrated efficient logistical capabilities exclusive of humanitarian organisations. This study proposes that CANs, as a result of their unique characteristics, present alternatives to established humanitarian approaches to logistics, while also mitigating the challenges commonly faced by traditional humanitarian organisations. Furthermore, CANs offer a more holistic, long-term approach to disaster management, owing to their impact on development through their involvement in humanitarian logistics. This research provides the foundation for further theoretical analysis of effective and efficient disaster management, and details opportunities for policy and practice.

An exploration into the impact of blogs on students’ learning: case studies in postgraduate business education

The research draws from four case studies to investigate the impact of using blogs within postgraduate education. The study explores how postgraduate business students engage with blogs, whether students’ learning preferences correlate with their degree of contribution and how student participation relates with overall achievement. A mixed methods’ approach is adopted using both qualitative and quantitative data. The findings indicate that blogs can contribute to students’ collective and reflective learning, and emphasise the importance of active feedback from the lecturer. Challenges pertinent to using blogs include the choice of assessment strategies and understanding postgraduate students’ goals and expectations. Statistical analyses revealed significant correlation between students’ degree of contribution and their achievement. No significant correlation was observed between the degree of contribution and students’ learning preferences.

MTAP-MaSim: A Multi-agent Simulator for the Mobile Task Allocation Problem

While two main approaches, namely the centralized and the market-based approaches, have been used and compared in the literature for task allocation problems, uncertainty and dynamism imposed in the Mobile Task Allocation Problem (MTAPs) require new techniques for comparing these two approaches due to the limitations of traditional mathematical modeling techniques. This paper proposes an in-progress work on a multi-agent based simulator (MTAP-MaSim) to compare centralized and market based approaches for the MTAP. The fundamental features and structure of MTAPs are presented and the simulation model of MTAP-MaSim is described. Then the basic random models for both approaches are compared to test MTAP-MaSim feasibility. Finally the future research direction is proposed to conclude the paper.

Minimizing energy consumption and makespan in a two-machine flowshop scheduling problem

Energy consumption has become a key concern for manufacturing sector because of negative environmental impact of operations. We develop constructive heuristics and multi-objective genetic algorithms (MOGA) for a two-machine sequence-dependent permutation flowshop problem to address the trade-off between energy consumption as a measure of sustainability and makespan as a measure of service level. We leverage the variable speed of operations to develop energy-efficient schedules that minimize total energy consumption and makespan. As minimization of energy consumption and minimization of makespan are conflicting objectives, the solutions to this problem constitute a Pareto frontier. We compare the performance of constructive heuristics and MOGAs with CPLEX and random search in a wide range of problem instances. The results show that MOGAs hybridized with constructive heuristics outperform regular MOGA and heuristics alone in terms of quality and cardinality of Pareto frontier. We provide production planners with new and scalable solution techniques that will enable them to make informed decisions considering energy consumption together with service objectives in shop floor scheduling.

Hybrid Genetic Algorithms for the Lot Production and Delivery Scheduling Problem in a Two-Echelon Supply Chain

This chapter addresses integrated production and delivery scheduling of several items in a two-echelon supply chain. A single supplier produces the items on a flexible flow line (FFL) under a cyclic policy and delivers them directly to an assembly facility over a finite planning horizon. A new mixed zero-one nonlinear programming model is developed, based on the basic period (BP) policy to minimize average setup, inventory-holding and delivery costs per unit time where stock-out is prohibited. This problem has not yet been addressed in literature. It is computationally complex and has not been solved optimally especially in real-sized problems. Two efficient hybrid genetic algorithms (HGA) are proposed using the power-of-two (PT-HGA) and non-power-of-two (NPT-HGA) policies. The solution’s quality of the proposed algorithms is evaluated and compared with the common cycle approach in a number of randomly generated problem instances. Numerical experiments demonstrate the merit of the NPT-HGA and indicate that it constitutes a very promising solution method for the problem.