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Optimizing shared resources across multiple clients is a complex challenge in the production, logistics, and service sectors. This study addresses the underexplored area of forecasting service costs for non-cooperative clients, which is essential for sustainable business management. We propose a framework that merges Operations Research (OR) and Machine Learning (ML) to fill this gap. It begins by applying the OR model to historical instances, optimizing resource allocation, and determining equitable service cost allocations for each client. These allocations serve as training targets for ML models, which are trained using a combination of original and augmented client data, aiming to reliably project service costs and support competitive, sustainable pricing strategies. The framework's efficacy is demonstrated in a reverse logistics case study, benchmarked against two traditional cost estimation methods for new clients. Comparative analysis shows that our framework outperforms these methods in terms of predictive accuracy, highlighting its superior effectiveness. The integration of OR and ML offers a significant decision-support mechanism, improving sustainable business strategies across sectors. Our framework provides a scalable solution for cost forecasting and resource optimization, marking progress toward a circular, sustainable economy by accurately estimating costs and promoting efficient operations. [ABSTRACT FROM AUTHOR]

As telehealth utilization for ambulatory and home-based care skyrockets, there has been a paradigm shift to a decentralized and hybrid care delivery modality integrating both in-person and telehealth services provided at different layers of the care delivery network, i.e., central hospitals, satellite clinics, and patient homes. The operations of such care delivery systems need to take into consideration patients' mobility and care needs, and rely on multiple types of nurses who can support and facilitate telehealth (with hospital physicians) in clinics and patient homes. We formulate an optimization problem, aiming at operationalizing the proposed care delivery network. Decisions regarding the type of care delivered, the location of care delivered, and the scheduling of all kinds of nurses are determined jointly to minimize operating costs while simultaneously satisfying patients' care needs. We propose a bi-level approximation that exploits the structure of the hybrid telehealth system, and develop column generation-based heuristic algorithms to identify the joint decision rules for clinic selection, patient assignment, and visiting nurse routing problems. Numerical experiment results demonstrate our algorithm's capability to achieve high-quality solutions in reasonable computation time, and is capable of solving instances with large patient sizes and time windows. Our work supports the efficient and effective operation of the proposed hybrid telehealth systems to improve patient access to care. [ABSTRACT FROM AUTHOR]

Low-carbon logistics is an emerging and sustainable development industry in the era of a low-carbon economy. The end-to-end deep reinforcement learning (DRL) method with an encoder-decoder framework has been proven effective for solving logistics problems. However, in most cases, the recurrent neural networks (RNN) and attention mechanisms are used in encoders and decoders, which may result in the long-distance dependence problem and the neglect of the correlation between query vectors. To surround this problem, we propose an improved transformer model (TAOA) with both multi-head attention mechanism (MHA) and attention to attention mechanism (AOA), and apply it to solve the low-carbon multi-depot vehicle routing problem (MDVRP). In this model, the MHA and AOA are implemented to solve the probability of route nodes in the encoder and decoder. The MHA is used to process different parts of the input sequence, which can be calculated in parallel, and the AOA is used to deal with the deficiency problem of correlation between query results and query vectors in the MHA. The actor-critic framework based on strategy gradient is constructed to train model parameters. The 2opt operator is further used to optimize the resulting routes. Finally, extensive numerical studies are carried out to verify the effectiveness and operation efficiency of the proposed TAOA, and the results show that the proposed TAOA performs better in solving the MDVRP than the traditional transformer model (Kools), genetic algorithm (GA), and Google OR-Tools (Ortools). [ABSTRACT FROM AUTHOR]

As an innovative public transport, the customized bus has rapidly grown. To improve the efficiency of customized bus and satisfy customers' personal requirement, a mixed-integer programming model is proposed to optimize the stop assignment for customers and route scheduling for buses with walking distance constraints and travel time constraints. As an variants of vehicle routing problem, the formulated model on large-scale instances is intractable to solve for commercial solvers. Therefore, an exact algorithm based on branch and price is developed to solve the model, in which a labeling algorithm is designed for the pricing problems. Numerical experiments and a real-world case in Dalian are conducted to validate the effectiveness of the proposed model. The computational results show that the tailored algorithm can yield an optimal solution within a significantly shorter time than that of CPLEX. Some managerial implications are also obtained based on sensitivity analysis, which may be potentially useful for bus companies. [ABSTRACT FROM AUTHOR]

