Nico Vandaele

Vehicle routing with dynamic travel times: A queueing approach

Transportation is an important component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routes is a crucial management problem. In this paper, a vehicle routing problem with dynamic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture travel times. The queueing approach is compared with other approaches and its potential benefits are described and quantified. Moreover, the optimization of the starting times of a route at the distribution center is evaluated. Finally, the trade-off between solution quality and calculation time is discussed. Numerous test instances are used, both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment, which is usually the case on European road networks.

A general approximation for the single product lot sizing model with queueing delays

Abstract The objective of this paper is to derive a general approximation for the single product lot sizing model with queueing delays, explicitly including a non-zero setup time. Most research focuses on bulk (batch) arrival and departure processes. In this paper we assume an individual arrival and departure process allowing the modelling of more realistic demand patterns. A general approximation of the expected lead time and the variance of the lead time is derived. The lead time probability distribution is approximated by means of a lognormal distribution. This allows the manufacturer to quote lead times satisfying a specified customer service level as a function of the lot size. The main result is a convex relationship of the expected lead time and the quoted lead time as a function of the lot size. The results are illustrated by means of numerical examples.

Multi-level reverse logistics network design under uncertainty

Localising facilities and assigning product flows in a reverse logistics environment is a crucial but difficult strategic management decision, certainly when value decay plays an important part. Despite numerous publications regarding closed-loop supply chain design, very few addressed the impact of lead times and the high level of uncertainty in reverse processes. In this article, a single product reverse logistics network design problem with multiple layers and multiple routings is considered. To this end, a new advanced strategic planning model with integrated queueing relationships is built that explicitly takes into account stochastic delays due to various processes like collection, production and transportation, as well as disturbances due to various sources of variability like uncertain supply, uncertain process times, unknown quality, breakdowns, etc. Their impact is measured by transforming these delays into work-in-process, which affects profit through inventory costs. This innovative modeling approach is difficult to solve because of both combinatorial and nonlinear continuous relationships. The differential evolution algorithm with an enhanced constraint handling method is proposed as an appropriate heuristic to solve this model close to optimality. A number of scenarios for a realistic case illustrate the power of this optimisation tool.

Load-Based POLCA: An Integrated Material Control System for Multiproduct, Multimachine Job Shops

This article proposes a supporting framework for the implementation of the material control system POLCA (paired-cell overlapping loops of cards with authorization). The POLCA system is particularly appropriate for environments that involve highly variable demand and large product variety, which force small batch (or even one-of-a-kind) production. We propose a load-based version of the POLCA control system (LB-POLCA), which determines the POLCA parameters (release authorizations, allowed workloads in the loops) according to an advanced resources planning (ARP) system that adequately captures the stochastic behavior of the production system and enables fine-tuning and high-level optimization of the manufacturing lot sizes. We also discuss the implementation of an electronic LB-POLCA system in a metal shop of Spicer Off-Highway Products Division, a subsidiary of the Dana Corporation.

Advanced resource planning as a decision support module for ERP

The planning and decision support capabilities of the manufacturing planning and control system, which provides the core of any enterprise resource planning package, can be enhanced substantively by the inclusion of a decision support module as an add-on at the midterm planning level. This module, called advanced resource planning (ARP), provides a parameter-setting process, with the ultimate goal of yielding realistic information about production lead times for scheduling purposes, sales and marketing, strategic and operational decision making, and suppliers and customers. This article illustrates the ARP approach with reports from several real-life implementations by large industrial companies.

Sustainable R&D portfolio assessment

Research and development portfolio management is traditionally technologically and financially dominated, with little or no attention to the sustainable focus, which represents the triple bottom line: not only financial (and technical) issues but also human and environmental values. This is mainly due to the lack of quantified and reliable data on the human aspects of product/service development: usability, ecology, ethics, product experience, perceived quality and the like. Even if these data are available, consistent decision support tools are not ready available. Based on the findings from an industry review, a DEA model has been developed that permits to support strategic R&D portfolio management. The usability of this approach is underscored with real life examples from two different industries: consumables and materials manufacturing (polymers).

