Allocation Of Customers Research Articles

Developing an incentive‐based model for efficient product recovery and reverse logistics

AbstractThis study presents an inventive model aligning with sustainable development goals (SDGs) 11, 12, and 9. SDG 11 emphasizes sustainable urban aspects, SDG 12 centers on responsible consumption, and SDG 9 highlights resilient infrastructure. Focused on enhancing operational profits, the model integrates a robust reverse logistics network and policy framework to ensure safe disposal and environmental preservation. Employing the CPLEX solver software, we evaluated various methodologies, including proximity‐based allocation, set covering problems, p‐median allocation, and capacity‐relaxed models, to maximize profitability and efficiency in battery return systems. Our findings underscored the limitations of conventional proximity‐based methods, emphasizing the necessity of advanced optimization. Scenario 3, utilizing the p‐median problem, emerged as the most profitable, optimizing customer allocation and reducing distance‐related costs. Additionally, our sensitivity analysis highlighted the collection rate parameter's pivotal role in influencing customer behavior and overall system profitability. The study also emphasizes the significance of accessible collection centers, revealing disparities in accessibility across customer zones. These findings call for nuanced analyses to ensure equitable access. Implications include advocating for strategic policies to enhance collection rates, optimize center accessibility, and promote responsible disposal, benefiting policymakers, industry professionals, and environmental stakeholders. Ultimately, this research contributes to sustainable practices, fostering eco‐conscious societies, and accelerating progress toward SDGs.

Integrated location and inventory planning in service parts logistics with last-mile delivery outsourcing

Outsourcing last-mile delivery to express companies provides an innovative strategy for capital goods manufacturers to improve service parts logistics. Given delivery dilemma due to geographically dispersed customers and low-frequency uncertain demands, we develop an integrated location and inventory problem with last-mile delivery outsourcing in a two-echelon service parts logistics. The proposed non-linear mixed integer programming model captures important decisions involving (1) location of local warehouses, (2) customer allocation to opened local warehouses, (3) inventory levels at these local warehouses, (4) outsourcing or self-operating last-mile delivery. We then replace service level constraints that cause nonlinearity with two new sets of constraints, and construct a linear mixed integer formulation that could be solved by GUROBI solver for some small-scale problems. It is still a challenging problem for large scale case, for which we propose an adaptive large neighbourhood search (ALNS) algorithm. Our extensive computational experiments verify the effectiveness of our model and algorithm. Computational experiments’ results indicate that outsourcing last-mile delivery amounting to around 12 % total cost savings on average, compared to self-operating in a real-world large instance from our research partner. Furthermore, we test some key parameters with computational experiments, and provide some managerial implications.

Analysis of Voltage Profiles, Unbalance Voltages and Phase Balance Optimization of Single-Phase Customer and Photovoltaic Connection at Real Low Voltage Feeder

Abstract Voltage unbalance is common issue encountered in low voltage distribution networks, caused by uneven allocation of single-phase customers among phases. This paper analyses part of real low voltage distribution network in Bosnia and Herzegovina. The impact of single-phase customers and single-phase connected micro photovoltaic power plants (MPPP-s) were analysed. To reduce unbalance and improve voltage profiles, Phase Balance Optimization toolbox in DIgSILENT PowerFactory software was performed in six scenarios with different distribution of customers and different percentage of penetration of MPPS-s. The aim was to find method with least number of changes in customer and photovoltaic phase connection that fits within defined limits of voltage variations and voltage unbalance of European standard for power quality (EN 50160). Conclusion is that MPPP-s cause voltage increases in the network, as well as an increase in voltage unbalance, but these effects can be mitigated by proper distribution of customer loads and MPPS-s among phases.

Logistics hub location for high-speed rail freight transport with road-rail intermodal transport network.

In this paper, we propose a new freight mode to describe how the designed HSR freight train serve for express delivery. We introduce the functions of the hubs and design the hybrid hub-and-spoke network of road-rail intermodal transportation from the perspective of planners, which features as single allocation rule and configures varying levels of hubs. The problem is accurately described by a mixed integer programming model with the objective to minimize total construction cost and total operation cost. We develop a hybrid heuristic algorithm based on a greedy strategy to obtain the levels of hubs, customer allocation and cargo routing. Taking the example of HSR freight network consisting of 50 cities in China, numerical experiments are conducted on the basis of forecasting data from the real-life express market to obtain the hub location schemes. The validity of the model and the performance of the algorithm are verified.

