Single Depot Research Articles

Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses

To address sustainable development issues of urban traffic, electric buses will join traditional bus system, and the scheduling of bus fleet should be adjusted due to the distinct features of electric buses. To this end, this paper develops a Multi-objective Bi-level programming model to collaboratively optimize the vehicle scheduling and charging scheduling of the mixed bus fleet under the operating conditions of a single depot. The upper level determines the vehicle scheduling to minimize the operating cost and carbon emissions under the constraints of connecting time between trips and the limited driving range of electric buses. The lower level is a charging scheduling problem that considers the charging time and the limited driving distance constraint to minimize the charging cost. The proposed model is solved with an integrated heuristic algorithm. The vehicle scheduling problem is addressed with the iterative neighborhood search algorithm based on simulated annealing, while the charging scheduling problem is solved with a greedy dynamic selection strategy based on the approach of multi-stage decision. Finally, case study is carried out based on a mixed bus fleet in Beijing, and the results validate the availability of the proposed model and solution algorithm.

Solving the vehicle routing problem with multi-compartment vehicles for city logistics

Logistics companies are under increasing pressure to overcome operational challenges and sustain profitable growth while dealing with the newest requirements of their customers. One of the remedies designed to cope with a higher number of shipments is to use multi-compartment city vans to ensure all forms of integration with deliveries. In the area of city logistics, the most common type of delivery involves storing inventory in a central warehouse and to deliver customers’ orders with multi-compartment vehicles. The problem under study is denoted as the vehicle routing problem with multi-compartment vehicles which are to operate from a single depot to visit customers within the chosen time period by minimizing major operational costs. We propose an enhanced adaptive large neighborhood search algorithm for the investigated routing problem. The computational results highlight the efficiency of the proposed algorithm in terms of both solution quality and solution time and also provide useful insights for city logistics.

The vehicle routing problem with simultaneous pickup and delivery and handling costs

In this paper we introduce the vehicle routing problem with simultaneous pickup and delivery and handling costs (VRPSPD-H). In the VRPSPD-H, a fleet of vehicles operates from a single depot to service all customers, which have both a delivery and a pickup demand such that all delivery items originate from and all pickup items go to the depot. The items on the vehicles are organized as a single linear stack where only the last loaded item is accessible. Handling operations are required if the delivery items are not the last loaded ones. We implement a heuristic handling policy approximating the optimal decisions for the handling sub-problem, and we propose two bounds on the optimal policy, resulting in two new myopic policies. We show that one of the myopic policies outperforms the other one in all configurations, and that it is competitive with the heuristic handling policy if many routes are required. We propose an adaptive large neighborhood search (ALNS) metaheuristic to solve our problem, in which we embed the handling policies. Computational results indicate that our metaheuristic finds optimal solutions on instances of up to 15 customers. We also compare our ALNS metaheuristic against best solutions on benchmark instances of two special cases, the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) and the traveling salesman problem with pickups, deliveries and handling costs (TSPPD-H), and on two related problems, the vehicle routing problem with divisible pickup and delivery (VRPDPD) and the vehicle routing problem with mixed pickup and delivery (VRPMPD). We find or improve 39 out of 54 best known solutions (BKS) for the VRPSPD, 36 out of 54 BKS for the VRPDPD, 15 out of 21 BKS for the VRPMPD, and 69 out of 80 BKS for the TSPPD-H. Finally, we introduce and analyze solutions for the variations of the VRPDPD and VRPMPD with handling costs – the VRPDPD-H and the VRPMPD-H, respectively.

Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome.

