Customer Locations Research Articles

Sistem Informasi Geografis Manajemen Aset di PT. PLN (Persero) Area Pelayanan Jaringan Surakarta

Electricity is one of major needs for human, therefore it is important to manage Electricity’s network. As the party who responsible in managing electricity in Indonesia, PT. PLN (Persero) keeps increasing and developing its quality including its speed and services. The increasing number of assets that are scattered in many locations, it will be faced with difficulties in finding or providing accurate information on the number and value of assets owned by PT.PLN (Persero). Asset Management Geographic Information System is one of the tools that are used to analyze spatial data then combine it with tabular data so easier information is obtained. Geographic Information System can analyze spatial data and non-spatial data. Geographic Information System can be used by individuals, organizations, and for those who need information related to positioning. Asset Management Geographic Information System helps PT.PLN (Persero) to fasten the decision-making process from field datas that have been visualized in application system; to monitor assets owned by PT.PLN (Persero) scattered both urban and rural areas. This monitoring process includes monitor the location of substations, poles, and customers.

Heuristics for the single source capacitated multi-facility Weber problem

The Single Source Capacitated Multi-facility Weber Problem (SSCMWP) is concerned with locating I capacitated facilities in the plane to satisfy the demand of J customers with the minimum total transportation cost of a single commodity such that each customer satisfies all its demand from exactly one facility. The SSCMWP is a non-convex optimization problem and difficult to solve. In the SSCMWP, customer locations, customer demands and facility capacities are known a priori. The transportation costs are proportional to the distance between customers and facilities. We consider both the Euclidean and rectilinear distance cases of the SSCMWP. We first present an Alternate Location and Allocation type heuristic and its extension by embedding a Very Large Scale Neighborhood search procedure. Then we apply a Discrete Approximation approach and propose both lower and upper bounding procedures for the SSCWMP using a Lagrangean Relaxation scheme. The proposed heuristics are compared with the solution approaches from the literature. According to extensive computational experiments on standard and randomly generated test sets, we can say that they yield promising performance.

Slow Infusion of Information Technology in Master Bank of Pakistan – A Case Study

E-Banking is now a well know phenomenon in the industry and amongst the banking users in Pakistan. This paper addresses the issues encountered and lessons derived by largest Pakistani bank while developing its e-banking capabilities. The paper explores some burning issues hindering key decisions of shifting to e-banking, e-banking delivery channel choice, enticing customers, and managing e-channel conflict to be made by bank. The findings indicated that the obstacles related to e banking customer location, customer satisfaction and the Bank’s software’s and communication infrastructure capabilities are influential factors in motivating the decision for shifting to a high-tech bank. The bank's E-channel choice is influenced by competence of application and software technologies that the bank acquires and the nature of diffusion of information and communications among its stakeholders. Technological development in Banking practices and awareness in customers due to presence of foreign and domestic banks in Pakistani financial market influenced the Master Bank to switch over to complete automated banking system. The sophistication of ebanking services and the further rapid improvement appears to influence strategies to entice E-channel conflict issues. The paper concludes by suggesting the Bank to optimally utilize its resources by deploying efficient applications and software’s and third party alliances and partnerships required to offer customized technology enabled banking services and develop appropriate e-banking risk mitigation strategies that bring the bank into a win-win position.

Dynamic approach to solve the daily drayage problem with transit time uncertainty

The intermodal transport chain can become more efficient by means of a good organization of drayage movements. Drayage in intermodal container terminals involves the pick up and delivery of containers at customer locations, and the main objective is normally the assignment of transportation tasks to the different vehicles, often with the presence of time windows. This scheduling has traditionally been done once a day and, under these conditions, any unexpected event could cause timetable delays. We propose to use the real-time knowledge about vehicle position to solve this problem, which permanently allows the planner to reassign tasks in case the problem conditions change. This exact knowledge of the position of the vehicles is possible due to the use of a geographic positioning system by satellite (GPS, Galileo, Glonass), and the results show that these additional data can be used to dynamically improve the solution.