The optimization of cargo loading and transportation are two highly considered optimization problems (namely "3L-CVRP"). The combination of both has attracted increasing interest in the past decades. Hereby, 2D or 3D items have to be transported from one depot to a given set of customers using a homogeneous fleet of vehicles. Each route must be provided with a feasible packing plan taking various constraints into account. Combining the two optimization problems increases the complexity of the solution approaches, leading to a higher difficulty to check the results for correctness. To support the research progress and to enable transparency of solution structures, this paper provides an overview of recent literature, problem formulations, and best-known solutions. Furthermore, we introduce two open-source tools: The "Solution Validator" checks the feasibility of solutions in terms of considered constraints. The "Visualizer" provides two views and visualizes solutions. In the vehicle routing view, the tour plan and the corresponding schedule are displayed. In the loading view, the position of each item in the cargo space is demonstrated. In both views, it is possible to check the feasibility of the solution and highlight violated items. Besides the combined problem, the tool can be used also for one optimization problem (e.g. vehicle routing problem or container loading). The source codes for both tools are available at GitHub in C++ and Java and can be easily integrated into other researchers' code. [ABSTRACT FROM AUTHOR]

The classical multi-period inventory routing problem (MIRP) combines inventory management and vehicle routing problems, attempting to maintain customer inventory levels via autonomous deliveries, with the assumption that stock-out never occurs at the supply side. While this is no more the truth when a common third-party logistics company operates a central warehouse for a group of small and medium sized retailing companies. Procurements have to be made to refill inventory at the central warehouse. This study thus extends the traditional MIRP by incorporating procurement decisions (MIRP-PD) and tries to minimize the total cost of procurement, inventory holding and transportation. To our knowledge, this paper is among the first to formally describe MIRP-PD and model it as a mixed integer linear program. A hybrid two-level heuristic is proposed to address large-scale instances. The upper level of the algorithm determines the visiting schedule while the lower level works out procurement and routing decisions accordingly. Components from tabu search and adaptive threshold acceptance are also embedded to help escape from local optima. Extensive numerical instances are generated from a real case in Chengdu (China), based on which the computational results reveal the effectiveness and efficiency of the proposed algorithm compared with the commercial solver CPLEX. The comparison between MIRP and MIRP-PD has demonstrated the cost advantage of incorporating procurement decisions into inventory and routing plans. Impacts on costs of key factors including delivery frequency and buffer size of stores have also been examined to provide managerial implications to practitioners. [ABSTRACT FROM AUTHOR]

Range anxiety of electric vehicles (EVs) still poses a major barrier in their adoption in the logistics operations despite the advancements in the battery technology. The need for recharging the battery during the day brings additional complexities to the operational planning of commercial EVs in last mile deliveries. The driving range of an EV may vary according to different factors including ambient temperature, weight, speed, acceleration/deceleration, and the road profile. In this study, we revisit the well-known electric vehicle routing problem with time windows by taking into account the weight of the load carried. Cargo weight may play a crucial role in the operational efficiency of the EVs since it may affect the energy consumption significantly. We first present two alternative mathematical formulations of the problem and test their performances on small-size instances that can be solved using a commercial solver. Next, we develop a matheuristic approach that integrates an optimal repair procedure in the large neighbourhood search method and validate its performance. Then, we present an extensive numerical study to investigate the influence of load on the routing decisions. Our results show that cargo weight may create substantial changes in the route plans and fleet size, and neglecting it may cause severe disruptions in service and increase the costs. [ABSTRACT FROM AUTHOR]

Every year, natural disasters such as earthquakes, floods, volcanos, etc. cause millions of victims. So a quick response to these disasters is vital to reduce their negative consequences. Vehicle routing models can make important contributions to faster response, and thus, save lives. This paper proposes a vehicle routing problem to deliver relief resources from origins to destinations in response to disasters. For this purpose, a multi-period, multi-depot, multi-trip mixed-integer linear programming model is developed. Minimizing the sum of arrival times is considered as a service-based objective function for the increase of the survival rate. For the first time, the problem is solved using a grouping metaheuristic algorithm. Then its performance is compared with two other grouping algorithms. To evaluate the solution method, the algorithms are implemented on various test problems and compared statistically. Additionally, to show the validity of the model, sensitivity analyses are performed and managerial insights are given. [ABSTRACT FROM AUTHOR]

