Order Acceptance Research Articles

Integrated order acceptance and inventory policy optimization in a multi-period, multi-product hybrid production system

Integrated order acceptance and inventory policy optimization in a multi-period, multi-product hybrid production system

Solving distributed assembly blocking flowshop with order acceptance by knowledge-driven multiobjective algorithm

In the era of Industry 4.0, industrial artificial intelligence technologies make production planning and scheduling systems more flexible. A new distributed assembly blocking flowshop problem with order acceptance and scheduling decisions (DABFSP_OAS) was investigated in this paper. Specifically, three objectives—the makespan, total energy consumption (TEC), and total profit (TP)—were addressed simultaneously. To address this problem, we established a knowledge-driven non-dominated sorting genetic algorithm-II (KDNSGAII). First, three initialization schemes based on the problem-specific property were introduced to generate diverse initial population. Then, to accelerate the convergence process, we developed multiple Pareto-based crossover and mutation operators. In addition, two novel destructive reinsertion strategies based on product and job sequence length were implemented to enhance the development ability of the algorithm. Finally, the designed strategies were evaluated. Comparisons and discussions showed that the KDNSGAII outperformed the other state-of-art multi-objective algorithms in solving DABFSP_OAS.

Assessing Medical Students' Understanding of Do-Not-Resuscitate (DNR) Orders.

The evolution of medical directives surrounding cardiopulmonary resuscitation (CPR) has led to the prominence of do-not-resuscitate (DNR) orders. These orders aim to respect patient autonomy and ensure dignified end-of-life care. While regulations in the United States emphasize patient choice and comprehensive discussions, Saudi Arabia's approach to DNR orders integrates Islamic principles alongside expert medical judgment. This study aimed to assess the awareness and comprehension of DNR orders among medical students at Umm Al-Qura University, Makkah, Saudi Arabia. This study employed a cross-sectional design at Umm Al-Qura University in 2023. Convenience sampling yielded a sample of 145 participants, encompassing medical students from all years (first-sixth) and medical interns. A self-administered online questionnaire assessed participants' knowledge, beliefs, and attitudes regarding DNR orders. The questionnaire specifically addressed their familiarity with DNR orders, their understanding of the Islamic perspective on DNR, and the factors influencing their attitudes toward DNR. Results: The majority of respondents (62%) were familiar with the term "DNR," but only 52% correctly defined it. Social media was the primary source of information about DNR for most respondents (56%). A significant portion of respondents (60.7%) agreed with the concept of DNR, and 54% had personal experience with the term. Patient dignity (54%), religious concerns (66.7%), and legal concerns (60.7%) were identified as important factors in DNR decisions. A majority of respondents (67%) agreed that it is acceptable to discontinue life support for DNR patients, and 49.3% believed that organ donation should be encouraged among patient families. The study found that as medical students progressed through their training (from pre-clinical to clinical years and internship), their acceptance and understanding of DNR orders generally increased. The study found that medical students possess some basic familiarity with DNR orders, but there is considerable room for advancement in their comprehension and acceptance of the topic. Addressing these knowledge gaps may lead to improved patient outcomes. The findings also underscore the need to equip future physicians with a comprehensive understanding of DNR orders, their ethical ramifications, and effective communication strategies for engaging with patients and their families.

Identity Disclosure and Anthropomorphism in Voice Chatbot Design: A Field Experiment

Fueled by the widespread adoption of algorithms and artificial intelligence, the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging the two design features identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to around 11% reduction in the response probability. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response probability, conversation length, and the probability of order acceptance intention by over 5.6%, 24.9%, and 10.1%, respectively. Moreover, even when the chatbot’s identity is disclosed along with humanizing features, the operational outcomes still improve. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation for the performance improvement—the enhanced trust between humans and algorithms—and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform. This paper has been This paper was accepted by Felipe Caro for the Special Issue on the Human-Algorithm Connection. Funding: This study is supported by the National Natural Science Foundation of China [Grants 72172169 and 91646125], Program for Innovation Research at the Central University of Finance and Economic, and Shanghai Pujiang Program. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03833 .

