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Production and distribution are critical components of the furniture supply chain, and achieving optimal performance through their integration has become a vital focus for both the academic and business communities. Moreover, as economic globalization progresses, distributed manufacturing has become a pioneering production technique. Via leveraging a distributed flexible manufacturing system, mass flexible production at lower costs can be achieved. To this end, this study presents an integrated distributed flexible job shop and distribution problem to minimize makespan and total tardiness. In our research, a set of custom furniture orders from different customers are processed among flexible job shops and then delivered by vehicles to customers as the due date. To distinctly show the presented problem, a mixed integer mathematical programming model is created, and a multi-objective brain storm optimization method is introduced considering the problem's features. In comparison to the other three advanced methods, the superiority of the algorithm created is showcased. The findings of the experiments demonstrate that the constructed model and the introduced algorithm have remarkable competitiveness in addressing the problem being examined.

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The wild horse optimizer (WHO) is a novel metaheuristic algorithm, which has been successfully applied to solving continuous engineering problems. Considering the characteristics of the wild horse optimizer, a discrete version of the algorithm, named discrete wild horse optimizer (DWHO), is proposed to solve the capacitated vehicle routing problem (CVRP). By incorporating three local search strategies-swap operation, reverse operation, and insertion operation-along with the introduction of the largest-order-value (LOV) decoding technique, the precision and quality of the solutions have been enhanced. Experimental results conducted on 44 benchmark instances indicate that, in most test cases, the solving capability of discrete wild horse optimizer surpasses that of basic wild horse optimizer (BWHO), hybrid firefly algorithm, dynamic space reduction ant colony optimization (DSRACO), and discrete artificial ecosystem-based optimization (DAEO). The discrete wild horse optimizer provides a novel approach for solving the capacitated vehicle routing problem and also offers a new perspective for addressing other discrete problems.

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This study presents a comprehensive analysis of greenhouse gas (GHG) emissions associated with waste transfer and transport, incorporating derived leachate treatment-a factor often overlooked in existing research. Employing an integration model of life cycle assessment and a vehicle routing problem (VRP) methods, we evaluated the GHG reduction potential of waste transfer and transport system. Two Chinese counties with different topographies and demographics were selected, yielding 80 scenarios that factored in waste source separation as well as vehicle capacity, energy sources, and routes. The functional unit (FU) is transferring and transporting 1 tonne waste and treating derived leachate. The GHG emissions varied from 12 to 39 kg CO2 equivalent per FU. Waste source separation emerged as the most impactful mitigation strategy, not only for the studied system but for an integrated waste management system. Followings are the use of larger capacity vehicles and electrification of the vehicles. These insights are instrumental for policymakers and stakeholders in optimizing waste management systems to reduce GHG emissions.

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To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

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In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm's convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT's average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT.

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Precision medicine's emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the vehicle routing problem (VRP) assembler: an approach that transforms this task into a vehicle routing problem, an optimization formulation solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google's OR-Tools, achieving a haplotype-resolved local assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to Hifiasm with phasing accuracy approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

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Logistics and transportation industry is not only a major energy consumer, but also a major carbon emitter. Developing green logistics is the only way for the sustainable development of the logistics industry. One of the main factors of environmental pollution is caused by carbon emissions in the process of vehicle transportation, and carbon emissions of vehicle transportation are closely related to routing, road conditions, vehicle speed, and speed fluctuations. The low-carbon vehicle routing problem with high granularity time-dependent speeds, speed fluctuations, road conditions, and time windows is proposed and formally described. In order to finely evaluate the effects of vehicle speed and speed fluctuations on carbon emissions, a graph convolutional network (GCN) is used to predict the high granularity time-dependent traffic speeds. To solve this complicated low-carbon vehicle routing problem, a hybrid genetic algorithm with adaptive variable neighborhood search is proposed to obtain vehicle routing with low carbon emissions. Finally, this method is validated using a case study with the logistics and traffic data in Jingzhou, China, and also the results show the effectiveness of this proposed method.