Over the past decade, great progress has been made in understanding the complexity of adipose tissue biology and its role in metabolism. This includes new insights into the multiple layers of adipose tissue heterogeneity, not only differences between white and brown adipocytes, but also differences in white adipose tissue at the depot level and even heterogeneity of white adipocytes within a single depot. These inter- and intra-depot differences in adipocytes are developmentally programmed and contribute to the wide range of effects observed in disorders with fat excess (overweight/obesity) or fat loss (lipodystrophy). Recent studies also highlight the underappreciated dynamic nature of adipose tissue, including potential to undergo rapid turnover and dedifferentiation and as a source of stem cells. Finally, we explore the rapidly expanding field of adipose tissue as an endocrine organ, and how adipose tissue communicates with other tissues to regulate systemic metabolism both centrally and peripherally through secretion of adipocyte-derived peptide hormones, inflammatory mediators, signaling lipids, and miRNAs packaged in exosomes. Together these attributes and complexities create a robust, multidimensional signaling network that is central to metabolic homeostasis.

An Improved Heuristic Based on Clustering and Genetic Algorithm for Solving the Multi-Depot Vehicle Routing Problem

Abstract: This paper introduces the multi depot vehicle routing problem (MDVRP) with location depot, a hard combinatorial optimization problem arising in several applications. The MDVRP with location depot based on two well-known NP-hard problems: the facility location problem (FLP) and the multi depot vehicle routing problem (MDVRP) with location depot. In the first phase, the problem consists of selecting on which sites to install a warehouse and assigning one and only one warehouse to each customer. In the second phase, for each depot, we search to solve a vehicle routing problem (VRP), multiple vehicles homogenous leave from a single depot and return to the same one. Each route must respect the capacity of each vehicle. The goal of this problem is to minimize the total distances to the performed routes. The MDVRP with location depots is classified as an NP-hard problem. Hence, the use of exact optimization methods may be difficult to solve this problem. In this work, we propose a method to solve it to optimality. The proposed procedure initially uses K-means algorithm to optimize the location selection and customer assignment, then planning the routes from the selected warehouses to a set of customers using Clarke and Wright saving method. The routes from the resulting multi-depot vehicle-routing problem (MDVRP) are improved using a Genetic Algorithm (GA). The proposed approach is tested and compared on a set of twelve benchmark instances from the MDVRP literature. The computational experiments confirm that our heuristic is able to find best solutions.

Empirical-type simulated annealing for solving the capacitated vehicle routing problem

ABSTRACTThe Capacitated Vehicle Routing Problem (CVRP) is a well-known combinatorial optimisation problem used to design an optimal route for a fleet of capacitated vehicles based at a single depot, to serve a set of customers. Over the few past years, the interest in solving real-world applications of the CVRP, especially in transportation and logistics, has grown tremendously. The Simulated Annealing (SA) algorithm is among the most effective employed techniques for finding the CVRP’s global optimums. However, because of its lack of flexibility, the SA algorithm may have some weakness, like its slowness and its wandering near the global minimum in the final stage of the search. For this reason, we define in this paper the Empirical-Type Simulated Annealing (ETSA) as a new dynamic version of the SA for effectively solving the CVRP and any other vehicle routing problem. The method operates incrementally by exploiting the last portion of worse feasible solutions, which are fitted using a parametric density function, to update the SA’s Boltzmann acceptance criterion. This leads to a more accurate decision within the searching process, and consequently, optimums are more rapidly reached. A comparison to state-of-the-art approaches has proven that the new algorithm is capable of locating all optimums while improving the convergence of the SA algorithm.

