The scheduling of order-picking problems is a critical aspect of warehouse and distribution centre operations. The efficient execution of order picking requires determining the sequence in which it is needed to pick i...
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The scheduling of order-picking problems is a critical aspect of warehouse and distribution centre operations. The efficient execution of order picking requires determining the sequence in which it is needed to pick items from storage locations, as well as the assignment of employees to different picking tasks. In this paper, we present an approach to optimise the process using the Flexible Flow Shop Scheduling Problem (FFSSP), which encompasses multiple order fulfilment stages performing a set of operations on various machines (groups of employees) assigned to the picking stages (e.g., order creating, product collection, quality control, packing, shipment). The aim of the study was to determine the sequence of operations on the machines to minimise the order completion time while taking into account the available resources. We used IBM'sCP Optimizer. The computational experiments showed, on the one hand, the advantages of solver usage for relatively small instances providing optimal solutions, while on the other hand, it demonstrated its disadvantages as for 11 large instances from 24, optimal solutions were not found. Moreover, in 6 cases from 24 ones, the solution which was found was 94% worse than the optimal one.
constraint programming (CP) is an emergent software technology for declarative description and effective solution of large combinatorial problems, which has proven to be useful, especially in such areas as integrated ...
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constraint programming (CP) is an emergent software technology for declarative description and effective solution of large combinatorial problems, which has proven to be useful, especially in such areas as integrated production planning. In that context, the CP can be considered as a well-suited framework for the development of decision-making software supporting small and medium size enterprises (SME) in the course of Production Process Planning (PPP). The aim of the paper is to present the CP modelling framework as well as to illustrate its application to decision making in the case of a new production order evaluation. The paper emphasises benefits derived from CP-based Decision Support Systems and focuses on constraint satisfaction driven decision making rather than on optimal solution searching.
This paper addresses a new problem of redirecting freight trains to revised destinations as a last-minute risk mitigation strategy. The problem is approached from a consignee’s perspective as the demand for a change ...
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This paper addresses a new problem of redirecting freight trains to revised destinations as a last-minute risk mitigation strategy. The problem is approached from a consignee’s perspective as the demand for a change of destination is made by the consignee. A constraint programming (CP) model is formulated to minimize the cost of redirecting trains subject to various constraints. The case of a government organization in India which is involved in food grain distribution is considered. The model is solved using an open source CP solver and is found to be highly efficient in terms of computation time.
We investigate the production planning and detailed scheduling of multiple-stage flexible flow shops, making products that require different production cycles. The production process can be configured in several ways,...
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We investigate the production planning and detailed scheduling of multiple-stage flexible flow shops, making products that require different production cycles. The production process can be configured in several ways, involving both different processing phases and times, depending on specific treatments required by the processed job. Jobs require operations on a unique raw material item and its quality features can require specific processing phases as well as lead to different processing times. We investigate, through the succession of a MILP and a CP model, the impact of quality-related aspects on processing times and hence on the overall planning and scheduling problem. An actual case from a leather tannery industry derived from the M2H – Machine To Human” (project code CBYX592), INNONETWORK 2017, Regione Puglia is investigated.
The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system...
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The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system uses a daily schedule of train runs to meet the needs of both the harvesters and the mills (An Integrated Approach to Optimise Cane Rail Operations (M. Masoud, E. Kozan, G. Kent, Liu, Shi Qiang, 2016b) [1]). Producing an efficient daily schedule to optimise the rail operations requires integration of the main elements of harvesting, transporting and milling in the value chain of the Australian agriculture industry. The data utilised in this research involve four main tables: sidings, harvesters, sectional rail network and trains. The utilised data were collected from Australian sugar mills as a real application. Operations Research techniques such as metaheuristic and constraint programming are used to produce the optimised solutions in an analytical way. (C) 2016 Published by Elsevier Inc. This is an open access article under the CC BY license.
This paper presents a method for the multiple autonomous vehicles mission flight planning in changing weather conditions. We model UAVs fleet servicing spatially-dispersed customers in terms of declarative modelling f...
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This paper presents a method for the multiple autonomous vehicles mission flight planning in changing weather conditions. We model UAVs fleet servicing spatially-dispersed customers in terms of declarative modelling framework. The considered problem boils down to a predictive and reactive planning of delivery missions within a specified timeframe. Due to the need to implement an emergency return of a UAV to its base, or to handle variations in delivery periods, conditions sufficient to allow eliminating unfeasible solutions, and thus allowing to speed up the calculations, have been developed. The results of numerous computer experiments have confirmed experiments these expectations.
Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we ...
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Machine scheduling is a hard combinatorial problem having many manifestations in real life. Due to the schedule followed, the possibility of installations of machines operating sub-optimally is high. In this work, we examine the problem of a single machine with time-dependent capacity that performs jobs of deterministic durations, while for each job, its due time is known in advance. The objective is to minimize the aggregated tardiness in all tasks. The problem was motivated by the need to schedule charging times of electric vehicles effectively. We formulate an integer programming model that clearly describes the problem and a constraint programming model capable of effectively solving it. Due to the usage of interval variables, global constraints, a powerful constraint programming solver, and a heuristic we have identified, which we call the "due times rule", the constraint programming model can reach excellent solutions. Furthermore, we employ a hybrid approach that exploits three local search improvement procedures in a schema where the constraint programming part of the solver plays a central role. These improvement procedures exhaustively enumerate portions of the search space by exchanging consecutive jobs with a single job of the same duration, moving cost-incurring jobs to earlier times in a consecutive sequence of jobs or even exploiting periods where capacity is not fully utilized to rearrange jobs. On the other hand, subproblems are given to the exact constraint programming solver, allowing freedom of movement only to certain parts of the schedule, either in vertical ribbons of the time axis or in groups of consecutive sequences of jobs. Experiments on publicly available data show that our approach is highly competitive and achieves the new best results in many problem instances.
In this paper we present a constraint Logic programming (CLP) based model, and hybrid solving method for the Scheduling of Maintenance Activities in the Power Transmission Network. The model distinguishes from others ...
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Class models are often employed to represent domains. In order for class models to conform to their intended domain semantics, we need to ensure their precision and consistency. Precision can be achieved by augmenting...
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Answer Set programming (ASP) is a powerful modeling formalism for combinatorial problems. However, writing ASP models can be hard. We propose a novel method, called Sketched Answer Set programming (SkASP), aimed at fa...
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ISBN:
(纸本)9781538674499
Answer Set programming (ASP) is a powerful modeling formalism for combinatorial problems. However, writing ASP models can be hard. We propose a novel method, called Sketched Answer Set programming (SkASP), aimed at facilitating this. In SkASP, the user writes partial ASP programs, in which uncertain parts are left open and marked with question marks. In addition, the user provides a number of positive and negative examples of the desired program behaviour. SkASP then synthesises a complete ASP program. This is realized by rewriting the SkASP program into another ASP program, which can then be solved by traditional ASP solvers. We evaluate our approach on 21 well known puzzles and combinatorial problems inspired by Karps 21 NP-complete problems and on publicly available ASP encodings.
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