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Frequently, various sectors within the same company often find themselves at odds with one another due to conflicting objectives. For example, the sales and logistics departments frequently fail to synchronize, with sales viewing logistics merely as a cost center. Conversely, sales place demanding delivery tasks on logistics, particularly evident in scenarios involving out-of-stock or non-assortment items. To address this pervasive issue, the primary objective of this paper is to propose a model for assessing the efficiency of logistical processes, distribution centers, and drivers. Hence, it can be asserted that this study, through its proposed model, aims to accomplish several aims: (i) determine the effectiveness of distribution centers (DCs), (ii) analyze how the efficiency of DCs evolves over time, (iii) evaluate the performance of distribution drivers, and (iv) rank proficient drivers for bonus allocation based on their performance. To achieve these objectives, the suggested model relies on the application of the CI-DEA-Malmquist Index-DEA-MEREC-ADAM methodology. Specifically, the model is delineated into three distinct phases. The initial phase entails employing the CI-DEA and Malmquist index methodologies to condense the multitude of parameters considered in the study by utilizing the CI-DEA approach. Additionally, Malmquist analysis is applied to observe the multi factor productivity change in the efficiency of DCs and to identify the most efficient DC across all time periods. Following the identification of the most efficient DC, the subsequent phase focuses on selecting drivers from that center, followed by applying the DEA method to isolate only the most efficient drivers, who are then further evaluated and ranked accordingly. Through the application of these methodologies, it was determined that out of a total of 10 drivers, only four were deemed efficient. In the final phase of the model, these drivers were assessed based on seven criteria for ranking purposes. During this phase, the MEREC method was utilized to ascertain the criteria weights, while the ADAM method was employed for driver ranking. The findings revealed that the second driver ranked the highest and thus should be granted the highest bonus.

Frequently, various sectors within the same company often find themselves at odds with one another due to conflicting objectives. For example, the sales and logistics departments frequently fail to synchronize, with sales viewing logistics merely as a cost center. Conversely, sales place demanding delivery tasks on logistics, particularly evident in scenarios involving out-of-stock or non-assortment items. To address this pervasive issue, the primary objective of this paper is to propose a model for assessing the efficiency of logistical processes, distribution centers, and drivers. Hence, it can be asserted that this study, through its proposed model, aims to accomplish several aims: (i) determine the effectiveness of distribution centers (DCs), (ii) analyze how the efficiency of DCs evolves over time, (iii) evaluate the performance of distribution drivers, and (iv) rank proficient drivers for bonus allocation based on their performance. To achieve these objectives, the suggested model relies on the application of the CI-DEA-Malmquist Index-DEA-MEREC-ADAM methodology. Specifically, the model is delineated into three distinct phases. The initial phase entails employing the CI-DEA and Malmquist index methodologies to condense the multitude of parameters considered in the study by utilizing the CI-DEA approach. Additionally, Malmquist analysis is applied to observe the multi factor productivity change in the efficiency of DCs and to identify the most efficient DC across all time periods. Following the identification of the most efficient DC, the subsequent phase focuses on selecting drivers from that center, followed by applying the DEA method to isolate only the most efficient drivers, who are then further evaluated and ranked accordingly. Through the application of these methodologies, it was determined that out of a total of 10 drivers, only four were deemed efficient. In the final phase of the model, these drivers were assessed based on seven criteria for ranking purposes. During this phase, the MEREC method was utilized to ascertain the criteria weights, while the ADAM method was employed for driver ranking. The findings revealed that the second driver ranked the highest and thus should be granted the highest bonus. [ABSTRACT FROM AUTHOR]

