Quantitative Risk Management Models for Newsvendor Supply Chains

As the practices of offshoring and outsourcing force the supply chain networks to keep on expanding geographically in the globalised environment, the logistics processes are becoming more exposed to risk and disruptions. Thus, modern supply chains seem to be more vulnerable than ever. It is clear that efficient logistics risk and security management emerges as an issue of pivotal importance in such competitive, demanding and stochastic environment and is thus vital for the viability and profitability of a company. In this context, this chapter focuses on a set of stochastic quantitative models that study the impact of one or more supply chain disruptions on optimal determination of single period inventory control policies. The purpose of this research is to provide a critical review of state-of-the-art methodologies to be used as a starting point for further research efforts.

Optimal Design and Operation of Supply Chain Networks under Demand Uncertainty

This chapter considers a detailed mathematical formulation for the problem of designing supply chain networks comprising multiproduct production facilities with shared production resources, warehouses, distribution centers and customer zones and operating under time varying demand uncertainty. Uncertainty is captured in terms of a number of likely scenarios possible to materialize during the life time of the network. The problem is formulated as a mixed-integer linear programming problem and solved to global optimality using standard branch-and-bound techniques. A case study concerned with the establishment of Europe-wide supply chain is used to illustrate the applicability and efficiency of the proposed approach. The results obtained provide a good indication of the value of having a model that takes into account the complex interactions that exist in such networks and the effect of inventory levels to the design and operation.

An Analytical Model to Estimate the Optimum Production Rate of Picking Processes in a Modular Warehouse Environment

One of the most important, complicated and expensive processes in a warehouse is order-picking. The cost associated with order preparation and picking typically varies between 40% and 60% of the total cost of all the processes in a warehouse; therefore, improving the productivity in order picking would result directly in cost reduction. In any attempt to reduce costs in order picking, one has to take into account: (i) the design of the warehouse so that the pickers’ work may be controlled at all instances, (ii) the existence of standards on the pickers’ work so that performance measurements may be compared and contrasted reliably, and (iii) the analysis of the phases of the picking process so that the pickers’ productivity may be measured and maximized. Such a series of concerns and parameters leads to the necessity of developing a mathematical parametric model which may serve as a useful tool for the warehouse manager in his efforts to not only measure productivity but also to intervene in the process and proceed to improvements. The present work deals with the development of such an analytical parametric model for the order picking process in a modular warehouse. The research attempts to address and solve three distinct, yet relevant, areas of focus: (i) to produce a generic and analytical framework to model the order picking process, (ii) to define practical and easy to adopt performance measures for the order picking process, and (iii) to provide the tools for a warehouse manager to set goals, measure performance and identify areas of improvement in his areas of responsibility. In addition to these, the research sets the foundations to further expand on other warehouse processes, such as loading/unloading, products receipt, etc., that supersede the boundaries of order picking. The analysis is corroborated by a real case study, among the many monitored in a pragmatic setup, accompanied by ABC analysis of the warehouse operation and a presentation of a fair frame to measure workers’ performance.

Generating Supply Chain Ordering Policies using Quantum Inspired Genetic Algorithms and Grammatical Evolution

Contributions to a supply chain’s overall cost function (such as the bullwhip effect) are sensitive to the different players’ ordering policies. This chapter addresses the problem of developing ordering policies which minimise the overall supply chain cost. Evolutionary Algorithms have been used to evolve such ordering policies. The authors of this chapter extend existing research in a number of ways. They apply two more recent evolutionary algorithms to the problem: Grammatical Evolution (GE), using a standard Genetic Algorithm (GA) search engine; and Quantum Inspired Genetic Algorithm (QIGA), used both as a standalone algorithm, and as an alternative search engine for GE. The authors benchmark these against previous work on the linear Beer Game supply chain, and extend our approaches to arborescent supply chains (without gaming), and capacitated inventory. The ordering-policy-generating grammars investigated range from simple — only using the demand presented at that point — to complex — which may incorporate lagged demands, forecasting approaches such as Moving Average or Simple Exponential Smoothing, conditional statements and other operators. The different grammars and search engines are compared for deterministic demand, and various stochastic demand distributions. Overall, GE outperforms other approaches by discovering more efficient ordering policies. However, its performance is sensitive to the choice of grammar: simple grammars do best on deterministic demand, while grammars using conditionals, information sharing and forecasting do better on stochastic demand. GE with a QIGA search engine has similar performance overall to GE with a standard GA search engine: typically QIGA is better if demand follows a Poisson distribution, with GA better for Normal demand.

