Scheduling around a small common due date

A set of n jobs has to be scheduled on a single machine which can handle only one job at a time. Each job requires a given positive uninterrupted processing time and has a positive weight. The problem is to find a schedule that minimizes the sum of weighted deviations of the job completion times from a given common due date d, which is smaller than the sum of the processing times. We prove that this problem is NP-hard even if all job weights are equal. In addition, we present a pseudopolynomial algorithm that requires O(n2d) time and O(nd) space

The Impact of Complexity, Rate of Change and Information Availability on the Production Planning and Control Structure

The organizational theory literature argues that the more uncertain the environment, the more likely the firm’s operational decision structure is decentralized. However, it remains unclear which uncertainty dimensions (i.e. complexity, rate of change and lack of information) impacts the production planning and control structure the most given today’s turbulent manufacturing environments. Based on 206 responses from medium sized Dutch discrete parts manufacturing firms, this study retests the impact of these uncertainty dimensions. This study indicates that each dimension of uncertainty affects the production planning and control structure in a different way. In general, complexity, rate of change and lack of information result in a decentralization of the operational planning and control decision structure, but at the same time a centralization of the customer-order processing decision structure

Approximation algorithms for the parallel flow shop problem

We consider the NP-hard problem of scheduling n jobs in m two-stage parallel flow shops so as to minimize the makespan. This problem decomposes into two subproblems: assigning the jobs to parallel flow shops; and scheduling the jobs assigned to the same flow shop by use of Johnson's rule. For m = 2, we present a 32-approximation algorithm, and for m = 3, we present a 127-approximation algorithm. Both these algorithms run in O(n log n) time. These are the first approximation algorithms with fixed worst-case performance guarantees for the parallel flow shop problem

Stronger Lagrangian bounds by use of slack variables: applications to machine scheduling problems

Lagrangian relaxation is a powerful bounding technique that has been applied successfully to manyNP-hard combinatorial optimization problems. The basic idea is to see anNP-hard problem as an easy-to-solve problem complicated by a number of nasty side constraints. We show that reformulating nasty inequality constraints as equalities by using slack variables leads to stronger lower bounds. The trick is widely applicable, but we focus on a broad class of machine scheduling problems for which it is particularly useful. We provide promising computational results for three problems belonging to this class for which Lagrangian bounds have appeared in the literature: the single-machine problem of minimizing total weighted completion time subject to precedence constraints, the two-machine flow-shop problem of minimizing total completion time, and the single-machine problem of minimizing total weighted tardiness

Crisis performance predictability in supply chains

It is widely acknowledged that supply chain ‘glitches’ may have detrimental effects on company per

The impact of innovation and organizational factors on APS adoption: Evidence from the Dutch discrete parts industry

Advanced Planning and Scheduling (APS) systems have gained renewed interest from academics and practitioners. However, literature on APS adoption is scant. This study explores the impact of organizational and innovation related factors on the adoption of APS systems from a factors approach. The results from our field survey of 136 Dutch discrete manufacturing firms, show that management support, cost of purchase, number of end-products, and the value that firms attach to other users’ opinions are key-factors that directly influence the adoption of APS systems. In addition, professionalism, external communications, and innovation experience indirectly influence APS adoption

Minimizing makespan in flowshops with pallet requirements: computational complexity

Studies the minimization in flowshops with pallet requirements. Importance of pallets in automated or flexible manufacturing environments; Mounting and dismounting of work pieces; Planning problems involved

The Lot Sizing and Scheduling of Sand Casting Operations

We describe a real world case study that involves the monthly planning and scheduling of the sand-casting department in a metal foundry. The problem can be characterised as a single-level multi-item capacitated lot-sizing model with a variety of additional process-specific constraints. The main objective is to smooth production. We present a hierarchical approach, in which we use a combination of mixed integer linear programming, shortest path algorithms, and iterative local improvement. The quality of the production schedules obtained in this way is by far superior to the quality of the schedules constructed by a very expert production planner with no other tool than a plan board. Furthermore, the planning effort is significantly reduced: the manual method requires about 2–3 days, whereas a typical planning session with a prototype decision support system takes no more than half an hour

A branch-and-bound algorithm for single-machine earliness-tardiness scheduling with idle time

Presents a branch-and-bound algorithm which is based upon many dominance rules and various lower bound approaches, including relaxation of the machine capacity, data manipulation and Lagrangian relaxation. Insertion of the idle time for a given sequence; Properties of the proposed lower bounds

Earliness-tardiness scheduling around almost equal due dates

Discusses the existence of another class of problems that are structurally less complicated than the general earliness-tardiness problem. Details of common due date problems; Logic behind Emmons' matching algorithm; List of earliness-tardiness problems to which the optimality principle of the dynamic algorithm applies; Properties that apply to the variants of dynamic programming