Izabela Ewa Nielsen

Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry

Abstract The incorporation of environmental criteria into the conventional supplier selection practices is essential for organizations seeking to promote green supply chain management. Challenges associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. The development and implementation of practical decision making tools that seek to address these challenges are rapidly evolving. This article contributes to this knowledge area by comparing the application of three popular multi-criteria supplier selection methods in a fuzzy environment. The incorporation of fuzzy set theory into TOPSIS, VIKOR and GRA methods is thoroughly discussed. The methods are then utilized to complete a green supplier evaluation and selection study for an actual company from the agri-food industry. Our comparative analysis for this case study indicates that the three fuzzy methods arrive at identical supplier rankings, yet fuzzy GRA requires less computational complexity to generate the same results. Additional analyses of the numerical results are completed on the normalization functions, distance metrics, and aggregation functions that can be used for each method.

Scheduling a single mobile robot for part-feeding tasks of production lines

This study deals with the problem of sequencing feeding tasks of a single mobile robot which is able to provide parts for feeders of machines on production lines. The mobile robot has to be scheduled in order to stoppage from lack of parts in the production line. A method based on the characteristics of feeders and inspired by the (

Iterative Multimodal Processes Scheduling

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Analyzing and evaluating product demand interdependencies

) inventory system, is thus applied to define time windows for the feeding tasks of the robot. The capacity of the robot is also taken into consideration. The performance criterion is to minimize total traveling time of the robot for a given planning horizon. A genetic algorithm-based heuristics is presented which results in a significant increase in the speed of finding near-optimal solutions. To evaluate the performance of the genetic algorithm-based heuristic, a mixed-integer programming model has been developed for the problem. A case study is implemented at an impeller production line in a real factory and computational experiments are also conducted to demonstrate the effectiveness of the proposed approach.

Advanced planning and scheduling technology

The paper describes a Multimodal Transportation Network (MTN) in which several unimodal networks (Automated Guided Vehicles (AGVs), hoists, lifts, etc.) interact with each other via common shared workstations as to provide a variety of demand-responsive material handling operations. The material handling transport modes provide movement of work pieces between workstations along their manufacturing routes in the MTN. The main contribution of this work is the solution to a constraint satisfaction problem aimed at AGVs fleet match-up scheduling while taking into consideration assumed itineraries of concurrently manufactured product types. In other words, assuming a given topology of the MTN and schedules of operation sequences modeling concurrently manufactured product types, the main objective is to provide a declarative framework aimed at determining conditions allowing one to adjust the AGVs fleet schedule due to the timetable of operations executed in an assumed multi-product manufacturing environment.

Green Supplier Selection Criteria: From a Literature Review to a Flexible Framework for Determination of Suitable Criteria

Abstract The paper considers the problem of Automated Guided Vehicles (AGVs) fleet scheduling subject to the right match-up of local cyclic acting AGV schedules to given workpiece machining schedules. The main contribution of this work is the solution to a constraint satisfaction problem aimed at AGVs fleet match-up scheduling while taking into consideration assumed itineraries of concurrently manufactured product types. In other words, assuming a given layout of FMS’s material handling and production routes of simultaneously manufactured work orders as well as cyclic schedules of concurrently manufactured product types, the goal is to provide a declarative model enabling multimodal processes, i.e. employing AGVs, hoists, lifts, etc. demand-responsible scheduling of transportation/handling services. An algebra-like driven approach to cyclic scheduling based on step-by-step composition of multimodal transportation network sub-structures is proposed. Results of computational experiments assessing scalability of the method provided are presented as well.

Optimizing supply chain waste management through the use of RFID technology

Emissions from idle truck engines are a main source of pollution at container terminals. In this study, we focus on reducing such emission from waiting trucks as well as the related crane operations with a new truck arrival control method that gives individual truck limited time slots for entry. We develop a method to optimize the time slot assignment for individual trucks, aiming at minimizing total emissions from trucks and cranes at import yards. The method applies discrete event simulation to estimate total truck waiting times and crane moving distance, and then applies a genetic algorithm to minimize the generated emissions from these trucks and cranes. The experiment result shows that the truck arrivals should be controlled based on the stacking of import containers, and that such control is necessary for reducing truck idling emissions at a congested container terminal.

A methodology for implementation of mobile robot in adaptive manufacturing environments

Demand-driven manufacturing is an extremely unstable planning environment compared to forecast-driven manufacturing. This requires preparation and makes knowledge of demand behaviour even more important for planning and control. The basic assumptions of pre-ante allocation based on forecast of independent end-products demand are critical for manufacturing planning and control in general. However, the importance is higher for demand-driven manufacturing than forecast-driven manufacturing. This is due to the sensitivity of demand-driven manufacturing to demand fluctuations, e.g. time and interdependency of demand rates, due to the customer order decoupling point. This paper presents a method to establish time and interdependency of demand rates (the Time- and Interdependent Demand Rate Method), which can improve the planning and control performance as well as the order management performance in a MTO environment. The method is tested on data from two cases. For both cases results and demand planning implications are presented. Use guidelines for the method are also presented along with avenues of further research.