A Computational Intelligence Approach to Supply Chain Demand Forecasting

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.

Coalitional Added Services in a Linear Neutral e-Marketplace

The increase of transactions by electronic commerce (e-commerce) in Business to Business applications has a constant trend during last years. Many research reports have focused on negotiation and auction mechanisms in this context, but a smaller number of related research attempts, has chosen to develop coalition approaches This research attempt tries to overcome this gap by an innovative coalition model for a private neutral linear e-marketplace that combines a full integration between customer’s request and supplier’s planning activity. The Shapley value approach is proposed to manage the profit sharing activity among the coalition participants. The Shapley value is an approach of game theory used to share a gain in coalition games. A proper simulation environment has been designed and modeled in order to measure the “stay-together economy” achievable within the proposed innovative e-marketplace. The simulation results highlight how the proposed approach increases the performance level of the e-marketplace: specifically the suppliers gain more benefits than the customers through the possibility of establishing coalitions.

Dynamic Travel Time Estimation Techniques for Urban Freight Transportation Networks Using Historical and Real-Time Data

Effective travel time prediction is of great importance for efficient real-time management of freight deliveries, especially in urban networks. This is due to the need for dynamic handling of unexpected events, which is an important factor for successful completion of a delivery schedule in a predefined time period. This chapter discusses the prediction results generated by two travel time estimation methods that use historical and real-time data respectively. The first method follows the k-nn model, which relies on the non-parametric regression method, whereas the second one relies on an interpolation scheme which is employed during the transmission of real-time traffic data in fixed intervals. The study focuses on exploring the interaction of factors that affect prediction accuracy by modelling both prediction methods. The data employed are provided by real-life scenarios of a freight carrier and the experiments follow a 2-level full factorial design approach.

Constrained Optimization of JIT Manufacturing Systems with Hybrid Genetic Algorithm

This research explores the use of a hybrid genetic algorithm in a constrained optimization problem with stochastic objective function. The underlying problem is the optimization of a class of JIT manufacturing systems. The approach investigated here is to interface a simulation model of the system with a hybrid optimization technique which combines a genetic algorithm with a local search procedure. As a constraint handling technique we use penalty functions, namely a “death penalty” function and an exponential penalty function. The performance of the proposed optimization scheme is illustrated via a simulation scenario involving a stochastic demand process satisfied by a five–stage production/inventory system with unreliable workstations and stochastic service times. The chapter concludes with a discussion on the sensitivity of the objective function in respect of the arrival rate, the service rates and the decision variable vector.

Multi-period routing in Hybrid Courier Operations

Appointment-based logistics systems, such as special courier services, or repair / maintenance services, face ever increasing competitive pressures for efficiency and on-time performance. For example, in addition to typical (core) operations, courier service providers lately deal with micrologistics activities, such as bulk product deliveries. The promise dates of such deliveries have some flexibility within a pre-specified service level. In this hybrid environment, bulk deliveries are typically planned on an ad hoc basis, without taking explicitly into account the workload for core operations, a practice that may lead to inefficiencies. This chapter proposes a new method to perform assignment of service requests (calls) with some flexibility taking into account expected routes in a multi-period horizon. The problem is solved on a rolling horizon basis in order to address the dynamics of arriving calls. The method is tested through several theoretical examples, as well as in an extensive industrial case, and appears to be superior to current methods used in practice.

Relief Distribution Networks

Logistics area is often recognized as one of the key elements in achieving effective disaster preparedness and response efforts. This chapter presents modeling and solution approaches for both the problem of prepositioning emergency supplies prior to a disaster as well as the problem of their distribution after the disaster onset. Depending on whether uncertainty is taken into account or not, work in these areas will be classified into two major categories: stochastic or deterministic. A distinction will also be made between exact methods and heuristics. In addition, the advantages and limitations of each of these two classes of approaches will be discussed. Am emphasis will be put on the particularities and characteristics of relief distribution networks. More advanced issues in the design and operations of these networks will also be discussed as interesting research avenues.