Effects of distribution planning systems on the cost of delivery in unique make-to-order manufacturing

This thesis investigates the effects of simulation through the use of a distribution planning system (DPS) on distribution costs in the setting of unique make-to-order manufacturers (UMTO). In doing so, the German kitchen furniture industry (GKFI) serves as an example and supplier of primary data. On the basis of a detailed market analysis this thesis will demonstrate that this industry, which mostly works with its own vehicles for transport, is in urgent need of innovative logistics strategies. Within the scope of an investigation into the current practical and theoretical use of DPS, it will become apparent that most known DPS are based on the application of given or set delivery tour constraints. Those constraints are often not questioned in practice and in theory nor even attempted to be omitted, but are accepted in day-to-day operation. This paper applies a different approach. In the context of this research, a practically applied DPS is used supportively for the removal of time window constraints (TWC) in UMTO delivery. The same DPS is used in ceteris paribus condition for the re-routing of deliveries and hereby supports the findings regarding the costliness of TWC. From this experiment emerges an overall cost saving of 50.9% and a 43.5% reduction of kilometres travelled. The applied experimental research methodology and the significance of the resulting savings deliver the opportunity to analyse the removal of delivery time window restrictions as one of many constraints in distribution logistics. The economic results of this thesis may become the basis of discussion for further research based on the applied methodology. From a practical point of view, the contributions to new knowledge are the cost savings versus the change of demand for the setting of TWC between the receiver of goods and the UMTO supplier. On the side of theoretical knowledge, this thesis contributes to filling the gap on the production – distribution problem from a UMTO perspective. Further contributions to knowledge are delivered through the experimental methodology with the application of a DPS for research in logistics simulation

A multi-objective centralised agent-based optimisation approach for vehicle routing problem with unique vehicles

Motivated by heterogeneous service suppliers in crowd shipping routing problems, vehicles’ similarity assumption is questioned in the well-known logistical Vehicle Routing Problems (VRP) by considering different start/end locations, capacities, as well as shifts in the Time Window variant (VRPTW). In order to tackle this problem, a new agent-based metaheuristic architecture is proposed to capture the uniqueness of vehicles by modelling them as agents while governing the search with centralised agent cooperation. This cooperation aims to generate near optimum routes by minimising the number of vehicles used, total travelled distance, and total waiting times. The innovative architecture encapsulates three individual core modules in a flexible metaheuristic implementation. First, the problem is modelled by an agent-based module that includes its components in representing, evaluating, and altering solutions. A second metaheuristic module is then designed and integrated, followed by a multi-objective module introduced to sort solutions generated by the metaheuristic module based on Pareto dominance. Tests on benchmark instances were run, resulting in better waiting times, with an average reduction of 2.21-time units, at the expense of the other objectives. Benchmark instances are modified to tackle the unique vehicle's problem by randomising locations, capacities, and operating shifts and tested to justify the proposed model's applicability

A hyper-heuristic with two guidance indicators for bi-objective mixed-shift vehicle routing problem with time windows

In this paper, a Mixed-Shift Vehicle Routing Problem is proposed based on a real-life container transportation problem. In a long planning horizon of multiple shifts, transport tasks are completed satisfying the time constraints. Due to the different travel distances and time of tasks, there are two types of shifts (long shift and short shift) in this problem. The unit driver cost for long shifts is higher than that of short shifts. A mathematical model of this Mixed-Shift Vehicle Routing Problem with Time Windows (MS-VRPTW) is established in this paper, with two objectives of minimizing the total driver payment and the total travel distance. Due to the large scale and nonlinear constraints, the exact search showed is not suitable to MS-VRPTW. An initial solution construction heuristic (EBIH) and a selective perturbation Hyper-Heuristic (GIHH) are thus developed. In GIHH, five heuristics with different extents of perturbation at the low level are adaptively selected by a high level selection scheme with the Hill Climbing acceptance criterion. Two guidance indicators are devised at the high level to adaptively adjust the selection of the low level heuristics for this bi-objective problem. The two indicators estimate the objective value improvement and the improvement direction over the Pareto Front, respectively. To evaluate the generality of the proposed algorithms, a set of benchmark instances with various features is extracted from real-life historical datasets. The experiment results show that GIHH significantly improves the quality of the final Pareto Solution Set, outperforming the state-of-the-art algorithms for similar problems. Its application on VRPTW also obtains promising results