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Distribution costs remain consistently high in crowded city road networks, posing challenges for traditional distribution methods in efficiently handling dynamic online customer orders. To address this issue, this paper introduces the Proactive Dynamic Vehicle Routing Problem considering Cooperation Service (PDVRPCS) model. Based on proactive prediction and order-matching strategies, the model aims to develop a cost-effective and responsive distribution system. A novel solution framework is proposed, incorporating a proactive prediction method, a matching algorithm and a hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm. To validate the effectiveness of the proposed model and algorithm, a case study is conducted. The experimental results demonstrate that the dynamic scheme can significantly reduce the number of vehicles required for distribution, leading to cost reduction and increased efficiency.

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Generalized Vehicle Routing Problem (GVRP) is a challenging operational research problem which has been widely studied for nearly two decades. In this problem, it is assumed that graph nodes are grouped into a number of clusters, and serving any node of a cluster eliminates the need to visit the other nodes of that cluster. The general objective of this problem is to find the set of nodes to visit and determine the service sequence to minimize the total traveling cost. In addition to these general conditions, GVRP can be formulated with different assumptions and constraints to practically create different sub-types and variants. This paper aims to provide a comprehensive survey of the GVRP literature and explore its various dimensions. It first encompasses the definition of GVRP, similar problems, mathematical models, classification of different variants and solution methods developed for GVRPs, and practical implications. Finally, some useful suggestions are discussed to extend the problem. For this review study, Google Scholar, Scopus, Science Direct, Emerald, Springer, and Elsevier databases were searched for keywords, and 160 potential articles were extracted, and eventually, 45 articles were judged to be relevant.

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The vehicle routing problem (VRP) is a highly significant and extensively studied issue in post-disaster rescue. In recent years, there has been widespread utilization of helicopters for post-disaster rescue. However, efficiently dispatching helicopters to reach rescue sites in post-disaster rescue is a challenge. To address this issue, this study models the issue of dispatching helicopters as a specific variant of the VRP with time window (VRPTW). Considering that the VRPTW is an NP-hard problem, the genetic algorithm (GA) as one of the prominent evolutionary algorithms with robust optimization capabilities, is a good candidate to deal with this issue. In this study, an improved GA with a local search strategy and global search strategy is proposed. To begin, a cooperative initialization strategy is proposed to generate an initial population with high quality and diversity. Subsequently, a local search strategy is presented to improve the exploitation ability. Additionally, a global search strategy is embedded to enhance the global search performance. Finally, 56 instances extended from Solomon instances are utilized for conducting simulation tests. The simulation results indicate that the average relative percentage increase (RPI) of the distance travelled by helicopters as obtained by the proposed algorithm is 0.178, 0.027, 0.075 and 0.041 times smaller than the average RPIs obtained by the tabu search algorithm, ant colony optimization algorithm, hybrid GA and simulated annealing algorithm, respectively. Simulation results reveal that the proposed algorithm is more efficient and effective for solving the VRPTW to reduce the driving distance of the helicopters in post-disaster rescue.

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The bamboo forest growth optimization (BFGO) algorithm combines the characteristics of the bamboo forest growth process with the optimization course of the algorithm. The algorithm performs well in dealing with optimization problems, but its exploitation ability is not outstanding. Therefore, a new heuristic algorithm named orthogonal learning quasi-affine transformation evolutionary bamboo forest growth optimization (OQBFGO) algorithm is proposed in this work. This algorithm combines the quasi-affine transformation evolution algorithm to expand the particle distribution range, a process of entropy increase that can significantly improve particle searchability. The algorithm also uses an orthogonal learning strategy to accurately aggregate particles from a chaotic state, which can be an entropy reduction process that can more accurately perform global development. OQBFGO algorithm, BFGO algorithm, quasi-affine transformation evolutionary bamboo growth optimization (QBFGO) algorithm, orthogonal learning bamboo growth optimization (OBFGO) algorithm, and three other mature algorithms are tested on the CEC2017 benchmark function. The experimental results show that the OQBFGO algorithm is superior to the above algorithms. Then, OQBFGO is used to solve the capacitated vehicle routing problem. The results show that OQBFGO can obtain better results than other algorithms.