Simple Summary: The cassava mealybug (Phenacoccus manihoti) represents a significant quarantine pest affecting cassavas. This study seeks to elucidate the patterns of the suitable habitat and distribution centers of cassava mealybugs in China, as well as their dynamic ecological niche in invaded areas under the influence of climate change. Climate factors have primarily concentrated suitable habitats for cassava mealybugs in the southern regions of China, with minimal observed changes and a slight northward expansion. The dynamic ecological niche is also projected to expand slightly. Based on these predictions, relevant quarantine agencies can implement measures to prevent the introduction and spread of cassava mealybugs. The changing global climate has significantly impacted the spread of plant pests. The cassava mealybug (Phenacoccus manihoti) is among the most dangerous quarantine pests affecting cassavas worldwide, causing substantial losses in agricultural production and food security across several regions. Although China is currently free of the cassava mealybug, its proximity to affected countries and extensive trade with these regions necessitate a detailed understanding of the pest's distribution pattern and dynamic ecological niche changes. Using the Biomod2 model, we selected two historical climate scenarios and two future climate scenarios (SSP1-2.6 and SSP5-8.5) to investigate the distribution patterns, potential habitats, distribution centers, and dynamic ecological niches of cassava mealybugs in China. Key environmental variables influencing the distribution were identified, including bio4, bio8, bio12, bio18, and bio19. The potential habitat of cassava mealybugs is mainly located in several provinces in southern China. In the future, the suitable habitat is projected to expand slightly under the influence of climate change, maintaining the overall trend, but the distribution center of suitable areas will shift northward. Dynamic ecological niche prediction results indicate the potential for further expansion; however, the ecological niches may be unequal and dissimilar in the invaded areas. The predictions could serve as a valuable reference for early warning systems and management strategies to control the introduction of cassava mealybugs. [ABSTRACT FROM AUTHOR]

Copyright of Tobacco Science & Technology is the property of Tobacco Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the logistics center (warehouse or distribution center), customer orders need to be picked up by the pickers. In this research, we examine the order-picking problem with sequencedependent constraints with two decision variables (container start time and product quantity) in a distribution center with a one-directional conveyor. The decision-making is based on the developed two variations of two-step matheuristics. At first, the main order-picking problem is divided into two subproblems. Next, each step of each variant of the subproblem is solved using a mathematical programming-based technique. Both matheuristics were better in 85 of 120 test instances compared to the initial model solved by mathematical programming. Pickers matheuristics were better on average at 46.56%, while Buffers matheuristics were better on average at 46.87%. The proposed matheuristics approach allows distributors to schedule orders in the logistics center fast enough and with fewer resources. [ABSTRACT FROM AUTHOR]

The distribution center is a transit place for goods in the logistics network, used to achieve the distribution of goods. Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness due to the low temperature or ultra-low temperature requirements of transport objects. Aiming at the problems of high cost and low efficiency of cold chain distribution center location, a new location model of cold chain distribution center is developed. The Affinity Propagation (AP) clustering algorithm is used to simplify location selection. And combine the binary semantics with entropy to reduce the subjectivity of the binary semantics in the process of experiment. The results show that the location selection using the research method is optimal, and the problem that multiple secondary distribution centers are the same retailer will not appear. The research method is more objective and scientific for the location of cold chain distribution centers. Compared with ordinary logistics, cold chain logistics has higher requirements for timeliness. Distribution center is the transfer place of goods in the logistics network, which plays an important role in the logistics supply chain system. A new location model of cold chain distribution center is developed. Using Affinity Propagation (AP) clustering algorithm to simplify location selection and combining binary semantics with entropy method can further improve the objectivity of influencing factor index weights. The results show that the problem of multiple secondary distribution centers providing logistics services for the same retailer will not occur in the research method, and the location selection results are optimal. And the logistics cost can be reduced by 0.042%. This study improves the distribution efficiency, enhances the customer experience of cold chain logistics distribution, and provides certain technology and reference value for the development of cold chain logistics distribution. [ABSTRACT FROM AUTHOR]