The influence of task- and location-specific complexity on the control and coordination costs in global outsourcing relationships

Several reputable industry sources have recognized that many organizations fail to realize the financial benefits sought with outsourcing. Further, prior research has found that outsourcing organizations struggle to estimate accurately the so called “hidden costs” associated with managing these inter-organizational relationships. This is especially true of complex, globally distributed outsourced services. In this study, we use dyadic data on 102 outsourcing relationships to investigate how dimensions of task- and location-specific complexity influence the degree of control and coordination costs incurred by the customer organization. Results from our hierarchical regression analysis demonstrate that the scale of the service and the geographic distance between the customer and provider locations are associated with higher levels of both control and coordination costs. Task breadth and geographic dispersion are significantly associated with increased control costs, but not coordination costs. Counter to our expectations, control costs decrease with the degree of service customization, whereas both control and coordination costs are negatively related to the average cultural distance between provider and customer organizations. These findings contribute unique empirical evidence to the outsourcing, offshoring, and international service operations literature.

Data Security Model Enhancement In Cloud Environment

Cloud computing is one of the most emerging technologies which plays an important role in the next generation architecture of IT Enterprise. It has been widely accepted due to its ability to reduce costs associated with computing while increasing flexibility and scalability for computer processes. During the past few years, cloud computing has grown from being a promising business idea to one of the fastest growing parts of the IT industry. In the cloud computing system, both application software and databases are moved to the large data centers, where the data should not be secure in the hands of providers. IT organizations have expressed concerns about the various security aspects that exist with the widespread implementation of cloud computing. These types of concerns originate from the fact that data is stored remotely from the customer's location. From the consumers' perspective, cloud computing security concerns, especially data security and privacy protection issues, remain the primary inhibitor for adoption of cloud computing services. This paper describes an enhancement for the already existing data security model in cloud environment. The proposed data security model provides user authentication and data protection. It also ensures fast recovery of data. Keyword - AES Algorithm, Byzantine fault tolerance, Data Security Model, Distributed Denial of Service (DDoS)

Constrained optimisation of distribution network using new hybrid PSO optimiser

The efficient and effective movement of goods from raw material sites to processing facilities, component fabrication plants, finished goods assembly plants, distribution centres, retailers and customers are critical in today’s competitive environment. These in turn depend on facility location decisions taken during strategic planning. These facilities are expensive to construct and difficult to modify. Inefficient locations for production and assembly plants as well as distribution centres will result in excess costs being incurred throughout the lifetime of the facilities, no matter how well the production plans, transportation options, inventory management, and information sharing decisions are optimised in response to changing conditions. This paper addresses design of network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. If a facility is located at a candidate site, a known fixed location cost is incurred. There is a known unit shipment cost between each candidate site and each customer location. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the customers to minimise the combined facility location and shipment costs subject to a requirement that all customer demands be met. To optimise this, the distribution network model is mathematically represented and solved using hybrid PSO algorithm.

Local search algorithms for multiple-depot vehicle routing and for multiple traveling salesman problems with proved performance guarantees

We consider two related problems: the multiple-depot vehicle routing problem (MDVRP) and the Multiple traveling salesman problem (mTSP). In both of them, given is the complete graph on n vertices $$G = (V,E)$$ with nonnegative edge lengths that form a metric on V. Also given is a positive integer k. In typical applications, V represents locations of customers and k represents the number of available vehicles. In MDVPR, we are also given a set of k depots $$\{O_1,\ldots ,O_k\} \subseteq V$$ , and the goal is to find a minimum-length cycle cover of G of size k, that is, a collection of k (possibly empty) cycles such that each $$v \in V$$ is in exactly one cycle, and each cycle in the cover contains exactly one depot. In mTSP, no depots are given, so the goal is to find (any) minimum-length cycle cover of G of size k. We present local search algorithms for both problems, and we prove that their approximation ratio is 2.

Mixed Binary Integer Programming Formulation for the Two-Echelon Supply Chain System with Two Customer Locations

Coordinating works between production and delivery is a crucial issue for the management of production and logistics. This study developed a single machine scheduling model that incorporated delivery vehicle routing decisions and serves two customer locations. The objective was to minimize the mean arrival time. The problem is NP-hard in the strong sense in general. A mixed binary integer programming (BIP) model was developed to optimally solve this problem. In addition, computational results of the illustrative example are reported using the mixed BIP model to solve the proposed problem.

