A new three phase method (SDP method) for the multi-objective vehicle routing problem with simultaneous delvery and pickup (VRPSDP)

Transportation service operators are witnessing a growing demand for bi-directional movement of goods. Given this, the following thesis considers an extension to the vehicle routing problem (VRP) known as the delivery and pickup transportation problem (DPP), where delivery and pickup demands may occupy the same route. The problem is formulated here as the vehicle routing problem with simultaneous delivery and pickup (VRPSDP), which requires the concurrent service of the demands at the customer location. This formulation provides the greatest opportunity for cost savings for both the service provider and recipient. The aims of this research are to propose a new theoretical design to solve the multi-objective VRPSDP, provide software support for the suggested design and validate the method through a set of experiments. A new real-life based multi-objective VRPSDP is studied here, which requires the minimisation of the often conflicting objectives: operated vehicle fleet size, total routing distance and the maximum variation between route distances (workload variation). The former two objectives are commonly encountered in the domain and the latter is introduced here because it is essential for real-life routing problems. The VRPSDP is defined as a hard combinatorial optimisation problem, therefore an approximation method, Simultaneous Delivery and Pickup method (SDPmethod) is proposed to solve it. The SDPmethod consists of three phases. The first phase constructs a set of diverse partial solutions, where one is expected to form part of the near-optimal solution. The second phase determines assignment possibilities for each sub-problem. The third phase solves the sub-problems using a parallel genetic algorithm. The suggested genetic algorithm is improved by the introduction of a set of tools: genetic operator switching mechanism via diversity thresholds, accuracy analysis tool and a new fitness evaluation mechanism. This three phase method is proposed to address the shortcoming that exists in the domain, where an initial solution is built only then to be completely dismantled and redesigned in the optimisation phase. In addition, a new routing heuristic, RouteAlg, is proposed to solve the VRPSDP sub-problem, the travelling salesman problem with simultaneous delivery and pickup (TSPSDP). The experimental studies are conducted using the well known benchmark Salhi and Nagy (1999) test problems, where the SDPmethod and RouteAlg solutions are compared with the prominent works in the VRPSDP domain. The SDPmethod has demonstrated to be an effective method for solving the multi-objective VRPSDP and the RouteAlg for the TSPSDP.

A hybrid genetic algorithm for the multi-depot vehicle routing problem

The distribution of finished products from depots to customers is a practical and challenging problem in logistics management. Better routing and scheduling decisions can result in higher level of customer satisfaction because more customers can be served in a shorter time. The distribution problem is generally formulated as the vehicle routing problem (VRP). Nevertheless, there is a rigid assumption that there is only one depot. In cases, for instance, where a logistics company has more than one depot, the VRP is not suitable. To resolve this limitation, this paper focuses on the VRP with multiple depots, or multi-depot VRP (MDVRP). The MDVRP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To deal with the problem efficiently, two hybrid genetic algorithms (HGAs) are developed in this paper. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method and the nearest neighbor heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different problem sizes. It is proved that the performance of HGA2 is superior to that of HGA1 in terms of the total delivery time.

A branch and efficiency algorithm for the optimal design of supply chain networks

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named “efficiency cuts”, selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples.

Performance of five different electrospray ionisation sources in conjunction with rapid monolithic column liquid chromatography and fast MS/MS scanning

The performances of five different ESI sources coupled to a polystyrene-divinylbenzene monolithic column were compared in a series of LC-ESI-MS/MS analyses of Escherichia coli outer membrane proteins. The sources selected for comparison included two different modifications of the standard electrospray source, a commercial low-flow sprayer, a stainless steel nanospray needle and a coated glass Picotip. Respective performances were judged on sensitivity and the number and reproducibility of significant protein identifications obtained through the analysis of multiple identical samples. Data quality varied between that of a ground silica capillary, with 160 total protein identifications, the lowest number of high quality peptide hits obtained (3012), and generally peaks of lower intensity; and a stainless steel nanospray needle, which resulted in increased precursor ion abundance, the highest-quality peptide fragmentation spectra (5414) and greatest number of total protein identifications (259) exhibiting the highest MASCOT scores (average increase in score of 27.5% per identified protein). The data presented show that, despite increased variability in comparative ion intensity, the stainless steel nanospray needle provides the highest overall sensitivity. However, the resulting data were less reproducible in terms of proteins identified in complex mixtures -- arguably due to an increased number of high intensity precursor ion candidates.

Premium e-grocery:exploring value in logistics integrated service solutions

E-grocery is gradually becoming viable or a necessity for many families. Yet, most e-supermarkets are seen as providers of low value "staple" and bulky goods mainly. While each store has a large number of SKU available, these products are mainly necessity goods with low marginal value for hedonistic consumption. A need to acquire diverse products (e.g., organic), premium priced products (e.g., wine) for special occasions (e.g., anniversary, birthday), or products just for health related reasons (e.g., allergies, diabetes) are yet to be served via one-stop e-tailers. In this paper, we design a mathematical model that takes into account consumers' geo-demographics and multi-product sourcing capacity for creating critical mass and profit. Our mathematical model is a variant of Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), which we extend by adding intermediate locations for trucks to meet and exchange goods. We illustrate our model for the city of Istanbul using GIS maps, and discuss its various extensions as well as managerial implications.

The new generation: characteristics and motivations of BME graduate eEntrepreneurs

The purpose of this research is to profile the characteristics and entrepreneurial motivations of graduate entrepreneurs from black and minority ethnic (BME) communities. The authors found that: BME graduate entrepreneurs were diverse in terms of their characteristics: size, gender, ethnicity and when they started the business. Almost all interviewees had worked for someone before they started their business. The two most compelling motivations for start up were 'being your own boss', especially for Indians and Bangladeshis; and making more money (31%), in particular for African Caribbeans. Over half of interviewees started a business in a sector in which they had prior experience, knowledge or skills. Two thirds of interviewees obtained advice from family and friends, while just over a third had completed any kind of training or course. This study has provided an insight into characteristics and entrepreneurial motivations of BME graduate entrepreneurs. Though the results of this study are indicative, there is a compelling case for further research into this relatively unexplored group.

CFD and bubble column reactors:simulation and experiment

The simulation of two-phase flow in bubble columns using commercially available software fromFluent Incorporated is presented here. Data from a bubble column with a ratio of height to thecolumn diameter of 5 : 1 are compared with simulations and experimental results for time-averaged velocity and Reynolds stress proles are used to validate transient, two-dimensional simulations.The models are based on multiphase biological reactors with applications in the food industry. An example case of the mass transfer of oxygen through the liquid phase is also presented.

Productivity and price performance in the privatized water and sewerage companies of England and Wales

After its privatization in 1989, the water and sewerage industry of England and Wales faced a new regulatory régime and implemented a substantial capital investment program aimed at improving water and environmental standards. A new RPI + K regulatory pricing system was designed to compensate the industry for its increased capital costs, encourage increased efficiency, and maintain fair prices for customers. This paper evaluates how successful privatization and the resulting system of economic regulation has been. Estimates of productivity growth, derived with quality adjusted output indices, suggest that despite reductions in labor usage, total factor productivity growth has not improved since privatization. Moreover, total price performance indices reveal that increases in output prices have outstripped increases in input costs, a trend which is largely responsible for the increase in economic profits that has occurred since privatization. * We would like to thank Emmanuel Thanassoulis, Joshy Easaw, Jim Love, John Sawkins, and an anonymous referee for helpful comments on earlier drafts of this paper. The usual disclaimer applies.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Price policy and price decision making in small companies

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