Background: Endogenous and exogenous factors impact the operational characteristics of supply chains, affecting wholesale warehouses. The survival of a warehouse is often threatened by disruptive events that alter infrastructure and performance. The emergence of COVID-19 exemplified the need for adaptability in retail goods supply chains, emphasizing the necessity for responding to external shocks. Methods: The MARLIN (Method wArehouse ResiLience dIstruptioN) method, founded on theories and models of resilience engineering is introduced. MARLIN is a practical tool designed to identify key areas requiring intervention in response to disruptive events. An empirical test was conducted in an Italian warehouse. Results: The conducted test yielded tangible results, demonstrating the efficacy of the method. It successfully pinpointed areas necessitating intervention and identified Key Performance Indicators (KPIs) associated with disruptions. The study not only underscores the importance of data collection but also highlights the often-overlooked significance of warehouse management. Conclusions: The study establishes MARLIN as a valuable asset for stakeholders involved in disruption management. Its application has proven instrumental in recognizing areas of intervention and identifying KPIs related to disruptions. Ongoing research endeavors to broaden its applicability across diverse supply chain scenarios, aiming to enhance situational awareness and enable proactive risk assessment through what-if analysis. [ABSTRACT FROM AUTHOR]

The changing global climate has significantly impacted the spread of plant pests. The cassava mealybug (Phenacoccus manihoti) is among the most dangerous quarantine pests affecting cassavas worldwide, causing substantial losses in agricultural production and food security across several regions. Although China is currently free of the cassava mealybug, its proximity to affected countries and extensive trade with these regions necessitate a detailed understanding of the pest’s distribution pattern and dynamic ecological niche changes. Using the Biomod2 model, we selected two historical climate scenarios and two future climate scenarios (SSP1-2.6 and SSP5-8.5) to investigate the distribution patterns, potential habitats, distribution centers, and dynamic ecological niches of cassava mealybugs in China. Key environmental variables influencing the distribution were identified, including bio4, bio8, bio12, bio18, and bio19. The potential habitat of cassava mealybugs is mainly located in several provinces in southern China. In the future, the suitable habitat is projected to expand slightly under the influence of climate change, maintaining the overall trend, but the distribution center of suitable areas will shift northward. Dynamic ecological niche prediction results indicate the potential for further expansion; however, the ecological niches may be unequal and dissimilar in the invaded areas. The predictions could serve as a valuable reference for early warning systems and management strategies to control the introduction of cassava mealybugs.

The distribution or delivery process is one factor that affect customer satisfaction as the goal of supply chain. In order for supply chain to be competitive in competition, delivery time is an important factor to manage, so that it could provide high service level value. One factor that could affects distribution or delivery is distribution center location. A strategic distribution center location would facilitate and speed up distribution process. This research discusses model for determining location of distribution center so that delivery could be made at the right amount and in the right time by analyzing of time-based using set covering model and maximal covering model. The numerical example in this research is case study on paint supply chain in Bandung which has a number of delivering clusters. Each cluster consists of a number of building material stores which are located scattered. There is one cluster that has problems in fulfilling demands and delivery times. The analysis made could be used to determine the distribution center location so that it could serve all consumer demands. Based on the calculation results, it was found that there were two distribution center locations. Two distribution centers could fulfill the objective function of cost minimization and satisfy all demands. The results of the sensitivity analysis show that there are several factors that influence the determination of location number.

AbstractEnabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity. [ABSTRACT FROM AUTHOR]

The necessity for undertaking this research is driven by the prevailing challenges encountered in logistic centers. This study addresses a logistic order-picking issue involving unidirectional conveyors and buffers, which are assigned to racks and pickers with the objective of minimizing the makespan. Subsequently, two variations of a two-step matheuristic approach are proposed as solution methodologies. These matheuristics entail decomposing the primary order-picking problem into two subproblems. In the initial step, the problem of minimizing the free time for pickers/buffers is solved, followed by an investigation into minimizing order picking makespan. An experimentation phase is carried out across three versions of a distribution center layout, wherein one or more pickers are allocated to one or more buffers, spanning 120 test instances. The research findings indicate that employing a mathematical programming-based technique holds promise for yielding solutions within reasonable computational timeframes, particularly when distributing products to consumers with limited product variety within the order. Furthermore, the proposed technique offers the advantages of expediency and simplicity, rendering it suitable for adoption in the process of designing and selecting order-picking systems. [ABSTRACT FROM AUTHOR]