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Almost all perishable crops deteriorate due to improper and tardy transportation and storage. Vehicle Routing Problem, or VRP, might be of great aid since it takes into account a number of aspects of any transportation and storage issues and optimizes them in such a way as to reduce the overall cost of the carrier. This study attempts to widen the scope of the commonly used VRP model by including traffic and energy consumption features and transforming it into the Aggregated Vehicle Routing Problem (AVRP). Traditional VRP focuses on minimizing distance. Generally, it is unable to find out the optimal number of aggregation points required to serve a system. So, cost optimization of the AVRP approach was designed with two specialized steps. Firstly, the destination data are divided into multiple clusters employing the X-means clustering. And then the best route was found to execute the delivery thus minimizing cost, required time, and carbon footprint. The study was implemented on the Chattogram zone and discovered that the optimal number of aggregation points (AP) required to serve Chattogram is only three namely- AP 1, AP 2, and AP 3. VRP analysis was stretched further with AVRP model using AP 1 and found to reduce the operating cost by 10.96%.

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The environmental damage caused by air pollution has recently become the focus of city council policies. The concept of the green city has emerged as an urban solution by which to confront environmental challenges worldwide and is founded on air pollution levels that have increased meaningfully as a result of traffic in urban areas. Local governments are attempting to meet environmental challenges by developing public traffic policies such as air pollution protocols. However, several problems must still be solved, such as the need to link smart cars to these pollution protocols in order to find more optimal routes. We have, therefore, attempted to address this problem by conducting a study of local policies in the city of Madrid (Spain) with the aim of determining the importance of the vehicle routing problem (VRP), and the need to optimise a set of routes for a fleet. The results of this study have allowed us to propose a framework with which to dynamically implement traffic constraints. This framework consists of three main layers: the data layer, the prediction layer and the event generation layer. With regard to the data layer, a dataset has been generated from traffic data concerning the city of Madrid, and deep learning techniques have then been applied to this data. The results obtained show that there are interdependencies between several factors, such as weather conditions, air quality and the local event calendar, which have an impact on drivers' behaviour. These interdependencies have allowed the development of an ontological model, together with an event generation system that can anticipate changes and dynamically restructure traffic restrictions in order to obtain a more efficient traffic system. This system has been validated using real data from the city of Madrid.

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The traveling tournament problem is a well-known sports league scheduling problem famous for its practical hardness. Given an even number of teams with symmetric distances between their venues, a double round-robin tournament has to be scheduled minimizing the total travel distances over all teams. We consider the most common constrained variant without repeaters and a streak limit of three, for which we study a beam search approach based on a state-space formulation guided by heuristics derived from different lower bound variants. We solve the arising capacitated vehicle routing subproblems either exactly for small- to medium-sized instances up to 18 teams or heuristically also for larger instances up to 24 teams. In a randomized variant of the search, we employ random team ordering and add small amounts of Gaussian noise to the nodes' guidance for diversification when multiple runs are performed. This allows for a simple yet effective parallelization of the beam search. A final comparison is done on the NL, CIRC, NFL, and GALAXY benchmark instances with 12 to 24 teams, for which we report a mean gap difference to the best known feasible solutions of 1.2% and five new best feasible solutions.

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Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces the availability of time windows for future customers. In this paper, we explore using historical order data to manage scarce delivery capacities efficiently. We propose a sampling-based customer acceptance approach that is fed with different combinations of these data to assess the impact of the current request on route efficiency and the ability to accept future requests. We propose a data-science process to investigate the best use of historical order data in terms of recency and amount of sampling data. We identify features that help to improve the acceptance decision as well as the retailer's revenue. We demonstrate our approach with large amounts of real historical order data from two cities served by an online grocery in Germany.