Bi‐objective optimization of distribution scheduling using MOPSO optimizer

PurposeThe paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.Design/methodology/approachTo optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.FindingsThis paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.Originality/valueVery few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

The rollon–rolloff waste collection vehicle routing problem with time windows

This study introduces a rollon–rolloff waste collection vehicle routing problem involving large containers that accumulate huge amounts of garbage at construction sites and shopping districts. In this problem, tractors move one container at a time between customer locations, a depot, disposal facilities, and container storage yards. The complicated constraints discussed in this study arise from having multiple disposal facilities, multiple container storage yards, seven service types of customer demands, different time windows for customer demands and facilities, various types and sizes of containers, and the lunch break of tractor drivers. In addition, real-world issues, such as changing service types, multiple demands at a customer’s location, and tractors with different work schedules, are dealt with. This study proposes a large neighborhood search based iterative heuristic approach consisting of several algorithms for the problem. The effectiveness of the proposed methods is demonstrated by computational experiments using benchmark data, some instances of which are derived from real-world problems.

An effective heuristic for the P-median problem with application to ambulance location

We consider the p-median problem which is to find the location of p-facilities so as to minimize the average weighted distance or time between demand points and service centers. Many heuristic algorithms have been proposed for this problem. In this paper we present a simple new heuristic which is effective for moderately size problem. The heuristic uses a reduction and an exchange procedure. Our methodology is tested on 400 randomly generated problems with 10 to 50 customer locations as well as 6 well known literature test problems. We also compare our method with the Branch and Bound method in terms of quality and computational time using a larger problem size of 150 customer locations. For the random problems the generated solutions were on average within 0.61 % of the optimum. A similar result was achieved for the literature test problems. A comparative analysis with literature heuristics supports the superiority of our method. The computational time of our heuristic is 0.75 % of the Branch and Bound Method. We also apply our heuristic to a case study involving the location of emergency vehicles (ambulances) in Perth City (Australia).

A Comparison of Anticipatory Algorithms for the Dynamic and Stochastic Traveling Salesman Problem

Advances in information technology and telecommunications, together with ever-growing amounts of data, offer opportunities for transportation companies to improve the quality of the service that they provide to their customers. This paper compares two methods motivated by the opportunity that the availability of data and technology gives to improve on current practice. In particular, the two solution approaches are explored in the context of a dynamic and stochastic routing problem in which a single, uncapacitated vehicle serves a set of known customers locations. One approach, sample-scenario planning, offers the potential for higher-quality solutions, but at the expense of greater computational effort. On the other hand, anticipatory insertion offers reduced computation and increased managerial ease, but with the potential for reduced solution quality due to restrictions on solution structure. Our results show that anticipatory insertion can often match the quality of sample-scenario planning, particularly when the degree of dynamism is low.

A methodology to solve large-scale cooperative transportation planning problems

In this paper, we suggest a methodology to solve a cooperative transportation planning problem and to assess its performance. The problem is motivated by a real-world scenario found in the German food industry. Several manufacturers with same customers but complementary food products share their vehicle fleets to deliver their customers. After an appropriate decomposition of the entire problem into sub problems, we obtain a set of rich vehicle routing problems (VRPs) with time windows for the delivery of the orders, capacity constraints, maximum operating times for the vehicles, and outsourcing options. Each of the resulting sub problems is solved by a greedy heuristic that takes the distance of the locations of customers and the time window constraints into account. The greedy heuristic is improved by an appropriate Ant Colony System (ACS). The suggested heuristics to solve the problem are assessed within a dynamic and stochastic environment in a rolling horizon setting using discrete event simulation. We describe the used simulation infrastructure. The results of extensive simulation experiments based on randomly generated problem instances and scenarios are provided and discussed. We show that the cooperative setting outperforms the non-cooperative one.

The minimum duration truck driver scheduling problem

Truck driver scheduling problems are important subproblems of real-life vehicle routing and scheduling problems, because rest periods required by government regulations have a significant impact on travel and arrival times. Vehicle routes generated without considering these regulations are often practically infeasible. This paper identifies common constraints imposed by hours of service regulations world wide and presents a mixed integer programming formulation for a variant of the truck driver scheduling problem in which truck drivers may only rest at customer locations and at suitable rest areas. The objective of the problem is to find a feasible truck driver schedule with minimal duration. The model presented in this paper is very flexible and can be configured to consider different sets of rules imposed by government regulations and union contracts. A dynamic programming approach is presented and its effectiveness is demonstrated for working hour regulations in the United States and in the European Union.

Agent based mobile negotiation for personalized pricing of last minute theatre tickets

This paper proposes an agent based mobile negotiation framework for personalized pricing of last minutes theatre tickets whose values are dependent on the time remaining to the performance and the locations of potential customers. In particular, case based reasoning and fuzzy cognitive map techniques are adopted in the negotiation framework to identify the best initial offer zone and adopt multi criteria decision in the scoring function to evaluate offers. The proposed framework is tested via a computer simulation in which personalized pricing policy shows higher market performance than other policies therefore the validity of the proposed negotiation framework.

