Planning and Scheduling in Manufacturing and Services

What is Scheduling About? Applied operations research Models Algorithms Solution using computers Implement algorithms Draw on common databases Integration with other systems February 19, 2019

Application Areas Procurement and production Transportation and distribution Information processing and communications February 19, 2019

Manufacturing Scheduling Short product life-cycles Quick-response manufacturing Manufacture-to-order More complex operations must be scheduled in shorter amount of time with less room for errors! February 19, 2019

Scope of Course Levels of planning and scheduling Long-range planning (several years), middle-range planning (1-2 years), short-range planning (few months), scheduling (few weeks), and reactive scheduling (now) These functions are now often integrated February 19, 2019

Scheduling Systems Enterprise Resource Planning (ERP) Common for larger businesses Materials Requirement Planning (MRP) Very common for manufacturing companies Advanced Planning and Scheduling (APS) Most recent trend Considered advanced feature of ERP February 19, 2019

Scheduling Problem Allocate scarce resources to tasks Combinatorial optimization problem Maximize profit Subject to constraints Mathematical techniques and heuristics February 19, 2019

Our Approach Scheduling Problem Model Conclusions Problem Formulation Solve with Computer Algorithms Conclusions February 19, 2019

Scheduling Models Project scheduling Job shop scheduling Flexible assembly systems Lot sizing and scheduling Interval scheduling, reservation, timetabling Workforce scheduling February 19, 2019

General Solution Techniques Mathematical programming Linear, non-linear, and integer programming Enumerative methods Branch-and-bound Beam search Local search Simulated annealing/genetic algorithms/tabu search/neural networks. February 19, 2019

Scheduling System Design Order master file Shop floor data collection Databases Schedule generation User interfaces Database Management Automatic Schedule Generator Performance Evaluation Schedule Editor Graphical Interface User February 19, 2019

LEKIN Generic job shop scheduling system User friendly windows environment C++ object oriented design Can add own routines February 19, 2019

Advanced Topics Uncertainty, robustness, and reactive scheduling Multiple objectives Internet scheduling February 19, 2019

Setting up the Scheduling Problem Topic 1 Setting up the Scheduling Problem

Modeling Three components to any model: Decision variables This is what we can change to affect the system, that is, the variables we can decide upon Objective function E.g, cost to be minimized, quality measure to be maximized Constraints Which values the decision variables can be set to February 19, 2019

Decision “Variables” Three basic types of solutions: A sequence: a permutation of the jobs A schedule: allocation of the jobs in a more complicated setting of the environment A scheduling policy: determines the next job given the current state of the system February 19, 2019

Model Characteristics Multiple factors: Number of machine and resources, configuration and layout, level of automation, etc. Our terminology: Resource = machine (m) Entity requiring the resource = job (n) February 19, 2019

Notation Static data: Dynamic data: Processing time (pij) Release date (rj) Due date (dj) Weight (wj) Dynamic data: Completion time (Cij) February 19, 2019

Machine Configuration Standard machine configurations: Single machine models Parallel machine models Flow shop models Job shop models Real world always more complicated. February 19, 2019

Constraints Precedence constraints Routing constraints Material-handling constraints Storage/waiting constraints Machine eligibility Tooling/resource constraints Personnel scheduling constraints February 19, 2019

Other Characteristics Sequence dependent setup Preemptions preemptive resume preemptive repeat Make-to-stock versus make-to-order February 19, 2019

Objectives and Performance Measures Throughput (TP) and makespan (Cmax) Due date related objectives Work-in-process (WIP), lead time (response time), finished inventory Others February 19, 2019

Throughput and Makespan Defined by bottleneck machines Makespan Minimizing makespan tends to maximize throughput and balance load February 19, 2019

Due Date Related Objectives Lateness Minimize maximum lateness (Lmax) Tardiness Minimize the weighted tardiness February 19, 2019

Due Date Penalties Lateness Tardiness Late or Not In practice February 19, 2019

WIP and Lead Time Work-in-Process (WIP) inventory cost Minimizing WIP also minimizes average lead time (throughput time) Minimizing lead time tends to minimize the average number of jobs in system Equivalently, we can minimize sum of the completion times: February 19, 2019

Other Costs Setup cost Personnel cost Robustness Finished goods inventory cost February 19, 2019