Dynamic pricing services to minimise CO2 emissions of delivery vehicles

In recent years, companies delivering goods or services to customers have been under increasing legal and administrative pressure to reduce the amount of CO2 emissions from their delivery vehicles, while the need to maximise profit remains a prime objective. In this research, we aim to apply revenue management techniques, in particular incentive/dynamic pricing to the traditional vehicle routing and scheduling problem while the objective is to reduce CO2 emissions. With the importance of accurately estimating emissions recognised, emissions models are first reviewed in detail and a new emissions calculator is developed in Java which takes into account time-dependent travel speeds, road distance and vehicle specifications. Our main study is a problem where a company sends engineers with vehicles to customer sites to provide services. Customers request for the service at their preferred time windows and the company needs to allocate the service tasks to time windows and decide on how to schedule these tasks to their vehicles. Incentives are provided to encourage customers choosing low emissions time windows. To help the company in determining the schedules/routes and incentives, our approach solves the problem in two phases. The first phase solves time-dependent vehicle routing/scheduling models with the objective of minimising CO2 emissions and the second phase solves a dynamic pricing model to maximise profit. For the first phase problem, new solution algorithms together with existing ones are applied and compared. For the second phase problem, we consider three different demand modelling scenarios: linear demand model, discrete choice demand model and demand model free pricing strategy. For each of the scenarios, dynamic pricing techniques are implemented and compared with fixed pricing strategies through numerical experiments. Results show that dynamic pricing leads to a reduction in CO2 emissions and an improvement in profits

A Particle Swarm Optimisation for Vehicle Routing Problem with Time Windows

A heuristic based on Particle Swarm Optimisation (PSO) algorithm for solving VRPTW, which is an extension of PSO application for the Capacitated Vehicle Routing Problem (CVRP) (Ai and Kachitvichyanukul,2007), is presented in this paper. A computational experiment is carried out by running the proposed algorithm with the VRPTW benchmark data set of Solomon (1987). The results show that the proposed algorithm is able to provide VRPTW solutions that are very close to its optimal solutions for problems with 25 and 50 customers within reasonably short of computational tim

Multi-objective tools for the vehicle routing problem with time windows

Most real-life problems involve the simultaneous optimisation of two or more, usually conflicting, objectives. Researchers have put a continuous effort into solving these problems in many different areas, such as engineering, finance and computer science. Over time, thanks to the increase in processing power, researchers have created methods which have become increasingly sophisticated. Most of these methods have been based on the notion of Pareto dominance, which assumes, sometimes erroneously, that the objectives have no known ranking of importance. The Vehicle Routing Problem with Time Windows (VRPTW) is a logistics problem which in real-life applications appears to be multi-objective. This problem consists of designing the optimal set of routes to serve a number of customers within certain time slots. Despite this problem’s high applicability to real-life domains (e.g. waste collection, fast-food delivery), most research in this area has been conducted with hand-made datasets. These datasets sometimes have a number of unrealistic features (e.g. the assumption that one unit of travel time corresponds to one unit of travel distance) and are therefore not adequate for the assessment of optimisers. Furthermore, very few studies have focused on the multi-objective nature of the VRPTW. That is, very few have studied how the optimisation of one objective affects the others. This thesis proposes a number of novel tools (methods + dataset) to address the above- mentioned challenges: 1) an agent-based framework for cooperative search, 2) a novel multi-objective ranking approach, 3) a new dataset for the VRPTW, 4) a study of the pair-wise relationships between five common objectives in VRPTW, and 5) a simplified Multi-objective Discrete Particle Swarm Optimisation for the VRPTW