Customer Locations Research Articles

Marketing strategies and profitability analysis of restaurants in Sokoto metropolis, Nigeria

The study examined the marketing strategies and profitability of restaurants in Sokoto metropolis. Both primary and secondary data were used for the investigation. Proportional sampling method was used to collect data from 52 restaurants in the study area in 2004. The Primary data were collected using well structured questionnaire. The Analytical tools employed were Descriptive statistics and cost benefit analysis. The study revealed that the cost of starting the business was very high, with the majority (71.2%) starting with more than N100.000.00. The marketing strategies employed by the marketers in order to adapt to market conditions include; preparation of very delicious meals (88.5%), treating customers well (84.6%), setting signboards (78.9%), distributing food to customers location (42.3%), selling food at cheaper rates (17.3%), selling food on credit (11.5%) and advertisement (3.8%). A profit of N766.10 was realized daily by a typical marketer in the survey area. Cost- Benefit ratio indicated that for every N1.00 of invested capital, N1.60 was realized as daily sales revenue indicating that there was 6% turnover. This shows that the market is profitable. It is recommended that the marketers should improve on their marketing strategies, form co-operative societies and the government should provide them with credit facilities in order to enable them procure generators to circumvent the attendant incessant problem of power failure.Key words: Marketing, profitability analysis, restaurants,investment

The multi-commodity capacitated multi-facility Weber problem: heuristics and confidence intervals

The Capacitated Multi-facility Weber Problem (CMWP) is concerned with locating I capacitated facilities so as to satisfy the demand of J customers with the minimum total transportation cost of a single commodity. This is a non-convex optimization problem and is difficult to solve. This work focuses on a multi-commodity extension and considers the situation where K distinct commodities are shipped to the customers subject to capacity and demand constraints. Customer locations, demands, and capacities for each commodity are known a priori. The transportation costs, which are proportional to the distance between customers and facilities, depend on the commodity type. A mathematical programming formulation of the problem is presented and two alternate location-allocation heuristics and a discrete approximation method are proposed and subsequently used to statistically estimate confidence intervals on the optimal objective function values. Computational experiments on standard and randomly generated test instances are also presented.

The Traveling Salesman Problem with Pickups, Deliveries, and Handling Costs

This paper introduces a new variant of the one-to-many-to-one single vehicle pickup and delivery problems (SVPDP) that incorporates the handling cost incurred when rearranging the load at the customer locations. The simultaneous optimization of routing and handling costs is difficult, and the resulting loading patterns are hard to implement in practice. However, this option makes economical sense in contexts where the routing cost dominates the handling cost. We have proposed some simplified policies applicable to such contexts. The first is a two-phase heuristic in which the tour having minimum routing cost is initially determined by optimally solving an SVPDP, and the optimal handling policy is then determined for that tour. In addition, branch-and-cut algorithms based on integer linear programming formulations are proposed, in which routing and handling decisions are simultaneously optimized, but the handling decisions are restricted to three simplified policies. The formulations are strengthened by means of problem specific valid inequalities. The proposed methods have been extensively tested on instances involving up to 25 customers and hundreds of items. Our results show the impact of the handling aspect on the customer sequencing and indicate that the simplified handling policies favorably compare with the optimal one.

The Delivery Man Problem with time windows

In this paper, a variant of the Traveling Salesman Problem with Time Windows is considered, which consists in minimizing the sum of travel durations between a depot and several customer locations. Two mixed integer linear programming formulations are presented for this problem: a classical arc flow model and a sequential assignment model. Several polyhedral results are provided for the second formulation, in the special case arising when there is a closed time window only at the depot, while open time windows are considered at all other locations. Exact and heuristic algorithms are also proposed for the problem. Computational results show that medium size instances can be solved exactly with both models, while the heuristic provides good quality solutions for medium to large size instances.

