Set Of Customers Research Articles

The incremental connected facility location problem

We consider the incremental connected facility location problem (incremental ConFL), in which we are given a set of potential facilities, a set of interconnection nodes, a set of customers with demands, and a planning horizon. For each time period, we have to select a set of facilities to open, a set of customers to be served, the assignment of these customers to the open facilities, and a network that connects the open facilities. Once a customer is served, it must remain served in subsequent periods. Furthermore, in each time period the total demand of all customers served must be at least equal to a given minimum coverage requirement for that period. The objective is to minimize the total cost for building the network given by the investment and maintenance costs for the facilities and the network summed up over all time periods.We propose a mixed integer programming approach in which, in each time period, a single period ConFL with coverage restrictions has to be solved. For this latter problem, which is of particular interest in itself, new families of valid inequalities are proposed: these are set union knapsack cover (SUKC) inequalities, which are further enhanced by lifting and/or combined with cut-set inequalities, which are primarily used to ensure connectivity requirements. Details of an efficient branch-and-cut implementation are presented and computational results on a benchmark set of large instances are given, including examples of telecommunication networks in Germany.

Improved Approximation Algorithm for the Fault-Tolerant Facility Placement Problem with Rejection

SYNOPTIC ABSTRACTIn this article, we revisit an interesting variant of the fault-tolerant facility placement problem with rejection (FTFPWR, for short). In the FTFPWR, we are given a set of potential locations to open facilities, and a set of customers to be connected to a number of facilities to satisfy their demands. At each location we are allowed to open any number of facilities, each with its corresponding opening cost. Each customer has an integral demand which specifies the number of open facilities that it should be connected to. Some facilities that a customer is connected to could be located at the same location, as long as they are all different and open. For each location and customer pair we are also given a distance between them that represents the cost to connect one unit of demand from the customer to a facility at its location. We assume the distance function to be metric. The task is to choose a subset of locations to open facilities, and choose a subset of customers to connect their demands to the open facilities at locations with the remaining customers to be rejected by paying the rejection costs such that the sum of the facility opening costs, the connection costs, and the rejection costs is minimized. For the FTFPWR, the performance ratio of currently best approximation algorithm is 2.515. By introducing a randomized rounding approach and the derandomizing technique, we propose an improved approximation algorithm with the performance ratio of 2.07.

Green vehicle routing problem with queues

Organizations spend considerable amount of time and money in distributing goods from production centres to customers who are geographically distributed. When vehicles are engaged to make long trips, along with serving the customers it may halt at facility centres like toll booths, warehouses, refuelling stations etc., for some service. The cost and time associated with these halts will have an impact on the overall cost of the route. But, when organizations plan a route with minimum cost, the costs associated with halts in the facility centres are generally not accounted. When these facility centres are limited in number, vehicles should wait for their turn to get the service which has an impact on the overall cost of the tour. This paper intends to address the issues that are associated with the waiting time at the refuelling station. Green Vehicle Routing Problem with Queues (GVRP-Q) is introduced in this paper. GVRP-Q aims to serve a set of customers, but includes halts in between for refuelling in the refuelling stations. Each refuelling station is modelled as M/M/1 queue model, where vehicle is allowed to wait in the queue. The impact of this wait time on the overall cost of the route is studied in this paper. GVRP-Q is solved using an enhanced Chemical Reaction Optimization (e-CRO) where the performance of CRO is enhanced by a bacterial transformation. Experiments are conducted on bench mark data sets and the results predict the influence of the wait time on the overall route cost.

