Multitrip vehicle routing with delivery options: a data-driven application to the parcel industry

To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products

Behavioural operational research: returning to the roots of the OR profession

We witness and welcome the resurgence of interest in the study of behavioural issues in the conduct of operational research (OR). The use of the term ‘resurgence’ is deliberate: the consideration of human factors in models and model-supported processes can be traced back to debates in the 1960s and 1970s (e.g. Ackoff, 1977; Churchman, 1970; Dutton & Walton, 1964). However, whilst the socially situated nature of OR in practice has long been recognised (e.g. Keys, 1997), it was not until the wave of recent activity triggered by Hamalainen et al.’s (2013) paper in this journal that the role and impact of behaviour in OR practice regained centrality in academic and practitioners circles alike

CQ-free optimality conditions and strong dual formulations for a special conic optimization problem

In this paper, we consider a special class of conic optimization problems, consisting of set-semidefinite (orK-semidefinite) programming problems, where the setKis a polyhedral convex cone. For these problems, we introduce theconcept of immobile indices and study the properties of the set of normalized immobile indices and the feasible set. Thisstudy provides the main result of the paper, which is to formulate and prove the new first-order optimality conditions inthe form of a criterion. The optimality conditions are explicit and do not use any constraint qualifications. For the case of alinear cost function, we reformulate theK-semidefinite problem in a regularized form and construct its dual. We show thatthe pair of the primal and dual regularized problems satisfies the strong duality relation which means that the duality gap is vanishing.publishe

Human Stem Cell - European National Innovation Systems and Patents

The purpose of this paper is for the reader to realise how national innovation systems are deeply intertwined with the legal background of a country and to understand the processes that involves national innovation systems specifically regarding the stem cell / genetics research and how the need for specific community law must be considered targeting the stem cell patents. The legal part will try to answer: Why is it important given the actual state of the European stem cell national innovation systems for the European Commission to take a stand and tackle issues regarding the patenting of the human stem cell innovations? This is done from a country industry analysis (business approach) and then linking it with competition law from a community stand point of view (that tackles biotechnology issues). In order to achieve this the paper is divided into three separate analyses beginning with a theoretical background of general biotechnology / genetic terms that will enable the reader to have a general understanding of the importance of this kind or research ( genetics / stem cell research). The chosen countries case studies exemplify very diverse economies and development perspective from the traditionally R&D intensive to the least and from the biggest countries in Europe to one of the smallest, thus giving cultural, legal, economic and scientific variety

Handbook of Computational Intelligence in Manufacturing and Production Management

Artificial intelligence (AI) is simply a way of providing a computer or a machine to think intelligently like human beings. Since human intelligence is a complex abstraction, scientists have only recently began to understand and make certain assumptions on how people think and to apply these assumptions in order to design AI programs. It is a vast knowledge base discipline that covers reasoning, machine learning, planning, intelligent search, and perception building. Traditional AI had the limitations to meet the increasing demand of search, optimization, and machine learning in the areas of large, biological, and commercial database information systems and management of factory automation for different industries such as power, automobile, aerospace, and chemical plants. The drawbacks of classical AI became more pronounced due to successive failures of the decade long Japanese project on fifth generation computing machines. The limitation of traditional AI gave rise to development of new computational methods in various applications of engineering and management problems. As a result, these computational techniques emerged as a new discipline called computational intelligence (CI)

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paper explores the consistency of some of its standard techniques. We focus in particular on prevailing wholesale electricity trading simulation methods. We include different supply and demand representations and propose the Experience-Weighted Attractions method to include several behavioural algorithms. We compare the results across assumptions and to economic theory predictions. The match is good under best-response and reinforcement learning but not under fictitious play. The simulations perform well under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The overall conclusion is that agent-based simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model wholesale electricity markets.Agent-based computational economics, electricity, market design, experience-weighted attraction (EWA), learning, supply functions, demand aggregation, initial beliefs.

Information-rich quality controls prediction model based on non-destructive analysis for porosity determination of AISI H13 produced by electron beam melting

The number of materials processed via additive manufacturing (AM) technologies has rapidly increased over the past decade. As of these emerging technologies, electron beam powder bed fusion (EB-PBF) process is becoming an enabling technology to manufacture complex-shaped components made of thermal-cracking sensitive materials, such as AISI H13 hot-work tool steel. In this process, a proper combination of process parameters should be employed to produce dense parts. Therefore, one of the first steps in the EB-PBF part production is to perform the process parameter optimization procedure. However, the conventional procedure that includes the image analysis of the cross-section of several as-built samples is time-consuming and costly. Hence, a new model is introduced in this work to find the best combination of EB-PBF process parameters concisely and cost-effectively. A correlation between the surface topography, the internal porosity, and the process parameters is established. The correlation between the internal porosity and the melting process parameters has been described by a high robust model (R-adj(2) = 0.91) as well as the correlation of topography parameters and melting process parameters (R-adj(2) = 0.77-0.96). Finally, a robust and information-rich prediction model for evaluating the internal porosity is proposed (R-adj(2) = 0.95) based on in situ surface topography characterization and process parameters. The information-rich prediction model allows obtaining more robust and representative model, yielding an improvement of about 4% with respect to the process parameter-based model. The model is experimentally validated showing adequate performances, with a RMSE of 2% on the predicted porosity. This result can support process and quality control designers in optimizing resource usage towards zero-defect manufacturing by reducing scraps and waste from destructive quality controls and reworks

The development and use of tools to support the strategy process

This document presents a collection of peer reviewed journal articles, book chapters and books which together form the submission for PhD by published work. The document demonstrates that the collection submitted forms a significant contribution to knowledge primarily to the field of operational research (OR) and strategy. The contribution covers four key areas: the practice of tool use by practitioners to support the strategy process and one of its particular activities (visioning); the development and application of two specific tools (visioning and scenario planning); the support of the strategy process through tool use; and, teaching the subject of OR and strategy