This paper applies the truck and drone cooperative delivery model to humanitarian logistics and proposes a multi-objective humanitarian pickup and delivery vehicle routing problem with drones (m-HPDVRPD) which contains two subproblems: cooperative routing subproblem and relief supplies allocation subproblem. The m-HPDVRPD is formulated as a multi-objective mixed integer linear programming (MILP) model with two objectives, which simultaneously minimizes the maximum cooperative routing time and maximizes the minimum fulfillment rate of demand nodes. We develop a hybrid multi-objective evolutionary algorithm with specialized local search operators (HMOEAS) and a hybrid multi-objective ant colony algorithm (HACO) for the problem. A set of numerical experiments are performed to compare the performance of the two algorithms and their three variants. And the experimental results prove that HMOEAS is more effective than other methods. Taking the Corona Virus Disease 2019 (COVID-19) epidemic in Wuhan as a case, we compare the truck-drone cooperative delivery model with the truck-only delivery and the drone-only delivery models, and find that our model has advantages in the delivery efficiency of anti-epidemic materials. Moreover, based on the real-world case, a sensitivity analysis is conducted to investigate the impact of different drone parameters on the efficiency of truck-drone cooperative delivery. [ABSTRACT FROM AUTHOR]

Many packing, routing, and knapsack problems can be expressed in terms of integer linear programming models based on set covering. These models have been exploited in a range of successful heuristics and exact techniques for tackling such problems. In this paper, we show that integer linear programming models based on set covering can be very useful for their use within an algorithm called “Construct, Merge, Solve & Adapt”(CMSA), which is a recent hybrid metaheuristic for solving combinatorial optimization problems. This is because most existing applications of CMSA are characterized by the use of an integer programming solver for solving reduced problem instances at each iteration. We present applications of CMSA to the variable-sized bin packing problem and to the electric vehicle routing problem with time windows and simultaneous pickups and deliveries. In both applications, CMSA based on a set covering model strongly outperforms CMSA when using an assignment-type model. Moreover, state-of-the-art results are obtained for both considered optimization problems. [ABSTRACT FROM AUTHOR]

In light of the growing consumer emphasis on delivery speed and corporate environmental responsibility, it becomes paramount to simultaneously address the alignment of customer expectations with corporate objectives. We undertake a comprehensive examination of delivery delays and carbon emissions stemming from e-commerce logistics, leading us to formulate a delivery delay penalty function informed by customer behavior traits such as loss aversion. Concurrently, we analyze various factors influencing customer satisfaction and integrate them into our model. Similarly, we incorporate multiple determinants impacting vehicle emissions, devising a logistics cost-minimization model encompassing carbon emissions and cooling expenses. By amalgamating considerations of customer satisfaction, logistics expenses, and environmental concerns, we devise a dual-objective optimization model. To tackle this complex challenge, we introduce a multi-objective Artificial Bee Colony algorithm based on MOEA/D principles, substantiating its efficacy through extensive numerical experiments. Our findings demonstrate the algorithm's ability to intelligently optimize logistics routes, thus reducing vehicle utilization. Finally, we present a Pareto front, illustrating how mitigating customer satisfaction can alleviate logistics and carbon emission costs. [ABSTRACT FROM AUTHOR]

The vehicle routing problem is a traditional combinatorial problem with practical relevance for a wide range of industries. In the literature, several specificities have been tackled by dedicated methods in order to better reflect real-world situations. Following this trend, this article addresses the fleet size and mix vehicle routing problem with time windows in which companies hire a third-party logistics company. The shipping charges considered in this work are calculated using step cost functions, in which values are determined according to the type of vehicle and the total distance traveled, with fixed values for predefined distance ranges. A mixed integer linear programming model is introduced and two sequential insertion heuristics are proposed. The introduced methods are examined through a computational comparative analysis in small-sized instances with known optimal solution, 168 benchmark instances from the literature, and 3 instances based on a real-world problem from the civil construction industry. The numerical experiments show that the proposed methods are efficient and show good performance in different scenarios. [ABSTRACT FROM AUTHOR]

Pharmaceutical supply chain (PSC) is one of the most important healthcare supply chains and the recent pandemic (COVID-19) has completely proved it. Also, the environmental and social impacts of PSCs are undeniable due to the daily entrance of a large amount of pharmaceutical waste into the environment. However, studies on closed-loop PSCs (CLPSC) are rarely considered real-world requirements such as competition among diverse brands of manufacturers, the dependency of customers' demand on products' price and quality, and diverse reverse flows of end-of-life medicines. In this study, a scenario-based Multi-Objective Mixed-Integer Linear Programming model is developed to design a sustainable CLPSC, which investigates the reverse flows of expired medicines as three classes (must be disposed of, can be remanufactured and can be recycled). To study the competitive market and deal with demand uncertainty, a novel scenario-based game theory model is proposed. The demand function for each brand depends on the price and quality provided. Then, a hybrid solution approach is provided by combining the LP-metrics method with a heuristic algorithm. Furthermore, a real case study is investigated to evaluate the application of the model. Finally, sensitivity analysis and managerial insights are provided. The numerical results show that the proposed classification of reverse flows leads to proper waste management, making money, and reducing both disposal costs and raw material usage. Moreover, competition increases PSCs performance and improves the supply of products to pharmacies. [ABSTRACT FROM AUTHOR]