Bi-level programming for joint order acceptance and production planning in industrial robot manufacturing enterprise

Effectively addressing the Order Acceptance and Production Planning (OAP) problem is crucial for industrial robot manufacturers, particularly considering the rapid expansion of the industrial robot market. A bi-level programming model is structured to enhance synergies in response to department reformation within an industrial robot manufacturer. The upper-level model focuses on order acceptance, considering rejection and delay penalty costs, aiming to maximize enterprise profit. The lower-level model concentrates on product production, aiming to minimize the total sum of production costs and Work-In-Progress (WIP) holding costs. Detailed constraints related to material readiness, including inventory levels and procurement lead times, are comprehensively examined. Given the NP-hard complexity of this multi-objective problem, a meta-heuristic algorithm Whale Optimization Algorithm (WOA) is implemented. To enhance WOA’s optimization capabilities, two heuristic algorithms, Solution Improvement policy (I-algorithm) and Heuristic-based acceleration strategy (H-algorithm), are introduced, resulting in the development of WOA-IH. The experimental evidence suggests that the proposed model facilitates decision coordination for manufacturers, particularly in order management, production planning, and material procurement. Furthermore, this study contributes to applying bi-level programming theory in managing supply chains for industrial robotics.

Does Reframing Do-Not-Resuscitate to Beneficial Care Only Increase Acceptance of No-CPR Orders?

BackgroundThe terminology of a do-not-resuscitate order (DNR) can be confusing and controversial for patients at the end of life. We examined whether changing the name to “Beneficial Care Only” (BCO) would increase patient acceptance. Research QuestionWould individuals be more willing to forgo full code status and accept a no-CPR order if the order title was “Beneficial Care Only”? Study Design and MethodsWe conducted a cross-sectional survey of 599 adults residing in the United States, presenting participants with a hypothetical scenario of a terminal patient. Half were given a choice between “full code” (FC) and “DNR” status, and half were given a choice between FC and BCO status. The 20-item survey included multiple-choice responses and one free-response question. ResultsIn our nationally representative survey of U.S. participants who were 50% female and 26% nonwhite (99% response rate, n = 599/600), there was no difference in participant preference for BCO or DNR overall (p = 0.7616) and across participant sociodemographic characteristics. While themes of participant reasons for choosing against CPR were similar for both DNR and BCO preferences, including (1) harms imposed by CPR, (2) lack of quality of life, (3) trust in the medical team, (4) avoidance of suffering, two additional themes appeared only for BCO responses: (1) CPR would be useless, and (2) the patient would continue to receive beneficial care. InterpretationWe found no statistically significant difference in preference between BCO and DNR orders for a terminally ill patient. These findings suggest changing the terminology of DNR to BCO may not lead to changes in decisions to forgo CPR. The additional themes identified with the use of BCO support the concept that BCO terminology conveys to the recipient that all beneficial care will continue to be provided to the patient.

Multi-Period Profit-Oriented Location Routing Problem in Multi-User Warehouses with Backordering and Order Acceptance: A Relax-Fix-Optimize Heuristic

Multi-Period Profit-Oriented Location Routing Problem in Multi-User Warehouses with Backordering and Order Acceptance: A Relax-Fix-Optimize Heuristic

COMPARATIVE ASSESSMENT BY NEURAL NETWORKS MODELING

Current manufacturing industries are experiencing a paradigm shift towards more flexibility to respond quickly and efficiently to constant changing customers requirements, new technologies and increasing product variety. In today’s manufacturing environment it is necessary to make diverse decisions, which mainly concern to all stages of the manufacturing activity: bidding, negotiating, order acceptance, product design, processes planning and jobs scheduling. The decision consists in selecting the most suitable alternative from the potential ones, therefore a comparative assessment of the potential alternatives is required. In this paper we present a different approach to performing the comparative assessment, based on modeling with neural networks. Neural networks are used for establishing the dependence relations between the key process parameters and measured datasets e.g. cost, time span, consumed energy etc. A numerical simulation for comparative assessment by modeling with neural networks, with the help of an instances artificial database is also presented.

