Order Acceptance Research Articles

Dual deep reinforcement learning agents-based integrated order acceptance and scheduling of mass individualized prototyping

Coordinating order acceptance decisions with production scheduling to maximize revenue is challenging for Mass Individualized Prototyping (MIP) service providers. This paper presents a dual deep reinforcement learning agents-based (DDRLA) integrated order acceptance and scheduling (IOAS) for improving revenue. Firstly, a deep reinforcement learning-based virtual production scheduling (VPS) agent together with 8 state features and 11 action rules is designed. The VPS agent quickly and virtually reschedules a dynamically-arriving accepted order to evaluate the overall impact of accepting this order, including consumed capacity and increased revenue. Then, a deep reinforcement learning-based order acceptance decision (OAD) agent is designed. Based on the information guidance resulting from an interaction with the VPS agent, the OAD agent selectively accepts orders to maximize long-term gains, as well as to improve system resilience in the presence of a high ratio of urgent orders. The experiment results show that the proposed DDRLA method has better performance, compared with other IOAS approaches.

An improved hybrid ICA-SA metaheuristic for order acceptance and scheduling with time windows and sequence-dependent setup times

An improved hybrid ICA-SA metaheuristic for order acceptance and scheduling with time windows and sequence-dependent setup times

Logic-based Benders decomposition for order acceptance and scheduling in distributed manufacturing

This paper studies an order acceptance and scheduling problem in distributed manufacturing to minimize the total costs consisting of rejection cost, production cost, transportation cost and tardiness cost. Two mixed-integer programming models are formulated, which are further improved by the proposed enhancement techniques. A logic-based Benders decomposition (LBBD) method and a branch and check search framework are developed in an attempt to realize the optimal and intelligent decisions of order acceptance, order assignment and scheduling. The proposed LBBD method following the principle of “divide and conquer” divides the original problem into determining the master problem of order acceptance and assignment, and determining the subproblem of order sequencing in each factory. A dynamic programming algorithm is also proposed to efficiently solve the subproblems. Extensive computational experiments are conducted, and the results demonstrate the effectiveness and efficiency of the enhancement techniques for the formulations, as well as the LBBD method. Besides, the value of distributed manufacturing is verified and the sensitivity of key cost factors is presented.

Nanobiotechnology to advance stress resilience in plants: Current opportunities and challenges.

A sustainable and resilient crop production system is essential to meet the global food demands. Traditional chemical-based farming practices have become ineffective due to increased population pressures and extreme climate variations. Recently, nanobiotechnology is considered to be a promising approach for sustainable crop production by improving the targeted nutrient delivery, pest management efficacy, genome editing efficiency, and smart plant sensor implications. This review provides deeper mechanistic insights into the potential applications of engineered nanomaterials for improved crop stress resilience and productivity. We also have discussed the technology readiness level of nano-based strategies to provide a clear picture of our current perspectives of the field. Current challenges and implications in the way of upscaling nanobiotechnology in the crop production are discussed along with the regulatory requirements to mitigate associated risks and facilitate public acceptability in order to develop research objectives that facilitate a sustainable nano-enabled Agri-tech revolution. Conclusively, this review not only highlights the importance of nano-enabled approaches in improving crop health, but also demonstrated their roles to counter global food security concerns.

Process Improvement in PT. Docil Berkah Abadi Using Business Process Improvement (BPI) Framework

PT. Docil Berkah Abadi is a retailer that offers motorcycle accessories, primarily windshields, via e-commerce, resellers, distributors, and an offline store. Additionally, subsidiary companies sell additional varieties of accessories. Mission of this organization is to dominate the market for motorcycle accessories in Indonesia and to innovate our products via subsidiary companies. PT. Docil Berkah Abadi has serious problems in its business process because the previously running business processes are not effective and result in too large fixed costs incurred by PT.Docil berkah Abadi This is the time from the beginning of the order acceptance until the goods are sold too long. The number of employees who have been discharged does not correspond to the salary costs already incurred by PT. Docil Berkah Abadi This is a very expensive rental because you have to store a lot of stock. The aim of this study is to improve business processes by understanding the roots of the problem with Root Cause Analysis (RCA) and improve the business process with Business Process Improvement. (BPI). The author uses five whys on Root Cause Analysis (RCA) analysis and performs identification on business early processes, and improves business by streamlining with Bureaucracy Elimination and Upgrading. This study was conducted using qualitative methods. The researchers conducted in-depth interviews with several divisions of the company to gain insight into the business processes of the enterprise. The results of the interviews showed that the business processes used by the company are still less accurate. Using interview insights, the researchers built a new process business using flowchart.