In vehicle routing problems (VRPs) the decisions to be taken concern the assignment of customers to vehicles and the sequencing of the customers assigned to each vehicle. Additional decisions may need to be jointly taken, depending on the specific problem setting. In this paper, after discussing the different kinds of decisions taken in different classes of VRPs, the class where the decision about when the routes start from the depot has to be taken is considered and the related literature is reviewed. This class of problems, that we call VRPs over time, includes the periodic routing problems, the inventory routing problems, the vehicle routing problems with release dates, and the multi-trip vehicle routing problems. [ABSTRACT FROM AUTHOR]

In marine container drayage (CD), which refers to the transport of goods over a short distance, empty containers (ECs) are required to be transported long distances between a terminal and a shipper/consignee by conventional transportation. This causes truck traffic congestion in harbor districts that increases CO2 emissions (CO2EM). This study addresses the vehicle dispatch problem with respect to the environmental impact of marine CD, considering the tractor dwell time is long at the customer site due to cumbersome container loading/unloading (L/U). We model this problem as an extended version of the vehicle routing problem with precedence constraints to minimize the total CO2 weighted travel distance. To improve CD operation in long L/U time, we propose a new practice for CD operations. Additionally, we develop a mixed integer programing model (MIP) for the new practice representation, and propose a Simulated Annealing (SA) based heuristic approach to solving the new practice instance. By implementing our proposed new operation, in a long L/U time, the CO2EM, EC move and number of tractors required can also be reduced by allowing uncoupled tractor move. [ABSTRACT FROM AUTHOR]

Big Data brings about a mix of opportunities for and challenges to the development of intelligent transportation and logistics industries. Incorporating the tremendous trove of data and data analytics into operations research greatly increases capabilities to solve real-world complex problems. "DEA under big data: data enabled analytics and network data envelopment analysis" by Zhu, proposes that data envelopment analysis (DEA) should be viewed as a method for data-oriented analytics in performance evaluation and benchmarking, and demonstrate that network DEA can be different from conventional DEA in many aspects. [Extracted from the article]

Limited supply can be an emergent issue in refined oil distribution, which may increase operating cost and decrease gasoline station satisfaction with shortage. Hence, how to devise an optimal distribution scheme is the central problem for oil distribution companies. The main problem with limited supply involves: (I) depicting the dynamic efforts on vehicle routing driven by the demand and priority of gasoline stations, and (II) incorporating the efforts into variable distribution region division associated with oil depots. In this paper, we propose a multi-objective optimization model for dynamic vehicle routing problem with limited supply in oil distribution with variable neighborhood region. First, a preliminary multi-stage model for dynamic vehicle routing problem is designed, which takes operating cost, gasoline station satisfaction and priority into consider in the setting of limited supply. Based on the preliminary model, a variable neighborhood region division model is presented for oil depot supply and tanker delivery, in light of Fuzzy C-means algorithm and justifiable granularity principle. Finally, the experimental results show that the dynamic vehicle programming model with variable neighborhood performs better than other comparable scenarios at cost savings and satisfaction improvement. [ABSTRACT FROM AUTHOR]

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra's algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm. [ABSTRACT FROM AUTHOR]

The vehicle routing problem (VRP) is a combinatorial optimization management problem that seeks the optimal set of routes traversed by a vehicle to deliver products to customers. A recognized problem in this domain is to serve 'prioritized' customers in the shortest possible time where customers with known demands are supplied by one or several depots. This problem is known as the Vehicle Routing with Prioritized Customers (VRPC). The purpose of this work is to present and compare two artificial intelligence-based novel methods that minimize the traveling distance of vehicles when moving cargo to prioritized customers. Various studies have been conducted regarding this topic; nevertheless, up to now, few studies used the Cuckoo Search-based hyper-heuristic. This paper modifies a classical mathematical model that represents the VRPC, implements and tests an evolutionary Cuckoo Search-based hyper-heuristic, and then compares the results with those of our proposed modified version of the Clarke Wright (CW) algorithm. In this modified version, the CW algorithm serves all customers per their preassigned priorities while covering the needed working hours. The results indicate that the solution selected by the Cuckoo Search-based hyper-heuristic outperformed the modified Clarke Wright algorithm while taking into consideration the customers' priority and demands and the vehicle capacity. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the Multi-Depot Heterogeneous VRP with Backhauls. Though the problem is a generalisation of three existing routing problems, this is the first time this combined routing problem is investigated. A mathematical formulation is first presented followed by some tightening. A powerful and novel hybridisation of Variable Neighbourhood Search (VNS) with the Greedy Randomized Adaptive Memory Programming Search is proposed. As there are no problem instances available for bench-marking and evaluation purposes, we generated data sets by combining those from existing vehicle routing problems. The proposed meta-heuristic obtains a number of optimal solutions for small instances and yields about 13 % gap from the lower bounds compared to nearly 40 % and 20 % average gap values for our CPLEX implementation and the VNS without hybridisation, respectively. [ABSTRACT FROM AUTHOR]