A decision support system for technician routing with time windows

Propósitopresentar un sistema de soporte a las decisiones (Decision Support System, DSS) para una empresa colombiana de servicios públicos con el fin de apoyar el proceso de toma de decisiones a nivel operativo en lo relacionado con la planeación de rutas y el tiempo de servicio. El objetivo es suministrar una herramienta que ayude a los técnicos a desempeñar el servicio de corte y reconección de servicios domiciliarios para aproximadamente 2000 clientes por día.Diseño/metodología/enfoqueel problema de una empresa real es modelado como un problema de enrutamiento de vehículos un único depósito y ventanas de tiempo (Single Depot Vehicle Routing Problem with Time Windows, SDVRP-TW). Éste es un problema de optimización muy conocido en Investigación de Operaciones / Ciencias de la Administración. Se presenta un enfoque de dos etapas integrado en un software de ayuda a la toma de decisiones. La primera etapa considera el agrupamiento de los clientes generado por una combinación de los algoritmos del barrido y el k-media, mientras que la segunda fase define el plan de rutas para los técnicos utilizando las heurísticas de vecino más cercano y Or-opt. El enfoque propuesto es validado empleando datos reales.Hallazgosen comparación con el plan de rutas actualmente utilizado por la empresa, el método propuesto es capaz de obtener ahorros en el tiempo total de viaje incrementando la eficiencia operativa en un 22.2%.Limitaciones de la invstigación/implicacionespuesto que el análisis se lleva a cabo a partir de un modelo matemático, los supuestos sobre las relaciones entre las variables y los elementos del sistema real complejo podrían simplificarse. Además, aunque el sistema propuesto realiza la planeación de rutas, la decisión final es tomada finalmente por las personas.Implicaciones prácticasel DSS propuesto tiene un impacto crítico en la práctica operativa real de la empresa. La productividad y el nivel de servicio se mejoran, mientras se reducen los costos operativos. El proceso de toma de decisiones en sí mismo se verá mejorado pues los técnicos y los tomadores de decisiones pueden enfocarse en realizar otras tareas.Originalidad/valorel problema real es modelado utilizando programación matemática y se resuelve de forma efectiva con un procedimiento de dos etapas sencillo y básicamente intuitivo que no ha sido puesto en marcha antes para tales empresas de servicios. Además, puesto que datos reales de la empresa son utilizados en la experimentación, el enfoque de solución es validado y su eficiencia y eficacia son comprobadas en un ambiente real, suministrando así un comportamiento real para los tomadores de decisiones en la empresa.

A Multi-Objective Solution of Green Vehicle Routing Problem

Abstract Distribution is one of the major sources of carbon emissions and this issue has been addressed by Green Vehicle Routing Problem (GVRP). This problem aims to fulfill the demand of a set of customers using a homogeneous fleet of Alternative Fuel Vehicles (AFV) originating from a single depot. The problem also includes a set of Alternative Fuel Stations (AFS) that can serve the AFVs. Since AFVs started to operate very recently, Alternative Fuel Stations servicing them are very few. Therefore, the driving span of the AFVs is very limited. This makes the routing decisions of AFVs more difficult. In this study, we formulated a multi-objective optimization model of Green Vehicle Routing Problem with two conflicting objective functions. While the first objective of our GVRP formulation aims to minimize total CO 2 emission, which is proportional to the distance, the second aims to minimize the maximum traveling time of all routes. To solve this multi-objective problem, we used ɛ-constraint method, a multi-objective optimization technique, and found the Pareto optimal solutions. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model in IBM OPL CPLEX. To test our proposed method, we generated two hypothetical but realistic distribution cases in Izmir, Turkey. The first case study focuses on an inner-city distribution in Izmir, and the second case study involves a regional distribution in the Aegean Region of Turkey. We presented the Pareto optimal solutions and showed that there is a tradeoff between the maximum distribution time and carbon emissions. The results showed that routes become shorter, the number of generated routes (and therefore, vehicles) increases and vehicles visit a lower number of fuel stations as the maximum traveling time decreases. We also showed that as maximum traveling time decreases, the solution time significantly decreases.