Improvement of demand fulfillment in Advanced Planning System through decentralized decision support system

In a tight supply situation, industries such as the semiconductor sector face challenges in allocating supply to customers through complex hierarchical supply chain planning systems, which involve the use of Advanced Planning Systems (APS) and human planners in updating allocation plans. This article focuses on designing, developing, and testing a prototype Decision Support System (DSS) based on a mathematical model to assist human planners and improve demand fulfillment systems in APS when demand exceeds supply. By incorporating digitalization and supporting human interventions in planning, our approach enhances these systems.The bi-objective mathematical model aims to maximize customer service levels while maintaining a maximum amount of stock available for unforeseen planning situations. We developed the proposed mathematical model into a web application decision support tool, called the Regional Customer Allocation Support Tool (ReCAST). The ReCAST prototype was applied to a semiconductor case study, demonstrating its effectiveness in supporting decision-making processes by planners in a real-world context.

The balanced [formula omitted]-median problem with unitary demand

We consider a bi-objective variant of the p-median problem where p facilities must be located to serve a set of n customers with unitary demand. The considered objectives are: minimizing the average traveled distance between customers and facilities, and balancing the number of allocated customers per facility. We denote the latter by customer allocation inequity and measure it as the mean absolute deviation of the number of customers assigned to each median. We formulate this new problem as a bi-objective mixed-integer linear program ,and use a weighted sum method to generate a representative set of Pareto optimal solutions. Considering the single-objective subproblem solved by the weighted sum method, we develop a primal–dual algorithm that handles large-scale instances by combining a Lagrangian relaxation heuristic within a variable neighborhood search metaheuristic. This algorithm relies on the solution of a tailored minimum cost flow problem for the case where the locations of the facilities are known. We evaluate the proposed formulation and algorithm on test instances from the literature. After demonstrating the effectiveness of the developed algorithm, we test it on a series of large instances derived from an industrial application of districting for last-mile delivery. We analyze the trade-off between the assignment cost and customer allocation inequity, and evaluate the quality of the solutions by comparing them with those attained through alternative inequity measures.

Modeling and solving an economies‐of‐scale service system design problem

AbstractThis paper studies the design of a service system in the presence of economies of scale (EoS). The goal is to decide on the number, location, service capacities of facilities, and the allocation of customers to the opened facilities to minimize the total cost of the system. The total cost includes the opening and serving costs of facilities as well as customers' transportation and waiting costs. To reflect the EoS in the modeling, a general opening cost function is assumed for each service facility with the characteristic of being concave and nondecreasing in its service capacity. A Lagrangian relaxation algorithm is developed for solving the problem in its general form. The algorithm decomposes the relaxed model into homogeneous subproblems where each one can be optimally solved in polynomial time with no need for an optimization solver. Furthermore, the optimal service capacity of each open facility is determined as a closed‐form function. Our computational experiment shows that the developed algorithm is both efficient and effective in solving the proposed problem.

Integrated production and inventory routing planning of oxygen supply chains

In this work, we address a production and inventory routing problem for a liquid oxygen supply chain comprising production facilities, distribution network, and distribution resources. The key decisions of the problem involve production levels of production plants, delivery schedule and routing through heterogeneous vehicles, and inventory strategies for national stock-out prevention. Due to the problem complexity, we propose a two-level hybrid solution approach that solves the problem using both exact and metaheuristic methods. At the upper level, we develop a mixed-integer linear programming (MILP) model that determines production and inventory decisions and customer allocation. In the lower level, the original problem is reduced to several multi-trip heterogeneous vehicle routing problems by fixing the optimal production, inventory, and allocation decisions and clustering customers. A well-recognised metaheuristic, guided local search method, is adapted to solve the low-level routing problems. A real-world case study in the UK illustrates the applicability and effectiveness of the proposed optimisation framework.

Designing a service system with price- and distance-sensitive demand: A case study in mining industry

This study incorporates pricing decision into a congested facility location problem with immobile servers. The operations of facilities are modelled as queueing systems with price- and distance-sensitive streams of customer arrivals. Given a set of potential locations, a central service provider decides on the location of facilities, the allocation of customers to open facilities and also the service rate and price at each facility. The service provider’s profit maximization problem is investigated in two settings, i.e., continuous and discrete service rates, each representing a class of real-world problems. In either case, the resulting mixed-integer non-linear program is reformulated as a mixed-integer second-order cone program which enables us to solve it in a reasonable time using commercial software packages. To demonstrate the applicability of the proposed models, a real-world case in the mining industry is presented and solved.