We consider a home health care routing problem with scarce resources which is a variation of the vehicle routing problem with resource constraints. In this problem, the services are provided by nurses and aids at patients' homes. There are different types of patients that are categorized based on the services they demand and should be serviced by appropriate staff teams or single personnel where the teams are transported by rental vehicles. In this problem, the objective is to minimize the total transportation cost, by considering the patients' requirements and the qualification of the staff, as well as the resource limitations. In this paper, we present a mathematical formulation of the problem that is modeled similar to a vehicle routing problem and propose a branch-and-price algorithm to solve it. Our proposed algorithm utilizes speed up techniques from the literature to enhance the effectiveness of the proposed solution method. A comprehensive computational study is conducted on Solomon based instances and the results illustrate the efficiency of the algorithm and the effective use of available resources. [ABSTRACT FROM AUTHOR]

Since early 2010s, the Green Routing Problem (GRP) has dominated the literature of logistics and transportation. The problem itself consists of finding a set of vehicle routes for a set of customers while minimizing the detrimental effects of transportation activities. These negative externalities have been intensively tackled in the last decade. Operations research studies have particularly focused on minimizing the energy consumption and emissions. As a result, the rich literature on GRPs has already reached its peak, and several early literature reviews have been conducted on various aspects of related vehicle routing and scheduling problem variants. The major contribution of this paper is that it represents a comprehensive review of the current reviews on GRP studies. In addition to that, it is an up-to-date review based on a new chronological taxonomy of the literature. The detailed analysis provides a useful framework for understanding the research gaps for the future studies and the potential impacts for the academic community. [ABSTRACT FROM AUTHOR]

In recent years, reasoning under uncertainty in multi-objective problems has become an active research area. However, solving this kind of problems reveals many performance and robustness issues that have been so far been neglected. Most of existing studies in this area are focused on dealing with robustness in the field of mono-objective optimization. The aim of the present paper is to address robustness of multi-objective optimization problems over uncertain inputs data. In particular, we focus on the specific case of fuzziness propagation to the multiple objectives in such problems. Then in order to avoid the loss of efficiency of fuzzy-valued objective values, we introduce new robustness techniques combining fuzziness and multi-objective context. [ABSTRACT FROM AUTHOR]

The multiperiod vehicle routing problem with profit (mVRPP) is a selective vehicle routing problem where the planning horizon of each vehicle is divided into several periods. The aim of solving mVRPP is to design service itineraries so that the total amount of collected profit is maximized and the travel time limit of each period is respected. This problem arises in many real life applications, as the one encountered in cash-in-transit industry. In this paper, we present a metaheuristic approach based on the particle swarm optimization algorithm (PSO) to solve the mVRPP. Our approach incorporates an efficient optimal split procedure and dedicated local search operators proposed to guarantee high search intensification. Experiments conducted on an mVRPP benchmark show that our algorithm outperforms the state of the art metaheuristic approaches in terms of performance and robustness. Our PSO algorithm determines all the already known optimal solutions within a negligible computational time and finds 88 strict improvements among the 177 instances of the benchmark. [ABSTRACT FROM AUTHOR]

Motivated by a real-life application, this research considers the multi-objective vehicle routing and loading problem with time window constraints which is a variant of the Capacitated Vehicle Routing Problem with Time Windows with one/two-dimensional loading constraints. The problem consists of routing a number of vehicles to serve a set of customers and determining the best way of loading the goods ordered by the customers onto the vehicles used for transportation. The three objectives pertaining to minimisation of total travel distance, number of routes to use and total number of mixed orders in the same pallet are, more often than not, conflicting. To achieve a solution with no preferential information known in advance from the decision maker, the problem is formulated as a Mixed Integer Linear Programming (MILP) model with one objective—minimising the total cost, where the three original objectives are incorporated as parts of the total cost function. A Generalised Variable Neighbourhood Search (GVNS) algorithm is designed as the search engine to relieve the computational burden inherent to the application of the MILP model. To evaluate the effectiveness of the GVNS algorithm, a real instance case study is generated and solved by both the GVNS algorithm and the software provided by our industrial partner. The results show that the suggested approach provides solutions with better overall values than those found by the software provided by our industrial partner. [ABSTRACT FROM AUTHOR]

