Due-Date Management Through Iterative Bidding

Abstract

This paper proposes an iterative bidding framework for integrated due date management decision making. We focus on a type of make-to-order environment, in which a firm needs to quote due dates and prices and to schedule the production of a variety of job orders required by a large group of customers. In most cases, customers prefer shorter due dates. However, given limited production capacity and various cost constraints, the firm has to balance the attractiveness of their due date quotations and the reliability in terms of scheduling and delivering accepted job orders. The key issue here is how to integrate due date management decisions such that high quality solutions which benefit both the firm and the customers can be obtained. We study the integrated due date management in an economic setting where customers are modeled as self-interested agents and the objective of the firm is to maximize social welfare.We present an iterative bidding framework as a decentralized decision support tool which enables the integration of key due date management decisions. Effective solutions are achieved through the automated negotiation between the firm and its customers. We provide analytical results on the application of the proposed framework to two special cases of the integrated due date management. We also evaluate the performance of the framework on general due date management problems through a computational study. the terms of the transactions, in particular, on the price and the due date. If the price or the due date quoted by the firm is too high compared to what the customer is willing to accept, the customer may choose not to place the order. Alternatively, if the price the customer is willing to pay is low or the due date requested is too short to make it a profitable transaction for the firm, the firm might decide not to accept the order. As the DDM decisions are tightly coupled, it is desirable to model the interrelations among them and consider them simultaneously. However, given the complexities of these decisions, in practice, they are often made sequentially. The way that a firm makes DDM decisions is reflected by the time-based competition strategy it adopts.There are three time-based competition strategies used by firms [3], (1) quick service with minimal wait; (2) "uniform" short lead time 1 guarantee; (3) due date quotation. The first strategy does not involve order acceptance and due date quotation decisions. The focus here is how to schedule job orders such that they can be served as fast as possible. The second strategy promises a uniform lead time guarantee to all customers. Although, a firm can influence the demand rate by adjusting the length of the guaranteed lead time, there is no direct integration between the decisions of order acceptance and scheduling. In fact, under this strategy, there is a risk that demand may exceed the firms' capacity to respond. Uniform lead time guarantee is widely adopted in the service and make-to-order manufacturing sectors [3,4]. While the strategy may be easy to ...

An iterative combinatorialauctionmechanism formulti-agentparallel machinescheduling

“…Bidders in these approaches submit one bid or bundle of time slots per round, which cannot reduce the resource allocation conflicts and solve the large-scale problem effectively. Wang et al (2011) proposed an iterative bidding framework for due-date management with submitting more than one bid. Amir, Sharon, and Stern (2015) handled the situation where many bundles are submitted by each agent in the multi-agent pathfinding problem.…”

A Price-Based Iterative Double Auction for Charger Sharing Markets

“…Compared with one shot auctions, such as VCG [13]- [15], the iterative bidding structure of the proposed auction promises reduced computation at the auctioneer side and partial revelation of the private information at buyers' and sellers' sides [32], [35]. In addition, compared with single-sided multi-round auctions, such as those proposed in [29]- [31], the two-sided structure of P-IDA facilitates trade between two groups in one market, which is more efficient than combining several single-sided auctions [36].…”

“…Therefore, this approach can generate a large number of distinct time units, which inflicts heavy computation burden on double auctions in terms of bids evaluation, communication, and winner determination [27], [28]. In [29] and [30], the authors use scheduling specific bidding language for decentralized scheduling problems, which models scheduling related constraints naturally and reduces computation costs. However, both papers focus only on one-sided settings where there is only one seller in the market.…”

Balancing setup workers load of flexible job shop scheduling using hybrid genetic algorithm with tabu search strategy

“…Production scheduling in make-to-order (MTO) manufacturing has several important points: meeting the due-date of a customer (Wang et al, 2011;Kaminsky, 2008;Sawik, 2011), balancing the workload of resources that are constrained (Thuerer et al, 2012), and reducing work-in-process for proper cash flow management (Nenes et al, 2014). These points dictate that enterprises with flexible job shop manufacturing systems for MTO production need multi-objective flexible job shop scheduling systems.…”