Customers seek items at low prices, excellent quality, and customization in today's market. Additionally, the need for quick product delivery is rising because of the expansion of e-commerce. Order picking is an essential element of order fulfillment and is regarded as one of the most costly and time-consuming tasks for warehouses. The study aims to create a model for the ordering in logistics that involves the group of racks with assigned one-way conveyor and buffers. The focus of this study is on improving the customer order completion time because of appropriate tasks assigned to pickers. The simulation was performed using the CPLEX solver. The findings highlight the need for picking operations to manage time resources, as well as the importance of sustainable work of order pickers in logistic centers in fostering sustainable performance outcomes. [ABSTRACT FROM AUTHOR]

PT Asagri Selaras Asia is an agribusiness company in Jakarta, Indonesia that provides food supply for entrepreneurs in the tourism sector, such as hotels and restaurants. In running their business, the company has a supply chain network that effectively integrates suppliers, manufacturers, warehouses, and stores. Supply chain management optimization is deemed necessary to maximize efficiency in every process carried out at PT Asagri Selaras Asia. One of the processes that can be optimized is the determination of facility location. The right facility location can minimize distance, delivery time, and operational costs and optimize the company's profit. Currently, the contract period for the distribution center (DC) used by PT Asagri Selaras Asia is about to expire therefore a decision is needed regarding the determination of the DC location for the next period, either extending the current DC contract or moving to a new location. This study aims to maximize the company's profit by comparing the profit obtained under existing and alternative DC, and the proposed DC from the experimental results using the Green Field Analysis (GFA) method. This study also aims to determine the feasibility of the three DC locations using the Network Optimization (NO) method with Anylogistix software. Based on the data analysis, the new DC is recommended to be located in the West Pejaten area, South Jakarta with latitude -6.2729150574 and longitude 106.8230798418 which will give an increase in profit of 80.53% compared to the existing DC.

Strategic planning for the supply chain is part of the planning process for the supply chain management. One part of this process is figuring out the supply chain's infrastructure and physical structure. The design of supply chain networks has been used for facility location models for more than 20 years. The hub and spoke system is a two-level distribution network with a hub that handles shipments and spokes that connect with consumers. This study integrates optimization techniques with decision-making tools to determine the best location for distribution centers. The optimization method is modified by considering population, income, and distance factors. The location of the distribution center is determined at two levels. The first level uses the set covering method, while the second uses the Analytical Hierarchy Process (AHP). A case study is drawn from this research: a company that will determine regional and local distribution centers in East Java. The result is four regional distribution centers: Pacitan, Kediri, Banyuwangi, and Sumenep. Each regional distribution center has two local distribution centers: Pacitan (Tulungagung and Madiun), Kediri (Bojonegoro and Jombang), Banyuwangi (Pasuruan and Malang), and Sumenep (Surabaya and Gresik). This study illustrates how the integration of two-level method can be applied in the context of strategic decision making. The selected locations have a minimum total cost and can serve consumers with maximum coverage in the region. Finally, an efficient supply chain network with a high service level will be obtained.

For logistics enterprises, site selection and path layout are related to the cost and efficiency of distribution, which is a very critical issue and has an important impact on the development of enterprises. Compared with land logistics, the cost of marine ship logistics is higher due to the high cost of ships, so the research on the location and path layout of its distribution centers is also particularly important. This paper established a two-layer model under the assumption that unit transportation costs and administration expenses are known for the site selection and path layout problems of marine ship logistics distribution centers. Corresponding constraint conditions were set. The upper layer was the optimization model of the site selection problem of the distribution center, and the objective function was to minimize operating and construction costs and was solved using the quantum particle swarm optimization (QPSO) algorithm. The lower layer was the optimization model of the distribution path layout, and the objective function was to minimize the logistics distribution cost and was solved using the ant colony optimization (ACO) algorithm. The model was veri-fied through an example analysis. It was assumed that there were three ships, five candidate distribution centers, and ten customer points. The model was solved in MATLAB software. The results of the example analysis showed that com-pared with K-means, genetic algorithm (GA), and particle swarm optimization (PSO)-ACO algorithms, the QPSO-ACO algorithm had the shortest running time, about 60 s, which saved about 50% compared to the K-means algorithm. The optimal cost of the QPSO-ACO algorithm was 293,400 yuan, which was significantly lower than the K-means, GA, and PSO-ACO algorithms (459,600 yuan, 398,300 yuan, and 357,700 yuan). In this example, the site obtained by the QPSO-ACO algorithm was distribution center 2, and the obtained path distribution was 1-7-5-4, 2-6-3, and 10-8-9. The results verify the effectiveness of the QPSO-ACO algorithm in solving the problem of site selection and path layout. The QPSO-ACO algorithm can be applied in the actual marine ship logistics.

