Tactical fixed job scheduling with spread-time constraints

We address the tactical fixed job scheduling problem with spread-time constraints. In such a problem, there are a fixed number of classes of machines and a fixed number of groups of jobs. Jobs of the same group can only be processed by machines of a given set of classes. All jobs have their fixed start and end times. Each machine is associated with a cost according to its machine class. Machines have spread-time constraints, with which each machine is only available for L consecutive time units from the start time of the earliest job assigned to it. The objective is to minimize the total cost of the machines used to process all the jobs. For this strongly NP-hard problem, we develop a branch-and-price algorithm, which solves instances with up to 300 jobs, as compared with CPLEX, which cannot solve instances of 100 jobs. We further investigate the influence of machine flexibility by computational experiments. Our results show that limited machine flexibility is sufficient in most situations

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

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

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

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

Crisis performance predictability in supply chains

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

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