Analysing trade-offs in container loading: Combining load plan construction heuristics with agent-based simulation

This is the accepted version of the following article: Analysing Trade-offs in Container Loading: Combining Load Plan Construction Heuristics with Agent-based Simulation. International Transactions in Operational Research, 20(4): 471-491which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/itor.12017/abstractIn this paper we describe two operations research techniques, cutting and packing optimisation (CPO) and simulation, and present a multi-methodology approach for analysing the trade-offs between loading efficiency and various important practical considerations in relation to the cargo, such as its stability, fragility or possible cross-contamination between different types of items over time. The feasibility of this approach is demonstrated by considering a situation where the items to be loaded have differing degrees of perishability and where badly deteriorated items can affect those in their immediate vicinity (e.g. through the spread of mould). Our approach uses the output of the CPO algorithms to create agents that simulate the spread of mould through proximity-based interactions between the agents. The results show the trade-offs involved in container utilisation and the propagation of mould, without evidence of any correlation between them. The contribution of this research is the methodology and the feasibility study

Measuring the social responsibility of European companies: a goal programming approach

[EN] Corporate social responsibility (CSR) can be measured by a number of different criteria, some of which are similar to each other, while others can be manifestly contrary to the general tendency. This means that some companies can obtain a good valuation in some criteria but a bad valuation in others, which makes it difficult to assess the company¿s overall CSR valuation. It is not easy to find a single measure that covers all aspects of corporate social performance. This paper aims to estimate multicriteria CSR performance through different models of goal programming and by taking into account all the dimensions that make up CSR. An illustrative example shows the result of applying these models to a database composed of 212 European companies, which enabled us to identify the most socially responsible group, regardless of the approach considered in the construction of the multicriteria performance. The results show that environmental and corporate governance dimensions are the most important elements in measuring this performanceGarcía-Martínez, G.; Guijarro, F.; Poyatos-León, JÁ. (2019). Measuring the social responsibility of European companies: a goal programming approach. International Transactions in Operational Research. 26(3). doi:10.1111/itor.12438S26

Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version

Distribution planning in a weather-dependent scenario with stochastic travel times: a simheuristics approach

In real-life logistics, distribution plans might be affected by weather conditions (rain, snow, and fog), since they might have a significant effect on traveling times and, therefore, on total distribution costs. In this paper, the distribution problem is modeled as a multi-depot vehicle routing problem with stochastic traveling times. These traveling times are not only stochastic in nature but the specific probability distribution used to model them depends on the particular weather conditions on the delivery day. In order to solve the aforementioned problem, a simheuristic approach combining simulation within a biased-randomized heuristic framework is proposed. As the computational experiments will show, our simulation-optimization algorithm is able to provide high-quality solutions to this NP-hard problem in short computing times even for large-scale instances. From a managerial perspective, such a tool can be very useful in practical applications since it helps to increase the efficiency of the logistics and transportation operations.Peer ReviewedPostprint (published version

A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system

With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches

Desirability–doability group judgment framework for the collaborative multicriteria evaluation of public policies

Desirability–doability framework (2 × D) is a novel framework for the collaborative evaluation of public policies. Fundamental objectives and performance indicators are agreed upon in workshops, policies are characterised, and barriers to implementation identified. MACBETH interactive protocols are then applied in decision conferences to elicit qualitative judgments about the desirability of policies, within and across objectives; and about their doability under the expected graveness of barriers on contrasting scenarios. Elicited judgments allow, respectively, to construct a shared multicriteria model measuring the overall desirability of policies; and, to measure their doability. Desirability–doability graphs enable visual interactive classification of policies, with sensitivity/robustness analyses of uncertainties. 2 × D was successfully tested in a real-world urban-health policymaking case to evaluate spatial policies. The main novelty of 2 × D is that it bridges the socio-technical gap, present in OR, between the support required by a complex social decision-making process, and that usually offered by analytic techniques – while keeping modeling theoretically sound and simple

Evaluating blockchain as a participatory organisational system: looking for transaction efficiency

The article presents a decision-making model that can be used with blockchain technology. Blockchain is used as an alternative transaction mechanism to authority and the market, where the decision is decentralised within the organisation. Thus, the process is parameterised around the acceptance or not of a project, depending on individual levels of expertise, consensus level and including deliberation time. In addition, commission and omission errors are also evaluated.We show that this technology should be imposed naturally because, at the same level of information, it can obtain better results than any other decision mechanism in a systematic way. In addition, the blockchain ensures that both commission errors and omission errors are reduced with decision times that do not increase the expected opportunity loss on omission errors

Comparison of different approaches to multistage lot sizing with uncertain demand

We study a new variant of the classical lot sizing problem with uncertain demand where neither the planning horizon nor demands are known exactly. This situation arises in practice when customer demands arriving over time are confirmed rather lately during the transportation process. In terms of planning, this setting necessitates a rolling horizon procedure where the overall multistage problem is dissolved into a series of coupled snapshot problems under uncertainty. Depending on the available data and risk disposition, different approaches from online optimization, stochastic programming, and robust optimization are viable to model and solve the snapshot problems. We evaluate the impact of the selected methodology on the overall solution quality using a methodology-agnostic framework for multistage decision-making under uncertainty. We provide computational results on lot sizing within a rolling horizon regarding different types of uncertainty, solution approaches, and the value of available information about upcoming demands

A decomposition approach for multidimensional knapsacks with family-split penalties

The optimization of Multidimensional Knapsacks with Family-Split Penalties has been introduced in the literature as a variant of the more classical Multidimensional Knapsack and Multi-Knapsack problems. This problem deals with a set of items partitioned in families, and when a single item is picked to maximize the utility, then all items in its family must be picked. Items from the same family can be assigned to different knapsacks, and in this situation split penalties are paid. This problem arises in real applications in various fields. This paper proposes a new exact and fast algorithm based on a specific Combinatorial Benders Cuts scheme. An extensive experimental campaign computationally shows the validity of the proposed method and its superior performance compared to both commercial solvers and state-of-the-art approaches. The paper also addresses algorithmic flexibility and scalability issues, investigates challenging cases, and analyzes the impact of problem parameters on the algorithm behavior. Moreover, it shows the applicability of the proposed approach to a wider class of realistic problems, including fixed costs related to each knapsack utilization. Finally, further possible research directions are considered