Order acceptance and scheduling in a single-machine environment: exact and heuristic algorithms.

In this paper, we develop exact and heuristic algorithms for the order acceptance and scheduling problem in a single-machine environment. We consider the case where a pool consisting of firm planned orders as well as potential orders is available from which an over-demanded company can select. The capacity available for processing the accepted orders is limited and orders are characterized by known processing times, delivery dates, revenues and the weight representing a penalty per unit-time delay beyond the delivery date promised to the customer. We prove the non-approximability of the problem and give two linear formulations that we solve with CPLEX. We devise two exact branch-and-bound procedures able to solve problem instances of practical dimensions. For the solution of large instances, we propose six heuristics. We provide a comparison and comments on the efficiency and quality of the results obtained using both the exact and heuristic algorithms, including the solution of the linear formulations using CPLEX.Order acceptance; Scheduling; Single machine; Branch-and-bound; Heuristics; Firm planned orders;

Bütünleşik tedarik zinciri çizelgeleme modelleri: Bir literatür taraması

Research on integration of supply chain and scheduling is relatively recent, and number of studies on this topic is increasing. This study provides a comprehensive literature survey about Integrated Supply Chain Scheduling (ISCS) models to help identify deficiencies in this area. For this purpose, it is thought that this study will contribute in terms of guiding researchers working in this field. In this study, existing literature on ISCS problems are reviewed and summarized by introducing the new classification scheme. The studies were categorized by considering the features such as the number of customers (single or multiple), product lifespan (limited or unlimited), order sizes (equal or general), vehicle characteristics (limited/sufficient and homogeneous/heterogeneous), machine configurations and number of objective function (single or multi objective). In addition, properties of mathematical models applied for problems and solution approaches are also discussed.Bütünleşik Tedarik Zinciri Çizelgeleme (BTZÇ) üzerine yapılan araştırmalar nispeten yenidir ve bu konu üzerine yapılan çalışma sayısı artmaktadır. Bu çalışma, bu alandaki eksiklikleri tespit etmeye yardımcı olmak için BTZÇ modelleri hakkında kapsamlı bir literatür araştırması sunmaktadır. Bu amaçla, bu çalışmanın bu alanda çalışan araştırmacılara rehberlik etmesi açısından katkı sağlayacağı düşünülmektedir. Bu çalışmada, BTZÇ problemleri üzerine mevcut literatür gözden geçirilmiş ve yeni sınıflandırma şeması tanıtılarak çalışmalar özetlenmiştir. Çalışmalar; tek veya çoklu müşteri sayısı, sipariş büyüklüğü tipi (eşit veya genel), ürün ömrü (sınırlı veya sınırsız), araç karakteristikleri (sınırlı/yeterli ve homojen/heterojen), makine konfigürasyonları ve amaç fonksiyonu sayısı (tek veya çok amaçlı) gibi özellikler dikkate alınarak kategorize edildi. Ayrıca problemler için uygulanan matematiksel modellerin özellikleri ve çözüm yaklaşımları da tartışılmıştır

Project portfolio management: capacity allocation, downsizing decisions and sequencing rules.

This paper aims to gain insight into capacity allocation, downsizing decisions and sequencing rules when managing a portfolio of projects. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. In previous work, we have determined the amount of critical capacity that is optimally allocated to concurrently executed projects with deterministic or stochastic workloads when the impact of downsizing is known. In this paper, we extend this view with the possibility of sequential processing, which implies that a complete order is imposed on the projects. When projects are sequenced instead of executed in parallel, two effects come into play: firstly, unused capacity can be shifted to later projects in the same period; and secondly, reinvestment revenues gain importance because of the differences in realization time of the sequenced projects. When project workloads are known, only the second effect counts; when project workloads are stochastic, however, the project's capacity usage is uncertain so that unused capacity can be shifted to later projects in the same period. In this case, both effects need to be taken into account. In this paper, we determine optimal sequencing rules when the selection and capacity-allocation decisions for a set of projects have already been made. We also consider a combination of parallel and sequential planning and we perform simulation experiments that confirm the appropriateness of our capacity-allocation methods.Project portfolio management; Downsizing; Sequencing;

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

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

This is the final version. Available on open access from Springer Verlag via the DOI in this recordAdditive 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.National High Technology Research and Development Program of Chin

Multiobjective Order Acceptance and Scheduling on Unrelated Parallel Machines with Machine Eligibility Constraints

This paper studies the order acceptance and scheduling problem on unrelated parallel machines with machine eligibility constraints. Two objectives are considered to maximize total net profit and minimize the makespan, and the mathematical model of this problem is formulated as multiobjective mixed integer linear programming. Some properties with respect to the objectives are analysed, and then a classic list scheduling (LS) rule named the first available machine rule is extended, and three new LS rules are presented, which focus on the maximization of the net profit, the minimization of the makespan, and the trade-off between the two objectives, respectively. Furthermore, a list-scheduling-based multiobjective parthenogenetic algorithm (LS-MPGA) is presented with parthenogenetic operators and Pareto-ranking and selection method. Computational experiments on randomly generated instances are carried out to assess the effectiveness and efficiency of the four LS rules under the framework of LS-MPGA and discuss their application environments. Results demonstrate that the performance of the LS-MPGA developed for trade-off is superior to the other three algorithms