A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption

The electricity consumption of Heating Ventilating and Air Conditioning (HVAC) systems has a significant share in the energy consumption of buildings, which account for 75% of total electricity produced in the US. Therefore, improving the energy efficiency in HVAC systems is an essential goal in facility management (FM) industry. Building Automation Systems (BASs) deployed in buildings provide an enormous amount of data on HVAC operations, which can be leveraged to extract hidden knowledge and insights about operational signatures of these systems (i.e., parameter-value pairs set for running the equipment) and their relationship to energy profiles. This study aims to identify critical parameters of HVAC systems that drive the changes in the building energy-use profiles and develop an automated approach for identifying HVAC operational signatures and their energy profiles in buildings. The approach relies on data-driven methodologies and is composed of three major steps: data preprocessing, feature selection, and signature discovery and analysis. The approach was tested on four air handling units (AHUs) in different buildings. The results showed that it is possible to define operational signatures for facility operators to run AHUs at these custom settings and achieve about 30% saving in electric power, given the profiles across the operational signatures.

A branch and cut algorithm for the time-dependent profitable tour problem with resource constraints

In this paper we study the time-dependent profitable tour problem with resource constraints (TDPTPRC), a generalization of the profitable tour problem (PTP) which includes variable travel times to account for road congestion. In this problem, the set of customers to be served is not given and must be determined based on the profit collected when visited, keeping a balance with the total travel time. We propose a mixed integer linear programming (MILP) formulation that exploits the travel time function to reduce the size of a standard formulation from the literature. We derive four new families of valid inequalities and study the connections among them, as well as their associated separation problems. We develop a tailored Branch and Cut (BC) algorithm including these new families in addition to some well known valid inequalities from related problems. Computational results on four different problems, with alternative resources and objectives, show that the approach is flexible and effective. The algorithm achieves significant reductions in the computing times on benchmark instances from the related literature, and outperforms a recent method proposed for the time-dependent traveling salesman problem with time windows.

ε-Constraint method for bi-objective competitive facility location problem with uncertain demand scenario

We consider a model of two parties’ competition organized as a Stackelberg game. The parties open their facilities intending to maximize profit from serving the customers that behave following a binary rule. The set of customers is unknown to the party which opens its facilities first and is called the Leader. Instead, a finite list of possible scenarios specifying this set is provided to the Leader. One of the scenarios is to be realized in the future before the second party, called the Follower, would make their own decision. The scenarios are supplied with known probabilities of realization, and the Leader aims to maximize both the probability to get a profit not less than a specific value, called a guaranteed profit, and the value of a guaranteed profit itself. We formulate the Leader’s problem as a bi-objective bi-level mathematical program. To approximate the set of efficient solutions of this problem, we develop an ε -constraint method where a branch-and-bound algorithm solves a sequence of bi-level problems with a single objective. Based on the properties of feasible solutions of a bi-level program and mathematical programming techniques, we developed three upper bound procedures for the branch-and-bound method mentioned. In numerical experiments, we compare these procedures with each other. Besides that, we discuss relations of the model under investigation and the stochastic competitive location model with uncertain profit values.

Abstract 5006: Comprehensive gene expression analysis of the tumor microenvironment in patients with advanced cancer treated with a personalized neoantigen vaccine, NEO-PV-01, in combination with anti-PD1

