E. H. Grosse

The effect of worker learning and forgetting on storage reassignment decisions in order picking systems

Order picking is a time-intensive and costly logistics activity as it involves a high amount of manual work. Prior research has mostly neglected the influence of human factors on the efficiency of order picking systems. This paper develops a mathematical model that investigates the impact of learning and forgetting of a heterogeneous workforce on order picking time and, consequently, on storage assignment decisions. In particular, the paper investigates when to change a storage assignment and when to keep it if learning and forgetting occur among the members of an order picking workforce. The results show that learning and forgetting should be considered in order to achieve a proper planning of storage assignment strategies.

Human factors in order picking: a content analysis of the literature

Order picking (OP) is one of the most labour- and time-intensive processes in internal logistics. Over the last decades, researchers have developed various mathematical planning models that help to increase the efficiency of OP systems, for example, by optimising storage assignments or by specifying routes for the order pickers that minimise travel distance in the warehouse. Human characteristics that are often a major determinant of OP system performance have, however, widely been ignored in this stream of research. This paper systematically evaluates the literature on manual OP systems and conducts a content analysis to gain insights into how human factors (HF) have been considered and discussed in the scientific literature. The results of the analysis indicate that management-oriented efficiency criteria dominated prior research on OP, and that there is a clear lack of attention to HF in the design and management of OP systems. This poses an opportunity for research and design of manual OP systems.

Human energy expenditure in order picking storage assignment

Considers human energy expenditure in order picking.Develops a bi-objective approach for a class-based storage assignment problem.Estimates rest allowance based on energy consumption.Compares traditional class-based storage and bi-objective storage assignment. Order picking is the most time-consuming and labor-intensive activity in warehousing. Due to the need to frequently handle items, order picking requires high human energy expenditure and poses a risk environment for workers to develop musculoskeletal disorders. The storage assignment policy in use has a significant impact on human energy expenditure and fatigue during the picking process, but this impact is usually not considered in (management-oriented) decision support models for storage assignment.This paper models and analyzes the integration of human energy expenditure as one dimension of ergonomics into the storage assignment problem using a bi-objective approach that considers both total order picking time and human energy expenditure. Time and energy expenditure depend on the main features of the order picking system, such as item characteristics, item popularity, order profiles, and physical dimensions of the shelf and locations. Pareto frontiers are constructed to understand the impact of the storage assignment policy on the objective functions. Subsequently, a quantitative approach is developed to integrate the energy expenditure rate into the time estimation for a general order picking system based on the introduction of rest allowance. Finally, the results of the model are analyzed and suggestions for the practical application of the model are presented.

Decision support models for production ramp-up: a systematic literature review

Production ramp-up is a critical step in the life cycle of a new product, and efficiently managing ramp-ups is a key to business success and market leadership. To support the planning of ramp-ups in practice, researchers have developed decision support models in the past that help to solve problems that arise during the ramp-up phase, such as lot sizing, the assignment of workers to workplaces or the determination of the capacity of the production equipment. Decision support models for production ramp-up typically consider the specific characteristics of this phase, such as uncertainty, growth in demand, worker learning or imperfect production processes. The aim of this paper is to provide a comprehensive overview of decision support models for production ramp-up and to identify areas where more research is needed. First, the paper develops a conceptual framework of production ramp-up by categorising typical planning problems and process characteristics of the ramp-up phase. Secondly, a systematic literature review with a focus on mathematical planning models for the ramp-up phase is conducted. The analysis shows that various decision support models that help to realise an efficient production ramp-up exist, but that there are still many opportunities for future research in this area.

Analysis of economic and ergonomic performance measures of different rack layouts in an order picking warehouse

Abstract Manual order picking ranks among the most time- and cost-intensive activities in warehouses, and it has frequently been studied in the past. The aim of existing studies was to improve the operational efficiency of order picking processes mainly by developing planning models that help to reduce the time that is needed for order picking. As order picking is still performed manually with technical support in most warehouses, human workers play an important role for order picking performance. Although it is recognized that manual material handling activities in warehouses expose workers to a high risk of developing musculoskeletal disorders, integrated planning approaches that consider both economic and ergonomic objectives in order picking design are still rare. This paper contributes to closing this research gap by developing economic and ergonomic performance measures for the case where orders are picked from pallets, half-pallets and half-pallets equipped with a pull-out system. The comprehensive analysis of the different rack layouts shows that there are opportunities to replace the traditional pallet storage system by half-pallets with a pull-out system on the lower rank to improve both ergonomics and economic performance.

Maverick picking: the impact of modifications in work schedules on manual order picking processes

Order picking, the process of retrieving items from their storage locations to fulfil customer orders, ranks among the most labour- and time-intensive processes in warehousing. Prior research in this area had a strong focus on the development of operating policies that increase the efficiency of manual order picking, for example by calculating optimal routes for the order pickers or by assigning products to storage locations. One aspect that poses a major challenge to many warehouse managers in practice has, curiously enough, remained largely unexplored by academic research: modifications in workflows (i.e. workplace deviance in a positive or negative sense) in order picking, which we define as ‘maverick picking’. The purpose of this paper is to characterise maverick picking and to study its causes, its forms of appearance and its potential impact on order picking performance. To gain insights into maverick picking, we first survey the literature to illustrate the state-of-knowledge of maverick picking. Subsequently, we report the results of a multi-case study on maverick picking and deduct a related content framework. The results of our case study support the proposition that maverick picking is highly relevant in practice and that it is a major determinant of order picking performance.