A scrumban board-based approach to improve material flow in engineering to order (ETO) companies: an industrial application based on action research

This paper focuses on lean management in Engineering-To-Order (ETO) companies. The objective is to streamline the management of an order and to reduce the time to market, from order acceptance to delivery. To this aim, we propose a visual tool that allows a better organisation and coordination of all the activities carried out by the departments involved in the management of an order. The proposed tool is a readaptation of the ‘scrumban’ methodology, which originated from an ‘action research-based project’, carried on in cooperation with an important ETO manufacturer. Specifically, using this implementation as use case, the proposed tool and its benefits are analysed in detail, showing that it allows to: (i) reorganise and operate the shop floor in a lean way, (ii) encourage a continuous improvement of the manufacturing processes and, above all, (iii) achieve a full coordination of physical and information flows. A step-by-step implementation framework is also provided, aiming to support and guide managers and practitioners willing to introduce visual tools, for a lean-oriented management of shop floor activities in ETO.

Makespan estimation in a flexible job-shop scheduling environment using machine learning

ABSTRACT A production plan gives the quantity of products to release on the shop floor in each period, where a period may represent a week or a month. The plan is the basis for negotiating order acceptance and delivery dates with customers and suppliers. The production plan must respect the available capacity on the shop floor, as underloading the shop floor leads to a loss of opportunity, and thus, a loss of competitiveness for the company. To properly manage the production capacity while negotiating with suppliers and customers, the production planners need a tool to accurately estimate the capacity consumption in each period. The computation of capacity consumption requires creating a detailed production schedule which is a complex task. Algorithms that find close to optimal schedules in a complex manufacturing environment are often time-consuming, which is impractical in a negotiation context. We investigate machine learning models to predict capacity consumption. We consider a flexible job-shop as commonly encountered in practice, and proposed several machine learning models. Namely, several variants of linear regression, decision trees, and artificial neural networks. Numerical experiments showed that our models outperform those found by both an exact approach and dispatching rules when computation time is short.

Applicability of a Web-based 24-hour Dietary Recall Tool for Japanese Populations in Large-scale Epidemiological Studies.

Recent innovations in information and communication technology have made it possible to assess diet using web-based methods; however, their applicability in the general population remains unclear. Hence, we aimed to examine the applicability of a web-based 24-hour dietary recall (24HR) tool to large-scale epidemiological studies by determining the sampling rate and characteristics of randomly selected participants from a Japanese cohort study. In total, 5,013 individuals were recruited from a cohort of 21,537 individuals, and 975 agreed to participate in this study. The participants selected either self-administered web-based dietary 24HR (self-administered 24HR) or interviewer-administered telephone-based 24HR (interviewer-administered 24HR) as the method for the dietary assessment and answered questions regarding the acceptability of the system. The response rate of the 975 participants was 19.4%, corresponding to approximately 4.5% of the total study sample. About half of them chose the self-administered 24HR (46.9%). The median time required for the self-administered and interviewer-administered 24HR was 25 and 27 minutes, respectively. In the self-administered 24HR, older people, regardless of sex, tended to require a longer time, and approximately 60% of the participants rated the ease of use of the system as "somewhat difficult" or "difficult." Characteristics of the participants in this study were not systemically different from those of the entire study sample. Improvements in the approach to entering cooking details and the dish name selection may be necessary for better acceptability in order to be accepted as a self-administered dietary recall tool.

Acceptance Ordering Scheduling Problem: The impact of an order-portfolio on a make-to-order firm’s profitability

Firms’ growth, the darling measure of investors, comes from higher revenues. Thus, sales and marketing departments make extreme efforts to accept as many customer orders as possible. Unfortunately, not all orders contribute equally to profits, and some orders may even reduce net profits. Thus, saying no (i.e., not accepting an order) may be a necessary condition for net profits growth. For understanding the impact of rejecting orders on profitability, we propose an order acceptance and scheduling problem (OAS). Although the OAS has extensively been studied in the literature, there is still some gap between these papers and real-life problems in industry. In an attempt to close that gap, the OAS we propose considers orders revenues, machines costs, holding costs and tardiness costs. We develop a mixed integer linear programming (MILP) model for solving this problem. Since the complexity of the problem makes it impossible for the MILP to solver large-scale instances, we also propose a metaheuristic algorithm. Numerical experiments show that the metaheuristic finds good quality solutions in short computational times. In the last part of the paper we confirm some managerial insights: higher holding and tardiness costs imply a lower acceptance of orders, forcing production has a concave negative impact on net profits, and accurately estimating costs is essential for good planning.

