Solving a harvest scheduling optimization problem with constraints on clearcut area and clearcut proximity

This study aims at solving a harvesting scheduling optimization problem with constraints on the clearcut area with additional constraints on clearcut proximity. The objective function is defined as the net present value generated by harvesting discounted by a penalty for each clearcut. This problem arises to reduce the negative environmental impact of excessive harvesting. We propose the connected-bucket model, the so-called bucket model with additional constraints on bucket connectivity and two definitions of stand adjacency, and a Dantzig–Wolfe decomposition. The decomposed model is solved by branch-and-price and the connected-bucket model by a general-purpose mixed integer programming solver (CPLEX). We compare the quality of the solutions obtained with both approaches for real instances. The branch-and-price approach found better solutions for the majority of the instances.This research was supported by the Center of Mathematics, Fundamental Applications and Operations Research - project UIDB/04561/2020, the INESC TEC - Institute for Systems and Computer, Engineering, Technology and Science - project LA/P/0063/2020, and also by the R&D project entitled “An Optimization Framework to reduce Forest Fire” - PCIF/GRF/0141/2019, all funded by FCT - Fundação para a Ciência e Tecnologia

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

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

The matching relaxation for a class of generalized set partitioning problems

This paper introduces a discrete relaxation for the class of combinatorial optimization problems which can be described by a set partitioning formulation under packing constraints. We present two combinatorial relaxations based on computing maximum weighted matchings in suitable graphs. Besides providing dual bounds, the relaxations are also used on a variable reduction technique and a matheuristic. We show how that general method can be tailored to sample applications, and also perform a successful computational evaluation with benchmark instances of a problem in maritime logistics.Comment: 33 pages. A preliminary (4-page) version of this paper was presented at CTW 2016 (Cologne-Twente Workshop on Graphs and Combinatorial Optimization), with proceedings on Electronic Notes in Discrete Mathematic

The Economic Impact of Container-loading Problem

Thousands of containers with different types of cargo are loaded every day in multiple manufacturing and logistics centres in the world. The main problem arising from these handlings is how to make the maximum use of all the available container capacities, while keeping the overall costs of transport per cargo unit as low as possible. The previous research mostly focuses on studying different algorithms for optimising container loading with cargo that has already been assigned based on its dimensions and weight. However, this paper will emphasise the importance of using algorithms in the planning and preparation of the cargo itself during the manufacturing processes before it is dispatched for loading into containers. Besides the length, width, height, and weight of the cargo itself, a fifth component influencing the overall transport costs will be considered, i.e. the manner of loading a container. The research will be carried out on an example of a container shipment of wooden sawn timber materials

Operations capability, productivity and business performance: the moderating effect of environmental dynamism

Purpose – The purpose of this study is to investigate the relationships between operations capability, productivity and business performance in the context of environmental dynamism. Design/methodology/approach – A proposed conceptual framework grounded in the resourcebased view (RBV) and dynamic capability view (DCV) is analysed using archival data from 193 automakers in the UK. Findings – The results show that operations capability, as an important dynamic capability, has a significant positive effect on productivity, which in turn leads to improved business performance. The results also suggest that productivity fully mediates the relationship between operations capability and business performance, and that environmental dynamism significantly moderates the relationship between operations capability and productivity. Practical implications – The research findings provide practical insights that will help managers develop operations capability to gain greater productivity and business performance in a dynamic environment

On hub location problems in geographically flexible networks

The authors were partially supported by research groups SEJ-584 and FQM-331 (Junta de Andalucia) and projects MTM2016-74983-C02-01 (Spanish Ministry of Education and Science/FEDER), FEDER-US-1256951, P18-FR-1422, P18-FR-2369 (Junta de Andalucia), CEI-3FQM331 (Andalucia Tech), and NetmeetData (Fundacion BBVA - Big Data 2019). We also would like to acknowledge Elena Fernandez (Universidad de Cadiz) for her useful and detailed comments on previous versions of this manuscript.In this paper, we propose an extension of the uncapacitated hub location problem where the potential positions of the hubs are not fixed in advance. Instead, they are allowed to belong to a region around an initial discrete set of nodes. We give a general framework in which the collection, transportation, and distribution costs are based on norm-based distances and the hub-activation setup costs depend not only on the location of the hub that are opened but also on the size of the region where they are placed. Two alternative mathematical programming formulations are proposed. The first one is a compact formulation while the second one involves a family of constraints of exponential size that we separate efficiently giving rise to a branch-and-cut algorithm. The results of an extensive computational experience are reported showing the advantages of each of the approaches.Junta de Andalucia SEJ-584 FQM-331 FEDER-US-1256951 P18-FR-1422 P18-FR-2369Spanish Government European Commission MTM2016-74983-C02-01Andalucia Tech CEI-3FQM331NetmeetData (Fundacion BBVA - Big Data 2019