Logic-based Benders decomposition for order acceptance and scheduling on heterogeneous factories with carbon caps

We study an integrated order acceptance and scheduling problem for heterogeneous factories with carbon caps, order-dependent processing speeds, and setup times. Three joint decisions including order selection, order assignment and order sequencing are made to maximize profits, i.e., total revenue minus total production and tardiness cost. Firstly, we develop a mixed integer programming model to simultaneously optimize the three decisions and propose several dominance rules to enhance the model. Secondly, due to the particular structure of the problem, we propose a logic-based Benders decomposition (LBBD) method that decomposes the complicated original problem into a master problem and a number of subproblems. The master problem aims to determine order acceptance and order assignment, and each subproblem is for determining order sequencing in each factory. A branch and bound algorithm is then designed to quickly solve the subproblems. The branch and check framework is implemented to accelerate the solving process of the LBBD. Finally, numerical experiments verify that the proposed dominance rules play a promising role in enhancing the model, and the results of the algorithm comparison experiments show the significant advantages of proposed LBBD algorithm combined with the branch and check framework. Sensitivity experiments reveal that carbon cap serves as a major constraint on order acceptance.

Determination of Nutritional Composition and organoleptic Properties of African Locust Beans Wrapped with Different Packaging Materials

African Locust beans is a popular condiment used as taste and flavor enhancer in soup and dishes in Africa. It is one of the major sources of plant protein in African diet but packaging materials used to extend its shelf life has different effects on the products. This study therefore examined the nutritional composition and organoleptic properties of African locust beans processed and separately wrapped with plastic container, nylon and dry banana leaves and allowed to ferment for 5days. Proximate analysis and Mineral content were determined using standard methods while the sensory attributes were assessed using a 9-point Hedonic scale. The results showed that African locust bean packaged with different packaging materials for moisture content ranged between 31.27% and 36.13%, Ash content (3.46% and 4.62%), protein content (17.90 and 30.21%), carbohydrate content (17.92% and 25.32%), fat content (5.76% and 10.60%) and crude fibre content (7.92% and 10.4%). Result of mineral analysis showed that African locust bean packaged with dry banana leaf had the highest values for Sodium, Calcium, Iron, Magnesium and Zinc while the sample packaged with plastic container had the highest values for potassium and manganese. However, Vitamin A content was higher in sample packaged with plastic container. Locust beans wrapped with plastic container was mostly preferred based on sensory attributes. Findings from this study showed that locust beans wrapped with plastic containers should be encouraged due to its high protein content and general acceptability in order to alleviate protein deficiency and its associated diseases in Africa.

Big Data Scheduling with Order Acceptance Consideration in Supply Chain

The efficient job scheduling schemes can make full use of resources, and then achieve different goals, such as maximizing efficiency, minimizing cost and saving energy. In supply chain, there are a lot of members and enormous data. Therefore, a suitable scheduling scheme has become the most common and effective method to optimize the execution of big data in supply chain. A scheduling problem with production and delivery consideration, which is usually in supply chain has been considered. For the reason of highly time emergency and random coming orders in quick production and delivery system, the effective algorithms for order acceptance scheduling problem are required. This paper addressed the production and delivery problem which has one manufacturer and multiple customers. There is single machine for order production at the manufacturer. For the consideration of order acceptance or not, the manufacturer need to choose order set to be accepted for processing. The paper aims at finding a balance between orders profit, delivery cost and time-based cost to maximize the total revenue. We consider three main objective functions in scheduling theory, analyze the problem complexity. The complexity of two of the problems are weakly NP hard, and the other one is strongly NP hard. For three weakly NP hard problems, we give pseudo-polynomial time optimal algorithms.

Economic Modelling of the Manufacturing Machines Competitiveness

The paper refers to a method of competitive management of the machines of the manufacturing systems in the conditions that they execute small series works to order. In practice, order acceptance decisions and production planning are usually taken separately. The sales department makes decisions regarding the acceptance of orders, while the production department is responsible for production planning for the fulfillment of accepted orders. In most cases, the decision to accept the order does not involving the production department and the information about the planning of available production capacities is incomplete. In this context, the purpose of the paper is to highlight the econometric correlations between technique-technology and competitiveness, to study the role of the parameters related to them in the acquisition, and increase the manufacturing machines' competitiveness.

