Chris N. Potts

Scheduling with batching: A review

There is an extensive literature on models that integrate scheduling with batching decisions. Jobs may be batched if they share the same setup on a machine. Another reason for batching occurs when a machine can process several jobs simultaneously. This paper reviews the literature on scheduling with batching, giving details of the basic algorithms, and referencing other significant results. Special attention is given to the design of efficient dynamic programming algorithms for solving these types of problems.

Supply chain scheduling: Batching and delivery

Although the supply chain management literature is extensive, the benefits and challenges of coordinated decision making within supply chainscheduling models have not been studied. We consider a variety of scheduling, batching, and delivery problems that arise in an arborescent supply chain where a supplier makes deliveries to several manufacturers, who also make deliveries to customers. The objective is to minimize the overall scheduling and delivery cost, using several classical scheduling objectives. This is achieved by scheduling the jobs and forming them into batches, each of which is delivered to the next downstream stage as a single shipment. For each problem, we either derive an efficient dynamic programming algorithm that minimizes the total cost of the supplier or that of the manufacturer, or we demonstrate that this problem is intractable. The total system cost minimization problem of a supplier and manufacturer who make cooperative decisions is also considered. We demonstrate that cooperation between a supplier and a manufacturer may reduce the total system cost by at least 20%, or 25%, or by up to 100%, depending upon the scheduling objective. Finally, we identify incentives and mechanisms for this cooperation, thereby demonstrating that our work has practical implications for improving the efficiency of supply chains.

Scheduling a batching machine

textabstractWe study the problem of scheduling a chain-reentrant shop, in which each job goes for its processing first to a machine called the primary machine, then to a number of other machines in a fixed sequence, and finally back to the primary machine for its last operation. The problem is to schedule the jobs so as to minimize the makespan. This problem is unary NP-hard for a general number of machines. We focus in particular on the two-machine case that is also at least binary NP-hard. We prove some properties that identify a specific class of optimal schedules, and then use these properties in designing an approximation algorithm and a branch-and-bound type optimization algorithm. The approximation algorithm, of which we present three versions, has a worst-case performance guarantee of f32 along with an excellent empirical performance. The optimization algorithm solves large instances quickly. Finally, we identify a few well solvable special cases and present a pseudo-polynomial algorithm for the case in which the first and the last operations of any job (on the primary machine) are identical.

An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem

This paper introduces a new neighborhood search technique, called dynasearch, that uses dynamic programming to search an exponential size neighborhood in polynomial time. While traditional local search algorithms make a single move at each iteration, dynasearch allows a series of moves to be performed. The aim is for the lookahead capabilities of dynasearch to prevent the search from being attracted to poor local optima. We evaluate dynasearch by applying it to the problem of scheduling jobs on a single machine to minimize the total weighted tardiness of the jobs. Dynasearch is more effective than traditional first-improve or best-improve descent in our computational tests. Furthermore, this superiority is much greater for starting solutions close to previous local minima. Computational results also show that an iterated dynasearch algorithm in which descents are performed a few random moves away from previous local minima is superior to other known local search procedures for the total weighted tardiness scheduling problem.

Constraint satisfaction problems: algorithms and applications

A constraint satisfaction problem requires a value, selected from a given finite domain, to be assigned to each variable in the problem, so that all constraints relating the variables are satisfied. Many combinatorial problems in operational research, such as schedulling and timetabling, can be formulated as constraint satisfaction problems. Researchers in artificial intelligence usually adopt a constaint satisfaction approach as their prefererd method when tackling such problems. However constraint satisfaction approches are not widely known amongst operational researchers. The aim of this paper is to introduce constraint statisfaction to the operational researchers.

On the Complexity of Scheduling with Batch Setup Times

Many practical scheduling problems involve processing several batches of related jobs on common facilities where a setup time is incurred whenever there is a switch from processing a job in one batch to a job in another batch. We extend various scheduling models to include batch setup times. The models include the one-machine maximum lateness, total weighted completion time, and number of late jobs problems. In all these cases, a dynamic programming approach results in an algorithm that is polynomially bounded in the number of jobs, but is exponential in the number of batches. We also study the parallel machine model with preemption and show that the maximum completion time, maximum lateness, total weighted completion time, and number of late jobs problems are NP-hard, even for the case of two identical parallel machines, and sequence independent setup times.

A decomposition algorithm for the single machine total tardiness problem

The problem of sequencing jobs on a single machine to minimize total tardiness is considered. An algorithm, which decomposes the problem into subproblems which are then solved by dynamic programming when they are sufficiently small, is presented and is tested on problems with up to 100 jobs.

A Branch and Bound Algorithm for the Total Weighted Tardiness Problem

This paper presents a new branch and bound algorithm for the single machine total weighted tardiness problem. It obtains lower bounds using a Lagrangian relaxation approach with subproblems that are total weighted completion time problems. The well-known subgradient optimization technique is replaced by a multiplier adjustment method that leads to an extremely fast bound calculation. The method incorporates various devices for checking dynamic programming dominance in the search tree. Extensive computational results for problems with up to 50 jobs show the superiority of the algorithm over existing methods.

A Review of Machine Scheduling: Complexity, Algorithms and Approximability

The scheduling of computer and manufacturing systems has been the subject of extensive research for over forty years. In addition to computers and manufacturing, scheduling theory can be applied to many areas including agriculture, hospitals and transport. The main focus is on the efficient allocation of one or more resources to activities over time. Adopting manufacturing terminology, a job consists of one or more activities, and a machine is a resource that can perform at most one activity at a time. We concentrate on deterministic machine scheduling for which it is assumed that all data that define a problem instance are known with certainty.

Single Machine Tardiness Sequencing Heuristics

Abstract This paper presents a collection of heuristics for the single machine total (weighted) tardiness problem. The methods considered range from simple quick and dirty heuristics to more sophisticated algorithms exploiting problem structure. These heuristics are compared to interchange and simulated annealing methods on a large set of test problems. For the total tardiness problem a heuristic based on decomposition performs very well, whereas for the total weighted tardiness problem simulated annealing appears to be a viable approach. Our computational results also indicate that straightforward interchange methods perform remarkably well.