scholarly journals Simultaneous optimization scheduling with two agents on an unbounded serial-batching machine

2021 ◽  
Author(s):  
Cheng He ◽  
Shisheng Li ◽  
Jing Wu
Keyword(s):  
Cost Function ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Simultaneous Optimization ◽  
Pareto Optimal ◽  
Batching Machine ◽  
The Cost ◽  
Completion Times ◽  
Item Availability ◽  
Optimization Scheduling

This paper considers a class of simultaneous optimization scheduling with two competitive agents on an unbounded serial-batching machine. The cost function of each agent depends on the completion times of its jobs only. According to whether the jobs from different agents can be processed in a common batch, compatible model and incompatible model are investigated. For the incompatible model, we consider batch availability and item availability. For each problem, we provide a polynomial-time algorithm that can find all Pareto optimal schedules.

BATCHING MACHINE SCHEDULING WITH BICRITERIA: MAXIMUM COST AND MAKESPAN

2014 ◽  
Vol 31 (04) ◽  
pp. 1450025 ◽  
Author(s):  
CHENG HE ◽  
HAO LIN ◽  
JINJIANG YUAN ◽  
YUNDONG MU
Keyword(s):  
Processing Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Maximum Cost ◽  
Parallel Batching ◽  
Multicriteria Problem ◽  
Batching Machine

In this paper, the problem of minimizing maximum cost and makespan simultaneously on an unbounded parallel-batching machine is considered. An unbounded parallel-batching machine is a machine that can handle any number of jobs in a batch and the processing time of a batch is the largest processing time of jobs in the batch. The main goal of a multicriteria problem is to find Pareto optimal solutions. We present a polynomial-time algorithm to produce all Pareto optimal solutions of this bicriteria scheduling problem.

scholarly journals Polynomial Algorithm for Constructing Pareto-Optimal Schedules for Problem 1∣rj∣Lmax,Cmax

2020 ◽  
Author(s):  
Alexander A. Lazarev ◽  
Nikolay Pravdivets
Keyword(s):  
Completion Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Single Machine Scheduling ◽  
Maximum Lateness ◽  
Pareto Optimal ◽  
Due Dates ◽  
Processing Times ◽  
Pareto Optimal Set ◽  
Maximum Completion Time

In this chapter, we consider the single machine scheduling problem with given release dates, processing times, and due dates with two objective functions. The first one is to minimize the maximum lateness, that is, maximum difference between each job due date and its actual completion time. The second one is to minimize the maximum completion time, that is, to complete all the jobs as soon as possible. The problem is NP-hard in the strong sense. We provide a polynomial time algorithm for constructing a Pareto-optimal set of schedules on criteria of maximum lateness and maximum completion time, that is, problem 1 ∣ r j ∣ L max , C max , for the subcase of the problem: d 1 ≤ d 2 ≤ … ≤ d n ; d 1 − r 1 − p 1 ≥ d 2 − r 2 − p 2 ≥ … ≥ d n − r n − p n .

scholarly journals Selection model of work technology based on multi–criteria evaluations

2020 ◽  
Vol 164 ◽  
pp. 08030
Author(s):  
Sergey Barkalov ◽  
Pavel Kurochka ◽  
Anton Khodunov ◽  
Natalia Kalinina
Keyword(s):  
Cost Function ◽  
Optimal Solution ◽  
Budget Constraint ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Customer Preferences ◽  
Optimal Solution Set ◽  
The Cost ◽  
Comprehensive Indicator ◽  
Selection Of

A model for the selection of options for the production of work in a construction project is considered, when each option is characterized by a set of criteria. The number of analyzed options is being reduced based on the construction of the Pareto-optimal solution set. The remaining options are used to solve the problem based on the network model,\ in which the solution will be a subcritical path that meets budgetary constraints. At the same time, the proposed comprehensive indicator characterizing the preferences of the customer makes it possible to determine alternative options for performing work in the energy project in such a way that the amount of costs allocated to implement the set of work under consideration is minimal. Another statement of the problem is also considered when it is necessary to determine a strategy for the implementation of an energy project that, given a planned budget constraint, maximizes the growth of a comprehensive indicator that characterizes customer preferences in this project. The solution of the tasks is given under the assumption of the convexity of the cost function.

THE PROBLEM OF PARTITIONING WITH DUPLICATIONS AND ITS APPLICATIONS

1994 ◽  
Vol 03 (03) ◽  
pp. 395-405
Author(s):  
J. HARALAMBIDES ◽  
S. TRAGOUDAS
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Vlsi Layout ◽  
Optimal Polynomial ◽  
Partitioning Problem ◽  
Polynomially Solvable ◽  
The Cost ◽  
Parallel Graph ◽  
Special Case

The problem of partitioning the elements of a graph G=(V, E) into two equal size sets A and B that share at most d elements such that the total number of edges (u, v), u∈A−B, v∈B−A is minimized, arises in the areas of Hypermedia Organization, Network Integrity, and VLSI Layout. We formulate the problem in terms of element duplication, where each element c∈A∩B is substituted by two copies c′∈A and c″∈B As a result, edges incident to c′ or c″ need not count in the cost of the partition. We show that this partitioning problem is NP-hard in general, and we present a solution which utilizes an optimal polynomial time algorithm for the special case where G is a series-parallel graph. We also discuss special other cases where the partitioning problem or variations are polynomially solvable.