The combined manpower-vehicle routing problem for multi-staged services

This paper introduces a combined problem of vehicle routing and scheduling of manpower teams to perform multi-stage tasks at customer locations. Each customer demands several tasks in a given order. These tasks are performed by a set of teams and each team can perform a single task. The teams should be moved by a set of vehicles. The objective is to find an efficient schedule for the teams and to minimize the cost of vehicle routing. The problem differs from the traditional vehicle routing problem in that the tasks are not done by vehicles but done by teams, so teams must be assigned to customers and teams to vehicles. However, teams and vehicles are not bound to each other, so they can be scheduled separately but should be synchronized. Moreover, the multi-stage nature of the tasks in a given order makes the problem more complex. In this paper, a mixed integer programming model is developed for the problem, and a particle swarm optimization based heuristic algorithm is proposed. Some benchmark problems and the computational results of the proposed algorithm are also presented.

A new mixed integer linear model for a rich vehicle routing problem with docking constraints

In this paper we address a rich vehicle routing problem that arises in real-life applications. Among other aspects we consider time windows, simultaneous delivery and pick-up at customer locations and multiple use of vehicles. To guarantee a coordinated material flow at the depot, we include the timed allocation of vehicles to loading bays at which the loading and unloading activities can occur. The resulting vehicle routing problem is formulated as a two-index vehicle-flow model which integrates the routing under real-life conditions and the assignment of vehicles to loading bays at the depot. We use CPLEX 11.0 to solve medium-sized instances that are derived from the extended Solomon test set. The selective implementation of preprocessing techniques and cutting planes improves the solver performance significantly.

A library of local search heuristics for the vehicle routing problem

The vehicle routing problem (VRP) is a difficult and well-studied combinatorial optimization problem. Real-world instances of the VRP can contain hundreds and even thousands of customer locations and can involve many complicating constraints, necessitating the use of heuristic methods. We present a software library of local search heuristics that allows one to quickly generate solutions to VRP instances. The code has a logical, object-oriented design and uses efficient data structures to store and modify solutions. The core of the library is the implementation of seven local search operators that share a similar interface and are designed to be extended to handle additional options with minimal code change. The code is well-documented, straightforward to compile, and is freely available online. The code contains several applications that can be used to generate solutions to the capacitated VRP. Computational results indicate that these applications are able to generate solutions that are within about one percent of the best-known solution on benchmark problems.

A Geometrical Center based Two-way Search Heuristic Algorithm for Vehicle Routing Problem with Pickups and Deliveries

Abstract: The classical vehicle routing problem (VRP) can be extended by including customers who want to send goods to the depot. This type of VRP is called the vehicle routing problem with pickups and deliveries (VRPPD). This study proposes a novel way to solve VRPPD by introducing a two-phase heuristic routing algorithm which consists of a clustering phase and uses the geometrical center of a cluster and route establishment phase by applying a two-way search of each route after applying the TSP algorithm on each route. Experimental results show that the suggested algorithm can generate bet-ter initial solutions for more computer-intensive meta-heuristics than other existing methods such as the giant-tour-based partitioning method or the insertion-based method. Keywords: Vehicle Routing Problem, Heuristic Algorithm, Initial Solution 1. Introduction The vehicle routing problem (VRP) is a combinatorial optimization and nonlinear programming problem seeking to service a number of customers with a fleet of vehicles. VRP has been an important problem in the fields of trans-portation, distribution and logistics since Dantzig and Ramser [1] first proposed the problem. The vehicle routing problem with pickups and deliveries (VRPPD) is an exten-sion of the classical VRP. Recently, research on VRPPD has peaked essentially because pickup demands for pack-aging and used product returns from customer locations have increased substantially due to environmental and gov-ernment regulations, and the fact that integrating pickups with deliveries maximally utilizes vehicle capacity and saves money. So, VRPPD must take into account the goods that customers return to the delivery vehicle. This restric-tion makes the planning problem more difficult and in-creases travel distances or the number of vehicles. Re-stricted situations, in which there are no interchanges of goods between customers and all delivery demands start from the depot and all pickup demands are brought back to the depot, are usually considered in VRPPD. The VRPPD model can be classified into three: Deliv-ery-first and pickup-second; mixed pickups and deliveries; and simultaneous pickup and deliveries. The assumption of the delivery-first and pickup-second model is that all deliv-eries must be made before any pickups. This assumption was due to the fact that vehicles were rear-loaded and be-cause rearranging delivery loads onboard to accommodate new pickup loads was difficult. However, in recent days, most vehicles have side-loading as well as rear-loading functions. To accommodate new pickup loads, rearranging delivery loads onboard is no longer a requirement. Hence, the assumption that all deliveries must be made before any pickup can occur can be relaxed, allowing for the mixed pickups and deliveries model in which deliveries and pick-ups may occur in any sequence on a vehicle route. When customers can simultaneously receive and send goods, it is referred to as simultaneous pickup and delivery. A VRPPD solution is feasible only if the following three conditions are satisfied: delivery-feasibility, pickup-feasibility and load-feasibility. Delivery-feasibility and pickup-feasibility mean that both the total delivery and the total pickup de-mands on any vehicle route do not exceed the maximum capacity of the vehicle; and load-feasibility means that the maximum capacity of the vehicle is not exceeded at any point on the route. This study focuses on solving mixed pickups and deliv-eries by applying the two-phase heuristic algorithm. The first phase selects cluster seed as the farthest node (cus-tomers) among unclustered nodes and cluster nodes by using the notion of the geometrical center of a cluster. The result of the first phase satisfies the delivery-feasibility and pickup-feasibility since the total deliveries and pickups on any route is less than or equal to the maximum capacity of the vehicle. And the second phase of the algorithm applies the TSP algorithm on each cluster to find the shortest route regardless of load-feasibility and then finds routes that sat-DOI : 10.3745/JIPS.2009.5.4.237