The effects of human behavior on the efficiency of routing policies in order picking

An exploratory case study is used to highlight how order pickers perceive order picking routes.A simulation model is developed to quantify the effects of route deviations on routing policies.Optimal routing policy keeps its performance advantage even when routing deviations occur. Retrieving items from storage locations in warehouses, commonly referred to as order picking, is often performed by human workers. The high amount of human work involved in order picking turns this activity into a time- and cost-intensive process step in warehouse operations. Due to the cost impact of manual order picking, researchers have developed various planning methods that support practitioners in realizing an efficient order picking process. Among these planning approaches, methods that support the routing of order pickers through the warehouse have been a very popular research topic in recent years, with the focus being both on the development of optimal and heuristic routing policies. Surprisingly, problems that may arise when implementing picker routes for human workers in practice have not been investigated so far. There is, however, empirical evidence that order pickers tend to deviate from optimal routes, putting the efficiency of these routing approaches at stake.This paper presents a detailed evaluation of the relative efficiency of order picker routing policies when order pickers deviate from pre-specified routes. First, different behavioral factors that may result in deviations from pre-specified routes are identified in a state-of-the-art literature review and using the results of an exploratory case study. Subsequently, an agent-based simulation model is developed to quantify the effects of deviations from pre-specified routes. The simulation model is then used to compare different routing policies in an extensive simulation experiment. The results of this paper indicate that even in case order pickers deviate from given routes, implementing the optimal routing policy should be the preferred option in most real-world scenarios.

Applying fuzzy multi-objective linear programming to a project management decision with nonlinear fuzzy membership functions

This paper investigates a multi-objective project management problem where the goals of the decision maker are fuzzy. Prior research on this topic has considered linear membership functions to model uncertain project goals. Linear membership functions, however, are not much flexible to model uncertain information of projects in many situations, and therefore, fuzzy models with linear membership functions are not suitable to be applied in many practical situations. Hence, the purpose of this paper is to apply nonlinear membership functions in order to develop a better representation of fuzzy project planning in practice. This approach supports managers in examining different solution strategies and in planning projects more realistically. In doing so, a fuzzy mathematical project planning model with exponential fuzzy goals is developed first which takes account of (a) the time between events, (b) the crashing time for activities, and (c) the available budget. Following, a weighted max–min model is applied for solving the multi-objective project management problem. The performance of the developed solution procedure is compared with the literature that applied linear membership functions to this problem, and it is shown that the model developed in this paper outperforms the existing solution.

Machine scheduling problems in production

Presents a systematic review of 129 literature reviews on machine scheduling problems in production.Develops a conceptual framework that considers main attributes of machine scheduling problems in production.Presents a comprehensive overview of the state-of-knowledge on machine scheduling problems in production.Derives promising ideas for future research on machine scheduling problems in production from the results of the review. This paper presents the results of a comprehensive and systematic review of 129 literature reviews on machine scheduling problems in production (MSPP). The paper first proposes a conceptual framework that considers the main attributes of MSPP in seven categories and 75 sub-categories. After a descriptive analysis of the sampled papers that give insights into publication patterns for MSPP, a quantitative analysis of the sampled review papers is carried out based on the proposed framework. A synthesis of research findings describes the state-of-knowledge and unveils general deficiencies of literature reviews on MSPP. In addition, the paper provides a comprehensive overview of MSPP, which supports researchers in positioning their own work in the literature and in finding potential innovative research areas. The paper concludes with an outlook on future research opportunities in the area of MSPP.

Environmental impact of warehousing: a scenario analysis for the United States

In recent years, there has been observed a continued growth of global carbon dioxide emissions, which are considered as a crucial factor for the greenhouse effect and associated with substantial environmental damages. Amongst others, logistic activities in global supply chains have become a major cause of industrial emissions and the progressing environmental pollution. Although a significant amount of logistic-related carbon dioxide emissions is caused by storage and material handling processes in warehouses, prior research mostly focused on the transport elements. The environmental impact of warehousing has received only little attention by research so far. Operating large and highly technological warehouses, however, causes a significant amount of energy consumption due to lighting, heating, cooling and air condition as well as fixed and mobile material handling equipment which induces considerable carbon dioxide emissions. The aim of this paper is to summarise preliminary studies of warehouse-related emissions and to discuss an integrated classification scheme enabling researchers and practitioners to systematically assess the carbon footprint of warehouse operations. Based on the systematic assessment approach containing emissions determinants and aggregates, overall warehouse emissions as well as several strategies for reducing the carbon footprint will be studied at the country level using empirical data of the United States. In addition, a factorial analysis of the warehouse-related carbon dioxide emissions in the United States enables the estimation of future developments and facilitates valuable insights for identifying effective mitigation strategies.