Human papillomavirus vaccine acceptance among adolescent girls in Ethiopia: a systematic review and meta-analysis

BackgroundCervical cancer is the fourth most common cancer affecting females. Human papillomavirus vaccination of adolescent girls is the primary strategy for cervical cancer prevention but in Ethiopia, it lacks emphasis. Despite different studies done and found a highly variable level of vaccine acceptance; however, there was no summarized evidence on the issues as a nation. Thus this systematic review and meta-analysis aimed to assess the pooled prevalence of human papillomavirus vaccine acceptance by adolescent girls and its associated factors in Ethiopia.MethodA systematic review and meta-analysis were conducted using PRISMA guidelines. Comprehensive literature was searched in PubMed, Google Scholar, and African Online Journal databases. A weighted inverse variance random effect model was used to estimate pooled prevalence. Cochrane Q-test and I2 statistics were computed to assess heterogeneity among studies. Funnel plot and Eggers test were done to assess publication bias. Review manager software was used to identify factors associated with vaccine acceptance.ResultOverall, 157 articles were retrieved and finally 7 articles were included in this review. The pooled prevalence of adolescent human papillomavirus vaccine acceptance was 46.52% (95%CI; 30.47-62.57%). Subgroup analysis showed that adolescent vaccine acceptance was highest in the Oromia region and lowest in Addis Ababa. Knowledge about human papillomavirus vaccination (AOR = 3.89, 95% CI: (2.85–5.32)) and attitude (AOR = 2.65, 95% CI: (2.03– 3.44)) were significantly associated with adolescent’s vaccine acceptance.ConclusionHuman papillomavirus vaccine acceptance of adolescent girls in Ethiopia was low. Knowledge about the vaccine and attitude to vaccination were positively associated with their vaccine acceptance. Therefore, policymakers and program planners should target school-aged adolescents in increasing their awareness and changing their attitudes to enhance their vaccine acceptance in order to prevent and control cervical cancer.

Order acceptance and scheduling with delivery under generalized parameters

AbstractInspired by the concept of generalized due dates introduced by Hall (IIE Transactions, 18(2), 220–222), in this article we first define four new generalized parameters in machine scheduling: the generalized release dates (GRD), generalized processing times (GPT), generalized rejection costs (GRC), and generalized delivery times (GDT). We then study order acceptance and scheduling problems with delivery consideration under the generalized parameters of GRD, GPT, GRC, and GDT, respectively. The objective of each scheduling problem in consideration is to minimize the maximum delivery completion time of the accepted jobs plus the total rejection penalty cost of the rejected jobs. We show that two of the six problems studied in this article are weakly NP‐hard while the other four ones can be solved in polynomial time. For each NP‐hard problem, we provide a pseudo‐polynomial time algorithm, a 2‐approximation algorithm and a fully polynomial‐time approximation scheme.

Order Acceptance and Scheduling Model Based on Improved Genetic Algorithm

As the manufacturing industry’s industrial structure continues to evolve, many enterprises have shifted from traditional make-to-store modes (MTS) to make-to-order modes (MTO). This paper focuses on MTO manufacturing enterprises’ order acceptance and scheduling problem, namely, how to exclude low-margin orders under capacity constraints and schedule them. We establish a hypothetical model with the goal of maximizing corporate profits, taking into account production capacity constraints and delivery time constraints. To solve the model, we propose a heuristic solution method based on genetic algorithms. The feasibility of the method is verified through randomly generated examples. The research results demonstrate that our proposed solution method can quickly solve large-scale order decision-making problems and has practical significance as a guiding tool.

Ecological momentary assessment of state fluctuations in mindfulness and symptoms in psychotic disorders

Mindfulness skills are a component of many modern cognitive-behavioral therapies that are used to treat a wide range of disorders, including psychotic disorders. While habitual (i.e., trait) mindfulness is associated with clinical outcomes, the effects of momentary (i.e., state) mindfulness are unclear. This is due in part to previous studies using cross-sectional designs relying on trait self-report questionnaires. Although such approaches are invaluable, they lack temporal specificity to evaluate momentary changes and effects of mindfulness. To address these limitations, the current study used ecological momentary assessment (EMA) to evaluate state levels of two mindfulness skills, acceptance and monitoring, and their association with state fluctuations in symptoms. Participants included individuals with affective and non-affective psychotic disorders (PD; n = 49) and healthy controls (CN; n = 53) who completed six days of EMA. Results indicated that the PD group endorsed lower state acceptance than CN; however, the groups did not significantly differ in monitoring. Further, greater state mindfulness skills in both acceptance and monitoring were associated with greater positive affect, reduced negative affect, and reduced negative symptoms. However, participants with a predominantly affective psychosis presentation showed differential effects compared to those with non-affective presentations. These findings suggest that mindfulness training for people with psychotic disorders may benefit from focusing on improving acceptance in order to improve emotional experience and build on existing monitoring skills. Further, mindfulness based psychosocial interventions may offer a novel means of treating negative symptoms in people with PD, which are currently stalled and largely unresponsive to other treatments.

