Design and Analysis of Efficient Freight Transportation Networks in a Collaborative Logistics Environment

The increase in total freight volumes, reducing volume per freight unit, and delivery deadlines have increased the burden on freight transportation systems of today. With the evolution of freight demand trends, there also needs to be an evolution in the freight distribution processes. Today\u27s freight transportation processes have a lot of inefficiencies that could be streamlined, thus preventing concerns like increased operational costs, road congestion, and environmental degradation. Collaborative logistics is one of the approaches where supply chain partners collaborate horizontally or/and vertically to create a centralized network that is more efficient and serves towards a common goal or objective. In this dissertation, we study intermodal transportation, and cross-docking, two major pillars of efficient, cheap, and faster freight transportation in a collaborative environment. We design an intermodal network from a centralized network perspective where all the participants intermodal operators, shippers, carriers, and customers strive towards a synchronized and cost-efficient freight network. Also, a cross-dock scheduling problem is presented for competitive shippers using a centralized cross-dock facility. The problem develops a fast heuristic and meta-heuristic approach to solve large-scale real-world problems and draws key insights from a cross-dock operator and inbound carrier\u27s perspectives

A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation

ADAPTIVE SCHEDULING FOR OPERATING ROOM MANAGEMENT

The perioperative process in hospitals can be modelled as a 3-stage no-wait flow shop. The utilization of OR units and the average waiting time of patients are related to makespan and total completion time, respectively. However, minimizations of makespan and total completion time are NP-hard and NP-complete. Consequently, achieving good effectiveness and efficiency is a challenge in no-wait flow shop scheduling. The average idle time (AIT) and current and future idle time (CFI) heuristics are proposed to minimize makespan and total completion time, respectively. To improve effectiveness, current idle times and future idle times are taken into consideration and the insertion and neighborhood exchanging techniques are used. To improve efficiency, an objective increment method is introduced and the number of iterations is determined to reduce the computation times. Compared with three best-known heuristics for each objective, AIT and CFI heuristics can achieve greater effectiveness in the same computational complexity based on a variety of benchmarks. Furthermore, AIT and CFI heuristics perform better on trade-off balancing compared with other two best-known heuristics. Moreover, using the CFI heuristic for operating room (OR) scheduling, the average patient flow times are decreased by 11.2% over historical ones at University of Kentucky Health Care

Preventive maintenance effect on the aggregate production planning model with tow-phase production systems: modeling and solution methods

This paper develops two mixed integer linear programming (MILP) models for an integrated aggregate production planning (APP) system with return products, breakdowns and preventive maintenance (PM). The goal is to minimize the cost of production with regard to PM costs, breakdowns, the number of laborers and inventory levels and downtimes. Due to NP-hard class of APP, we implement a harmony search (HS) algorithm and vibration damping optimization (VDO) algorithm for solving these models. Next, the Taguchi method is conducted to calibrate the parameter of the metaheuristics and select the optimal levels of factors influencing the algorithm’s performance. Computational results tested on a set of randomly generated instances show the efficiency of the vibration damping optimization algorithm against the harmony search algorithm. We find VDO algorithm to obtain best quality solutions for APP with breakdowns and PM, which could be efficient for large scale problems. Finally, the computational results show that the objective function values obtained by APP with PM are better than APP with breakdown results

Reinforcement Learning on Job Shop Scheduling Problems Using Graph Networks

This paper presents a novel approach for job shop scheduling problems using deep reinforcement learning. To account for the complexity of production environment, we employ graph neural networks to model the various relations within production environments. Furthermore, we cast the JSSP as a distributed optimization problem in which learning agents are individually assigned to resources which allows for higher flexibility with respect to changing production environments. The proposed distributed RL agents used to optimize production schedules for single resources are running together with a co-simulation framework of the production environment to obtain the required amount of data. The approach is applied to a multi-robot environment and a complex production scheduling benchmark environment. The initial results underline the applicability and performance of the proposed method.Comment: 8 pages, pre-prin

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

The Integration of Maintenance Decisions and Flow Shop Scheduling

In the conventional production and service scheduling problems, it is assumed that the machines can continuously process the jobs and the information is complete and certain. However, in practice the machines must stop for preventive or corrective maintenance, and the information available to the planners can be both incomplete and uncertain. In this dissertation, the integration of maintenance decisions and production scheduling is studied in a permutation flow shop setting. Several variations of the problem are modeled as (stochastic) mixed-integer programs. In these models, some technical nuances are considered that increase the practicality of the models: having various types of maintenance, combining maintenance activities, and the impact of maintenance on the processing times of the production jobs. The solution methodologies involve studying the solution space of the problems, genetic algorithms, stochastic optimization, multi-objective optimization, and extensive computational experiments. The application of the problems and managerial implications are demonstrated through a case study in the earthmoving operations in construction projects

Complexity of Scheduling Few Types of Jobs on Related and Unrelated Machines

The task of scheduling jobs to machines while minimizing the total makespan, the sum of weighted completion times, or a norm of the load vector, are among the oldest and most fundamental tasks in combinatorial optimization. Since all of these problems are in general NP-hard, much attention has been given to the regime where there is only a small number k of job types, but possibly the number of jobs n is large; this is the few job types, high-multiplicity regime. Despite many positive results, the hardness boundary of this regime was not understood until now. We show that makespan minimization on uniformly related machines (Q|HM|C_max) is NP-hard already with 6 job types, and that the related Cutting Stock problem is NP-hard already with 8 item types. For the more general unrelated machines model (R|HM|C_max), we show that if either the largest job size p_max, or the number of jobs n are polynomially bounded in the instance size |I|, there are algorithms with complexity |I|^poly(k). Our main result is that this is unlikely to be improved, because Q||C_max is W[1]-hard parameterized by k already when n, p_max, and the numbers describing the speeds are polynomial in |I|; the same holds for R|HM|C_max (without speeds) when the job sizes matrix has rank 2. Our positive and negative results also extend to the objectives ??-norm minimization of the load vector and, partially, sum of weighted completion times ? w_j C_j. Along the way, we answer affirmatively the question whether makespan minimization on identical machines (P||C_max) is fixed-parameter tractable parameterized by k, extending our understanding of this fundamental problem. Together with our hardness results for Q||C_max this implies that the complexity of P|HM|C_max is the only remaining open case