Scheduling jobs on non-identical IFR processors to minimize general cost functions

1991 ◽  
Vol 23 (04) ◽  
pp. 909-924 ◽  
Author(s):  
Rhonda Righter ◽  
Susan H. Xu
Keyword(s):  
Cost Function ◽  
Optimal Policy ◽  
Expected Number ◽  
Expected Cost ◽  
Processing Times ◽  
Special Cases ◽  
The Cost ◽  
General Cost Functions ◽  
Tardy Jobs ◽  
Completion Times

We consider the problem of scheduling n jobs non-preemptively on m parallel, non-identical processors to minimize a weighted expected cost function of job completion times, where the weights are associated with the jobs. The cost function is assumed to be increasing and concave but otherwise arbitrary. Processing times are IFR with different distributions for different processors. Jobs may be processed on any processor and there are no precedences. We show that the optimal policy orders the jobs in decreasing order of their weights and then uses the individually optimal policy for each job. In other words, processors are offered to jobs in order, and each job considers its own expected cost function for its completion time to decide whether to accept or reject a processor. Therefore, the optimal policy does not depend on the weights of the jobs except through their order. Special cases of our objective function are weighted expected flowtime, weighted discounted expected flowtime, and weighted expected number of tardy jobs.

The cost of a cycle is a square

2002 ◽  
Vol 67 (1) ◽  
pp. 35-60 ◽  
Author(s):  
A. Carbone
Keyword(s):  
Lower Bounds ◽  
Polynomial Time ◽  
Heuristic Algorithms ◽  
Sequent Calculus ◽  
Propositional Calculus ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Proof Search ◽  
The Cost ◽  
Flow Graphs

AbstractThe logical flow graphs of sequent calculus proofs might contain oriented cycles. For the predicate calculus the elimination of cycles might be non-elementary and this was shown in [Car96]. For the propositional calculus, we prove that if a proof of k lines contains n cycles then there exists an acyclic proof with (kn+1) lines. In particular, there is a polynomial time algorithm which eliminates cycles from a proof. These results are motivated by the search for general methods on proving lower bounds on proof size and by the design of more efficient heuristic algorithms for proof search.

scholarly journals Economic lot-sizing problem with remanufacturing option: complexity and algorithms

2021 ◽  
Author(s):  
Ashwin Arulselvan ◽  
Kerem Akartunalı ◽  
Wilco van den Heuvel
Keyword(s):  
Polynomial Time ◽  
Lot Sizing ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Single Item ◽  
Inventory Costs ◽  
Time Period ◽  
Lot Sizing Problem ◽  
Dynamic Lot Sizing ◽  
The Cost

AbstractIn a single item dynamic lot-sizing problem, we are given a time horizon and demand for a single item in every time period. The problem seeks a solution that determines how much to produce and carry at each time period, so that we will incur the least amount of production and inventory cost. When the remanufacturing option is included, the input comprises of number of returned products at each time period that can be potentially remanufactured to satisfy the demands, where remanufacturing and inventory costs are applicable. For this problem, we first show that it cannot have a fully polynomial time approximation scheme. We then provide a polynomial time algorithm, when we make certain realistic assumptions on the cost structure.

scholarly journals A Note on Two-Agent Scheduling with Resource Dependent Release Times on a Single Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-4 ◽  
Author(s):  
Peng Liu ◽  
Lini Duan
Keyword(s):  
Cost Function ◽  
Single Machine ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Release Time ◽  
Scheduling Problem ◽  
Two Agents ◽  
Release Times ◽  
Agent Scheduling ◽  
Optimal Polynomial

We consider a scheduling problem in which both resource dependent release times and two agents exist simultaneously. Two agents share a common single machine, and each agent wants to minimize a cost function dependent on its own jobs. The release time of eachA-agent’s job is related to the amount of resource consumed. The objective is to find a schedule for the problem of minimizingA-agent’s total amount of resource consumption with a constraint onB-agent’s makespan. The optimal properties and the optimal polynomial time algorithm are proposed to solve the scheduling problem.

Scheduling jobs on non-identical IFR processors to minimize general cost functions

1991 ◽  
Vol 23 (4) ◽  
pp. 909-924 ◽  
Author(s):  
Rhonda Righter ◽  
Susan H. Xu
Keyword(s):  
Cost Function ◽  
Optimal Policy ◽  
Expected Number ◽  
Expected Cost ◽  
Processing Times ◽  
Special Cases ◽  
The Cost ◽  
General Cost Functions ◽  
Tardy Jobs ◽  
Completion Times

We consider the problem of scheduling n jobs non-preemptively on m parallel, non-identical processors to minimize a weighted expected cost function of job completion times, where the weights are associated with the jobs. The cost function is assumed to be increasing and concave but otherwise arbitrary. Processing times are IFR with different distributions for different processors. Jobs may be processed on any processor and there are no precedences. We show that the optimal policy orders the jobs in decreasing order of their weights and then uses the individually optimal policy for each job. In other words, processors are offered to jobs in order, and each job considers its own expected cost function for its completion time to decide whether to accept or reject a processor. Therefore, the optimal policy does not depend on the weights of the jobs except through their order. Special cases of our objective function are weighted expected flowtime, weighted discounted expected flowtime, and weighted expected number of tardy jobs.

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