Order Acceptance and Scheduling: A Taxonomy and Review

Over the past 20 years, the topic of order acceptance has attracted considerable attention from those who study scheduling and those who practice it. In a firm that strives to align its functions so that profit is maximized, the coordination of capacity with demand may require that business sometimes be turned away. In particular, there is a trade-off between the revenue brought in by a particular order, and all of its associated costs of processing. The present study focuses on the body of research that approaches this trade-off by considering two decisions: which orders to accept for processing, and how to schedule them. This paper presents a taxonomy and a review of this literature, catalogs its contributions and suggests opportunities for future research in this area

A survey of variants and extensions of the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts. --project scheduling,modeling,resource constraints,temporal constraints,networks

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;

Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Relevance and Applicability of Multi-objective Resource Constrained Project Scheduling Problem: Review Article

Resource-Constrained Project Scheduling Problem (RCPSP) is a Non Polynomial (NP) - Hard optimization problem that considers how to assign activities to available resources in order to meet predefined objectives. The problem is usually characterized by precedence relationship between activities with limited capacity of renewable resources. In an environment where resources are limited, projects still have to be finished on time, within the approved budget and in accordance with the preset specifications. Inherently, these tend to make RCPSP, a multi-objective problem. However, it has been treated as a single objective problem with project makespan often recognized as the most relevant objective. As a result of not understanding the multi-objective dimension of some projects, where these objectives need to be simultaneously considered, distraction and conflict of interest have ultimately lead to abandoned or totally failed projects. The aim of this article is to holistically review the relevance and applicability of multi-objective performance dimension of RCPSP in an environment where optimal use of limited resources is important

Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review

The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS

Group Scheduling in a Cellular Manufacturing Shop to Minimise Total Tardiness and nT: a Comparative Genetic Algorithm and Mathematical Modelling Approach

In this paper, family and job scheduling in a cellular manufacturing shop is addressed where jobs have individual due dates. The objectives are to minimise total tardiness and the number of tardy jobs. Family splitting among cells is allowed but job splitting is not. Two optimisation methods are employed in order to solve this problem, namely mathematical modelling (MM) and genetic algorithm (GA). The results showed that GA found the optimal solution for most of the problems with high frequency. Furthermore, the proposed GA is efficient compared to the MM especially for larger problems in terms of execution times. Other critical aspects of the problem such as family preemption only, impact of family splitting on common due date scenarios and dual objective scenarios are also solved. In short, the proposed comparative approach provides critical insights for the group scheduling problem in a cellular manufacturing shop with distinctive cases

Multi-Period Cell Loading and Job Sequencing in a Cellular Manufacturing System

In this paper, a multi-period cell loading problem is addressed, where the objectives are to minimise the number of tardy jobs (nT) in a multi-period planning horizon and optimise the scheduling of tardy jobs. Three cell loading and job scheduling strategies are proposed and tested with two newly developed mixed integer programming models. Additionally, three types of due dates (tight, medium and loose) and three different demand levels were considered. Finally, two tardy job assignment methods were proposed to observe the impact on nT. Case problems were solved based on minimising nT, Tmax and total tardiness (TT) objectives and cost sensitivity analysis was performed. Results indicated that, the first strategy, (early start allowance and tardy job assignment after each period) performed better in terms of nT. For the secondary objectives, tradeoffs were observed among different strategies depending on the type of due date, demand level and tardy job assignment method

Performance analysis and scheduling strategies for ambulatory surgical facilities

Ambulatory surgery is a procedure that does not require an overnight hospital stay and is cost effective and efficient. The goal of this research is to develop an ASF operational model which allows management to make key decisions. This research develops and utilizes the simulation software ARENA based model to accommodate: (a) Time related uncertainties – Three system uncertainties characterize the problem (ii) Surgery time variance (ii) Physician arrival delay and (iii) Patient arrival delay; (b) Resource Capture Complexities – Patient flows vary significantly and capture/utilize both staffing and/or physical resources at different points and varying levels; and (c) Processing Time Differences – Patient care activities and surgical operation times vary by type and have a high level of variance between patient acuity within the same surgery type. A multi-dimensional ASF non-clinical performance objective is formulated and includes: (i) Fixed Labor Costs – regular time staffing costs for two nurse groups and medical/tech assistants, (i i) Overtime Labor Costs – staffing costs beyond the regular schedule, (i i i) Patient Delay Penalty – Imputed costs of waiting time experienced patients, and (iv) Physician Delay Penalty – Imputed costs of physicians having to delay surgical procedures due to ASF causes (limited staffing, patient delays, blocked OR, etc.). Three ASF decision problems are studied: (i) Optimize Staffing Resources Levels - Variations in staffing levels though are inversely related to patient waiting times and physician delays. The decision variable is the number of staff for three resource groups, for a given physician assignment and surgery profile. The results show that the decision space is convex, but decision robustness varies by problem type. For the problems studied the optimal levels provided 9% to 28% improvements relative to the baseline staffing level. The convergence rate is highest for less than optimal levels of Nurse-A. The problem is thus amenable to a gradient based search. (ii) Physician Block Assignment - The decision variables are the block assignments and the patient arrivals by type in each block. Five block assignment heuristics are developed and evaluated. Heuristic #4 which utilizes robust activity estimates (75% likelihood) and generates an asymmetrical resource utilization schedule, is found to be statistically better or equivalent to all other heuristics for 9 out of the 10 problems and (iii) Patient Arrival Schedule – Three decision variables in the patient arrival control (a) Arrival time of first patient in a block (b) The distribution and sequence of patients for each surgery type within the assigned windows and (c) The inter arrival time between patients, which could be constant or varying. Seven scheduling heuristics were developed and tested. Two heuristics one based on Palmers Rule and the other based on the SPT (Shortest Processing Time) Rule gave very strong results