Order acceptance and scheduling in the instant delivery system

In response to highly time-sensitive and dynamically emerging orders in instant delivery systems, an efficient method for order acceptance and scheduling is required. This work describes the order acceptance and scheduling problem in the instant delivery system (OAS-IDS) as a multi-trip prize collection pickup and delivery routing problem with release time and deadline time (MT-PCPDP-RT&DT). The dynamic order acceptance and scheduling problem is then decomposed into groups of continuous static problems by discretizing the scheduling horizon into several scheduling time points. An online insertion if beneficial based on trajectory similarity between orders (OIB-TSO), is proposed to tailor the order acceptance and vehicle scheduling decisions. Numerical examples indicate the OIB-TSO strategy provides a high-quality order acceptance and vehicle scheduling scheme. Management insights demonstrate the advantage and potential applicability of the designed OIB-TSO strategy in practice.

A bi-level nested heuristic algorithm for divisional seru order acceptance and scheduling problems

This paper focuses on the order acceptance and scheduling problem with worker–operation assignment considering the precedence constraint in the divisional seru production system, which is a new production type derived from Japan and can achieve responsiveness, flexibility, and efficiency simultaneously. With limited production capacity and workers with different skill sets and skill levels, the problem considered in this paper includes three sub-problems: (i) assigning workers to each seru, (ii) making the order acceptance and scheduling plan, and (iii) allocating workers to operations for each order. Subsequently, a nonlinear integer programming model is established for improving the total net revenue, and a bi-level nested heuristic algorithm is designed due to its intractable computation. Computational experiments are made finally, and results show that the objective value obtained by the bi-level nested heuristic algorithm is 3% better than the bi-level genetic algorithm, and the running time is shortened by 99.23%. That is, the proposed bi-level nested heuristic algorithm can achieve better results and higher efficiency for divisional seru order acceptance and scheduling problems.

The efficiency of the formatting and milling module of the technological line for door frames production

By introducing a new technological line for formatting and milling mass-customized door frames for wooden doors, PORTA KMI Poland with an industrial plant located in Ełk, Poland, conducted appropriate performance tests of the line to determine the production capacity during technological acceptance in order to check whether the assumed capacity was met before its technical acceptance takes place. The work describes how the processing time changes for frames with changed beam lengths from the reference length of 2028 mm to 2600 mm and for frames whose width has been increased from 127 and 147 mm to 500 mm. On this basis, an average time of 25.53 s was calculated for door frames with beams 2600 mm long and 28.1 s for door frames with a width of 500 mm. Efficiency was also calculated, which is 2.35 for frames with a changed beam length of 2.14.

An Iterated Population-Based Metaheuristic for Order Acceptance and Scheduling in Unrelated Parallel Machines with Several Practical Constraints

This study considers order acceptance and scheduling problems in unrelated parallel machines with several practical constraints, including order release times, sequence-dependent setup times, machines’ unequal ready times, and preventive maintenance. In a make-to-order production environment, issues with order acceptance and scheduling are mainly caused by the limited production capacity of a factory, which makes it impossible to accept all orders. Consequently, some orders must be rejected in order to maximize profits and the accepted orders must be completed by the due date or no later than the deadline. An iterated population-based metaheuristic is proposed to solve the problems. The algorithm begins with an efficient initial solution generator to generate an initial solution, and then uses the destruction and construction procedure to generate a population with multiple solutions. Then, a solution is selected from the population, and a variable neighborhood descent search algorithm with several new reduced-size neighborhood structures is applied to improve the selected solution. Following the completion of the local search, a method for updating the members of the population was devised to enhance its diversity. Finally, the metaheuristic allows the populations to evolve for several generations until the termination condition is satisfied. To evaluate the performance of the proposed metaheuristic, a heuristic rule and an iterated local search algorithm are examined and compared. The computational experimental results indicate that the presented metaheuristic outperforms the other heuristics.

On the Unmixedness Problems of Colored Pushdown Automata

Recently, we introduced a new automata model, so-called colored finite automata (CFAs) whose accepting states are multi-colored (i.e., not conventional black-and-white acceptance) in order to classify their input strings into two or more languages, and solved the specific complexity problems concerning color-unmixedness of nondeterministic CFA. That is, so-called UV, UP, and UE problems are shown to be NLOG-complete, P, and NP-complete, respectively. In this paper, we apply the concept of colored accepting mechanism to pushdown automata and show that the corresponding versions of the above-mentioned complexity problems are all undecidable. We also investigate the case of unambiguous pushdown automata and show that one of the problems turns out to be permanent true (the others remain undecidable).

Integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints

Integrated optimization of real-time order acceptance and flexible job-shop rescheduling with multi-level imperfect maintenance constraints

Advancing the marketing‐operations interface in omnichannel retail

Advancing the marketing‐operations interface in omnichannel retail