Application of IOT CNC Technology to Increase Production Capacity of the Jepara Carving Industry of Samisuko Furnicraft

Jepara Regency is the center of the carving and furniture industries. The spread of the Jepara carving industry is one of them in the village of Sukosono, Kedung sub-district. The majority of the population's livelihoods are carpenters, furniture craftsmen, and finishing services. Along with the times, there were many factories in Jepara Regency, which had an impact on the lack of carving staff in Jepara. Samisuko Furnicraft is a service partner in this activity. Founded in 2009 by Mr. Muhammad Samiun who is engaged in making calligraphy carvings, door carvings, gebyok carvings, and losters. Problems that exist with Partners: 1) Lack of carving staff. 2) in the production process at the stage of the carving process, the carving staff at the partners have not been able to make carving images with new motifs so that it slows down the production process and acceptance of incoming orders must be limited, 2) Marketing management on the marketplace platform is not optimal. Based on the existing problems, the solutions offered are (1) Socialization and Assistance in the application of CNC MACH technology, (2) Training on marketing with internet marketing. The output of this activity is Publication in the Jawa Pos newspaper on December 23, 2022, Activity Videos will be uploaded on Youtube LPPM UNISNU Jepara and increased partner empowerment starting from increasing production capacity from 400 to 520 pcs, turnover increasing from 20 million to 30 million and assets an increase of 10%.

On the Impact of Flexibility on Demand-Side Management: Understanding the Need for Consumer-Oriented Demand Response Programs

While demand response programs inherently depend on consumer acceptance in order to be successful, consumer behavior is often overlooked when designing such programs. This paper addresses the impact of consumer flexibility in terms of appliance use on the success of a demand response program, measured through the overall grid stability assessed by the demand’s peak-to-average ratio. We employ a bootstrapping approach to simulate energy communities from real-life consumer data and implement a state-of-the-art demand response system. Results suggest that higher consumer flexibility under real-time energy tariffs implies higher degrees of grid stability, with real-time pricing decreasing the average peak-to-average ratio by 4.6% compared to time-of-use tariff and with highly flexible consumers showing a 23% lower peak-to-average ratio than regular consumers on average. Yet, it is possible for higher flexibility to be detrimental to grid stability by increasing the peak-to-average ratio under a time-of-use tariff. This result highlights the importance of understanding the interplay between different factors that influence energy consumer behavior, a research stream that has been under investigated thus far.

Online Optimization of Pickup and Delivery Problem Considering Feasibility

A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there are cases where demand forecasting should be considered (e.g., the case where a large number of orders are carried by a small number of agents). In this paper, we consider an online pickup and delivery problem considering fuel and demand forecasting. First, the pickup and delivery problem with fuel constraints is formulated. The information on demand forecasting is included in the cost function. Based on the orders, the agents’ paths (e.g., the paths from stores to customers) are calculated. We suppose that the target area is given by an undirected graph. Using a given graph, several constraints such as the moves and fuels of the agents are introduced. This problem is reduced to a mixed integer linear programming (MILP) problem. Next, in online optimization, the MILP problem is solved depending on the acceptance of orders. Owing to new orders, the calculated future paths may be changed. Finally, by using a numerical example, we present the effectiveness of the proposed method.

Using Information Technologies to Manage Printing Production: Integration and Optimisation Based on the System-Process Approach

Taking into account the characteristics of the system-process approach, the study considers the importance of the use of information technologies in the management of the production of printed products. The information structure is considered to be an organised system for collecting, processing, storing and distributing the necessary information required for management decision making. This structure aims to ensure that the information is available, accurate and relevant, which is required to manage the production process. By considering the interrelationships of all elements of the production process and establishing optimal information exchange procedures to achieve management objectives, the system-process management approach is used. This helps to improve coordination between the company’s departments. It also reduces production time and increases the level of quality and productivity. A wide range of tasks, from order processing and preparation of production documentation to control and analysis of production results, are involved in the creation of a company’s management information structure. Production planning, customer invoicing and scheduling of production processes are also important aspects of management. They help to ensure an efficient production process and customer satisfaction. A comprehensive system of information and management components is used to create and use an information structure for managing the production of printed products based on a system-process approach. These components include information technology, production process management systems, and logistics process management systems. The aim is to automate and optimise production processes in the printing industry. In particular, it is possible to optimise the processes of interaction between customers and producers through the development of print production using information technologies and automated management systems. This helps to improve the quality of the service and to make management decisions on a more informed basis. The study notes that effective operational management of receiving orders in the printing industry is critical to running a successful business. Key elements of this process include the proper collection and management of information. Initial data plays an important role in order acceptance and fulfilment, such as customer requirements, requirement structure, financial conditions and order volume. This information enables you to communicate effectively with customers, understand their needs, and estimate profitability and cost to produce. Key aspects of production management include responding quickly to changes in demand, optimising processes and fulfilling orders on time. In order to maintain the efficient functioning of the company, it is important that the data is current, updated and accessible to all parties involved in the process. It is concluded that a properly organised information structure based on the system-process approach is an important component of successful print production management. It contributes to the optimisation of work processes and increases the productivity of the company.