306-LB: Adipocyte Subpopulations Mediate the Inflammatory Response in Adipose Tissue

Recent studies suggest that, even within a single adipose depot, there may be distinct subpopulations of adipocytes. Our lab has uncovered three developmentally distinct subpopulations of white adipocytes. These adipocyte subpopulations have unique gene expression signatures, and display differential responses to exogenous stimuli including insulin, inflammatory cytokines, and insulin. These three adipocyte subtypes are distinguished by high expression of specific marker genes: Wilms’ tumor 1 (type 1), transgelin (type 2), and myxovirus 1 (type 3). Utilizing Cre transgenic mice, transcription of which is directed by the promoters of these marker genes, lineage tracing analysis indicate that these three preadipocyte subpopulations independently gave rise to adipocytes in vivo, and differentially contribute to the adipose tissue depots. During high fat diet (HFD) induced obesity, macrophages, organized into crown like structures (CLS) around dead and dying adipocytes, are detected in the vicinity of type 1 adipocytes, as assessed by kernel density estimation. This association between type 1 adipocytes and CLS is secondary to a ∼10-fold increase in the expression of macrophage chemoattractant protein 1 at both the mRNA and protein level. Furthermore, although CLS are found in the vicinity of type 1 cells, with very few of the CLS are in direct contact with type 1 adipocytes. Instead, over 80% of CLS are found in direct contact with type 2 adipocytes, indicating an increased death of these adipocytes during HFD-induced obesity. Taken together, these data suggest that type 1 and type2 adipocytes coordinately regulate the inflammatory response in adipose tissue. Disclosure Q. Luong: None. J. Huang: None. R. Sharma: None. K.Y. Lee: None. Funding American Diabetes Association (1-17-JDF-055 to K.Y.L.)

Flexible two-echelon location routing problem

Abstract This paper deals with an integrated routing problem in which a supplier delivers a commodity to its customers through a two-echelon supply network. Over a planning horizon, the commodity is first sent from a single depot to a set of Distribution Centers (DCs). Then, from the DCs, it is delivered to customers. Two sources of flexibility are analyzed: flexibility in network design and flexibility in due dates. The former is related to the possibility of renting any of the DCs in any period of the planning horizon, whereas the latter is related to the possibility of serving a customer between the period an order is set and a due date. The objective is to minimize the total cost consisting of the sum of the shipping cost from the depot to the DCs, the traveling cost from the DCs to the customers, the renting cost of DCs, and the penalty cost for unmet due dates. A mathematical programming formulation is presented, together with different classes of valid inequalities. Moreover, an exact method is proposed that is based on the interplay between two branch-and-bound algorithms. Computational results on randomly generated instances show the value of each of the two kinds of flexibility. Their combination leads to average savings of up to about 30%.

Reducing pollutant emissions in a waste collection vehicle routing problem using a variable neighborhood tabu search algorithm: a case study

This paper focuses on designing waste collection routes with a single landfill using eco-efficiency as a performance indicator. In this problem, there are a limited number of heterogeneous vehicles based at a single depot. Empty vehicles leave the depot, collect waste from a set of locations and drop off the collected waste at a specific landfill. Then, vehicles leave the landfill and may collect more waste from other locations or return empty to the depot. Traditional performance indicators in vehicle routing problems are mainly focused on economic objectives, not explicitly considering environmental issues. In this paper, a mathematical model is presented with an eco-efficient objective function that takes into account external costs (climate change and air pollution). The COPERT model is used for estimating fuel consumption, carbon dioxide and pollutant emissions. The problem is first heuristically solved using a semi-parallel construction algorithm. Then, solutions are improved by a variable neighborhood tabu search algorithm developed for this problem. The algorithm is validated for a real problem in the municipality of Alcala de Guadaira, within the metropolitan area of Seville (Spain). Results obtained on a set of case studies improve the solution that is currently implemented in the municipality, in terms of total distance traveled, carbon dioxide emissions and pollutant emissions.

