Bees Algorithm for multi-mode, resource-constrained project scheduling in molding industry

Öztemel, Ercan; Selam, Ayşe Ayçim

doi:10.1016/j.cie.2017.08.012

Cited by 31 publications

(10 citation statements)

References 37 publications

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“…The activities in this paper are under category A. Oztemel and Selam (2017) use a new meta-heuristic to select an effective single mode for MRCPSP. Bee Colony Optimization (BCO) approach has been used to complete the project on time.…”

Section: Litereture Reviewmentioning

confidence: 99%

New procedures with new activity assumptions for solving resource constrained project scheduling problems

Issa¹,

Tu²

2020

10.5267/j.jpm

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The resource-constrained project scheduling problem (RCPSP) is a well-known and widely studied topic. The underlying problem assumes that non-preemptions and that constant resources are restrictions imposed on project activities, which are to be scheduled, subject to precedence relation and limited resource constraints. Project activities, in RCPSP, are classified under category A. The problem is expanded to include various other activity assumptions categories, such as B and C. In the Preemptive-RCPSP, project activities are classified under category B, which refers to the activity that can be implemented using constant resources and constant durations. In the Flexible-RCPSP, project activities are classified under category C, which refers to the activities that can be executed using flexible resources over flexible durations, and preemptions are not allowed. However, in One-of-a-Kind Production companies (OKP), such as the housing industry, plastic injection moldings, and RV manufacturing, all known as "manufactured-to-order" operations, the activities are classified under category D in addition to A, B, and C, simultaneously. Category D refers to the activities that can be executed using flexible durations and flexible resources, and preemptions are allowed. In this paper, therefore, we present a new effective model in order to deal with the projects that consist of all the previous activity assumptions simultaneously to generate feasible project schedules. Case studies are included, and the results show that the resources usage is increased and the project makespan is reduced.

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Section: Litereture Reviewmentioning

confidence: 99%

New procedures with new activity assumptions for solving resource constrained project scheduling problems

Issa¹,

Tu²

2020

10.5267/j.jpm

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show abstract

“…Moreover, recently, bees algorithms [34], team-based approaches based on different agent cooperation strategies [35], and machine learning heuristics [1] have been developed for solving the MRCPSP. In this section, the related work is classified into 7 subsections and discussed as follows:…”

Section: )mentioning

confidence: 99%

An innovative four-layer heuristic for scheduling multi-mode projects under multiple resource constrains

Zamani

2019

RAIRO-Oper. Res.

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In this paper, an innovative four-layer heuristic is presented for scheduling multi-mode projects under multiple resource constraints. For this purpose, a biased-random sampling technique, a local search, a decomposition method, and an evolutionary search mechanism, each in a separate layer, are combined, with each layer passing its output to the next layer for improvement. The procedure has been designed based on the fact that what makes the scheduling of multi-mode projects hard to solve is a massive search space of modes compounded with the starting times of activities. That is why the procedure is aimed at balancing exploration versus exploitation in searching a massive search space. On the one hand, it exploits promising areas further and, on the other hand, it searches unexplored areas for expanding its range. Since the first layer provides an initial solution, and each of the other three layers can either improve the result of its previous layer or keep it unchanged, solutions never deteriorate and hence promising areas are exploited. Moreover, unexplored areas are searched effectively because each layer explores solution space differently than its previous layer. Based on whether or not an improvement each layer can make to the result of its previous layer, the effect of the corresponding layer on the performance of the procedure has been measured.Mathematics Subject Classification. 90B50, 90B35, 90B40, 90.08, 68R05.

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“…Some authors have employed a meta-heuristic algorithm for mold scheduling. Oztemel et al [15] developed a bee algorithm for multi-mode resource-constrained project scheduling in the mold industry to minimize the mold project duration. Wang et al [16] used an ant colony optimization algorithm for the selection of machines that are needed in each working procedure, and the job sequence was determined by the heuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

2020

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In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q-network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

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Bees Algorithm for multi-mode, resource-constrained project scheduling in molding industry

Cited by 31 publications

References 37 publications

New procedures with new activity assumptions for solving resource constrained project scheduling problems

New procedures with new activity assumptions for solving resource constrained project scheduling problems

An innovative four-layer heuristic for scheduling multi-mode projects under multiple resource constrains

Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

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