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Exploring sustainable urban distribution based on electric vehicles is crucial given the rise in global greenhouse gas emissions, especially for fast fashion industries with extremely high distribution frequencies. However, most studies have overlooked the impact of deep discharge on the distribution route scheme, and few studies fit the characteristics of the fast fashion industry. As a result, this study presents a novel electric vehicle routing problem considering deep discharge model (EVRP-DD) for distribution route optimization, which fully considers deep discharge under the emerging mode of vehicle-battery separation. The characteristics of fashion consumers and products were also integrated into the model, such as consumer satisfaction and 3D loading constraints. To solve this complex programming problem, a sophisticated hybrid ant colony optimization (HACO) algorithm was designed by combining the advantages of ACO and A-star algorithms. Using real-life data, the experimental results verify the effectiveness and superiority of the proposed solution. EVRP-DD achieved reduced driving distance, total distribution cost, and deep discharge distance. HACO can enhance the computation speed and reduce the total distribution cost compared with the two conventional algorithms. The proposed solution showed excellent flexibility and could effectively adjust the optimal route scheme according to the ever-changing external environment. Thus, it can be concluded that this solution is a powerful tool for enterprises to achieve sustainable distribution. This study has realized theoretical innovation in sustainable distribution under the new mode of vehicle-battery separation, and its successful application in the fast fashion industry reflects its valuable application value.

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The outbreak of major public health emergencies such as the coronavirus epidemic has put forward new requirements for urban emergency management procedures. Accuracy and effective distribution model of emergency support materials, as an effective tool to inhibit the deterioration of the public health sector, have gradually become a research hotspot. The distribution of urban emergency support devices, under the secondary supply chain structure of "material transfer center-demand point," which may involve confusing demands, is studied to determine the actual situation of fuzzy requests under the impact of an epidemic outbreak. An optimization model of urban emergency support material distribution, based on Credibility theory, is first constructed. Then an improved sparrow search algorithm, ISSA, was designed by introducing Sobol sequence, Cauchy variation and bird swarm algorithm into the structure of the classical SSA. In addition, numerical validation and standard test set validation were carried out and the experimental results showed that the introduced improved strategy effectively improved the global search capability of the algorithm. Furthermore, simulation experiments are conducted, based on Shanghai, and the comparison with existing cutting-edge algorithms shows that the designed algorithm has stronger superiority and robustness. And the simulation results show that the designed algorithm can reduce vehicle cost by 4.83%, time cost by 13.80%, etc. compared to other algorithms. Finally, the impact of preference value on the distribution of emergency support materials is analyzed to help decision-makers to develop reasonable and effective distribution strategies according to the impact of major public health emergencies. The results of the study provide a practical reference for the solution of urban emergency support materials distribution problems.

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Sustainable forest management is concerned with the management of forests according to the principles of sustainable development. As a contribution to the field, this paper combines the Vehicle Routing Problem (VRP) (in which the vehicles are harvesters) with the Multiple Stock Size Cutting Stock Problem under uncertainty (in which the stock is logs). We present an Integer Linear Program that dynamically combines the cutting of the uncertain stock with vehicle routing, and uses it to address real-life problems. In experiments on real data from the forestry harvesting industry, we show that it outperforms a commonly used metaheuristic algorithm.

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Municipal solid waste management is a paramount activity in modern cities due to environmental, social and economic problems that can arise when mishandled. In this work, the sequencing of micro-routes in the Argentine city of Bahía Blanca is addressed, which is modelled as a vehicle routing problem with travel time limit and the vehicle's capacity. Particularly, we propose two mathematical formulations based on mixed integer programming and we solve a set of instances of the city of Bahía Blanca based on real data. Moreover, with this model, we estimate the total distance and travel time of the waste collection and use this data to analyse the possibility of installing a transfer station. The results demonstrate the competitiveness of the approach to resolve realistic instances of the target problem and suggest the convenience of installing a transfer station in the city considering the reduction of the travelled distance.

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This research is based on the capacitated vehicle routing problem with urgency where each vertex corresponds to a medical facility with a urgency level and the traveling vehicle could be contaminated. This contamination is defined as the infectiousness rate, which is defined for each vertex and each vehicle. At each visited vertex, this rate for the vehicle will be increased. Therefore time-total distance it is desired to react to vertex as fast as possible- and infectiousness rate are main issues in the problem. This problem is solved with multiobjective optimization algorithms in this research. As a multiobjective problem, two objectives are defined for this model: the time and the infectiousness, and will be solved using multiobjective optimization algorithms which are nondominated sorting genetic algorithm (NSGAII), grid-based evolutionary algorithm GrEA, hypervolume estimation algorithm HypE, strength Pareto evolutionary algorithm shift-based density estimation SPEA2-SDE, and reference points-based evolutionary algorithm.