In recent years, the automotive industry has faced an unprecedented crisis. In particular, the zero-inventory approach, which has been widely pursued by many automobile companies, seems to be impractical in some real production contexts since it requires an inventory of all parts but in low amounts. In this paper, we investigate a new logistics method which collects automobile parts by integrating the progress-lane (P-LANE) into the corresponding vehicle routing problem. We propose a mixed integer programming formulation for this new model, which can simultaneously determines the trip routes to collect automobile parts, as well as the P-LANE that each collected part should be assigned to, so as to minimize the total costs of the production and inbound logistics. The comparison with the zero-inventory model shows that the use of the P-LANE within the milk-run system could significantly decrease the total costs and also improve the transportation efficiency. To be specific, for small and large size instances, the total costs of the zero-inventory model are about 10% and 30% higher than the ones with P-LANE, respectively, which suggests that the periodic part collection model with P-LANE could be more appropriate for automobile manufacturing. [ABSTRACT FROM AUTHOR]

Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm's legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty. [ABSTRACT FROM AUTHOR]

Modern-day logistics companies require increasingly shorter lead-times in order to cater for the increasing popularity of on-demand services. There is consequently an urgent need for fast scheduling algorithms to provide high quality, real-time implementable solutions. In this work we model a spare part delivery problem for an on-demand logistics company, as a variant of vehicle routing problem. For large delivery networks, the optimisation solution technique of column generation has been employed successfully in a variety of vehicle routing settings and is often used in combination with exact methods for solving problems with a large number of variables. Challenges may arise when the pricing subproblem is difficult to solve in a realistic period due to complex constraints or a large number of variables. The problem may become intractable when the network structure varies daily or is known with less certainty over longer period. In such instances, a high quality heuristic solution may be more preferable than an exact solution with excessive run time. We propose an improved version of column generation approach integrating an efficient genetic algorithm to obtain fast and high-quality solutions for a sustainable spare parts delivery problem. More specifically, we propose to retain the traditional column generation iterative framework, with master problem solved exactly, but with pricing subproblem solved using a metaheuristic. Computational results on a real dataset indicate that this approach yields improved solutions compared to the current best-case business-as-usual costs. It also substantially decreases the computational time; allowing for high-quality, tractable solutions to be obtained in few minutes. We propose to strike a balance between the practical and efficient solution aspects of metaheuristic algorithms, and the exact decomposition and iterative aspect of the column generation solution technique. [ABSTRACT FROM AUTHOR]

We propose an efficient algorithm to provide transportation routes and schedules to pick up medical specimens from clinics, physician's offices, and hospitals and deliver them to a central laboratory quickly. This healthcare vehicle routing and scheduling problem differs from existing vehicle routing problems primarily in that, instead of minimizing driving time, the objective is to minimize the completion time, that is, the time from when the specimen is available for pickup until it is delivered to the central laboratory. We combine the routing problem with scheduling of vehicles and formulate a mixed integer linear program. We present a new algorithm to solve this optimization problem, called the Vehicle Routing and Scheduling Algorithm (VeRSA). VeRSA uses an indexing method inspired by scheduling methods to efficiently traverse a branch-and-bound tree associated with the mixed integer program. Instead of using a linear programming relaxation, as is common, we prove several propositions that lead to expressions that are fast to compute. We also prove a theoretical lower bound to provide some information on an optimality gap. Numerical results on three small and three large test problems demonstrate the high quality of solutions provided by VeRSA. The data in the large test problems are based on data provided by the University of Washington Medical Center (with modifications to protect confidentiality). The computational speed of VeRSA makes it applicable to real-time operational decisions when demand may be updated at any time due to cancellations or additional pickups. This model is applicable to other types of pickup and delivery systems where the waiting time of a package is important, such as perishable items. [ABSTRACT FROM AUTHOR]

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). [ABSTRACT FROM AUTHOR]

Multi-compartment vehicle routing problems arise in a variety of problem settings in which different product types have to be transported separated from each other. In this paper, a problem variant which occurs in the context of glass waste recycling is considered. In this problem, a set of locations exists, each of which offering a number of containers for the collection of different types of glass waste (e.g. colorless, green, brown glass). In order to pick up the contents from the containers, a fleet of homogeneous disposal vehicles is available. Individually for each disposal vehicle, the capacity can be discretely separated into a limited number of compartments to which different glass waste types are assigned. The objective of the problem is to minimize the total distance to be travelled by the disposal vehicles. For solving this problem to optimality, a branch-and-cut algorithm has been developed and implemented. Extensive numerical experiments have been conducted in order to evaluate the algorithm and to gain insights into the problem structure. The corresponding results show that the algorithm is able to solve instances with up to 50 locations to optimality and that it reduces the computing time by 87% compared to instances from the literature. Additional experiments give managerial insights into the use of different variants of compartments with flexible sizes. [ABSTRACT FROM AUTHOR]