The Minimum Duration Truck Driver Scheduling Problem

Truck driver scheduling problems are important subproblems of real-life vehicle routing and scheduling problems because rest periods required by government regulations have a significant impact on travel and arrival times. Vehicle routes generated without considering these regulations are often practically infeasible. This paper identifies common constraints imposed by hours of service regulations world wide and presents a mixed integer programming formulation for a variant of the truck driver scheduling problem in which truck drivers may only rest at customer locations and at suitable rest areas. The objective of the problem is to find a feasible truck driver schedule with minimal duration. The model presented in this paper is very flexible and can be configured to consider different sets of rules imposed by government regulations and union contracts. A dynamic programming approach is presented and its effectiveness is demonstrated for working hour regulations in the United States and in the European Union.

Heuristic Scheme for Heterogeneous Vehicle Routing Problem on Trees Based on Generalized Assignment and Bin-Packing Upper Bounds

The vehicle routing problem (VRP) is a classical problem in logistics that aims to design minimum-cost delivery routes from a centralized depot. A special case of the VRP arises in situations in which the network has a tree structure (TVRP). Such tree networks arise when the cost of road construction and maintenance is much more than the routing cost or when the transportation network consists of a main highway (e.g., Interstate system) and the customer locations are located off the highway. A heuristic for a constrained case of TVRP in which the vehicle fleet is capacitated and heterogeneous is proposed. The heuristic first determines the customers that will be served by each vehicle by use of bin-packing and Lagrangian-based generalized assignment algorithms. The individual vehicle routes are then determined by use of a depth-first search method. A procedure for further refinement of the heuristic solution quality is also described. The heuristic algorithm was implemented on two real-world networks and on randomly generated networks that varied in size from 20 to 120 nodes. The heuristic solution was found to be between 2% and 10% for almost all of the 200 instances tested and took a fraction of the time taken to find the optimal solution.

Hierarchical Interactions between Shippers and Carriers in International Maritime Freight Transportation Networks

The paper discusses hierarchical relationships between shippers and different types of carriers in international maritime freight transportation networks. Ocean carriers, port terminal operators and land carriers provide transportation services at different parts of the multimodal transportation network. The carriers determine service charges and routing patterns through competition and other interactions among them. Shippers deliver their products to customer locations using the transportation services. Shippers choose a sequence of carriers, based on the carrier's pricing and routing decisions. These shipper-carrier relationships are captured in a bi-level modelling approach.

Innovative Logistics Model and Containers Solution for Efficient Last Mile Delivery

Urban goods distribution is important for the economy. However urban transport causes traffic, noise and pollution. The European Research project CityLog (part of Framework Programme 7) aims at increasing efficiency in urban distribution by proposing a logistics model based on two types of vehicles and containers solution in the context of parcel delivery. The urban delivery scheme currently consists of a number of vehicles that leave the hub and drive towards the city. The innovative logistics model for urban deliveries introduces a concept which make use of two types of vehicles: a Freight bus which is loaded at the depot with several load units that can carry generic parcels, and a Delivery van which is a light vehicle with high eco-compatible characteristics that make it very suitable to be used in the inner city Each load unit, when transferred from the Freight bus to the Delivery van, replaces the body of the van. These interoperable vehicles and containers form an improved logistics chain, reducing the number of vehicles entering the city, chilometers driven and pollutant emissions. Another source of unnecessary driven chilometers is unsuccessful deliveries when the receiver is not home during the delivery. The Modular BentoBox is the second concept introduced by the project. It allows to obtain a balance between optimisation of logistics and customer's interests by delivering goods to the bentobox, instead that to the customer location. There parcels are stored in the bentobox until the customer picks them up. The Modular BentoBox System introduces the concept of removable modules that is trolleys with various size drawers. These trolleys and their drawers are filled in with parcels at the depot. The carrier transports the trolleys downtown and inserts them into the bentobox. As soon as the customer is notified, the parcel is ready at the bentobox. To access the parcel, the customer enters his special code on a user interface. The Modular BentoBox System can also be provided with a weighting and payment system and used for shipping parcels. The trolleys with shipments are taken back to the depot by the carrier. These CityLog concepts will be implemented and tested in 2012 in three European cities: Berlin, Lyon and Turin.