An open close multiple travelling salesman problem with single depot

An open close multiple travelling salesman problem with single depot

Towards an efficient approximability for the Euclidean Capacitated Vehicle Routing Problem with Time Windows and multiple depots

We consider the Euclidean Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). For the long time, approximability of this well-known problem in the class of algorithms with theoretical guarantees was poorly studied. This year, for the case of a single depot, we proposed two approximation algorithms, which are the Efficient Polynomial Time Approximation Schemes (EPTAS) for any fixed given capacity q and the number p of mutually disjunctive time windows. The former scheme extends the celebrated approach proposed by M. Haimovich and A. Rinnooy Kan and allows the evident parallelization, while the latter one has an improved time complexity bound and the enlarged domain in terms q = q(n) and p = p(n), where it retains polynomial time complexity. In this paper, we announce an extension of these results to the case of multiple depots. So, the first scheme is also EPTAS for any fixed parameters q, p, and m, where m is the number of depots, and remains PTAS for q = o(loglog n) and mp = o(log log n). In other turn, the second one is a PTAS for p3q4 = O(log n) and (pq)2 log m = O(log n).

GREEDYALGORITHM FOR INVENTORY ROUTING PROBLEM IN A SUPPLY CHAIN-A Review

Abstract Inventory routing problem (IRP) is one of the emerging solutions for improving the supply chain efficiency. It gives the vendors the responsibility to monitor and decide the inventory replenishments of their customers. The Inventory Routing Problem (IRP) is an evolved component of VMI policies. Inventory routing provides a win-win situation for both vendors and customers. In this paper we consider a one or more distribution network consisting of a single depot and many geographically dispersed customers where each customer faces demands for a single item which, arise at a deterministic and customer-specific rate. The problem addressed in this paper is based on a finite horizon, multi- period and single product where a fleet of capacitated vehicles transport items from the depot to meet the demand specified by the customers for each period. Here a greedy algorithm is used for finding an optimal route and routing cost associated with it, finding an inventory and transportation cost that minimizes the total coste.

Developmental and functional heterogeneity of white adipocytes within a single fat depot.

Recent studies suggest that, even within a single adipose depot, there may be distinct subpopulations of adipocytes. To investigate this cellular heterogeneity, we have developed multiple conditionally immortalized clonal preadipocyte lines from white adipose tissue of mice. Analysis of these clones reveals at least three white adipocyte subpopulations. These subpopulations have differences in metabolism and differentially respond to inflammatory cytokines, insulin, and growth hormones. These also have distinct gene expression profiles and can be tracked by differential expression of three marker genes: Wilms' tumor 1, transgelin, and myxovirus 1. Lineage tracing analysis with dual-fluorescent reporter mice indicates that these adipocyte subpopulations have differences in gene expression and metabolism that mirror those observed in the clonal cell lines. Furthermore, preadipocytes and adipocytes from these subpopulations differ in their abundance in different fat depots. Thus, white adipose tissue, even in a single depot, is comprised of distinct subpopulations of white adipocytes with different physiological phenotypes. These differences in adipocyte composition may contribute to the differences in metabolic behavior and physiology of different fat depots.

Optimal configuration problem identification of electrical power cable in tidal turbine farm\xa0via traveling salesman problem modeling approach

Electrical power cables in tidal turbine farms contribute a significant share to capital expenditure (CAPEX). As a result, the routing of electrical power cables connecting turbines to cable collector hubs must be designed so as to obtain the least cost configuration. This is referred to as a tidal cable routing problem. This problem possesses several variants depending on the number of cable collector hubs. In this paper, these variants are modeled by employing the approach of the single depot multiple traveling salesman problem (mTSP) and the multiple depot mTSP of operational research for the single and multiple cable collector variants, respectively. The developed optimization models are computationally implemented using MATLAB. In the triple cable collector cable hub variant, an optimal solution is obtained, while good-quality suboptimal solutions are obtained in the double and single cable collector hub variants. In practice, multiple cable collector hubs are expected to be employed as the multiple hub configurations tend to be more economic than the single hub configurations. This has been confirmed by this paper for an optimal tidal turbine layout obtained with OpenTidalFarm. Suggestions are presented for future research studies comprising a number of heuristics.