Abstract Background: Neoantigens arise from DNA mutations in cancer cells and are important targets for T cell mediated anti-tumor immunity. NEO-PV-01 is a personal neoantigen vaccine of up to 20 peptides designed based on a patient’s neoantigen and HLA profile that is directed at inducing tumor-specific T cell responses to neoantigens. Here we report comprehensive immune-related gene expression analysis of longitudinal tumor biopsies from patients with metastatic melanoma, bladder, and non-small cell lung cancer treated on our NT-001 trial with NEO-PV-01 + adjuvant in combination with nivolumab (NCT02897765) to correlate with clinical outcomes. Methods: Tumor biopsies from all three tumor types were collected i) prior to treatment, ii) after 12 weeks of nivolumab monotherapy and iii) after completion of NEO-PV-01 vaccination. Targeted gene expression analysis on RNA extracted from FFPE blocks was performed using the NanoString™ nCounter platform. A custom set of 800 genes included markers for immune cell populations, cytolytic markers, immune activation and suppression, and the tumor microenvironment. Gene signatures of key immune features were calculated after normalization with housekeeping genes and used for subsequent analysis. Results: Changes in the immune cell populations and the tumor microenvironment were detected after treatment with nivolumab and NEO-PV-01. Increases in various immune cell subsets, including T cells and B cells, as well as an increase in cytolytic phenotype were observed in tumor biopsies following treatment. Moreover, changes in the tumor microenvironment, consistent with the absence of tumor by histologic evaluation, were detected in many of the post-vaccination biopsies. In addition, these observations were consistent with data from peripheral blood that demonstrated durable de novo neoantigen-specific immune responses after vaccination. Additional exploratory analyses of the data demonstrate differential gene expression in patients’ tumors that align with tumor responses to therapy. Conclusion: Treatment with nivolumab and NEO-PV-01 leads to changes in the tumor microenvironment that are consistent with cell types and phenotypes that could contribute to an anti-tumor response. Citation Format: Meghan E. Bushway, Ying Sonia Ting, Rana H. Besada, Tracey E. Sciuto, Jasmina Prabhakara, Julian Scherer, Kristen N. Balogh, April Lamb, Jennifer A. Kaplan, Lisa D. Cleary, Melissa A. Moles, Sarah E. Church, Yuqi Ren, Xing Ren, Richard B. Gaynor, Matthew J. Goldstein, Les H. Brail, Joel Greshock, Lakshmi Srinivasan. Comprehensive gene expression analysis of the tumor microenvironment in patients with advanced cancer treated with a personalized neoantigen vaccine, NEO-PV-01, in combination with anti-PD1 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5006.

More efficient formulations and valid inequalities for the Green Vehicle Routing Problem

The Green Vehicle Routing Problem (G-VRP) aims to efficiently route a fleet of Alternative Fuel Vehicles, based at a common depot, in order to serve a set of customers, minimizing the total travel distance. Because of the limited driving range of these vehicles, intermediate stops at the Alternative Fuel Stations must also be considered. For the G-VRP, we propose two Mixed Integer Linear Programming formulations allowing multiple visits to the stations without introducing dummy copies of them. In the first model, only one visit to a station between two customers or between a customer and the depot is allowed. While, in the second model, two consecutive visits to stations are also permitted. In addition, the two formulations are strengthened through both dominance criteria to a priori identify the stations that are more efficient to use in each route and valid inequalities, specifically tailored for the G-VRP. Computational results, carried out on benchmark instances, show that our formulations strongly outperform the exact solution approaches presented in the literature. Finally, in order to better investigate the issue of the consecutive refueling stops, a new set of instances is properly generated and significant transport insights are also provided.

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.

Exact solution methods for the multi-period vehicle routing problem with due dates

In this paper we study the multi-period vehicle routing problem with due dates. A supplier has to determine a distribution plan to visit a set of customers over a given planning horizon. Each customer is associated with a release date and a due date, that is, the date at which the goods required by the customer become available at the supplier’s depot, and the date by which the customer has to be visited, respectively. A fleet of capacitated vehicles is available at the depot to perform the distribution and the objective is to minimize the distribution costs and the costs related to delayed deliveries. New families of valid inequalities are presented that allow us to improve a branch-and-bound algorithm proposed in the literature. The new branch-and-bound algorithm reduces to 5.1% the optimality gap which is 12.1% for the known branch-and-bound on instances with one vehicle. A variable MIP neighborhood descent (VMND) algorithm is also presented, which speeds up the search for high quality solutions through a local search heuristic embedded in the branch-and-bound algorithm. Computational tests are performed to assess the quality of the VMND algorithm against the new branch-and-bound algorithm. The computational results show that the VMND algorithm improves 35 out of 80 solutions on instances with one vehicle, reducing the average optimality gap from 5.1% to 3.6%. It further matches the performance of the new branch-and-bound algorithm on another 35 instances. On instances with two and three vehicles the average optimality gap obtained by the VMND algorithm is 5.5% and 5.6%, respectively.