Application of Meta-Heuristic Algorithms in Solving a Supply Chain Model

Over the last three decades, the integrated optimization approach to logistics systems has become one of the most important aspects of supply chain management optimization. This approach simultaneously examines the relationships between facility locations, supplier / customer allocation to facilities, transportation route structure, and inventory planning and control. One of the most important issues in logistics decisions is location-routing. In this case, the number and location of facilities, the size of the transport fleet, and the structure of the routes are determined according to the location and characteristics of suppliers and customers. In this paper, a mathematical model of the problem with consideration of product decay time constraints and solving with efficient meta-heuristic methods based on ant colony optimization (ACO) and particle swarm optimization (PSO) is presented. The comparison of the results of the two algorithms shows that the ant colony optimization (ACO) is faster in terms of convergence rate and the number of solution iterations is less than the particle swarm algorithm.

Robust collaborative maintenance logistics network design and planning

Maintenance service providers to advanced technical devices are confronted with uncertain demand, high cost of components, and the need for certified operators. Collaboration in terms of sharing scarce resources among different facilities in a maintenance logistics network is expected to reduce the delays in the delivery of repaired devices. This study proposes a two-stage robust optimization model for collaborative design and planning of maintenance networks under demand uncertainty. The goal of this model is to determine the optimal allocation of customers to each maintenance center along with the initial stock level of different components in each facility so as to minimize the cost of late deliveries under worst-case demand scenarios. Component and operator sharing strategies are proposed as the recourse actions in this model to hedge against the demand surge. The proposed approach is compared with a deterministic model by the aid of Monte-Carlo simulation on several test instances inspired by a real case study. Our numerical experiments demonstrate the significance of adopting the proposed collaborative mechanisms among maintenance facilities in terms of cost reduction, especially when the demand fluctuation is relatively high.

Optimal Open-Loop Routing and Threshold-Based Allocation in TWO Parallel QUEUEING Systems with Heterogeneous Servers

In this paper, we study the problem of optimal routing for the pair of two-server heterogeneous queues operating in parallel and subsequent optimal allocation of customers between the servers in each queue. Heterogeneity implies different servers in terms of speed of service. An open-loop control assumes the static resource allocation when a router has no information about the state of the system. We discuss here the algorithm to calculate the optimal routing policy based on specially constructed Markov-modulated Poisson processes. As an alternative static policy, we consider an optimal Bernoulli splitting which prescribes the optimal allocation probabilities. Then, we show that the optimal allocation policy between the servers within each queue is of threshold type with threshold levels depending on the queue length and phase of an arrival process. This dependence can be neglected by using a heuristic threshold policy. A number of illustrative examples show interesting properties of the systems operating under the introduced policies and their performance characteristics.

Estimation of the Optimal Threshold Policy in a Queue with Heterogeneous Servers Using a Heuristic Solution and Artificial Neural Networks

This paper deals with heterogeneous queues where servers differ not only in service rates but also in operating costs. The classical optimisation problem in queueing systems with heterogeneous servers consists in the optimal allocation of customers between the servers with the aim to minimise the long-run average costs of the system per unit of time. As it is known, under some assumptions the optimal allocation policy for this system is of threshold type, i.e., the policy depends on the queue length and the state of faster servers. The optimal thresholds can be calculated using a Markov decision process by implementing the policy-iteration algorithm. This algorithm may have certain limitations on obtaining a result for the entire range of system parameter values. However, the available data sets for evaluated optimal threshold levels and values of system parameters can be used to provide estimations for optimal thresholds through artificial neural networks. The obtained results are accompanied by a simple heuristic solution. Numerical examples illustrate the quality of estimations.

Collaborative Optimization for Location-Inventory Problem of Commercial Vehicle Distribution Network

This paper studies a bi-level programming model addressing the location-inventory problem which can determine the location of distribution centers and the inventory strategy of distribution centers and stores at the same time. In the location-inventory joint decision model, the order quantity is a continuous decision variable associating the upper and lower models and the remaining decision variables are 0-1 variables. In this paper, a branch and bound method is proposed and the SCIP solver is used to solve the bi-level mixed integer non-linear programming problem. The results show that collaborative optimization can save more costs than the one-by- one optimization method and that the optimal solution has better stability under the condition of demand fluctuation and satisfies the optimal allocation of important customers.