Objectives Studies in the goods supply chain in areas outside of warehousing show evidence of gender and racial/ethnic inequalities in working conditions (i.e. in work organization, work environment, and employment conditions). This review aimed to identify, summarize, and discuss research focused on inequality in warehousing and its effects on warehouse working conditions. In the review, racial/ethnic inequality includes inequality related to country of birth and (im)migration status. Methods We performed a systematic search in the Scopus and Web of Science databases to identify warehouse studies that addressed working conditions and (in)equality at a workplace level. Screening of records was performed using the Rayyan systematic review tool. Risk of bias was assessed according to established methods and checklists. Results Database searches yielded 4910 articles. After title-abstract-keyword and full-text screenings, 21 articles were included. Results showed inequality based on gender and race/ethnicity in both work organization (different tasks were performed by different groups of employees), work environment conditions (physical and psychosocial aspects differed), and employment conditions (disparate employment types and incomes between groups of employees). Health differences, as a possible result of unequal working conditions, were evident between different racial/ethnic groups of employees. A hierarchy that included both gender and race/ethnicity was found, with (im)migrant and racialized women positioned at the bottom. Conclusions We found evidence that gender and race/ethnicity influenced work organization, work environment conditions, and employment conditions. Evidence was found for an intersection between gender and race/ethnicity. To improve working conditions, and subsequently occupational health, we encourage researchers to simultaneously consider gender and race/ethnicity factors at work, and to consider both why inequality is present and how it impacts working conditions in future studies of warehousing, particularly in online retailing. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The focus of this study is on the significance of location in establishing distribution centers. The key question when selecting a location is regarding which location would contribute the most to the growth of a company's business through the establishment of distribution centers. To answer this question, we conducted research in the Brčko District of BiH in order to determine the best location for a distribution center using expert decision-making based on linguistic values. In order to use these values when selecting locations, a fuzzy set was formed using the IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to the Ideal Solution) methods. The IMF SWARA method was utilized to determine the weights of the criteria, and the fuzzy CRADIS method was employed to rank the locations based on expert ratings. The location for the construction of distribution centers at Bodarište was rated the worst, while the McGowern Base location was rated the best. Based on these findings, the research question was answered, and it was demonstrated that fuzzy methods could be utilized in the selection of distribution center locations. Hence, we recommend that future research be performed on the application of fuzzy methods in the expert selection of potential sites for distribution centers. [ABSTRACT FROM AUTHOR]

The goal of this study was to evaluate the relationship between ratings of perceived exertion (RPE) and relative strength with respect to baseline for a fatiguing free dynamic task targeting the upper extremity, namely simulated order picking, and determine whether the relationship remains the same for different conditions (i.e., pace and weight) and with fatigue. Fourteen participants (seven males, seven females) performed four sessions that included two 45-min work periods separated by 15 min of rest. The work periods involved picking weighted bottles from shoulder height and packaging them at waist height for four combinations of bottle mass and picking rate: 2.5 kg–15 bottles per minute (bpm), 2.5 kg–10 bpm, 2.5 kg–5 bpm, and 1.5 kg–15 bpm. Participants reported their RPEs every 5 min and performed a maximum isometric shoulder flexion exertion every 9 min. Pearson product-moment correlation was used to evaluate the linear relationship between RPE and relative strength for each subject and work period. Then, the effects of condition and work period on the average relationship were assessed using a repeated-measures analysis of variance (ANOVA). For the first 45-min period, there were no significantly different correlations between RPE and relative strength across conditions (average r = −0.62 (standard deviation = 0.38); p = 0.57). There was a significant decrease in average correlation for the second work period (r = −0.39 (0.53)). These results suggest that individual subjective responses consistently increase while relative strength declines when starting from a non-fatigued state. However, correlations are weaker when re-engaging in work following incomplete recovery. Thus, starting fatigue levels should be accounted for when considering the expected relationship between RPE and relative strength. [ABSTRACT FROM AUTHOR]