The current global interest in improving the use of ever-scarcer natural resources calls for the re-alignment of supply chain operations to include not only economic factors, but environmental and social factors as well. Two of the most important supply chain activities that logistics managers have to deal with are the planning and improvement of the packing and distribution of products. Although the optimization of these two activities has been thoroughly studied by means of Vehicle Routing Problems and Packing Problems, their analysis is often done separately and, in most cases, they consider only the economic decisions. Independent optimization of these two operations may overlook the structural dependencies between them, resulting in impractical solutions; while the consideration of only the economic criteria can overlook the environmental and social impacts of distribution activities, in the scope of sustainable supply chains. With the objective of improving distribution logistics, the aim of this review is to provide an overview of recent optimization developments for integrating packing and routing problems, in order to propose a simple classification scheme for re-aligning the optimization criteria and operational constraints, taking into consideration the issues of sustainability. [ABSTRACT FROM AUTHOR]

The paper presents an optimization model and its implementation using a hybrid approach for the Capacitated Vehicle Routing Problem with Pick-up and Alternative Delivery (CVRPPAD). The development of the CVRPPAD was motivated by postal items distribution issues. The model proposed combines various features of Vehicle Routing Problem variants. The novelty of this model lies in the introduction of alternativeness of item delivery points, differentiation of delivery point types in terms of their capacity (parcel lockers) and possibility of collecting the items during the execution of delivery routes. The model also takes into account a possibility of introducing time windows related to delivery time. The proposed data structure in the form of sets of facts facilitates the implementation of the model in all environments, Constraint Logic Programming (CLP), Mathematical Programming (MP), metaheuristics, databases, etc. The model is implemented in the MP, hybrid CLP/MP, and hybrid CLP/heuristic environments. The hybrid CLP/MP approach is the authors' original solution, which has already been used to solve Supply Chain Management problems, scheduling problems, routing problems etc. For large size problems, considering their combinatorial character, the proposed CLP/MP approach is ineffective. Its effectiveness will improve when MP is replaced by a heuristic. All implementations were performed on the same data instances (facts), which made it possible to compare them according to the solution search time, number of decision variables and number of constraints. The directions into which the model can be further developed were also presented. [ABSTRACT FROM AUTHOR]

We consider a family of rich vehicle routing problems (RVRP) which have the particularity to combine a heterogeneous fleet with other attributes, such as backhauls, multiple depots, split deliveries, site dependency, open routes, duration limits, and time windows. To efficiently solve these problems, we propose a hybrid metaheuristic which combines an iterated local search with variable neighborhood descent, for solution improvement, and a set partitioning formulation, to exploit the memory of the past search. Moreover, we investigate a class of combined neighborhoods which jointly modify the sequences of visits and perform either heuristic or optimal reassignments of vehicles to routes. To the best of our knowledge, this is the first unified approach for a large class of heterogeneous fleet RVRPs, capable of solving more than 12 problem variants. The efficiency of the algorithm is evaluated on 643 well-known benchmark instances, and 71.70% of the best known solutions are either retrieved or improved. Moreover, the proposed metaheuristic, which can be considered as a matheuristic, produces high quality solutions with low standard deviation in comparison with previous methods. Finally, we observe that the use of combined neighborhoods does not lead to significant quality gains. Contrary to intuition, the computational effort seems better spent on more intensive route optimization rather than on more intelligent and frequent fleet re-assignments. [ABSTRACT FROM AUTHOR]

This article considers the heterogeneous fleet vehicle routing problem, as a variant of a well-known transportation problem: the vehicle routing problem. In order to solve this particular routing problem, a variable neighborhood search with a threshold accepting mechanism is developed and implemented. The performance of the algorithm was compared to other algorithms and tested on datasets from the available literature. Computational results show that our proposed algorithm is competitive and generates new best solutions. [ABSTRACT FROM AUTHOR]

This paper presents a survey on the multi-trip vehicle routing problem (MTVRP) and on related routing problems where vehicles are allowed to perform multiple trips and corresponds to the article by Cattaruzza et al. (4OR 14(3):223-259, 2016). The first part of the paper focuses on the MTVRP. It gives an unified view on mathematical formulations and surveys exact and heuristic approaches. The paper continues with variants of the MTVRP and other families of routing problems where multiple trips are sometimes allowed. For the latter, it specially insists on the motivations for having multiple trips and the algorithmic consequences. The expected contribution of the survey is to give a comprehensive overview on a structural property of routing problems that has seen a strongly growing interest in the last few years and that has been investigated in very different areas of the routing literature. [ABSTRACT FROM AUTHOR]