A multi-agent resource bidding algorithm for order acceptance and assembly job shop scheduling

This study uses an agent-based approach with a combinatorial auction mechanism to solve the joint order acceptance and assembly job shop scheduling problem. A set of jobs is offered. Each job has a revenue, ready time, due date, deadline, and consists of a set of operations with precedence relationships. Jobs that deviate from their due dates incur earliness/tardiness penalties. An operation may require several units of capacity per time unit and a resource could have multiple units of capacity. The manufacturer can reject any job to satisfy the capacity constraints and maximise the overall profit. We develop a mathematical model for the problem, then use an agent-based approach to solve it. First, the relaxed problem is decomposed into a set of job-level subproblems. Each job is optimised individually without considering the capacity constraints. Profitable jobs at the individual level submit their optimal schedules as combinatorial bids to an auctioneer to acquire combinations of resource capacity-time units. Then, the auctioneer records the profit upper bound, resolves capacity conflicts to reach a feasible solution, records the profit lower bound, and updates the dual variables. Experimental results show that the proposed methodology can solve large-sized problems in reasonable CPU times.

Integrated Control of the Manufacturing System

In this paper, we propose a method to control the entire production process, starting with customer enquiry up to product delivery, for the make-to-order (MTO) manufacturing system. Method for integrated control of the job shop type manufacturing system proposed in this paper aims to facilitate the connection between the two departments and to achieve integrated control of job shop type manufacturing system on the basis of Earning Power (EP) evaluation. It gives a more accurate picture of a firm's profitability than gross income. The purpose of this paper is to demonstrate the ability of the proposed method on a real case and to illustrate the applicability of mathematical models that were proposed. The main problems for a MTO company manager, i.e. problems related to order acceptance and machine control are solved by the new integrated control method, which is included in this paper.

Perancangan Sistem Informasi Manajemen Penerimaan Pesanan di PT Aulia Jaya Corp Berbasis Java

Abstract— The purpose of this research is to design an Order Acceptance Management Information System, Case Study: PT Aulia Jaya Corp. In addition, the authors hope that users can understand the purpose of this system. The research method used is to use interviews and field observations. After that the authors designed, analyzed, and drew the conclusion that previously the Order Acceptance Management Information System at PT Aulia Jaya Corp was not always developing, so that by making this application program it could be used to facilitate work such as data input, orders and delivery, data search, and making reports to be more accurate.
 Keywords— Design, Information Systems, Stock, Orders, Delivery

Integrated decision-making for repair order acceptance and service resource allocation that distinguishes between in-warranty and out-of-warranty

Manufacturers classify customers into in-warranty and out-of-warranty customers based on their warranty status at the time of product failure. However, previous studies on the decision-making optimization of orders and service resources for repair services ignored the differences between in-warranty and out-of-warranty customers, which will affect the implementation of manufacturers’ repair service businesses. Therefore, this paper proposes an integrated decision-making model for order acceptance and resource allocation for repair services that distinguishes between in-warranty and out-of-warranty and considers the manufacturer’s limited service resources. Considering the conflicts of interest between the manufacturer, in-warranty and out-of-warranty customers, the model aims to maximize the satisfaction of in-warranty and out-of-warranty customers and the manufacturer’s profit. According to the expectations of different customers and the benefits of the manufacturer, the optimal decision scheme can be derived by determining the accepted orders, adjusting the resource allocation scheme and the service sequence of the accepted orders. In addition, to obtain the optimal decision scheme more efficiently, a memetic algorithm based on the nondominated neighbor immune algorithm is proposed, in which two local search operators are designed to improve the search efficiency. Three hundred instances were used to test this model, and extensive experimental results show that the proposed algorithm is more effective than other widely used algorithms. Finally, the benefits that the proposed integrated decision-making model can bring to manufacturers are demonstrated through numerical experiments.