This research applies and modifies K-means clustering analysis from Data Mining to solving the location problem. First, a case study of Thailand's convenience store franchise in locating distribution centers (DCs) is conducted. Then, the final centroids are served at suggested DC locations. Besides the typical distance, Euclidean, used in K-means, Manhattan, and Chebyshev, is also experimented with. Moreover, due to the stores' different demands, a modification of the centroid calculation is needed to reflect the center-of-gravity effects. For the proposed centroid calculation, the above three distance metrics incorporating the demands as weights give rise to another three approaches and are thus named Weighted Euclidean, Weighted Manhattan, and Weighted Chebyshev, respectively. Besides the optimal locations, the effectiveness of these six clustering approaches is measured by the expected total distribution cost from DCs to their served stores and the expected Davies-Bouldin index (DBI). Concurrently, the efficiency is measured by the expected number of iterations to the final clusters. All these six clustering approaches are then implemented in the case study of locating eight DCs to distribute to 260 convenience stores in Eastern Thailand. The results show that though all approaches yield locations in close proximity, the Weighted Chebyshev is the most effective one having both the lowest expected distribution cost and lowest expected DBI. In contrast, Euclidean is the most efficient approach, with the lowest expected number of iterations to the final clusters, followed by Weighted Chebyshev. Therefore, the DC locations from Weighted Chebyshev could, ultimately, be chosen for this Thailand's convenience store franchise. [ABSTRACT FROM AUTHOR]

Background: Endogenous and exogenous factors impact the operational characteristics of supply chains, affecting wholesale warehouses. The survival of a warehouse is often threatened by disruptive events that alter infrastructure and performance. The emergence of COVID-19 exemplified the need for adaptability in retail goods supply chains, emphasizing the necessity for responding to external shocks. Methods: The MARLIN (Method wArehouse ResiLience dIstruptioN) method, founded on theories and models of resilience engineering is introduced. MARLIN is a practical tool designed to identify key areas requiring intervention in response to disruptive events. An empirical test was conducted in an Italian warehouse. Results: The conducted test yielded tangible results, demonstrating the efficacy of the method. It successfully pinpointed areas necessitating intervention and identified Key Performance Indicators (KPIs) associated with disruptions. The study not only underscores the importance of data collection but also highlights the often-overlooked significance of warehouse management. Conclusions: The study establishes MARLIN as a valuable asset for stakeholders involved in disruption management. Its application has proven instrumental in recognizing areas of intervention and identifying KPIs related to disruptions. Ongoing research endeavors to broaden its applicability across diverse supply chain scenarios, aiming to enhance situational awareness and enable proactive risk assessment through what-if analysis.

We theorize that employees use the performance feedback they receive to reassess their beliefs about the marginal benefit of their effort, which may lead them to increase or reduce their effort. To test our model, we conduct a field experiment at the distribution center of a Fortune 500 firm where employees receive individual performance pay, and we study two types of feedback, individual and relative. The results show that employees react to feedback content in a way that is consistent with the model: They increase their effort if the information provided implies that the marginal benefit of increasing effort is high and decrease it if they learn that it is low. Moreover, performance feedback has a greater impact on the lower quantiles of the distribution of productivity. [ABSTRACT FROM AUTHOR]

The necessity for undertaking this research is driven by the prevailing challenges encountered in logistic centers. This study addresses a logistic order-picking issue involving unidirectional conveyors and buffers, which are assigned to racks and pickers with the objective of minimizing the makespan. Subsequently, two variations of a two-step matheuristic approach are proposed as solution methodologies. These matheuristics entail decomposing the primary order-picking problem into two subproblems. In the initial step, the problem of minimizing the free time for pickers/buffers is solved, followed by an investigation into minimizing order picking makespan. An experimentation phase is carried out across three versions of a distribution center layout, wherein one or more pickers are allocated to one or more buffers, spanning 120 test instances. The research findings indicate that employing a mathematical programming-based technique holds promise for yielding solutions within reasonable computational timeframes, particularly when distributing products to consumers with limited product variety within the order. Furthermore, the proposed technique offers the advantages of expediency and simplicity, rendering it suitable for adoption in the process of designing and selecting order-picking systems.