Electron correlation in chemical bonds II. LiH and LiH+

The correlation energy in LiH and LiH+ molecules is studied as a function of bond distance, from the united atom limit to the complete bond dissociation, using customized basis sets and Full Configuration Interaction. The analysis reveals an unusual complexity of the correlation energy dependence on the bond distance. The obtained accurate correlation energy profiles are used to study the performance of the established correlation models. The density functional models are found to be reasonably accurate in the description of LiH molecule, and are deficient in LiH+.

Analysis of the organisational culture in two Croatian hospitals.

organisational culture is the character of the environment of individual bodies. It consists of a set of values, norms and customs that govern the relations between people within it. Its elements are a mission or goals, organisational climate and a working atmosphere, which affect motivation, effectiveness and, ultimately, management style. this research aimed to determine which organisational culture is prevalent in hospitals, and which staff consider is the most desirable. the research was conducted in two hospitals in Zagreb-University Hospital Sveti Duh and University Hospital Dubrava. The Organizational Culture Assessment Instrument was administered to 87 nurses. in both hospitals, a clan culture was prevalent, followed by a hierarchical culture, then a competitive culture. The adhocracy culture was the least common. Participants said a clan culture was the most desirable, then a hierarchical culture, followed by the adhocracy culture; the least desirable was a competitive culture. In Sveti Duh, a clan culture was predominant, while in Dubrava it was a hierarchical culture. The most desirable culture for staff of both hospitals is the clan culture, but it was more prevalent in Sveti Duh. in two Croatian university hospitals, according to staff, a clan organisational culture is the most common type and is also the most desirable.

Locating a semi-obnoxious facility in the special case of Manhattan distances

The aim of this work is to locate a semi-obnoxious facility, i.e. to minimize the distances to a given set of customers in order to save transportation costs on the one hand and to avoid undesirable interactions with other facilities within the region by maximizing the distances to the corresponding facilities on the other hand. Hence, the goal is to satisfy economic and environmental issues simultaneously. Due to the contradicting character of these goals, we obtain a non-convex objective function. We assume that distances can be measured by rectilinear distances and exploit the structure of this norm to obtain a very efficient dual pair of algorithms.

Fuel cells hybrid systems for resilient microgrids

Nowadays, resilient grids meet growing interest for their capability of let survive critical load even in case of power fault coming from grid disturbance and disasters.In particular, telecommunications infrastructures (point-to-point and cell infrastructure) have distributed systems (RBS, DCs) equipped with storage devices used to guarantee continuous operation of the supplied equipment. Since high value services are provided to a wide set of customers, unexpected service unavailability is source of economic losses.This embedded UPS function, despite avoiding service interruption and contract penalties to the service providers, requires batteries whose unused capacity could be fruitfully exploited to provide the grid with valuable and paid energy services (frequency regulation - power and/or energy regulation in time slots - peak smoothing).For this reason, the supply systems of such plants, beside the internal energy storage devices, had better to have redundancy of energy sources (e.g. electrical grid and natural gas network) and tailored power flows control strategies to optimize the equipment utilization and costs, even in the case of external energy shortage.In this work, a combined approach with Fuel Cell (FC) and Fuzzy Logic (FL) is investigated to determine a good compromise between equipment installation, control logic, and availability of the service (downtime probability), even accounting for weather forecast, energy price and storage system exploitation (depth of discharge).For the implemented case studies of the simulation, a landline station located in Italy is analyzed by simulating different functions and power equipment to assess the benefits achievable the presented approach.

A hybrid VNS/Tabu search algorithm for solving the vehicle routing problem with drones and en route operations

With the goal of integrating drones in last-mile delivery, the Vehicle Routing Problem with Drones (VRPD) uses a fleet of vehicles, each of them equipped with a set of drones, for serving a set of customers with minimal makespan. In this paper, we propose an extension of the VRPD that we call the Vehicle Routing Problem with Drones and En Route Operations (VRPDERO). Here, in contrast to the VRPD, drones may not only be launched and retrieved at vertices but also on some discrete points that are located on each arc. We formulate the problem as a Mixed Integer Linear Program (MILP) and introduce some valid inequalities that enhance the performance of the MILP solvers. Furthermore, due to limited performance of the solvers in addressing large-scale instances, we propose an algorithm based on the concepts of Variable Neighborhood Search (VNS) and Tabu Search (TS). In order to evaluate the performance of the introduced algorithm as well as the solver in solving the VRPDERO instances, we carried out extensive computational experiments. According to the numerical results, the proposed valid inequalities and the heuristic have a significant contribution in solving the VRPDERO effectively. In addition, the consideration of en route operations can increase the utilization of drones and lead to an improved makespan.