Contemporary vehicle routing requires ubiquitous computing and massive data in order to deal with the three aspects of transportation such as operations, planning and safety. Out of the three aspects, safety is the most vital and this study refers safety as the reduction of CO2 emissions and hazardous risks. Hence, this paper presents a data driven multi-objective differential evolution (MODE) algorithm to solve the safe capacitated vehicle routing problems (CVRP) by minimizing the greenhouse gas emissions and hazardous risk. The proposed data driven MODE is tested using benchmark instances associated with real time data which have predefined load for each of the vehicle travelling on a specific route and the total capacity summed up from the customers cannot exceed the stated load. Pareto fronts are generated as the solution to this multi-objective problem. Computational results proved the viability of the data driven MODE algorithm to solve the multi-objective safe CVRP with a certain trade-off to achieve an efficient solution. Overall the study suggests 5 % increment in cost function is essential to reduce the risk factors. The major contributions of this paper are to develop a multi-objective model for a safe vehicle routing and propose a MODE algorithm that can handle structured and unstructured data to solve the safe capacitated vehicle routing problem. [ABSTRACT FROM AUTHOR]

The capacitated green vehicle routing problem is considered in this paper as a new variant of the vehicle routing problem. In this problem, alternative fuel-powered vehicles (AFVs) are used for distributing products. AFVs are assumed to have low fuel tank capacity. Therefore, during their distribution process, AFVs are required to visit alternative fuel stations (AFSs) for refueling. The design of the vehicle routes for AFVs becomes difficult due to the limited loading capacity, the low fuel tank capacity and the scarce availability of AFSs. Two solution methods, the two-phase heuristic algorithm and the meta-heuristic based on ant colony system, are proposed to solve the problem. The numerical experiment is performed on the randomly generated problem instances to evaluate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Though there are many specialities of logistics distribution in unexpected events, the greatest uniqueness of the problem should be the feature that no historical data about some parameters are available. The paper defines the emergency logistics as the one of which the parameters have no historical data due to the occurrence of the unexpected events, and discusses an emergency logistics distribution routing problem in which demands of the affected areas and road travel times lack historical data and are given by experts’ estimations. Uncertain variables are used to describe the experts’ estimates of the parameters and the use of them is justified. An emergency logistics distribution routing model is developed based on uncertainty theory. To solve the problem, the equivalent model is provided and a cellular genetic algorithm is designed. In addition, an example is presented to illustrate the application of the proposed model and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

An Amphibious Ready Group (ARG) consists of ships capable of conducting flight operations that daily require the transport of personnel and cargo (PMC) to remain operationally viable. Planning daily PMC transport for ARG ships and nearby airfields is a unique vehicle routing problem characterized by a heterogeneous capacitated vehicle fleet, two cargo types, multiple depots, time windows, and synchronized routing of two aircraft required between some but not all node pairs. We formulate this problem as an integer linear program (ILP) with an objective function that expresses the cost of flight operations and a penalty for undelivered PMC. We perform extensive computational testing using an ILP solver and a tailored ant colony optimization with local search metaheuristic on test instances constructed to represent those found in practice. We find most instances difficult to solve optimally while our heuristic provides the best known or close to the best known solution in just a few minutes. We have embedded our heuristic in a decision support system complete with graphical user interface and sent this out for use by United States Navy ARG planners. [ABSTRACT FROM AUTHOR]

In this paper we address the Vehicle Routing Problem with Pickups and Deliveries (VRPPD), an extension of the classical Vehicle Routing Problem (VRP) where we consider precedences among customers, and develop an Integer Linear Programming (ILP) based local search procedure. We consider the capacitated one-to-one variant, where a particular precedence must be satisfied between pairs of origin-destination customers. We extend the scheme proposed in De Franceschi et al. (Math Program 105(2-3):471-499, 2006) for the Distance-Constrained Capacitated VRP, which has been successfully applied to other variants of the VRP. Starting from an initial feasible solution, this scheme follows the destroy/repair paradigm where a set of vertices is removed from the routes and reinserted by solving heuristically an associated ILP formulation with an exponential number of variables, named Reallocation Model. In this research, we propose two formulations for the Reallocation Model when considering pickup and delivery constraints and compare their behavior within the framework in terms of the trade off between the quality of the solutions obtained and the computational effort required. Based on the computational experience, the proposed scheme shows good potential to be applied in practice to this kind of problems and is a good starting point to consider more complex versions of the VRPPD. [ABSTRACT FROM AUTHOR]