The focus of this study is on the significance of location in establishing distribution centers. The key question when selecting a location is regarding which location would contribute the most to the growth of a company’s business through the establishment of distribution centers. To answer this question, we conducted research in the Brčko District of BiH in order to determine the best location for a distribution center using expert decision-making based on linguistic values. In order to use these values when selecting locations, a fuzzy set was formed using the IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to the Ideal Solution) methods. The IMF SWARA method was utilized to determine the weights of the criteria, and the fuzzy CRADIS method was employed to rank the locations based on expert ratings. The location for the construction of distribution centers at Bodarište was rated the worst, while the McGowern Base location was rated the best. Based on these findings, the research question was answered, and it was demonstrated that fuzzy methods could be utilized in the selection of distribution center locations. Hence, we recommend that future research be performed on the application of fuzzy methods in the expert selection of potential sites for distribution centers.

The warehouse, with its racking positions, oversees regulate the flow of goods in the distribution center (DC). An adequate distribution of the products in the warehouse (slotting), reduces the operating picking times once is done by a full pallet. This paper presents a mathematical model that optimizes slotting times in a selective shelving and random storage warehouse. The model places the products with the highest turnover rate and ABC inventory valuation close to the dispatch area. In addition, it places high-value products on the upper shelf levels and heavy pallets on the lower levels. The results applied to a company dedicated to the manufacture of cellulose pulp and derivatives, obtained a 19.24% reduction in operating time. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the city scale and the increasing population, people have put forward higher requirements for the speed, reliability and economy of logistics distribution. Although there have been many achievements in the research on the vehicle optimal scheduling decision model and its solution algorithm, there are some problems such as low solution efficiency, long time consumption, and easy to fall into the local optimal solution. The purpose of this paper is to study the model of vehicle scheduling problem in distribution center based on improved differential evolution algorithm. This paper firstly makes a simple analysis of the vehicle distribution route optimization problem, and points out the main factors that constitute the route optimization problem; then analyzes the principle of the differential evolution algorithm, and on this basis, the differential evolution algorithm is improved. In the experimental part, this paper takes a distribution center as the research object, and proposes the implementation steps of the algorithm of the vehicle scheduling problem based on the improved differential evolution algorithm. And test the running cost of the improved differential evolution algorithm and differential evolution algorithm. Experimental results show that applying the improved differential evolution algorithm to vehicle scheduling problems has certain practicality. When the number of iterations reaches 60, the running cost of the improved differential evolution algorithm is 1,200 yuan, and the running cost of the differential evolution algorithm is 1,300 yuan. [ABSTRACT FROM AUTHOR]

Cargo transportation is a key element for an effective and properly functioning logistics chain. Due to the role it plays in overall logistics chain performance, it is important for cargo firms to be at their peak level in terms of provided service quality and implementation. Continuous performance evaluation is critical for diagnosing and preventing any problems that might disrupt the firms' ability to keep providing their services and further prevent customer dissatisfaction. Therefore, the aim of this paper is identifying the problems that cause the performance of Company X's distribution center located in İzmir to stay below desired levels, which consequently lead to customer dissatisfaction, and to offer a feasible solution for the identified problems. In order to identify underlying problems, performances of two distribution centers located in different cities are compared and improvements for worker performance, workload distribution, and processes are suggested. [ABSTRACT FROM AUTHOR]