A lot sizing model with queueing delays: The issue of safety time

Abstract The objective of this paper is to introduce the concept of safety time in a make-to-order production environment. The production facility is represented as a queueing model, explicitly including a non-zero setup time. A methodology is presented to quantify the safety time and to compute the associated service level based on the queueing delay. The main result is a convex relationship of the expected waiting time, the variance of the waiting time and the quoted lead time as a function of the lot size and a concave relationship of the service level as a function of the lot size. Most models in the literature assume batch arrivals. We relax that assumption so that an individual customer arrival process is allowed. We therefore have to derive a new closed form analytical expression for the expected waiting time. Both the deterministic and a stochastic case are studied.

A multi-criteria approach to robust outsourcing decision-making in stochastic manufacturing systems

Robust DEA-based approach for multi-criteria decision-making developed.Partial views of DEA envelope curves provide instructive decision support.Enhanced aggregate planning approach for stochastic environments developed.Demand variability drives outsourcing volumes and reduces internal batch sizes.Higher setup variability increases insourcing volumes and average batch sizes. Manufacturing outsourcing is a key industry trend towards greater operations effectiveness and is related to the discussion of strategic core competencies. We study the issue of contract manufacturing at the strategic-tactical level aiming for robust decisions to accommodate stochastic manufacturing environments and immanent uncertainty of planning parameters. The topic is approached from a multi-criteria decision-making perspective, since service, cost, quality, and more long-term value-related aspects need to be considered to arrive at well-balanced decisions. Our contribution is twofold: first, we develop a scenario-based non-parametric ranking approach to determine beneficial outsourcing options at the strategic level. The ranking method uses both model-based Key Performance Indicators (KPIs), which are obtained from a tactical planning model, and non-model-based KPIs that are derived in an independent assessment from multiple stakeholders. Second, we provide an enhanced aggregate planning approach at the tactical level in order to evaluate the performance implications of the strategic outsourcing decisions which in turn serve as the model-based KPIs for the ranking method. A queuing network-based approach is incorporated in the aggregate planning model to anticipate the stochastic behavior of manufacturing systems. An industry-derived case example with distinct outsourcing options is used to highlight the benefits of the approach and to investigate tactical trade-offs when coordinating internal and external manufacturing decisions.

Supplier managed inventory in the OEM supply chain : the impact of relationship types on total costs and cost distribution

We investigate the impact of four variants of supplier managed inventory on total costs and cost distribution in a capital goods supply chain consisting of a parts supplier who delivers parts to an original equipment manufacturer’s assembly plant. The four supplier managed inventory variants differ in the components of inventory costs that the supplier has to carry. The performance of the supplier managed inventory relationships is benchmarked with the situation where the assembly plant manages the inventories. Interesting managerial insights follow from this comparison.

Differential evolution to solve the lot size problem in stochastic supply chain management systems

An advanced resource planning model is presented to support optimal lot size decisions for overall performance improvement of real-life supply chain management systems in terms of either total delivery time or total setup costs. Based on a queueing network, a model is developed for a mix of products, which follow a sequence of operations taking place at multiple interdependent supply chain members. At the same time, various sources of uncertainty, both in demand and process characteristics, are taken into account. In addition, the model includes the impact of parallel servers for multiple resources with period dependent time schedules. The corrupting influence of variabilities from rework and breakdown is also explicitly modeled. This integer non-linear problem is solved by standard differential evolution algorithms. They are able to find each product’s lot size that minimizes its total supply chain lead time. We show that this solution approach outperforms the steepest descent method, an approach commonly used in the search for optimal lot sizes. For problems of realistic size, we propose appropriate control parameters for an efficient differential evolutionary search process. Based on these results, we add a major conclusion on the debate concerning the convexity between lot size and lead time in a complex supply chain environment.