A location–allocation heuristic for the capacitated multi-facility Weber problem with probabilistic customer locations

Abstract The capacitated multi-facility Weber problem is concerned with locating m facilities in the Euclidean plane, and allocating their capacities to n customers at minimum total cost. The deterministic version of the problem, which assumes that customer locations and demands are known with certainty, is a non-convex optimization problem and difficult to solve. In this work, we focus on a probabilistic extension and consider the situation where the customer locations are randomly distributed according to a bivariate distribution. We first present a mathematical programming formulation, which is even more difficult than its deterministic version. We then propose an alternate location–allocation local search heuristic generalizing the ideas used originally for the deterministic problem. In its original form, the applicability of the heuristic depends on the calculation of the expected distances between the facilities and customers, which can be done for only very few distance and probability density function combinations. We therefore propose approximation methods which make the method applicable for any distance function and bivariate location distribution.

A stochastic dynamic traveling salesman problem with hard time windows

Abstract Just-in-time (JIT) trucking service, i.e., arriving at customers within specified time windows, has become the norm for freight carriers in all stages of supply chains. In this paper, a JIT pickup/delivery problem is formulated as a stochastic dynamic traveling salesman problem with time windows (SDTSPTW). At a customer location, the vehicle either picks up goods for or delivers goods from the depot, but does not provide moving service to transfer goods from one location to another. Such routing problems are NP-hard in deterministic settings, and in our context, complicated further by the stochastic, dynamic nature of the problem. This paper develops an efficient heuristic for the SDTSPTW with hard time windows. The heuristic is shown to be useful both in controlled numerical experiments and in applying to a real-life trucking problem.

The balanced billing cycle vehicle routing problem

AbstractUtility companies typically send their meter readers out each day of the billing cycle in order to determine each customer's usage for the period. Customer churn requires the utility company to periodically remove some customer locations from its meter‐reading routes. On the other hand, the addition of new customers and locations requires the utility company to add new stops to the existing routes. A utility that does not adjust its meter‐reading routes over time can find itself with inefficient routes and, subsequently, higher meter‐reading costs. Furthermore, the utility can end up with certain billing days that require substantially larger meter‐reading resources than others. However, remedying this problem is not as simple as it may initially seem. Certain regulatory and customer service considerations can prevent the utility from shifting a customer's billing day by more than a few days in either direction. Thus, the problem of reducing the meter‐reading costs and balancing the workload can become quite difficult. We describe this Balanced Billing Cycle Vehicle Routing Problem in more detail and develop an algorithm for providing solutions to a slightly simplified version of the problem. Our algorithm uses a combination of heuristics and integer programming via a three‐stage algorithm. We discuss the performance of our procedure on a real‐world data set. © 2009 Wiley Periodicals, Inc. NETWORKS, 2009