Impact of Customer Capital on Achieving the Competitive Advantage on Banking Sector: A Field Study on Banks in Taiz, Yemen

This study aimed to identify the effect of customer capital (including its dimensions: customer acquisition, allocation, market share, customer satisfaction, and customer loyalty) on achieving a competitive advantage in the banking sector in Yemen (including its dimensions: cost reduction, innovation excellence and flexibility). The study used the descriptive analytical method on a sample of 86 participants selected purposively from the study population, which was (380) members, covering directors, managers, heads of departments and workers in Yemeni banks in the governorate of Taiz. Using the SPSS package, the descriptive and inferential statistical methods were used to analyze data. Major findings revealed there was a direct impact relationship between the customer capital and the competitive advantage in the banking sector, where the customer satisfaction dimension was found to be the largest contributor to achieving competitive advantage. Furthermore, findings showed that there was a lack of training offered to workers on customer acquisition skills (as it is an important variable in acquiring customers). There was also a disparity between Yemeni banks in having databases to track customer preferences and keep them for use whenever the need arises. The study concluded that banks need to enhance their employees' skills of customer acquisition, and to develop a marketing system to track customer preferences which are imperative to achieve the competitive advantage.
 Keywords: customer capital, banking sector, competitive advantage.

The single-vehicle two-echelon one-commodity pickup and delivery problem

We present in this paper a generalization of the one-commodity pickup and delivery traveling salesman problem where each customer supplies or demands a given amount of a certain product. The objective is to design a minimum cost two-echelon transportation network. The first echelon is the route of a capacitated vehicle that visits some customers, and the second echelon consists in the allocation of the non-visited customers to visited ones. The customers that must be visited by the vehicle and the ones that must be allocated to others are not predefined. We present three mathematical models for the problem, design an exact branch-and-cut algorithm to solve it, and show extensive computational results on benchmark instances.

Bi-Objective Optimization of Service-Oriented Location-Pricing Model Using Electromagnetism-Like Mechanism Algorithm

This paper develops a multi-objective multi-layer location-pricing (MLLP) model with congested facilities in which the facilities act like a classic queuing system. The customers who arrive to this system receive service at all layers in a predetermined order to fulfill their demands. The goal is to determine (1) optimal number of the facilities required at each layer, (2) optimal allocation of customers to facilities, and (3) optimal price of providing service at each layer. The objective functions are to maximize the total profit of the system and to minimize the sum of travel and waiting times, simultaneously. The problem is formulated as a multi-objective nonlinear integer mathematical programming model. Since the problem is hard to be solved analytically, we present a multi-objective meta-heuristic algorithm (MHA) based on an electromagnetism-like mechanism (ELM) as a solution for multi-objective MLLP. This algorithm used an elitist mechanism to strengthen the structure of search engine in order to find better quality solutions. The results indicate the efficiency and effectiveness of the proposed algorithm in comparison with the traditional ELM.

Growing by the Masses: Revisiting the Link between Firm Size and Market Power

How are a firm’s size and market power related to one another? Combining micro-data about producers and consumers, we document that while firms mainly grow by selling to more customers, their markups are only associated with their average sales per customer. To study the macroeconomic implications of these facts, we develop a model of firm dynamics with endogenous customer acquisition and variable markups. Relative to a model without customer acquisition, our model generates higher concentration at the top, but a lower aggregate markup. Our quantitative analysis reveals large welfare and efficiency losses due to (mis)allocation of customers across firms. By increasing market concentration among the most productive firms, the efficient allocation achieves 11% higher aggregate productivity and 15% higher output.

Efficient Allocation of Customers to Facilities in the Multi-Objective Sustainable Location Problem

This paper aims to evaluate the impact of customer allocation on the facility location in the multi-objective location problem for sustainable logistics. After a new practical multi-objective location model considering vehicle carbon emissions is introduced, the NSGA-II and SEAMO2 algorithms are employed to solve the model. Within the framework of each algorithm, three different allocation rules derived from the optimization of customer allocation based on distance, cost, and emissions are separately applied to perform the customer-to-facility assignment so as to evaluate their impacts. The results of extensive computational experiments show that the allocation rules have nearly no influence on the solution quality, and the allocation rule based on the distance has an absolute advantage of computation time. These findings will greatly help to simplify the location-allocation analysis in the multi-objective location problems.

A vehicle-UAV operation scheme for instant delivery

Instant delivery by ground vehicles combined with unmanned aerial vehicles (UAVs) will greatly expand its coverage range while expanding its application. In this paper, a novel operation scheme with vehicles and UAVs for instant delivery is presented. This scheme consists of four key processes: the locations of vehicle stops, the allocation of customers to vehicle stops, the allocation of customers to UAVs, and route planning for vehicles. We formulate a capacitated set covering location model to determine the number and feasible locations of vehicle stops. Moreover, we present a multilevel model to optimize the decisions on the remaining processes and finally determine the locations of vehicle stops while minimizing the number of vehicles dispatched and the total travel time. Furthermore, we propose two advanced ant colony optimization (ACO) by introducing variable visibility and multilevel feedback pheromones. Experiments are applied to prove the effectiveness of the operation scheme with vehicles and UAVs.