A path-based solution approach for the Green Vehicle Routing Problem

The Green Vehicle Routing Problem concerns routing alternative fuel vehicles, based at a single depot, to handle a subset of customers while minimizing the total travel distance. Due to limited fuel autonomy, each vehicle may need to stop at Alternative Fuel Stations (AFSs) during its trip. We propose a two-phase solution approach in which a route is seen as the composition of paths, each one handling a subset of customers without intermediate stops at AFSs. In the first phase, all feasible paths are generated limiting their number through dominance rules. In the second phase, the paths are selected and properly combined to generate the routes via Mixed Integer Linear Programming. Our approach, tested on small- to medium-sized benchmark instances, outperforms the existing exact methods obtaining always the optimal solution in a smaller average computational time. Furthermore, the approach was converted into a heuristic one considering in the first phase only a subset of feasible non-dominated paths. In this way, we can also address larger- sized instances outperforming, in terms of solution quality, the best heuristic approach in the literature.

복수포장대 좁은통로 피킹시스템의 주문묶음 알고리즘

This study proposes a joint order batching and depot selecting model (JOBDS) in narrow-aisle order picking systems (OPSs). The two depots in a hypothetical narrow-aisle OPS distribute the operational burden imposed by inspectidon and packaging. We build an order batching model and develop a mixed integer programming (MIP) algorithm considering the travel distance to each depot’s location for a 48-order and a 72-order case. We also develop large-scale heuristic algorithms: Seed and Saving algorithms. A comparison to order batching model with single depot (OBSD) shows that the proposed algorithms shorten the travel distance on average 4.19 ~ 13.38%. We provide additional simulation studies to analyze total travel time by walk speed, order size, inspection and packaging times, number of pickers, and location of depots. Consistently, travel time in JOBDS is operationally advantageous compared to OBSD.

NanoSIMS imaging reveals unexpected heterogeneity in nutrient uptake by brown adipocytes

Heterogeneity in the metabolic properties of adipocytes in white adipose tissue has been well documented. We sought to investigate metabolic heterogeneity in adipocytes of brown adipose tissue (BAT), focusing on heterogeneity in nutrient uptake. To explore the possibility of metabolic heterogeneity in brown adipocytes, we used nanoscale secondary ion mass spectrometry (NanoSIMS) to quantify uptake of lipids in adipocytes interscapular BAT and perivascular adipose tissue (PVAT) after an intravenous injection of triglyceride-rich lipoproteins (TRLs) containing [2H]triglycerides (2H-TRLs). The uptake of deuterated lipids into brown adipocytes was quantified by NanoSIMS. We also examined 13C enrichment in brown adipocytes after administering [13C]glucose or 13C-labeled mixed fatty acids by gastric gavage. The uptake of 2H-TRLs-derived lipids into brown adipocytes was heterogeneous, with 2H enrichment in adjacent adipocytes varying by more than fourfold. We also observed substantial heterogeneity in 13C enrichment in adjacent brown adipocytes after administering [13C]glucose or [13C]fatty acids by gastric gavage. The uptake of nutrients by adjacent brown adipocytes within a single depot is variable, suggesting that there is heterogeneity in the metabolic properties of brown adipocytes.

Optimization of Vehicle Routing for Smart City: Real Case Study in Casablanca

Smart cities constitute the future of civilian habitation. Artificial intelligence permits optimal solutions exploiting the data coming from the information systems of the company and the Geographical Information Systems (GIS) based on the sensors embedded in the cities. Through this paper we presented a new approach to plan the distribution of food products from a single depot to multiple customers with variable demand rates. To do this, we used the exact and heuristic approach to optimize our routing optimization model that takes into account the customer lead time. These approaches used GIS to integrate real vehicle travel data and then choose the optimal configuration to satisfy customers, reduce operating costs, and avoid contributing to congestion in the city.