Más que una simple ideología, el management es una racionalidad pues ha sobrepasado la esfera de la dirección del trabajo y se ha convertido en una mentalidad. Se mostrarán algunos ejemplos en los cuales se observa que la lógica managerial aparece encarnada en los mandos medios y que cuando reproducen con total convicción y coherencia sus retóricas, obtienen grandes dosis de satisfacción. El sufrimiento de ellos, es causado por el fenómeno opuesto: la incapacidad de obedecer sus premisas gracias a las exigencias que la lógica del management les impone. El personal subalterno, por su parte entiende tal racionalidad como una fatalidad y, sin mucho darse cuenta, la reproduce con resignación y, para protegerse de las exigencias que le impone, se contenta creyendo que puede “escapársele” diariamente, cuando su jornada termina. More than a simple ideology, Management is a rationality since have overpassed the sphere of the direction of the work and have converted in a mentality. It will be displayed some examples in which is possible to notice that the managerial logic appears embodied in the middle management and when be reproduced with total conviction its rhetoric, they get big dosses of satisfaction. Suffering of these people, is caused by the opposite phenomenon: the disability to obey the mentioned their premises thanks to the requirements that the logic of the management impose to them. The subordinate personnel, by their way understand that rationality like a fatality and by these, without realizing it, reproduce it with resignation, and to protect their selves from the requirements that are imposed to them, been happy knowing that can “escape” daily when their working day ends. Mais do que apenas uma ideologia, a administração é uma racionalidade porque foi além da esfera da direção do trabalho e se tornou uma mentalidade. Alguns exemplos serão mostrados nos quais se observa que a lógica gerencial aparece incorporada na gerência intermediária e que, quando ela reproduz sua retórica com total convicção e coerência, obtém grandes doses de satisfação. O sofrimento é causado pelo fenômeno oposto: a incapacidade de obedecer a suas premissas, graças às exigências que a lógica gerencial impõe. Os funcionários juniores, por sua vez, entendem essa racionalidade como uma fatalidade e, sem perceber, a reproduzem com resignação e, para se protegerem das exigências impostas, contentam-se em acreditar que podem "escapar" diariamente, quando termina o dia.

Outbound logistics channels are of crucial importance for an efficient construction materials logistics management and impacts on customer satisfaction. However, there is limited knowledge of the outbound logistics channels for construction material in Nigeria. This study aims to identify and examine the current outbound logistics channels used by the Nigerian construction material manufacturing industries. A quantitative research method using a case study approach was adopted in this research. The purposive sampling technique was chosen, where six construction material manufactured and distributed within five states capital and Abuja in the North-central region of Nigeria were selected for this study. A research instrument was developed and used in conjunction with an observation protocol in the form of a template. The data were collected through observations, direct measurement onsite and archival records of transactions. A descriptive method of data analysis was employed to analyse the data. Our findings indicate that there exist six alternative outbound logistics channels that can be used separately or in combination with each other to deliver materials to end users. The study concludes that the research finding provides a potential knowledge and understanding of the manufacturers’ outbound logistics channels that can be used at the start of a project to accomplish effective planning and delivery of the whole project. The study also established the average transportation cost per average ton and average transportation cost per average distance driven for construction material delivery. This information can be used for construction material transportation management.

This paper considers heuristic approaches that can be used to assign stock keeping units (SKU) to individual slots in distribution center. Firstly, we propose two novel strategies, slot selection and frequent itemset grouping. The former is used to find the most suitable slot for a single SKU, while the latter is for sequencing SKUs in an appropriate order. Secondly, we develop several storage location assignment heuristics by applying the two strategies. Especially, the heuristics are designed to assign frequently ordered SKU to a slot close to I/O (input/output) point and SKUs frequently ordered together to slots close to each other. Consequently, the proposed heuristics are helpful to reduce the travel distance of order picker in distribution center. In this paper, travel distance of order picker is calculated based on a routing policy that enables order picker to move along a flexible route. For illustration, we applied the heuristics to real data collected from a large distribution center. The experiment results reveal that slot selection strategy is very helpful to reduce average travel distance of order picker, especially under greedy routing policy. Also, frequent itemset grouping strategy can provide additional reduction in average travel distance if it is applied together with slot selection strategy.