An oven explosion: Lessons learned on PSM applications

AbstractAn explosion occurred at one of our customer's locations in late 1998 involving unburned and partially burned natural gas while the unit was in high fire mode. Investigation of the incident led to many contributing causes, most of which can be categorized broadly as lack of process safety information, failure of a fuel combustion control system, contractor control and lack of management of change.Combustion systems are found throughout all industries and homes. Code‐based safeguards are thoroughly and adequately described in NFPA. But is the spirit and intent of the code always applied and interpreted safely? Instrumentation application and installation are similarly well described in manufacturer's literature and the ISA standards. But are these systems installed and arranged properly? Is it important to manage change in something as mundane as fuel fired equipment? Is explosion venting on industrial ovens a sound way to limit damage? Is it even possible in all instances? Are there alternatives?This article highlights the investigation activities, answers these questions, presents conclusions and suggests remedies while showing the applicability of PSM concepts to industries other than the CPI.This article will describe, in general terms: the process, critical changes in design, key components involved, applicable codes, discoveries during the investigation and solutions deployed during the rebuild to address these issues. © 2009 American Institute of Chemical Engineers Process Saf Prog 2010

The Periodic Vehicle Routing Problem: A Case Study

This paper deals with a case study which is a variant of the Periodic Vehicle Routing Problem (PVRP). As in the traditional Vehicle Routing Problem (VRP), customer locations each with a certain daily demand are given, as well as a set of capacitated vehicles. In addition, the PVRP has a horizon, say T days, and there is a frequency for each customer stating how often within this T-day period this customer must be visited.A solution to the PVRP consists of T sets of routes that jointly satisfy the demand constraints and the frequency constraints. The objective is to minimize the sum of the costs of all routes over the planning horizon. We develop different algorithms solving the instances of the case studied. Using these algorithms we are able to realize considerable cost reductions compared to the current situation.

Spatially enabled customer segmentation using a data classification method with uncertain predicates

Spatial attributes are important factors for predicting customer behavior. However, thorough studies on this subject have never been carried out. This paper presents a new idea that incorporates spatial predicates describing the spatial relationships between customer locations and surrounding objects into customer attributes. More specifically, we developed two algorithms in order to achieve spatially enabled customer segmentation. First, a novel filtration algorithm is proposed that can select more relevant predicates from the huge amounts of spatial predicates than existing filtration algorithms. Second, since spatial predicates fundamentally involve some uncertainties, a rough set-based spatial data classification algorithm is developed to handle the uncertainties and therefore provide effective spatial data classification. A series of experiments were conducted and the results indicate that our proposed methods are superior to existing methods for data classification.

Adaptive granular local search heuristic for a dynamic vehicle routing problem

The advance of communication and information technologies based on satellite and wireless networks have allowed transportation companies to benefit from real-time information for dynamic vehicle routing with time windows. During daily operations, we consider the case in which customers can place requests such that their demand and location are stochastic variables. The time windows at customer locations can be violated although lateness costs are incurred. The objective is to define a set of vehicle routes which are dynamically updated to accommodate new customers in order to maximize the expected profit. This is the difference between the total revenue and the sum of lateness costs and costs associated with the total distance traveled. The solution approach makes use of a new constructive heuristic that scatters vehicles in the service area and an adaptive granular local search procedure. The strategies of letting a vehicle wait, positioning a vehicle in a region where customers are likely to appear, and diverting a vehicle away from its current destination are integrated within a granular local search heuristic. The performance of the proposed approach is assessed in test problems based on real-life Brazilian transportation companies.

