Academic articles

Fashion is one of the world’s most important industries, driving a significant part of the global economy representing, if it was a country, the seventh-largest GDP in the world in terms of market size. Focusing on the footwear industry, assembly line balancing and sequencing represents one of the more significant challenges fashion companies have to face with.

This paper presents the results of a simulation-optimization framework implementation in such industry, highlighting the benefits of the use of simulation together with a finite capacity scheduling optimization model. Production planning and scheduling optimization includes the conduction of a scenario analysis that compares production KPIs (in terms of average advance, delay and resource saturation) related to different scenarios.

The goal of this study is to contribute to commercialization of the second-generation cellulosic biofuels (SGCBs) by reducing its operational cost. A hybrid simulation-based optimization approach is devised to design a cost-effective SGCB supply chain model. The approach includes feedstock yield estimation and location-allocation of feedstock storages between farms and refineries. An agent-based simulation (ABS) implemented in AnyLogic is utilized to estimate operational cost of a SGCB supply chain. The simulation-based optimization with adaptive replication (AR) is devised to find an appropriate SGCB network design in terms of operational cost. The approach is applied to a SGCB transportation network design problem in Southern Great Plains of U.S.

This paper addresses the problem of controlling the Work-In-Process (WIP) in semiconductor manufacturing by using a global scheduling approach and manufacturing scheduling software. A WIP balancing strategy is proposed to minimize the product mix variability in terms of throughput and cycle time. This strategy is enforced using a global scheduling optimization model which is formulated as a linear programming model. The global scheduling model is coupled with a generic multi-method simulation model built with manufacturing scheduling software for evaluation purpose.

During recent years, Autonomous Vehicle Storage and Retrieval Systems (AVS/RS) have been widely applied in warehouse optimization to meet the increasing demand for rapid and flexible large-scale storage and retrieval tasks. This paper focuses on the operations control strategies with regard to the conveyor system, rack storage system, and pick-up system in order to maximize the system’s throughput capacity and minimize the storage/retrieval times of items. The study is based on a large-scale shoe manufacturer’s warehouse optimization and provides insights for system management.

In this paper, we simulate allocation policies for a two-stage inventory system that receives perfect advance demand information (ADI) from customers belonging to different demand classes using AnyLogic as supply chain management software. Demands for each customer class are generated by independent Poisson processes while the processing times are deterministic. All customers in the same class have the same demand lead time (the difference between the due date and the requested date) and back-ordering costs.

Each stage in the inventory system follows order-base-stock-policies where the replenishment order is issued upon arrival of a customer order. The researchers employ discrete event simulation to obtain output parameters such as inventory costs, fill rates, waiting time, and order allocation times. A numerical analysis is conducted to identify a reasonable policy to use in this type of system.

The ever-increasing demand for fresh and healthy products initiated an urgency for transportation system analysis and effective planning for Agri-Fresh Produce Supply Chains (AFPSC). However, AFPSC faces many challenges, including product vulnerability to market disruption and limited shelf-life. In case of a no-deal Brexit (i.e., the UK leaving the EU without an agreement), trade between Ireland and the UK will most probably be subjected to customs control. In effect, transportation delays and products deterioration rates will increase.

Based on interviews with an Irish AFPSC forwarder, a simulation model was developed to investigate different systems’ dynamics and operating rules under different delay patterns on the (yet non-existent) inner-Irish border.

Besides transportation costs the punctual delivery of the goods is a key factor for mode choice in intermodal transportation networks. However, only a limited number of studies have included stochastic transportation time in Service Network Design, which refers to decisions regarding transportation mode and services, so far.

The paper on hand combines a Sample Average Approximation approach with Discrete Event Simulation for transportation network optimization with stochastic transportation times. This includes the corresponding vehicle routing problem for road vehicles. The share of orders transported by intermodal road-rail vs. unimodal road transportation in dependence of costs and delays of the trains is evaluated for a generic transportation relation in Central Europe. The data is backed by empirical data for transportation orders and delay distributions.

This study was designed to evaluate innovative last-mile delivery concepts involving autonomous parcel robots with simulation and optimization. In the proposed concept, the last mile of parcel delivery is split into a two-tiered system, where parcels are first transported to a transfer point by conventional trucks and then delivered with parcel robots on customer demand.

The purpose of this publication is to compare different time slot selection options for customers, namely due window and on demand selection, in the context of city logistics measures such as access regulations and driving bans for city centers. The researchers use AnyLogic as delivery optimization software. They build an agent-based simulation model, including a Geographic Information System environment and optimization algorithms for allocation and scheduling of delivery robots.

A common challenge in field service planning is to evaluate different design choices, related to staffing decisions, technician scheduling strategies, and technological improvements in order to make the system more efficient. This work provides a simulation-based optimization tool to support decision makers in tackling this challenging problem. The proposed framework relies on an optimization engine for the generation of the daily plans.

The study uses AnyLogic as field service planning software to evaluate the applicability of such plans by taking into account the stochastic factors. Furthermore, an interface manages the communication between these two components and allows a feedback loop between the simulator and the optimizer to achieve more robust plans. The applicability of the framework is demonstrated through a business case to evaluate different staffing decisions.

Klein Mechanisch Werkplaats Eindhoven (KMWE) is a precision manufacturing company situated in the Netherlands and recently relocated to a new location known as the 'Brainport Industries Campus' (BIC). This move allowed KMWE to improve the performance of its manufacturing facility by investing in vertical automated storage and retrieval systems (AS/RSs). However, these decisions needed to be made under input uncertainties since the move to BIC and modernization of existing equipment would cause changes in operating parameters inside the facility.

In this study, the researchers show how hybrid simulation modelling was used in production planning optimization, in particular to assess the impact of input uncertainties (such as operator productivity, vertical storage height) on the throughput performance of TSC.