Dynamic Order Acceptance Research Articles

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.

Multiple Precast Component Orders Acceptance and Scheduling

Precast components manufacturer generally operates under limited production capacity and produces products of one order which may delay another. This paper develops a precast component order acceptance and scheduling model that aims to maximize the total profit in a stochastic multiple orders environment. In that model, the increasing of the overall profit of the precast component manufacturer is achieved by using a heuristic algorithm and a dynamic order acceptance heuristic. Results of numerical examples indicate the proposed model realizes the increasing total profit in most cases comparing to accept all of the orders. Besides, this study tested three order acceptance criteria and found that the profit-based criterion is to be more stable in terms of maximum total profit. This approach is anticipated to provide support to precast component manufacturers when faced with multiple orders in long-term production.

A dynamic order acceptance and scheduling approach for additive manufacturing on-demand production

Additive manufacturing (AM), also known as 3D printing, has been called a disruptive technology as it enables the direct production of physical objects from digital designs and allows private and industrial users to design and produce their own goods enhancing the idea of the rise of the “prosumer”. It has been predicted that, by 2030, a significant number of small and medium enterprises will share industry-specific AM production resources to achieve higher machine utilization. The decision-making on the order acceptance and scheduling (OAS) in AM production, particularly with powder bed fusion (PBF) systems, will play a crucial role in dealing with on-demand production orders. This paper introduces the dynamic OAS problem in on-demand production with PBF systems and aims to provide an approach for manufacturers to make decisions simultaneously on the acceptance and scheduling of dynamic incoming orders to maximize the average profit-per-unit-time during the whole makespan. This problem is strongly NP hard and extremely complicated where multiple interactional subproblems, including bin packing, batch processing, dynamic scheduling, and decision-making, need to be taken into account simultaneously. Therefore, a strategy-based metaheuristic decision-making approach is proposed to solve the problem and the performance of different strategy sets is investigated through a comprehensive experimental study. The experimental results indicated that it is practicable to obtain promising profitability with the proposed metaheuristic approach by applying a properly designed decision-making strategy.

Dynamic order acceptance and capacity planning in a stochastic multi-project environment with a bottleneck resource

We study the integration of order acceptance and capacity planning in multi-project environments with dynamically arriving projects. We model this planning problem as a continuous-time Markov decision process to determine long-term optimal decisions. We examine whether macro-process planning should be performed before or after order acceptance. We characterise the structure of optimal policies, and explore the dependence on a number of parameters such as project payoff, project cost and order arrival time. We also look into the effects of set-up costs and the use of non-regular capacity.

Postponable Acceptance and Assignment: A Stochastic Dynamic Programming Approach

We study a new dynamic order acceptance and assignment problem of a make-to-order manufacturing or service system that produces heterogeneous products or services using heterogeneous servers. Unlike traditional dynamic order acceptance and assignment problems, in our settings the system can strategically postpone acceptance and assignment decisions for orders in hand. The system does this while waiting for more profitable orders to come and/or more productive servers to become available, with the risk of losing orders in hand, low server utilization, and high waiting penalties. We formulate this problem as a stochastic dynamic program, characterize the structure of the optimal policy, and discuss managerial insights gained from the optimal policy. The paper also provides a methodological contribution by finding general structural properties and their preservation conditions, which can be reused in related future operations management studies.

Dynamic order acceptance and scheduling problem with sequence-dependent setup time

This paper studies the order acceptance and scheduling problem under a single machine environment when the orders come stochastically during the planning horizon and a sequence-dependent setup time is required between the processing of different types of orders. The objective is to maximise the expected revenue subject to the due date constraints. The problem is formulated as a stochastic dynamic programming model. A rule based on the opportunity cost of the remaining system capacity for the current system state is proposed to make the order acceptance decisions. The remaining system capacity is estimated by a heuristic which generates a good schedule for the accepted orders. Its opportunity cost is estimated by both mathematical programme and greedy heuristic. Computational experiments show that the profit generated by the integrated dynamic programming decision model is much higher than the widely used first-come-first-accept policy in industries and the benefit increases with the length of planning horizon, the arrival rate and the length of lead time. Acceptance decision based on mathematical programming outperforms greedy heuristic by about 7% and its computational time is short. It also shows that the quality of the solutions generated by the opportunity cost based order acceptance rule is satisfactory.

Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing

Order acceptance under uncertainty is a critical decision-making problem at the interface between customer relationship management and production planning of order-driven manufacturing systems. In this work, a novel approach for simulation-based development and on-line adaptation of a policy for dynamic order acceptance under uncertainty in make-to-order manufacturing using average-reward reinforcement learning is proposed. Locally weighted regression is used to generalize the gain value of accepting or rejecting similar orders regarding attributes such as product mix, price, size and due date. The order acceptance policy is learned by classifying an arriving order as belonging either to the acceptance set or to the rejection set. For exploitation, only orders in the acceptance set must be chosen for shop-floor scheduling. For exploration some orders from the rejection set are also considered as candidates for acceptance. Comparisons made with different order acceptance heuristics highlight the effectiveness of the proposed ARLOA algorithm to maximize the average revenue obtained per unit cost of installed capacity whilst quickly responding to unknown variations in order arrival rates and attributes.

Single-pass and approximate dynamic-programming algorithms for order acceptance and capacity planning

This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequence, the reinvestment revenues as well as the available capacity for future arriving projects. In general, project proposals arise dynamically to the organization, and their actual characteristics are only revealed upon arrival. Dynamic solution approaches are therefore most likely to obtain good results. Although the problem can theoretically be solved to optimality as a stochastic dynamic program, real-life problem instances are too difficult to be solved exactly within a reasonable amount of time. Efficient and effective heuristics are thus required that supply a response without delay. For this reason, this paper considers both ‘single-pass’ algorithms as well as approximate dynamic-programming algorithms and investigates their suitability to solve the problem. Simulation experiments compare the performance of our procedures to a first-come, first-served policy that is commonly used in practice.

Dynamic order acceptance and capacity planning on a single bottleneck resource

AbstractWe present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for aggregate regular as well as nonregular capacity. The stream of incoming order arrivals is the main source of uncertainty in dynamic order acceptance and the company only has forecasts of the main properties of the future incoming projects. Project proposals arrive sequentially with deterministic interarrival times and a decision on order acceptance and capacity planning needs to be made each time a proposal arrives and its project characteristics are revealed. We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as to deterministically allocate a single bottleneck resource to the accepted projects, both with an eye on maximizing the expected revenues within the problem horizon. We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selection and aggregate capacity usage. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007

Dynamic Order Acceptance and Capacity Planning within a Multi-Project Environment

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for regular as well as nonregular capacity. We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as an optimal capacity allocation for accepted projects, both with an eye on maximizing the expected revenues within the problem horizon. We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selectionand capacity usage.

Efficient and Effective Solution Procedures for Order Acceptance and Capacity Planning

This paper investigates dynamic order acceptance and capacity planning under limited regular and non-regular resources. Our goal is to maximize the profits of the accepted projects within a finite planning horizon. The way in which the projects are planned affects their payout time and, as a consequence, there investment revenues as well as the available capacity for future arriving projects. In general, project proposals arise dynamically to the organization, and their actual characteristics are only revealed upon arrival. Dynamic solution approaches are therefore most likely to obtain good results. Although the problem can theoretically be solved to optimality as a stochastic dynamic program, real-life problem instances are too difficult to be solved exactly within areas on able amount of time. Efficient and effective heuristics are thus required that supply a response without delay. For this reason, this paper considers both 'single-pass' algorithms as well as approximate dynamic-programming algorithms and investigates their suitability to solve the problem. Simulation experiments compare the performance of our procedures to a first-come, first-served policy that is commonly used in practice.