Arc-Routing Models for Small-Package Local Routing

This paper studies the arc-routing problem that arises in small-package delivery. In practice, each service provider is encouraged to follow a master route—a predesigned sequence of street addresses—over an extended planning horizon (more than one day). The objective here is to construct efficient master routes. The focus on arc routing offers two advantages. First, real-world vehicle routing takes place over a street network, rather than in Euclidean space. Second, there are, typically, many fewer streets than customer locations. Currently, a deterministic arc-routing problem (DARP) model is used to solve the problem. However, this approach ignores the uncertainty in the street segment presence probability—the probability that a street segment requires (i.e., there is a demand for) a visit on a particular day. We introduce two new models, namely, the probabilistic arc-routing problem (PARP) model and the multiday arc-routing problem (MARP) model, which take into account the street segment presence probabilities. PARP attempts to minimize the expected length of the master route. It assumes that the street segment presence probabilities are independent. This model can require excessive amounts of computation time. On the other hand, MARP tries to minimize average length of the master route over prespecified days. This model can also be viewed as a Monte Carlo simulation approximation of the PARP. This approximation significantly reduces the computational burden. Additionally, by utilizing historical data, MARP incorporates real-world correlations among the street segment presence probabilities. Our computational results show that PARP and MARP may produce more efficient master routes than DARP by taking demand uncertainty into account.

Supply chain planning for water distribution in Central Asia

PurposeThe role of business logistics for a water distribution company in Central Asia has become a major concern. As the marketing environment is getting more and more competitive, the company is forced to focus on the efficiency of its supply chain management operations both by improving customer service, increasing its profitability and productivity. The purpose of this paper is to report upon the designing of a responsive supply chain for water distribution in Central Asia.Design/methodology/approachA logistic plan to satisfy customer requirement for water distribution in a Central Asian city subject to satisfactory service levels both in the number of distribution centers (DCs) and truck delivery schedule is outlined in this paper. The logistics plan includes repositioning the DCs in relation to the customer location for efficient distribution. The problem is formulated as truck delivery schedule using a new algorithm where single distribution centre is converted into a multiple warehouse location problem. The problem is solved using WINQSB software. Further, the current DCs are appraised with the software and suggested possible new locations for convenience.FindingsThe application part of this case study consists of identifying water DCs in city limits. By developing improved distribution and logistics management, the study aims at economical operations, convenient zonal distributions, and responsive SCM characteristics. To this end, a spatial distribution plan and route sequencing solution is developed for water distribution.Originality/valueThe paper shows how to improve logistic network that results in cost savings, convenient zonal distributions, and responsive SCM operations. To this end, a spatial distribution plan and route sequencing is developed for water distribution.

Automatically Rotating PDP TV Using Multiple Sensor Information

Most display units such as advertizing panels and mannequins in Exhibition halls or Big Shopping Malls are static. This study proposes the concept of active display system. An automatically rotating TV is chosen as an example device. The active display system detects the presence and location of customers and provides a clear screen view for customers. Initially, three passive infrared (PIR) sensors are used to detect the position of customers. Next, to cope with shortcoming of PIR sensor, a sensor fusion method combining three PIR sensors and an ultrasonic sensor is proposed. The performance of the proposed algorithms has been verified through experiment of a PDP panel TV. Finally, a sensor network is employed to control multiple PDP TVs simultaneously by sharing information between display units.

Solving probabilistic multi-facility Weber problem by vector quantization

The multi-facility Weber problem is concerned with locating in the plane m facilities having unlimited capacities and allocating them to n customers at minimum total cost. The deterministic version is a non-convex optimization problem and difficult to solve exactly. In this work we focus on a probabilistic extension and consider the situation where the customer locations are randomly distributed. For this problem, we propose new heuristics based on the principle of vector quantization which are capable of computing good quality solutions for general distance functions and customer location distributions.

A local search heuristic for the pre- and end-haulage of intermodal container terminals

Pre- and end-haulage of intermodal container terminals involves the pickup or delivery of containers at customer locations. The attractiveness of intermodal transport can be increased by organizing the road segment in the intermodal transport chain more efficiently. In this paper the drayage of containers in the service area of an intermodal terminal is modelled as a full truckload pickup and delivery problem with time windows (FTPDPTW). A two-phase insertion heuristic is proposed to construct an initial solution. This solution is improved with a local search heuristic based on three neighbourhoods. Numerical experiments are described to demonstrate the mechanisms of the heuristics.