Multi-objective vehicle routing and loading with time window constraints: a real-life application

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.

CAPACITATED VEHICLE ROUTING PROBLEM

Cost of transportation of goods and services is an interesting topic in today’s society. The Capacitated vehicle routing problem, which is been consider in this research, is one of the variants of the vehicle routing problem. In this research we develop a reinforcement learning technique to find optimal paths from a depot to the set of customers while also considering the capacity of the vehicles, in order to reduce the cost of transportation of goods and services. Our basic assumptions are; each vehicle originates from a depot, service the customers and return to the depot, the vehicles are homogeneous. We solve the CVRP with an exact method; column generation, goole’s operation research tool and reinforcement learning and compare their solutions. Our objective is to solve a large-size of vehicle routing problem to optimality.

The cafeteria problem: order sequencing and picker routing in on-the-line picking systems

This paper is dedicated to the cafeteria problem: given a single waiter operating multiple counters for different dishes arranged along a line and a set of customers with given subsets of dishes they desire, find a sequence of customers, which may not overtake each other, and a service schedule for the waiter, such that the makespan is minimized. This generic problem is shown to have different real-world applications in order picking with blocking restrictions. We present different heuristic and exact solution procedures for both problem parts, i.e., customer sequencing and waiter scheduling, and systematically compare these approaches. Our computational results reveal that the largest performance gains are enabled by not strictly processing order after order. Instead, the waiter should be allowed to flexibly swap between customers waiting along the line. Such a flexible service policy considerably reduces the makespan and the total walking distance of the waiter.

The Influence of Framing Effects on Consumer Decision-Making on Online Platforms

Focusing on online electronics sales and online travel service customization, the present research employed two experiments to examine the framing effects on consumer decision making in the context of internet marketing. Study1 showed that combined (vs. partitioned) pricing strategy can promote consumers’ purchase intention on e-commerce platforms. Study 2 demonstrated that downgrade (vs. upgrade) framing approach can lead to higher total sum price and less change on the default package in travel package customization settings. The results have significantly implications for marketing management in the context of internet marketing.

A hybrid large-neighborhood search algorithm for the cumulative capacitated vehicle routing problem with time-window constraints

This paper introduces a special vehicle routing problem, i.e. the cumulative capacitated vehicle routing problem with time-window constraints (Cum-CVRPTW). The problem can be defined as designing least-cost delivery routes from a depot to a set of geographically-scattered customers, subject to the constraint that each customer has to be served within a time window; accordingly, the objective costs are computed as the sum of arrival times at all the customers. The Cum-CVRPTW finds practical utility in many situations, e.g. the provision of humanitarian aids in the context of natural disasters. The Cum-CVRPTW can be viewed as a combination of two NP-hard problems, i.e. the vehicle routing problem with time windows and the cumulative vehicle routing problem. To effectively address this problem, an effective algorithm is designed, which is based on the frameworks of Large Neighborhood Search Algorithm and hybridizes with Genetic Algorithm. The proposed algorithm adopts a constraint-relaxation scheme to extend the search space, enabling the iterative exploration of both the feasible and infeasible neighborhood solutions of an incumbent solution. Furthermore, some speed-up techniques are designed to reduce the computational complexity. To elucidate its effectiveness, the proposed algorithm is examined on the benchmark instances from the literature. The resultant numerical findings show that the algorithm is able to improve and obtain some best-known solutions found by existing state-of-the-art methods.